How Do Emotions Influence Time Perception? Exploring Potential Psychological

Home , Pieters, Time perception

How do emotions influence time perception? Exploring potential psychological

factors directly underlying this relationship.

Ewa Siedlecka

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Psychology

Faculty of Science

September 2019

Supervised by:

Prof. Tom F. Denson1

Co-Supervised by:

Dr. Chris Donkin1

1School of Psychology, Faculty of Science, UNSW Sydney

Thesis/Dissertation Sheet

Surname/Family Name : Siedlecka Given Name/s : Ewa Abbreviation for degree as give : PhD in the University calendar Faculty : Science School : Psychology How do emotions influence time perception? Exploring potential psychological factors Thesis Title : directly underlying this relationship.

Abstract 350 words maximum: (PLEASE TYPE)

Emotions are one factor that significantly influence how people perceive time (Droit-Volet & Meck, 2007). Existing models of time perception differ in their explanations of how emotions influence time perception, and each model possesses unique strengths and limitations (Scherer, 2000). The lack of consistency between existing models indicates a need to expand and consolidate these models to clarify the theoretical literature. One way of achieving this goal may be directly build upon the evidence for specific psychological factors in these models. As such, in this thesis, I explored the effects of some of these psychological factors on time perception. The factors I examined were arousal, physiology of the autonomic nervous system emotional valence, attention, and a personality dimension called time perspective. In this thesis, participants overestimated stimulus durations following emotional and parasympathetic inductions, which heightened arousal, relative to lower levels of arousal. Participants also overestimated stimulus durations following exposure to negative stimuli, relative to positive stimuli. Additionally, participants induced to experience immersion during a videogame overestimated stimulus durations relative to participants distracted by a timer while playing. Stimulus durations were also overestimated by individuals who were high in a specific time perspective personality dimension. However, this result was limited to instances in which the emotional recall task they completed was incongruent with this dimension. Overall, this thesis provided evidence that arousal, the autonomic nervous system, emotional valence, attention, and personality may directly underlie how emotions influence time perception. These results might eventually illustrate constructive ways to update and expand existing models of time perception.

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☐ This thesis contains no publications, either published or submitted for publication

Some of the work described in this thesis has been published and it has been ☒ documented in the relevant Chapters with acknowledgement

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‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

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Acknowledgments

Thank you to my supervisor, PROFESSOR Tom Denson. You provided countless opportunities, advice, and assistance: both for my thesis and my career. You allowed me to learn independently, but never hesitated to provide extra help when I needed it. Your guidance helped shape me into a better researcher, mentor, and writer. I immensely appreciate your endless encouragement, enthusiasm, generosity, and support. It has been a pleasure to learn from you. Thank you to my co-supervisor, Dr. Chris Donkin. Your expertise guided the cognitive aspects of my research and helped me improve my ideas and writing. I appreciate all the time and effort you spent providing feedback and advice. Your humor and enthusiasm for my ideas helped me to relax during stressful periods and learn to be confident and proud of my work. I want to thank the Level 13 research lab – Joanne Beames, Miriam Capper, Siobhan O’Dean, Dilan Sellahewa, and Liz Summerell. You all provided much needed motivation, advice and support. The countless chats in 1306 never failed to provide perspective and solidarity. Fireside chats and walks along the beach were a highlight. I also appreciate the endless hours that the interns and volunteers put into collecting data. A special mention to Nick Levy, for being by my side throughout this journey since our first year. Thank you to Dr. Peter Geelan-Small for your initial assistance writing code. Thank you to Professor Barry Markovsky. Your advice was instrumental to my confidence as a researcher. You encouraged me to break down barriers, no matter how established, and believe in my ideas. Thank you to my partner Tek. You are the reason I started to love my research. Thank you for believing in me, for supporting me, and for listening to a never-ending record. Your flexibility and positive attitude helped ease my worries and reminded me what is important. Thank you for facilitating adventures and lifting my spirits when I needed it. Thank you to my friends. I appreciate all the times you let me rant about my research, made me dinner, helped me solve problems, made me laugh, drank countless cups of tea with me, and took me on adventures to get away from it all. Your friendship has kept my sanity intact and my spirits high. A special mention to Lindsay and Jason, for growing with me, inspiring me, and always being there. x

Abstract

In this thesis, participants overestimated stimulus durations following emotional and parasympathetic inductions, which heightened arousal, relative to lower levels of arousal. Participants also overestimated stimulus durations following exposure to negative stimuli, relative to positive stimuli. Additionally, participants induced to experience immersion during a videogame overestimated stimulus durations relative to participants distracted by a timer while playing. Stimulus durations were also overestimated by individuals who were high in a specific time perspective personality dimension. However, this result was limited to instances in which the emotional recall task they completed was incongruent with this dimension. Overall, this thesis provided evidence that arousal, the autonomic nervous system, emotional valence, attention, and personality may directly underlie how emotions influence time perception. These results might eventually illustrate constructive ways to update and expand existing models of time perception.

Table of Contents

INCLUSION OF PUBLICATIONS STATEMENT ...... IV

ORIGINALITY STATEMENT ...... VI

AUTHENTICITY STATEMENT...... VIII

ACKNOWLEDGEMENTS ...... IX

ABSTRACT ...... X

PUBLICATIONS ...... XV

PRESENTATIONS ...... XVI

LIST OF TABLES ...... XVII

LIST OF FIGURES ...... XIX

LIST OF KEY ABBREVIATIONS ...... XXI

CHAPTER 1. GENERAL INTRODUCTION ...... 1

OVERVIEW ...... 2 REVIEW OF EMOTION LITERATURE ...... 4 REVIEW OF TIME PERCEPTION LITERATURE ...... 6 EMOTIONS AND TIME PERCEPTION ...... 12 OVERALL RESEARCH AIMS OF THIS THESIS ...... 28

CHAPTER 2. EXPERIMENTAL METHODS FOR INDUCING BASIC EMOTIONS ...... 29

CHAPTER INTRODUCTION ...... 29

PUBLICATION I: A REVIEW OF BASIC EMOTIONS ...... 31

ABSTRACT ...... 32

INTRODUCTION ...... 33

BASIC EMOTION INDUCTION PARADIGMS ...... 33

METHODS ...... 34

LITERATURE SEARCH ...... 34

EFFICACY GUIDELINES ...... 35

CONTROL GROUPS ...... 37

RESULTS ...... 38

ANGER ...... 38 DISGUST ...... 40 SURPRISE ...... 42 xii

HAPPINESS ...... 44 FEAR ...... 46 SADNESS ...... 48

DISCUSSION ...... 50

SUBJECTIVE EMOTIONAL EXPERIENCE ...... 50 LIMITATIONS ...... 51 CONCLUSION ...... 51

CHAPTER DISCUSSION ...... 53

CHAPTER 3. EXPERIMENT 1: DO CHANGES IN AUTONOMIC NERVOUS SYSTEM ACTIVITY INFLUENCE HOW PEOPLE PERCEIVE TIME? ...... 54

INTRODUCTION ...... 54

PHYSIOLOGICAL AROUSAL AND TIME PERCEPTION ...... 55 EMOTIONAL AROUSAL AND TIME PERCEPTION ...... 57 THE PRESENT RESEARCH...... 58

METHODS ...... 59

PARTICIPANTS AND DESIGN ...... 59 MATERIALS AND PROCEDURE ...... 60

RESULTS ...... 63

CALCULATING TIME PERCEPTION VARIABLES ...... 63

PRELIMINARY ANALYSES ...... 64

PRIMARY ANALYSES ...... 68

MODERATION AND CORRELATION ANALYSES ...... 72

CHAPTER DISCUSSION ...... 73

GENERAL DISCUSSION ...... 73 IMPLICATIONS ...... 74

LIMITATIONS AND FUTURE RESEARCH ...... 76 CONCLUSIONS ...... 80

CHAPTER 4. EXPERIMENT 2: HOW DO AUTONOMIC AROUSAL AND EMOTIONAL VALENCE INFLUENCE TIME PERCEPTION? ...... 81

INTRODUCTION ...... 81

PHYSIOLOGICAL AROUSAL AND TIME PERCEPTION ...... 82 VALENCE ...... 82 SURPRISE ...... 85 THE PRESENT RESEARCH...... 85

METHODS ...... 86 xiii

PARTICIPANTS AND DESIGN ...... 86 MATERIALS AND PROCEDURE ...... 88

RESULTS ...... 90

CALCULATING TIME PERCEPTION VARIABLES ...... 90

PRELIMINARY ANALYSES ...... 91

PRIMARY ANALYSES ...... 102 CORRELATIONS ...... 105

CHAPTER DISCUSSION ...... 106

GENERAL DISCUSSION ...... 106 IMPLICATIONS ...... 110

LIMITATIONS AND FUTURE RESEARCH ...... 111 CONCLUSIONS ...... 113

CHAPTER 5. EXPERIMENT 3: ATTENTING TO TEMPORAL AND NON-TEMPORAL CUES DISRUPTS FLOW STATE IN VIDEOGAME PLAY, BUT ONLY ATTENDING TO TEMPORAL CUES SPEEDS UP TIME PERCEPTION ...... 115

INTRODUCTION ...... 115

ATTENTION AND TEMPORAL CUES ...... 115 FLOW STATE AND TIME PERCEPTION ...... 118 THE PRESENT RESEARCH...... 119

METHODS ...... 120

PARTICIPANTS AND DESIGN ...... 120 MATERIALS AND PROCEDURE ...... 121

RESULTS ...... 125

CALCULATING TIME PERCEPTION VARIABLES ...... 125

PRELIMINARY ANALYSES ...... 125

PRIMARY ANALYSES ...... 126

MODERATION ANALYSIS...... 134

CHAPTER DISCUSSION ...... 134

CUES AND OVERALL FLOW STATE AND GAME ENGAGEMENT ...... 136

FLOW STATE AND TIME PERCEPTION ...... 138 IMPLICATIONS ...... 139

LIMITATIONS AND FUTURE RESEARCH ...... 141 CONCLUSIONS ...... 142

CHAPTER 6. EXPERIMENT 4: DO INDIVIDUAL DIFFERENCES IN HOW PEOPLE THINK ABOUT TIME ALTER THE SUBJECTIVE PASSAGE OF TIME? ...... 143 xiv

INTRODUCTION ...... 143

PERSONALITY AND TIME PERCEPTION ...... 143 INDIVIDUAL DIFFERENCES IN TIME PERCEPTION: THE ROLE OF INCONGRUENCE ...... 144 THE PRESENT RESEARCH...... 147

METHODS ...... 147

PARTICIPANTS AND DESIGN ...... 147 MATERIALS AND PROCEDURE ...... 149

RESULTS ...... 150

CALCULATING TIME PERCEPTION VARIABLES ...... 150

PRELIMINARY ANALYSES ...... 151

PRIMARY ANALYSES ...... 152

CHAPTER DISCUSSION ...... 154

GENERAL DISCUSSION ...... 155 IMPLICATIONS ...... 157

LIMITATIONS AND FUTURE RESEARCH ...... 157 CONCLUSIONS ...... 159

CHAPTER 7. GENERAL DISCUSSION ...... 160 OVERVIEW ...... 160

THE ROLE OF AROUSAL IN TIME PERCEPTION...... 162 THE ROLE OF VALENCE IN TIME PERCEPTION ...... 167

THE ROLE OF ATTENTION IN TIME PERCEPTION...... 167 THE ROLE OF PERSONALITY IN TIME PERCEPTION ...... 169 IMPLICATIONS ...... 170 LIMITATIONS AND FUTURE DIRECTIONS ...... 172

CONCLUSIONS ...... 176

REFERENCES ...... 177

APPENDIX A: ADDITIONAL INFORMATION FOR CHAPTER 3 ...... 244

APPENDIX B: ADDITIONAL INFORMATION FOR CHAPTER 4 ...... 252

APPENDIX C: ADDITIONAL INFORMATION FOR CHAPTER 5 ...... 267

APPENDIX D: ADDITIONAL INFORMATION FOR CHAPTER 6 ...... 290

Publications

*Siedlecka, E., & Denson, T. F. (2019). Experimental methods for inducing basic

emotions: A qualitative review. Emotion Review, 11(1), 87-97.

https://doi.org/10.1177/1754073917749016

[Chapter 2]

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Presentations

I presented several studies reported in this thesis at conference and university proceedings as outlined below:

Conference and University Presentations

*Siedlecka, E. (2018, November). Creating a better model of emotion and time

perception. Paper presented at the University of New South Wales, Sydney,

Australia.

*Siedlecka, E. (2018, November). How to induce basic emotions. Paper presented at the

L’Oreal Girls in Science Day, University of New South Wales, Sydney,

Australia.

*Siedlecka, E. (2018, June). The role of psychological factors in time perception. Paper

presented at the Université d'Auvergne, Clermont-Ferrand, France.

*Siedlecka, E. (2018, April). Emotion and time perception: the role of physiology.

Paper presented at the Society of Australasian Social Psychologists Conference,

Wellington, New Zealand.

*Siedlecka, E. (2017, April). Emotion and time perception: the role of arousal. Paper

presented at the Society of Australasian Social Psychologists Conference,

Melbourne, Australia.

Poster Presentations

*Siedlecka, E. (2018, July). Emotions and time perception. Poster presented at the

European Association of Social Psychology Summer School, Zurich,

Switzerland.

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List of Tables

Table 1.1 Table of different paradigms within time perception research,

possible results with these paradigms, and how to interpret these

results.……………………………………………………………………… 2

Table 2.1 Examples of emotion induction techniques by category……………… 33

Table 2.2 Summary of how each basic emotion influences specific

physiological variables within the literature…………………………… 36

Table 2.3 Summary of the most effective induction methods for each emotion… 38

Table 3.1 Changes in sympathetic and parasympathetic activity over the

course of Experiment 1………………………………………………… … 65

Table 3.2 Changes in bisection point and Weber ratio over the course of

Experiment 1………………………………………………………………… 70

Table 4.1 Mean parasympathetic and sympathetic activity levels between

autonomic conditions, before and after the surprise manipulation… 92

Table 4.2 Changes in physiological measures following the surprise

manipulation………………………………………………………………… 99

Table 4.3 Means for physiological measures before and after the surprise

manipulation ……………………………………………………………… 99

Table 4.4 Levels of positive, negative, and overall emotional intensity for

autonomic and valence conditions………………………………………… 101

Table 4.5 ANOVA summary table for the effects of autonomic arousal,

emotional valence, and time on time perception………………………… 102

Table 4.6 Bisection point and Weber ratio between valence conditions, before

and after the surprise manipulation……………………………………… 104 xviii

Table 5.1 Mean bisection point and Weber ratio for attention conditions in

Experiment 3……………………………………………………………… 127

Table 5.2 Flow State and Flow State subscale scores for attention conditions

in Experiment 3…………………………………………………………… 129

Table 5.3 Game Engagement and Game Engagement subscale scores for

attention conditions in Experiment 3…………………………………… 132

Table 6.1 Past subscale and bisection point means for participants who were

and were not past-oriented………………………………………..…….… 152

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List of Figures

Figure 1.1 Stages of the pacemaker-accumulator model………………………… 7

Figure 2.1 Probability of answering ‘long’ in the Experiment 1 bisection task… 65

Figure 2.2 Changes in sympathetic activity over the course of Experiment 1… 66

Figure 2.3 Changes in parasympathetic activity over the course of Experiment

1……………………………………………………………………………… 67

Figure 2.4 Interactions between experimental block and stimulus content on

time perception and temporal sensitivity in Experiment 1……………. 69

Figure 2.5 Changes in bisection point and Weber ratio over the course of

Experiment 1……………………………………………………………… 70

Figure 3.1 Probability of answering ‘long’ in the Experiment 2 bisection task… 91

Figure 3.2 Parasympathetic activity between autonomic conditions over the

course of Experiment 2…………………………………………………… 94

Figure 3.3 Sympathetic activity between autonomic conditions over the course

of Experiment 2………………………………………..…………………… 96

Figure 3.4 The effects of valence and time on bisection point and Weber ratio

in Experiment 2…………………………………………………………… 103

Figure 3.5 The effects of autonomic activity and time on bisection point and

Weber ratio in Experiment 2…………………………………………..… 103

Figure 3.6 Pearson’s correlations between Weber ratio and low and high

frequency power in Experiment 2………………………………………. 105

Figure 4.1 Probability of answering ‘long’ in the Experiment 3 bisection task… 126

Figure 4.2 Effects of attention condition on time perception in Experiment 3… 127

Figure 4.3 Effects of attention condition on the probability of answering ‘long’

in Experiment 3…………………………………………………………… 128 xx

Figure 4.4 Overall flow state scores for attention conditions…………………… 130

Figure 4.5 Action-awareness merging and transformation of time scores for

attention conditions……………………………………………………… 131

Figure 4.6 Concentration on task at hand and perceived control scores for

attention conditions……………………………………………………… 132

Figure 4.7 Game engagement scores for attention conditions…………………… 133

Figure 4.8 Game engagement flow scores for attention conditions……………… 134

Figure 5.1 Probability of answering ‘long’ in the Experiment 4 bisection task… 151

Figure 5.2 Bisection points for participants who were and were not past-

oriented …………………………………………………………………… 152

Figure 5.3 Probability of answering ‘long’ for past-oriented and not past-

oriented participants in the Experiment 4 bisection task …………… 153

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List of Key Abbreviations

Time Perception Abbreviations

BP Bisection point

WR Weber ratio

Statistical Abbreviations

ANOVA Analysis of variance

Physiological Measurement Abbreviations

RR Intervals between consecutive heart beats

RMSSD Root mean square of successive differences between R-R intervals

SDRR Standard deviation of the R-R intervals

pRR50 Ratio of successive R-R intervals that differ by more than 50ms to

R-R intervals

CHAPTER 1: General Introduction

Time perception is the subjective judgment of the speed or duration of real time (Khan, Sharma, & Dixit, 2006). Accurate time perception is crucial for a range of everyday behaviours like driving and cooking, although various factors can cause people to misperceive time (e.g., emotions; Buhusi & Meck, 2005; Droit-Volet & Meck, 2007; Wittmann, 2013). Accurate time perception is important because time misperception can impair behavioural functioning. For instance, inaccurate time perception can lead to adverse consequences like a car accident or burning a meal (e.g., Schirmer, 2011).

Emotions are one factor that influence how people perceive time (for a review, see Droit-Volet & Meck, 2007). Existing models of time perception differ in their explanations of how emotions influence time perception. Some models focus on biological mechanisms, some models on neurological mechanisms, whereas other models focus on psychological mechanisms (Allman, Teki, Griffiths, & Meck, 2014; Ivry & Schlerf, 2008; Treisman, 1963; Zimbardo & Boyd, 1999). Moreover, some models measure time perception quantitatively in milliseconds and seconds, whereas other models focus on how the passage of time feels (Treisman, 1963; Zimbardo & Boyd, 1999). Each model of time perception possesses unique strengths and limitations. Despite the differences among these models, one common factor is that many of these models feature the influence of psychological factors (e.g., arousal and attention). One way of expanding knowledge about time perception may be to build upon existing evidence for specific psychological factors in these models.

To this end, this thesis aimed to evaluate the plausibility of explaining how emotions influence time perception using psychological factors that directly underlie this relationship. The factors I examined were arousal, physiology of the autonomic nervous system, emotional valence, attention, and a personality dimension called time perspective. I selected these factors because they are components featured in models of either emotion and/or time perception. Distinguishing between the effects of these factors on time perception may increase the theoretical understanding of how emotions and other phenomena influence time perception.

This thesis broadly examined psychological factors through which emotions influence time perception. I found evidence that arousal, autonomic physiology, 2

emotional valence, attention, and time perspective all influence time perception. As such, these experiments identified or suggested psychological factors through which emotions may cause individuals to misperceive time. The results from this thesis could constructively inform models of time perception by identifying how emotions and other related phenomena influence time perception. The results from this thesis have additional implications for both consequences of misperceived time (e.g., car accidents) and disorders that are characterised by deficits in time perception (e.g., depression; Schirmer, 2011; Thönes & Oberfeld, 2015).

Overview of the Research

Scientists typically measure time perception by determining whether participants judge or reproduce a specific duration of time as longer or shorter than it actually is. When I describe previous research findings in this thesis, I use the operational language that reflects the methodology used in the research. However, using operational language for the dependent measures when describing previous time perception experiments can be non-intuitive. For example, overestimated durations indicate slower time perception, whereas overproduced durations indicate faster time perception (for a review, see Pande & Pati, 2010). To aid in the interpretation of the findings discussed, Table 1.1 describes a few example paradigms within time perception research, possible results within these paradigms, and explains how to interpret these results.

Notably, there are many more paradigms used to measure time perception other than the example paradigms described in Table 1.1. For instance, temporal generalization and temporal production tasks are both paradigms that measure time perception (Gil & Droit-Volet, 2011b). Temporal generalization involves participants judging whether stimulus durations are shorter, longer, or equivalent to their memory of a specific duration. Temporal production involves participants pressing a key when they think a specific duration has elapsed. However, most of the paradigms used within time perception research operationalise and compare dependent measures by under/overproduction and under/overestimation.

Table 1.1

Table of different paradigms used in time perception research, possible results within these paradigms, and how to interpret these results. Estimation involves judging the duration of an 3

interval of time. Reproduction involves replicating the duration of time a stimulus (e.g., a sound) was presented for (e.g., by producing the sound for the same elapsed duration).

Paradigm Example Possible Result Interpretation Example Citation Participants view an Overestimated Slower time Smith, McIver, Di image, then estimate the duration perception Nella, & Crease, 2011 duration they viewed Underestimated Faster time Smith et al., 2011 the image for duration perception Participants listen to a Overproduced Faster time Mioni, Stablum, sound, then reproduce duration perception Prunetti, & Grondin, the duration they heard 2016 the sound for Underproduced Slower time Mioni et al., 2016 duration perception

The first part of this thesis reviewed the efficacy of different emotion induction techniques. By reviewing this evidence, I could ensure that any manipulations within this thesis were likely to induce the desired emotion. The results of this review, in addition to identifying effective emotion induction paradigms, revealed that basic emotions do not elicit homogenous physiological responses (e.g., Cacioppo, Berntson, Larsen, Poehlmann, & Ito, 2000). This review informed the designs of Experiments 1 and 2. These experiments examined how the two autonomic branches that comprise the physiological component of emotions (i.e., parasympathetic and sympathetic activity) change time perception.

In addition to autonomic physiology, Experiments 1 and 2 examined the effects of arousal and valence on time perception. In Experiment 1, participants with heightened parasympathetic activity overestimated stimulus durations relative heightened sympathetic activity, although Experiment 2 did not replicate this result. Both Experiments 1 and 2 additionally found that heightened parasympathetic activity correlated with improved temporal sensitivity relative to lower parasympathetic activity. Improved temporal sensitivity is an improved ability to discriminate between temporal durations (e.g., Droit-Volet, Mermillod, Cocenas-Silva, & Gil, 2010). In Experiment 2, improved temporal sensitivity also correlated with overestimated durations. Similarly, individuals in a negatively surprised state overestimated durations relative to individuals 4

in a positively surprised state. These experiments suggest that autonomic activity and emotional valence might be included within time perception models.

Experiment 3 examined the effects of attending to concurrent temporal and non- temporal cues compared to immersion on time perception. In this experiment, participants were either induced to experience immersion while playing a videogame or attended to concurrent cues while playing the game. Participants induced to experience immersion overestimated stimulus durations compared to participants who concurrently attended to a timer (i.e., a temporal cue). However, attending to cues unrelated to time did not change time perception relative to immersion. Within existing models of time perception, individuals with a broader distribution of attentional resources, such as when an individual attends to concurrent cues, underestimate stimulus duration (e.g., Brown, 1985). As such, this experiment provided novel evidence that individuals only underestimate stimulus durations when the temporal information is relevant.

Experiment 4 examined the effects of time perspective, a personality dimension assessing how individuals think of time in their everyday lives, on time perception. One of these perspectives, the past time perspective, measures how regularly individuals think about past memories. In this experiment, individuals who were high in the past time perspective personality dimension overestimated stimulus durations. However, this was limited to instances in which the emotional recall task they completed was incongruent with this personality dimension. The results from this experiment suggest that at least one relevant personality dimension might be included within models of time perception.

Review of Emotion Literature

This section reviews and discusses the components of emotions. There are two primary types of emotion models known as discrete and dimensional models, which reflect different approaches to the study of emotion. There is evidence supporting the validity of both model types, and some models account for both discrete and dimensional frameworks (e.g., Harmon-Jones, Harmon-Jones, & Summerell, 2017; Scherer, 2000). In a third type of model, referred to as componential or appraisal models, emotions are derived from cognitive evaluations of events (Scherer, Mortillaro, & Mehu, 2013). However, I did not examine components of appraisal models in this thesis. I did not review appraisal models because appraisals are more distal causes of 5

emotions than psychological factors featured in other models of emotion. That is, although appraisals are thought to elicit emotion, psychological factors like arousal determine the appraisal pattern generated.

Discrete models of emotion. Discrete models identify a universal set of unique primary emotions and predict different effects and functions for each emotion (Scherer, 2000). Primary emotions are those that automatically activate in response to a stimulus (Sokolova & Fernández-Caballero, 2015). For example, fear redirects blood flow to optimise mobility - this pattern of response should prepare an individual to fight or run from a threat (Misslin, 2003). As such, discrete models characterise emotions by their physiological response and function.

The most popular theoretical framework within discrete emotions is that of basic emotions (Gunes & Pantic, 2010). Coined by Paul Ekman (1982), a basic emotion is one that is intrinsic and universal. There is sufficient evidence to validate the existence of certain basic emotions. For instance, Ekman (1982) instructed individuals in different cultures to identify emotions in photographs of emotional faces. Ekman (1982) concluded that happiness, sadness, surprise, fear, anger and disgust were universally recognised. Considerable research replicated the evidence for the universality and intrinsic nature of these six basic emotions (for reviews, see Barrett & Wager, 2006; Izard, 1992; Scherer & Wallbott, 1994; Tracy & Randles, 2011). Because there is considerable evidence supporting the validity of discrete models, individual basic emotions (e.g., anger) may exert distinct and independent effects on time perception.

Dimensional models of emotion. Dimensional models define emotions by a combination of one or more continuous factors (i.e., dimensions) that underlie all emotions. Dimensional models consider all emotions to be systematically related but differ in the number of dimensions they propose. Unidimensional models focus on a single bipolar dimension underlying all emotional experience, such as emotional valence (Scherer, 2000). Bidimensional models include two independent bipolar dimensions. For example, in Plutchik’s (1980; 2001) wheel of emotions, differences in valence and intensity characterise four bipolar emotion pairs (e.g., joy-sadness). Multidimensional models consider the influence of 3 or more independent dimensions. For example, in the tridimensional positive-activation negative-activation model, changes in positive valence, negative valence and arousal underlie all emotions (Watson & Tellegen, 1985; Watson, Weise, Vaidya, & Tellegen, 1999). Dimensional emotion 6

models likewise differ in the dimensions thought to underlie emotion. Examples of different dimensions include activation/arousal, valence, dominance, pleasantness, potency, attention and unpredictability (e.g., Fontaine, Scherer, Roesch, & Ellsworth, 2007; Osgood, May, & Miron, 1975; Osgood, Suci, & Tannenbaum, 1957; Schlosberg, 1954). As such, dimensional models define emotions by their underlying components, but the defined dimensions and dimension numbers differ between models.

A popular dimensional model is the circumplex model, which blended properties from both discrete and dimensional models. The circumplex model identified a bipolar variant for each basic emotion on a continuum of emotional experience (Posner, Russell, & Peterson, 2005; Russell, 1980). Differences in valence (i.e., positive/pleasant vs. negative/unpleasant) and arousal (i.e., relaxed vs. aroused) dimensions explain the entire continuum of emotional experience (including basic emotions) in this model. This model further subcategorized these dimensions, such that positive and negative dimensions could involve either low or high levels of arousal.

There is much evidence supporting the validity of dimensional models, particularly the circumplex model. Some researchers used emotional stimuli, such as facial expressions and emotional words, to both computationally map and multidimensionally scale emotional response. In these studies, changes in valence and arousal could sufficiently explain most emotional responses and labels (e.g., Abelson & Sermat, 1962; Bush, 1973; Cliff & Young, 1968; Kring, Barrett, & Gard, 2003; Remington, Fabrigar, & Visser, 2000; Russell, 1980; 1983; Russell & Bullock, 1985; Schlosberg, 1952). Additionally, there are strong correlations between different self- reported emotions of the same valence (Russell & Carroll, 1999; Watson et al., 1999). For instance, individuals who report feeling a negative emotion are likely to report feeling other negative emotions (Watson & Clark, 1992). This finding supports the validity of dimensional models, including the circumplex model, as it suggests that emotions can be considered on a spectrum rather than necessitating distinct categories. Because there is considerable evidence in favour of dimensional models, emotions may influence time perception through their component dimensions (e.g., arousal and valence). In this thesis, I examine how components of the circumplex model influence time perception because there is substantial evidence for this dimensional model.

Review of Time Perception Literature 7

Emotions influence a wide range of cognitive factors, one of which is time perception (Droit-Volet & Meck, 2007). In this section, I review and evaluate the dominant frameworks within time perception models.

According to some researchers, any model of time perception must conform to two psychophysical laws that reflect properties of real time (Allman et al., 2011). The first law states that subjective time should linearly increase as a function of real time. Secondly, any model of time perception must conform to Weber’s law (i.e., scalar invariance). This latter law states that increases in stimulus intensity proportionally increase variability in time perception; that is, longer durations are more difficult to accurately perceive. Two models of time perception that conform to these laws are the pacemaker-accumulator model and intrinsic models.

Figure 1.1. Stages of the pacemaker-accumulator model. Adapted from Allman et al., (2014), Church (1984), Gibbon, Church, & Meck, (1984), Meck (1984), and Treisman (1963; 1984).

Pacemaker-accumulator model. The pacemaker-accumulator model is the dominant model of time perception and proposes that time perception changes due to factors that influence the internal clock (e.g., Fallow & Voyer, 2013; Treisman, 1963; 1984; Wearden, 2003). The internal clock is an assumed biological mechanism that people use to keep track of real time. According to the pacemaker-accumulator model, psychological factors such as emotional dimensions change time perception depending on which component within the internal clock that they influence. As seen in Figure 1.1, 8

these components are the pacemaker, switch, and accumulator. Arousal increases the rate of the pacemaker and, as such, causes individuals to overestimate stimulus durations. The broad or narrow distribution of attention, on the other hand, changes time perception by changing switch latency. Switch latency refers to the changes in the latency of the switch in the internal clock opening and closing (Lake, LaBar, & Meck, 2016). Broader distribution of attention causes individuals to underestimate stimulus durations by opening this switch (Tse, Intriligator, Rivest, & Cavanagh, 2004).

Arousal and attention are the two primary psychological variables featured in the pacemaker-accumulator model. Some researchers suggest that the effects of arousal might be isolated mathematically from the effects of attention (e.g., Ogden, Moore, Redfern, & McGlone, 2014). These researchers suggest that the effect of arousal on time perception increases in magnitude as the duration that an individual views an arousing stimulus for increases (i.e., a multiplicative effect; Meck, 1983). In contrast, the effect of attention on time perception stays constant regardless of duration (i.e., an additive effect). As such, researchers might be able to determine which psychological variable is driving an effect on time perception by examining the interaction between duration and time perception. However, some research suggests that this approach is only reliable for experiments that induce altered time perception using physiological manipulations (e.g., electric shock) and not emotional manipulations (e.g., images; Gil & Droit-Volet, 2012; Grommet et al., 2011). Moreover, as arousal and attention often influence time perception in conjunction, these effects are difficult to isolate (Ogden et al., 2014). This research suggests a need to better isolate the effects of attention from arousal when examining the effects of emotion on time perception.

Critique of the pacemaker-accumulator model. Psychological factors assumed to cause individuals to overestimate duration in the pacemaker-accumulator model are heightened arousal and dopamine levels. Psychological factors assumed to cause individuals to underestimate duration in this model are broader attentional distribution and higher cognitive load (Lejeune, 1998). Individuals often overestimate and underproduce stimulus durations when in a heightened state of arousal relative to lower levels of arousal (Buhusi & Meck, 2002; 2005; Cheng, Ali, & Meck, 2007; Cheng, MacDonald, & Meck, 2006; Droit-Volet & Meck, 2007). Similarly, dopaminergic drugs typically cause individuals to overestimate stimulus durations whereas dopaminergic agonists typically cause individuals to underestimate stimulus 9

durations (Droit-Volet & Meck, 2007). Furthermore, when attention is more widely distributed, or cognitive load is higher, individuals often underestimate stimulus durations (Brown, 1985). Thus, there is evidence for certain psychological factors in the pacemaker-accumulator model, such arousal, autonomic activity, dopamine, and attention. However, evidence that these factors influence time perception does not provide evidence that these effects are mediated by structural components (i.e., the switch, pacemaker, and internal clock) specified by the pacemaker-accumulator model.

There is little evidence suggesting the structural components of the pacemaker- accumulator model exist. A popular criticism of this model is the lack of plausibility for a neural structure that keeps track of time (Simen, Rivest, Ludvig, Balci, & Killeen, 2013). Despite evidence for neural areas involved in time perception, there is still no evidence for an internal receptor or clock with a constant rate (Block, 2003; Dragoi, Staddon, Palmer, & Buhusi, 2003; Gibbon, Malapani, Sale, & Gallistel, 1997; Simen et al., 2013). Moreover, proposing a direct internal receptor for time perception seems assumptive because only a few select experiences (e.g., pain) have direct receptors (Wicher, 2013). Because the psychological factors specified in this model directly influence time perception, the structural components that indirectly explain changes in these factors may be unnecessary.

One way of addressing these limitations may be to incorporate the psychological factors that influence time perception (e.g., arousal) more directly within the model. Another way of addressing these limitations may be to incorporate novel factors from other existing models of emotion and time perception. For example, Hogan (1978) proposed that personality is a key determinant of time perception, although other models of time perception did not incorporate his research. To achieve these goals, this thesis examined how various cognitive, personality, and emotional factors influence time perception.

Intrinsic models. In response to the limited evidence for an internal clock, many models emerged as alternatives to the pacemaker-accumulator model (e.g., Arcediano & Miller, 2002; Bizo & White, 1994; Church & Broadbent, 1990; Dragoi et al., 2003; Grossberg & Schmajuk, 1989; Karmarkar & Buonomano, 2007; Killeen, 1991; Killeen & Fetterman, 1988; Shi, Church, & Meck, 2013; Staddon & Higa, 1999; for reviews, see Buonomano & Karmarkar, 2002; Buhusi & Meck, 2005; Gibbon et al., 1997). Rather than using an internal clock, intrinsic models propose that different neural 10

systems coordinate to inform time perception. In these models, the duration of a stimulus is processed inherently with the other properties of the stimulus (e.g., colour; Maniadakis & Trahanias, 2014). The proposed mechanisms for the coordination of different neural systems in these models are modality and context (for a review, see Ivry & Schlerf, 2008).

Critique of intrinsic time perception models. In intrinsic models, all psychological factors are processed depending on modality and dynamics in the relevant neural network (Ivry & Schlerf, 2008). Modality refers to type of sensory perception (e.g., visual, auditory; Huestegge & Hazeltine, 2011). For example, visual stimuli influence time perception based on the dynamics of neurons in the visual cortex. Conditions that increase the magnitude of neural activity in certain areas likewise lengthen perceptions of duration for stimuli processed in those areas (Pariyadath & Eagleman, 2007; 2012).

How psychological factors influence time perception also depends on context. Context refers to the circumstances under which a stimulus is processed (Barch, Carter, MacDonald III, Braver, & Cohen, 2003). Contexts that decrease the magnitude of neural activation may impair the accuracy of time perception judgments. For example, participants in one experiment could better discriminate between millisecond durations when the target duration was presented in a fixed rather than variable context (Karmarkar & Buonomano, 2007).

There is evidence that time perception is specific to modality, although some modalities influence time perception more than others. Disrupting processing in the visual cortex, for instance, impairs time perception, but only for visual stimuli (Kanai, Lloyd, Bueti, & Walsh, 2011). Similarly, more salient stimuli (e.g., brighter, louder) can lengthen perceptions of time relative to less salient stimuli (Xuan, Zhang, He, & Chen, 2007). Some research may suggest that this longer time perception results from increased activity in relevant neural areas (Eagleman, 2008). However, visual stimuli generally produce more variable time perception estimates than auditory stimuli (Goldstone, Boardman, & Lhamon, 1959; Merchant, Zarco, & Prado, 2008). There is also evidence that context can anchor time perception judgments, or that some stimuli only influence time perception in relevant contexts. For example, people estimate the durations of number streams suffixed by a larger mass (e.g., kilogram) as longer than those suffixed by a smaller mass (e.g., gram; Lu, Hodges, Zhang, & Zhang, 2009). In 11

another experiment, men overestimated the duration of red stimuli relative to blue stimuli, but not women (Shibasaki & Masataka, 2014). The authors attributed this result to context, as men associate the colour red with competition, but women do not.

One limitation of intrinsic models is that they cannot easily explain the influence of psychological factors from other time perception models. Time perception in intrinsic models is specific to modality and context. Thus, these models cannot explain why psychological factors like attention can influence time perception when switching between modalities. In one experiment, attending to an auditory stimulus in a temporal discrimination task facilitated performance when attending to a visual stimulus in a comparable task (Warm, Stutz, & Vassolo, 1975). Similarly, intrinsic models cannot easily explain why certain emotions influence time perception similarly irrespective of changes in modality and context. For example, scared individuals overestimate stimulus durations over a wide range of modalities and induction methods. Furthermore, the research supporting intrinsic models only assessed time perception for durations less than 100ms (for a review, see Ivry & Schlerf, 2008). However, there is evidence that some psychological factors influence time perception differently at different perceptual stages (Smith et al., 2011). For example, individuals underestimate arousing images of shorter durations (less than 300ms) and overestimate images of longer durations (more than 400ms). These limitations of intrinsic models may obscure the effects of certain psychological factors on time perception.

Non-dominant models of time perception. The pacemaker-accumulator model and intrinsic models are the two main frameworks assessing how individuals perceive time (Ivry & Schlerf, 2008). However, there are many additional models of time perception that feature other psychological factors. These factors include personality dimensions, perceived feelings about time, awareness of the time, and memory decay of associations between time perception and other stimuli (e.g., body movement, previous events; Addyman, French, Mareschal, & Thomas, 2011; Hasselmo, 2009; Hogan, 1978; Trope & Liberman, 2010; Zimbardo & Boyd, 1999). However, these models often only include the influence of one or two specific psychological factors. For example, Addyman et al.’s (2011) model focused solely on memory associations between timing and limb movements. As such, these models likely oversimplify how phenomena like emotions influence time perception. However, some psychological factors from these models may be useful for explaining changes in time perception. In this thesis, I 12

examine how personality changes time perception. Personality is featured in Hogan’s (1978) model of time perception.

There are also some neurological models that may help explain how emotions influence time perception. In one of these models, the increased accuracy of perceived bodily information (e.g., depressive symptoms, cardiac activity) causes individuals to overestimate duration (Craig, 2009; Di Lernia, Serino, Pezzulo, Pedroli, Cipresso, & Riva, 2018). In this model, the primary determinant of time perception is activity in the anterior insular cortex. Some neurological models may prove useful in explaining how emotions influence time perception. However, the evidence which suggests that specific neural areas are involved in determining time perception is variable and not strong (e.g., Dragoi et al., 2003; Ivry & Spencer, 2004). As such, I did not examine neurology in this thesis. However, I did examine some non-neurological components of these models in my thesis. For instance, Craig’s (2009) model links changes in time perception to physiological activity. In this thesis, I examine how physiological activity changes time perception.

Updating and consolidating existing models. Despite the differences between time perception models, one common feature is that many of these models include the influence of psychological factors (e.g., arousal). However, these models differ in how they explain the influence of these psychological factors. Intrinsic models propose that all psychological factors are processed similarly through changes in modality and context (Ivry & Schlerf, 2008). The pacemaker-accumulator model proposes that certain psychological factors, such as arousal, influence time perception (Droit-Volet & Meck, 2007). However, that model only explains these effects of psychological factors through mediation of the putative internal clock (Burle & Casini, 2001; Taagepera, 2008). Less common models of time perception consider the influence of many psychological factors; however, only in isolation. Hence, these models likely oversimplify the psychological factors involved in time perception. The lack of consistency in featured psychological factors within different time perception models indicates a need to consolidate and expand different models of time perception. In this thesis, I generated new knowledge that may eventually be used to update models of time perception.

Emotions and Time Perception 13

This section reviews the evidence for the effects of specific basic emotions, emotional dimensions, and cognitive components of time perception models on time perception.

How basic emotions influence time perception. This section examines how fear, anger, happiness, sadness and disgust influence time perception. I reviewed the effects of basic emotions on time perception because basic emotions may exert independent effects on time perception. The most studied basic emotion within the context of time perception is fear. The effects of fear on time perception were often examined using visual stimuli, including facial expressions of fear, and threatening stimuli (e.g., scary movies, pictures of snakes; Droit-Volet, Fayolle, & Gil, 2011; Gil & Droit-Volet, 2011a; 2012; Grommet, Droit-Volet, Gil, Hemmes, Baker, & Brown, 2011). Some experiments also emulated real-life situations that induce fear, such as anticipating an aversive stimulus or moving towards a stairwell blindfolded (Falk & Bindra, 1954; Hare, 1963; Langer, Wapner, & Werner, 1961). Further experiments examined phobic individuals presented with matched phobic stimuli, such as showing arachnophobic individuals a live spider (Watts & Sharrock, 1984). In these studies, scared participants overestimated stimulus durations compared to neutral controls (e.g., Buetti & Lleras, 2012; Droit-Volet et al., 2011; Falk & Bindra, 1954; Gil & Droit-Volet, 2011a; 2012; Grommet et al., 2011; Hare, 1963; Langer et al., 1961; Watts & Sharrock, 1984).

To the best of my knowledge, the effects of anger on time perception were only examined using pictures of angry faces. Participants viewing angry faces consistently overestimated stimulus duration compared to participants viewing neutral and happy faces (e.g., Gil & Droit-Volet, 2011a; Mioni, Meligrana, Grondin, Perini, Bartolomei, & Stablum, 2015; Siedlecka & Denson, 2019; Thayer & Schiff, 1975; Tipples, 2008; Tipples, Brattan, & Johnston, 2015). However, visual stimuli are not a very effective method of inducing anger (Siedlecka & Denson, 2019). Moreover, angry faces may not elicit anger as individuals may perceive angry faces as threatening (Belopolsky, Devue, & Theeuwes, 2010). As individuals often feel fear when threatened, the findings for angry faces may thus reflect the effects of fear, rather than anger, on time perception (Siedlecka & Denson, 2019).

The effects of sadness on time perception are mixed. In some experiments, participants overestimated stimulus durations compared after viewing sad faces and 14

images (e.g., images of plane carnage following a crash) compared to neutral stimuli (Droit-Volet et al., 2004; Gil & Droit-Volet, 2011a; 2012). In other experiments, participants underestimated the durations of sad music and faces compared to neutral stimuli (Droit-Volet, Bigand, Ramos, & Bueno, 2010; Mioni, Grondin, Meligrana, Perini, Bartolomei, & Stablum, 2017). Furthermore, one experiment found a significant positive correlation between increased levels of sadness and underestimated stimulus durations (Gil & Droit-Volet, 2009). However, time perception in other experiments did not differ when viewing sad faces and films relative to neutral faces and films (Droit- Volet et al., 2011; Tipples, 2008).

The effects of disgust on time perception are similarly mixed. In one experiment, participants overestimated the durations of disgusting pictures (e.g., burned and mutilated bodies) compared to neutral images (Gil & Droit-Volet, 2012). However, in two other experiments, time perception did not change when viewing disgusting images of food or facial expressions of disgust relative to neutral controls (Gil & Droit- Volet, 2011a; Gil et al., 2009). Thus, the evidence regarding the effects of disgust on time perception is inconclusive. Further research is needed to confirm whether disgust influences time perception, and in which direction.

The effects of happiness on time perception are also mixed. Participants in most experiments overestimated the durations of happy facial expressions relative to neutral facial expressions (Droit-Volet et al., 2004; Effron, Niedenthal, Gil, & Droit-Volet, 2006; Gil & Droit-Volet, 2011a; Mioni et al., 2017; Tipples et al., 2015). Furthermore, in another experiment, increased scores on a happiness scale positively correlated with overestimated stimulus durations (Gil & Droit-Volet, 2009). In other experiments, participants underestimated the durations of happy music and facial expressions compared to neutral controls (Droit-Volet et al., 2010; Tipples, 2008).

Although some emotions exert reliable effects on time perception (e.g., fear), the evidence for other emotions on time perception (e.g., happiness) is mixed. Notably, to the best of my knowledge, there is no research examining the effects of anger or surprise on time perception. These results suggest that some basic emotions may exert distinct independent effects on time perception. However, these results also suggest that there may be other factors that moderate or contribute to the effects of other basic emotions on time perception. Thus, basic emotions do not all appear to exert unique effects on time perception. The next section of this thesis explores if dimensions of 15

emotion and components of time perception models can explain the effects of emotion on time perception.

How specific psychological dimensions influence time perception. Arousal, autonomic activity and emotional valence are dimensions of emotion that also influence time perception. Although neither attention nor personality is a dimension of emotion, these factors both influence time perception and are components of time perception models. As such, attention and personality are also potentially psychological factors through which emotions change time perception. This section reviews the evidence for specific components and dimensions of emotion, as well as cognitive factors within time perception models, on time perception. Changes in these factors may explain how emotions influence time perception.

Arousal. Arousal is characterised by the intensity of both emotional and physiological experience (Cappo & Holmes, 1984). Researchers can induce arousal using paradigms designed to increase either emotional or physiological intensity (Gil & Droit-Volet, 2012). In conjunction, these emotional and physiological elements aid in differentiating between different emotions. Anger and fear, for instance, involve similar levels of emotional intensity, but different physiological responses (Ax, 1953). Similarly, negative emotions often elicit higher emotional intensity than positive emotions (Lang et al., 1997). It is hence important to consider both components of arousal when examining the effects of arousal on time perception. Arousal is one mechanism proposed for how emotions influence time perception and a feature in some models of time perception (Droit-Volet et al., 2004).

The cognitive component of arousal. Some experiments examined arousal by manipulating emotional intensity. Within these experiments, some experiments directly manipulated low and high levels of arousal by using images that elicit low (e.g., cemeteries) and high emotional intensity (e.g., mutilated and burned bodies; e.g., Smith et al., 2011). In these experiments, participants overestimated the durations of highly arousing stimuli compared to less arousing stimuli (Gil & Droit-Volet, 2012; Smith et al., 2011), although there were also interactions based on emotional valence and stimulus duration in these experiments. Similarly, participants often overestimate and underproduce the durations of arousing stimuli compared to neutral stimuli (Bar-Haim, Kerem, Lamy, & Zakay, 2009; Droit-Volet, Fayolle, & Gil, 2016; Effron et al., 2006; Gil & Droit-Volet, 2011a; Gil, Niedenthal, & Droit-Volet, 2007; Grommet et al., 2011; 16

Lee, Seelam, & O’Brien, 2011; Mella, Conty, & Pouthas, 2010; Thayer & Schiff, 1973; Tipples, 2008; Yamada & Kawabe, 2011). These experiments primarily used facial expressions of emotions to heighten arousal, with the notable exception of a few experiments (e.g., Grommet et al., 2011; Mella et al., 2010; Yamada & Kawabe, 2011). Likewise, individuals overestimate the durations of other stimuli associated with heightened arousal, such as bodily expressions of fear, live spiders, temperature stressors, danger, anticipation of aversive stimuli, caffeine, and increased room temperature (Droit-Volet & Gil, 2016; Droit-Volet et al., 2010; Fox, Bradbury, & Hampton, 1967; Hoaglund, 1966; Langer et al., 1961; Watts & Sharrock, 1984). Overestimated stimuli indicate slower time perception, whereas underestimated stimuli indicate faster time perception (Pande & Pati, 2010). These findings may suggest that more emotionally intense stimuli generally slow time perception relative to less emotionally intense stimuli.

In some experiments manipulating arousal using emotional stimuli, however, participants did not overestimate and underproduce durations during heightened arousal. In some experiments that manipulated emotional intensity, participants underestimated the durations of highly arousing and positive stimuli (Smith et al., 2011). Similarly, participants in some experiments underestimated and overproduced durations of arousing music, arousing images, and emotional facial expressions (e.g., Gil et al., 2009; Kellaris & Mantel, 1994; Lee et al., 2011). In other experiments, time perception did not change for participants following the presentation of arousing emotional videos, arousing images and emotional facial expressions, including one experiment that directly manipulated emotional intensity (Angrilli, Cherubini, Pavese, & Manfredini, 1997; Chebat, Gélinas-Chebat, Vaninski, & Filiatrault, 1995; Droit-Volet & Gil, 2016; Droit-Volet et al., 2011; Tipples, 2008). These mixed results may suggest that changes in emotional intensity alone cannot explain the effects of arousal on time perception.

The physiological component of arousal. One component of arousal is the intensity of physiological response. Additionally, one component of emotion is heightened physiological activity of the autonomic nervous system (e.g., Candland, Fell, Keen, Leshner, Tarpy, & Plutchik, 2003; Evans, 2014). The autonomic nervous system is the physiological network responsible for regulating physiological processes that occur without conscious control (Llewellyn-Smith & Verberne, 2011). As such, the autonomic nervous system regulates processes such as blood pressure, body 17

temperature, metabolism, sweating, and sexual response. This system receives input from sections of the central nervous system that process and integrate internal and external stimuli, such as those processing emotional stimuli.

The two major divisions of the autonomic nervous system are the sympathetic nervous system and parasympathetic nervous system. The sympathetic nervous system is catabolic (i.e., involves destructive metabolic processes that break down molecules and release energy). Conversely, the parasympathetic nervous system is anabolic (i.e., involves constructive metabolic processes that synthesize molecules; Cardinali, 2017). Thus, the sympathetic nervous system generally activates physiological responses in preparation for energy expenditure. This response is often referred to as the “fight-or- flight” response. Similarly, the parasympathetic nervous system generally conserves and restores energy and stimulates digestion. This response is often referred to as the “rest- or-digest” response.

Ganglia are networks of nerve body cells (Gabella, 2012). The sympathetic ganglia connect to the heart, sweat, salivary and digestive glands, and smooth muscle of the pupils, blood vessels and lungs. The sympathetic ganglia are responsible for physiological responses such as accelerated heart rate, higher basal metabolic rate, greater muscular strength, sweaty palms, and ejaculation. Accelerated heart rate and higher galvanic skin response are hence generally considered reliable indicators of sympathetic activation.

The parasympathetic ganglia produce localized responses in the pupillary muscles, smooth muscle of the viscera, lacrimal and salivary glands, and blood vessels in the neck, head and thoracoabdominal viscera. The parasympathetic ganglia are responsible for producing physiological responses such as decelerated heart rate, reduced blood pressure, increased heart rate variability, and erection. Three quarters of all parasympathetic fibres are located in the vagus nerve (Czura, Rosas-Ballina, & Tracey, 2007). As such, heightened vagus nerve activation is a reliable indicator of parasympathetic activation. Within vagal activation, however, only heightened high- frequency ranges of heart rate variability indicate parasympathetic activity. Heightened low-frequency ranges conversely indicate sympathetic activity (Billman, 2011; 2013).

Although sympathetic and parasympathetic activity are often complementary (Lehne, 2013), different emotions elicit different levels of activation in each branch. 18

Sadness, for example, primarily involves heightened parasympathetic activity, whereas fear primarily involves heightened sympathetic activity (Ax, 1953; Christie & Friedman, 2004; Kreibig, Wilhelm, Roth, & Gross, 2007). Some emotions, such as disgust, involve heightened activity in both the sympathetic and parasympathetic branches of the autonomic nervous system (de Jong, van Overveld, & Peters, 2011). Although sympathetic and parasympathetic activity can involve complementary responses, such that increases in one branch can correspond with decreases in the other, this concept may not apply to the physiological component of emotions (Lehne, 2013). Examining the autonomic component of emotions may identify unique psychological factors through which emotions influence time perception.

Arousal, as determined by physiological activity, is also a component in some models of time perception. However, these models do not differentiate between sympathetic and parasympathetic activity (Schirmer, 2011). In the pacemaker- accumulator model, heightened physiological activity causes individuals to overestimate the time by increasing the rate of the pacemaker (Droit-Volet & Meck, 2007). This model specifies that increased dopamine increases the rate of the pacemaker. Dopamine is a neurotransmitter that acts on the sympathetic nervous system (Krawczak, Beutler, Kozłowska, & Olejnik, 1989). As such, this model may also predict that individuals overestimate stimulus durations when experiencing heightened sympathetic activity relative to lower sympathetic activity. However, I am not aware of any time perception experiments that examined sympathetic and parasympathetic activity.

High and low levels of physiological intensity. Some experiments examined the effects of arousal on time perception by manipulating level of overall physiological intensity. The effects of arousal on time perception in these experiments are mixed. In most experiments, participants overestimated and underproduced stimulus durations when they had a heightened level of arousal relative to having a lower level of arousal (Falk & Bindra, 1954; Kopell, Wittner, Lunde, Warrick, & Edwards, 1969; Lapp, Collins, Zywiak, & Izzo, 1994; Sewell et al., 2012; Tinklenberg, Roth, & Kopell, 1976; Vercruyssen, Hancock, & Mihaly, 1989; Warm, Smith, & Caldwell, 1967; Wittmann et al., 2007). These experiments heightened arousal through physiological stimuli such as physical activity, drug administration and electric shocks. In other studies, there were positive correlations between overestimated and underproduced stimulus durations and indices of heightened arousal. In these studies, increases in skin conductance levels, 19

heart rate, respiration rate, blood pressure, airway conductance, and body temperature indicated heightened arousal (Hawkes, Joy, & Evans, 1962; Hoagland, 1933; Kleber, Lhamon, & Goldstone, 1963; Kuriyama et al., 2003; Meissner & Wittmann, 2011; Mella et al., 2010; Pfaff, 1968; Tinklenberg et al., 1976; Vachon, Sulkowski, & Rich, 1974).

However, in other experiments, time perception did not change when participants experienced heightened skin conductance level, finger pulse volume, blood pressure, and an accelerated heart rate (Agué, 1974; Angrilli et al., 1997; Caldwell & Jones, 1985; de Wit, Enggasser, & Richards, 2002; Heishman, Arasteh, & Stitzer, 1996). In further experiments, there were no significant correlations between time perception and pulse rate, heart rate, respiration rate, lung work, blood pressure, or skin conductance (Cellini et al., 2015; Curton & Lordahl, 1974; Schaefer & Gilliland, 1938). In one experiment, participants overproduced durations of stimuli after consuming anaesthetic gas, despite no concurrent changes in respiration rate, heart rate, or body temperature (Adam, Rosner, Hosick, & Clark, 1971). Another experiment examined the effects of subjective (i.e., self-reported) and objective (i.e., heart rate) physiology on perceived time and found that solely subjective ratings of physiological activity predicted slower time perception (Schwarz, Winkler, & Sedlmeier, 2012). Additionally, participants did not experience changes in time perception in other experiments after consuming dopaminergic drugs associated with increased physiological activity (Agué, 1974; de Wit et al., 2002; Gruber & Block, 2003; Heishman et al., 1996; Terry, Doumas, Desai, & Wing, 2008).

One possible reason for these mixed effects for arousal is that previous research did not distinguish between sympathetic and parasympathetic physiological activity. Heightened sympathetic activity involves a different physiological response to heightened parasympathetic activity (Gabella, 2012). An alternative way of examining the effects of arousal on time perception is to differentiate between sympathetic and parasympathetic activity. In this thesis, I investigated how both sympathetic and parasympathetic activity influence time perception.

Sympathetic and parasympathetic autonomic activity. The effects of heightened sympathetic activity, relative to lower sympathetic activity, on time perception are similarly mixed. Most research examining the effects of arousal on time perception by manipulating physiology focused on increasing general physiological activity through 20

heightened sympathetic variables (e.g., elevated blood pressure; Agué, 1974; Angrilli et al., 1997). As such, much of the mixed evidence for heightened physiological intensity on time perception also reflects mixed evidence for heightened sympathetic activity on time perception. However, additional evidence for the effects of sympathetic activity on time perception derives from experiments that manipulated dopamine levels. Changes in dopamine reflect changes in sympathetic activity because dopamine acts on the sympathetic nervous system (Krawczak et al., 1989).

The evidence for heightened dopamine on time perception is likewise mixed. Drugs that heighten dopamine levels include marijuana, amphetamines, and hallucinogens (Ghuran & Nolan, 2000; Jones, 2002; Krawczak et al., 1989). In some experiments, participants overestimated and underproduced time intervals, and judged these intervals less accurately, after consuming marijuana, cortisol, alcohol, and hallucinogens (Kopell et al., 1969; Lake & Meck, 2013; Lapp et al., 1994; Sewell et al., 2012; Tinklenberg et al., 1976; Vachon et al., 1974; Wittmann et al., 2007). Additionally, individuals sometimes overestimate stimulus durations after ingesting psychostimulants, and underestimate stimulus durations after ingesting antipsychotics (e.g., Droit-Volet & Meck, 2007). Psychostimulants increase dopamine levels whereas antipsychotics decrease dopamine levels (Scigliano & Ronchetti, 2013).

However, in one experiment, participants consumed dopaminergic drugs associated with sympathetic activation (i.e., methamphetamine) and sympathetic inhibition (i.e., chlorpromazine; Hawkes et al., 1962). Despite a negative correlation between increased sympathetic variables (e.g., heart rate) and interval production, time perception did not differ between participants under the influence of different drugs. Another experiment did not find differences in time perception following dopaminergic drug administration, despite receiving multiple doses of the drug (McClure, Saulsgiver, & Wynne, 2005).

One possible reason for these mixed effects is that previous research examining the effects of autonomic activity solely examined changes in sympathetic activity. To the best of my knowledge, there is no research that examined the effect of parasympathetic activity on time perception. Notably, however, some experiments examined the effect of parasympathetic activity on temporal sensitivity (e.g., Cellini et al., 2015; Meissner & Wittmann, 2011). Activities that heighten parasympathetic activity, such as meditation and relaxation, can influence time perception, although the 21

direction for these effects differs between experiments (Gorn, Chattopadhyay, Sengupta, & Tripathi, 2004; Kramer, Weger, & Sharma, 2013). Similarly, parasympathetic activity can influence variables related to time perception, such as increased temporal sensitivity (Cellini et al., 2015; Meissner & Wittmann, 2011). Moreover, the physiological component of some emotions involves heightened parasympathetic activity, rather than sympathetic activity (e.g., sadness; Christie & Friedman, 2004). Examining the relationship between parasympathetic activity and time perception may help clarify the mixed data on arousal and time perception.

In sum, arousal is both a component of emotion and a feature within some models of time perception. Researchers may induce arousal by manipulating either emotional or physiological intensity. Moreover, researchers may operationalise physiological intensity either through heightened physiological activity in general, or through heightened sympathetic and parasympathetic activity. The effects of arousal, as induced by both emotional and physiological manipulations, on time perception are mixed (e.g., Kopell et al., 1969; Wittmann et al., 2007). As such, there may be a need to examine how these different components of arousal interact to understand the effects of arousal on time perception. Additionally, although the effects of parasympathetic activity on time perception are unknown, there is indirect evidence to suggest that parasympathetic activity may alter time perception (e.g., Gorn et al., 2004; Kramer et al., 2013). However, these experiments provide evidence for individuals both overestimating and underestimating stimulus durations during heightened parasympathetic activity. In this thesis, I examined the effects of parasympathetic activity on time perception. Examining parasympathetic activity provides evidence for the effects of arousal.

Valence. Emotional valence refers to the hedonic value of a stimulus, or the extent to which a stimulus is positive or negative, or pleasant and unpleasant (Anderson & Sobel, 2003). For example, an amusing stimulus has a positive or pleasant valence, whereas an aversive stimulus has a negative or unpleasant valence. This distinction between positively and negatively valenced stimuli is a core parameter within many psychological theories (Brendl & Higgins, 1996). Emotional valence is not a component of time perception models. Some research may suggest that changes in arousal and attention can explain the effects of emotional valence on time perception (e.g., Droit-Volet & Gil, 2009; Droit-Volet & Meck, 2007; Droit-Volet et al., 2011; 22

Mioni et al., 2016). However, other research suggests that arousal, attention, and emotional valence are separate constructs that can exert independent effects on time perception (e.g., Bayer, Sommer, & Schacht, 2010; Kensinger & Corkin, 2004; Kensinger & Schacter, 2006). Additionally, some research may suggest that valence is one of three independent factors pertinent to explaining how emotion influences time perception (Angrilli et al., 1997). Emotional valence may hence influence time perception independent of arousal and attention.

Some experiments directly compared the effects of positive, negative, and neutral stimuli on time perception. In most of these experiments, participants overestimated negative stimuli relative to both positive and neutral stimuli (Antoinides, Verhoeff, & van Aalst, 2002; Bailey & Areni, 2006; Corke, Bell, Goodhew, Smithson, & Edwards, 2018; Delay & Richardson, 1981; Droit-Volet et al., 2010; Goldstone, Lahmon, & Sechzer, 1978; Gorn et al., 2004; Hornik, 1992; Lambrechts, Mella, Pouthas, & Noulhiane, 2011; Mella et al., 2010; Noulhiane, Mella, Samson, Ragot, & Pouthas, 2007; Twenge, Catanese, & Baumeister, 2003; Yamada & Kawabe, 2011). These experiments utilized a wide variety of stimuli including negative sounds (e.g., crying), aversive images (e.g., weapons), unpleasant colour, unfamiliarity, negative information, social rejection, and harsh lighting (e.g., Antoinides et al., 2002; Bailey & Areni, 2006; Goldstone et al., 1978; Gorn et al., 2004; Noulhiane et al., 2007; Twenge et al., 2003; Yamada & Kawabe, 2011). Furthermore, individuals high in personality dimensions with a negative emotional valence (e.g., boredom-proneness, anxiety) overestimated stimulus durations compared to individuals with more positive personality traits (Bar-Haim et al., 2009; Danckert & Allman, 2005; Watt, 1991). Similarly, depressed individuals perceived time as passing more slowly in several experiments (for a meta-analysis, see Thönes & Oberfeld, 2015). Moreover, slower time awareness positively correlated with increased severity of mood disturbance in depressed individuals (Blewett, 1992). In one experiment, heightened negative affect positively correlated with longer duration estimates of daily events (Freedman, Conrad, Cornman, Schwarz, & Stafford, 2013). Overestimated stimuli indicate slower time perception, whereas underestimated stimuli indicate faster time perception (Pande & Pati, 2010). This evidence thus may suggest that exposure to negative stimuli generally slows down time perception. 23

In most experiments, participants also underestimated positive stimuli relative to both negative and neutral stimuli (Antoinides et al., 2002; Bailey & Areni, 2006; Baker & Cameron, 1996; Delay & Richardson, 1981; Droit-Volet et al., 2010; Goldstone et al., 1978; Gorn et al., 2004; Hornik, 1992; Kellaris & Kent, 1994; Noulhiane et al., 2007; Rudd, Vohs, & Aaker, 2011; Tobin & Grondin, 2009; Yalch & Spangenberg, 2000). These studies utilized a wide variety of positive stimuli including positive sounds (e.g., erotic sounds), pleasant videos, agreeable colour, videogames, familiarity, positive information, and low intensity lighting (e.g., Antoinides et al., 2002; Delay & Richardson, 1981; Gorn et al., 2004; Noulhiane et al., 2007; Rudd et al., 2011; Tobin & Grondin, 2009; Yalch & Spangenberg, 2000). Furthermore, participants high in personality dimensions with a positive valence (e.g., low boredom-proneness, low anxiety) underestimated stimulus durations compared to participants high in personality dimensions with a negative valence (Bar-Haim et al., 2009; Danckert & Allman, 2005; Watt, 1991). Underestimated stimuli indicate faster time perception (Pande & Pati, 2010). Faster time perception may also cause individuals to feel more positive. In some experiments, participants with faster time perception rated stimuli as more positive and experienced more positive affect. These participants also reported feeling that tasks were more enjoyable and absorbing, and that negative stimuli were less aversive (Converse, Sackett, & Meyvis, 2010; Pageau & Surgan, 2015; Sackett, Meyvis, Nelson, Converse, & Sackett, 2010; Sucala, Stefan, Szentagotai-Tatar, & David, 2010; Vliek & Rotteveel, 2012). This evidence suggests that individuals generally underestimate positive stimuli, although this effect can be bidirectional.

Generally, individuals overestimate the duration of negative stimuli and underestimate the duration of positive stimuli. However, there are some mixed effects for positive and negative valence on time perception. In some experiments, participants underestimated negative stimuli relative to positive and neutral stimuli (Gil et al., 2009; Hul, Dube, & Chebat, 1997; Kellaris & Kent, 1992; Kellaris, Mantel, & Altsech, 1996; Ogden, 2013). These experiments utilized negative stimuli such as unattractive faces, images of disliked foods, and unpleasant music. Similarly, participants overestimated positive stimuli in some experiments relative to negative and neutral stimuli (Corke et al., 2018; Hul et al., 1997; Kellaris et al., 1996; Kramer et al., 2013; Lambrechts et al., 2011). These experiments utilized positive stimuli such as pleasant music, images, and 24

meditation. These mixed effects may suggest that additional factors moderate or contribute to the effects of emotional valence on time perception.

In sum, emotional valence is a component within models of emotion that some research may suggest is also one of 3 factors pertinent to explaining how emotions influence time perception (Angrilli et al., 1997). Participants typically overestimate negative stimuli and underestimate positive stimuli (e.g., Noulhiane et al., 2007). However, in some experiments, participants overestimated positive stimuli and underestimated negative stimuli (e.g., Lambrechts et al., 2011; Ogden, 2013). These mixed findings suggest that there may be other factors moderating or contributing to the effects of emotional valence on time perception. Examining how other components of emotion and time perception models influence time perception may help explain these mixed effects. In this thesis, I examined if changes in arousal can explain how emotional valence influences time perception.

Attention. Attention refers to the concentration of awareness when engaging with a specific stimulus. People use more attentional resources and have a higher cognitive load when their attention is more broadly distributed (e.g., when attending to multiple stimuli or completing concurrent tasks) relative to when their attention is narrowly distributed (e.g., when attending to a single stimulus). Attention is included in models of emotional processing and is a prerequisite for emotional processing to occur (for a review, see Yiend, 2010). Indeed, people preferentially attend to emotional stimuli over neutral stimuli (e.g., Fox, Russo, & Dutton, 2002; Lui, Penney, & Schirmer, 2011; Stormark, Nordby, & Hugdahl, 1995). Most relevant for the current thesis is that attention is both a suggested mechanism for how emotions influence time perception and a feature in some models of time perception (Schirmer, 2011).

The effects of attention on time perception differ based on experimental design. Prospective designs are paradigms that instruct participants to estimate the duration of upcoming intervals. In prospective paradigms, participants are hence aware of the temporal nature of the experiment (Brown, 1985). In prospective designs, individuals underestimate duration during more broadly distributed attentional resources and higher cognitive load relative to narrower attentional distribution and lower cognitive load. For instance, participants overproduced and underestimated durations, and performed less accurately on timing tasks, when they completed two competing tasks simultaneously (Brown, 1997; Hicks, Miller, & Kinsbourne, 1976; Hicks, Miller, Gaes, & Bierman, 25

1977). Furthermore, individuals underestimate the durations of non-temporal stimuli (e.g., emotional images) relative to temporal stimuli (e.g., a clock). Participants underestimated stimulus duration when they directed their attention towards non- temporal stimuli or features such as colour, intensity, and emotional content (Brown, 2008; 2010; Burle & Casini, 2001; Casini & Macar, 1997; Coull, Vidal, Nazarian, & Macar, 2004; Franssen & Vandierendonck, 2002; Gil et al., 2009; Lui et al., 2011; Macar, Grondin, & Casini, 1994; Matthews & Meck, 2016; McClain, 1983; Zakay, 1992; 1998). Moreover, performance on timing tasks in these paradigms decreased proportionally with increased cognitive demands (Block, Hancock, & Zakay, 2010; Burle & Casini, 2001; Casini & Macar, 1997; Macar et al., 1994). In one experiment, participants underestimated duration when the number of choices in a decision-making task increased (Fasolo, Carmeci, & Misuraca, 2009). Consistent with these results, participants underestimated duration when their attention was divided among multiple stimuli, such as in trials with introduced distractors (e.g., Gautier & Droit-Volet, 2002). In sum, individuals underestimate duration in prospective paradigms due to more broadly distributed attentional resources, higher cognitive load, and increased attention directed away from temporal stimuli (e.g., clocks).

Retrospective designs are paradigms that instruct participants to estimate the duration of previously experienced intervals. As such, participants are unaware of the temporal nature of the experiment in retrospective designs (Brown, 1985). In contrast to prospective designs, in retrospective experimental designs, increasing cognitive load and broadening attentional resources causes individuals to overestimate duration (Block et al., 2010; French, Addyman, Mareschal, & Thomas, 2014; Matthews & Meck, 2016). One possible explanation for these mixed effects of attention on time perception may be that retrospective timing and prospective timing work through different mechanisms (Brown, 1985; Jones & Boltz, 1989; Zakay & Block, 1996). There is some evidence supporting this hypothesis. In one influential experiment, participants observed a beaker of liquid boiling in either a prospective or retrospective paradigm. Participants underestimated durations when they were told the experiment concerned time perception (i.e., a prospective paradigm) relative to visual perception (i.e., a retrospective paradigm; Block, George, & Reed, 1980). However, there may be confounds within these designs that can help explain the mixed effects of attention on time perception. Participants informed an experiment is about time perception, for 26

example, may use counting strategies to monitor the time. Thus, these participants possibly possess a more accurate representation of timing than participants who are not aware an experiment is about time perception. Identifying new factors that influence time perception may help explain additional variance in these mixed findings. In this thesis, I examined whether attending to temporal cues (i.e., a timer) changes time perception relative to non-temporal cues (i.e., another consistent stimulus) in a retrospective design.

In sum, attention is a feature in some models of time perception and emotional processing (Yiend, 2010; Zakay & Block, 1996). Individuals may underestimate or overestimate duration when experiencing changes in the distribution of attentional resources based on the experimental paradigm (e.g., Block et al., 2010). However, these paradigms may include other confounds that could help explain the mixed results for attention on time perception.

Personality. Personality dimensions are continuous individual traits that remain stable over time (Schaie & Parham, 1976). Although personality is not a dimension of emotion, personality dimensions can predispose individuals to feeling certain emotions (Revelle & Scherer, 2009). Individuals high in conscientiousness, for example, were less likely to experience anger following a provocation relative to individuals low in conscientiousness in one study (Jensen-Campbell, Knack, Waldrip, & Campbell, 2007). In another study, individuals high in extraversion were more likely to experience positive affect than less extraverted individuals (Komulainen et al., 2014). This evidence suggests that personality dimensions explain some individual differences in emotional experience.

Models of time perception typically omit personality dimensions. In one exception, Hogan’s (1978) model proposed that the interactive effects of stimulus complexity and introversion can alter time perception. According to the model, individual overestimate duration due to increases in stimulus complexity because more complex stimuli are more difficult to process. Furthermore, highly introverted individuals can tolerate less stimulus complexity than more extraverted individuals (Eysenck, 1967; Ludvigh & Happ, 1974). As such, when working on a complex task, highly introverted individuals might overestimate duration relative to more extraverted individuals. This model only provided one specific example of how a single personality dimension may inform time perception. Thus, although some personality dimensions 27

may play a role in time perception, there is little understanding regarding how they may do so.

In the limited experiments that examined how personality dimensions change time perception, highly extraverted individuals overestimated duration and perceived time less accurately compared to more introverted individuals (e.g., Rammsayer, 1997; Zakay, Lomranz, & Kaziniz, 1984). Another experiment examined several other personality dimensions such as social desirability, extraversion, neuroticism, and psychoticism (Rammsayer, 1997). In this experiment, there was only a positive correlation between high psychoticism and temporal overestimation (Rammsayer, 1997). Although these experiments provide preliminary evidence that personality dimensions can alter perceived time, the precise mechanisms underlying these relationships are unclear.

One particular personality dimension, time perspective, may be particularly relevant to how individuals perceive the passage of time. Time perspective is a personality dimension that describes the extent to which individuals tend to focus on the past, present, and/or future in their everyday lives (Zimbardo & Boyd, 1999). A predominantly past-oriented individual may frequently think about past experiences, whereas a predominantly future-oriented individual may frequently plan ahead for desired goals. Present-oriented individuals focus on living in the current moment. Past and present time perspectives can also vary as a function of emotional valence, such that individuals can think about these time orientations in a positive or negative manner. A positive past-oriented individual will tend to concentrate on re-experiencing positive memories, whereas a negative past-oriented individual will tend to focus on re- experiencing negative memories. Similarly, a positive present-oriented individual can make choices to optimise hedonism, whereas a negative present-oriented individual may feel like fate controls their choices. Future perspective is always considered positive, as research has not identified a reliable future negative dimension (e.g., Zimbardo & Boyd, 1999). In this thesis, I examined how time perspective dimensions influence time perception.

In sum, there is limited research exploring how various personality dimensions influence time perception. Despite this literature gap, there is sufficient evidence to suggest that certain personality dimensions inform how individuals perceive the passage of time. Moreover, personality dimensions explain some individual differences in 28

emotional experience (e.g., Revelle & Scherer, 2009). Examining the extent to which personality dimensions like time perspective influence time perception may identify new psychological factors through which emotions influence time perception. Identifying new psychological factors underling this relationship may aid in explaining the mixed effects of other factors (e.g., arousal) on time perception.

Aims of this Thesis

This thesis primarily examined how arousal, autonomic activity, personality, attention and emotional valence influence time perception. In achieving this goal, this thesis examined how some of these factors interact, novel factors that influence time perception, and unanswered questions about these factors (e.g., whether changes in arousal can explain why individuals overestimate negative stimuli). As such, this thesis examined the plausibility of explaining how emotions and other phenomena influence time perception using components featured in models of emotion and time perception. Although this thesis suggests some constructive ways to update existing models of time perception, the current body of work is only the first step in updating and consolidating these models. Together with future study, this research may eventually inform new psychological models of time perception.

Similarly, this thesis aimed to clarify the mixed effects for some of these factors (e.g., arousal) on time perception. In this thesis, I only examined if these mixed effects stem from other confounding psychological factors (e.g., autonomic activity). However, other unconsidered factors, such as methodology, may also contribute to these mixed results. For example, participants in at least one experiment demonstrated different changes in time perception depending on which task was used to measure time perception (Gil & Droit-Volet, 2011b). I attempted to minimize methodological confounds in my research. For example, I used the same dependent measure of time perception throughout this thesis. However, future researchers could consider if factors unrelated to dimensions of emotion and time perception confound the effects of certain psychological factors (e.g., attention) on time perception. These factors could constructively inform future psychological models of time perception.

CHAPTER 2: Experimental Methods for Inducing Basic Emotions

Chapter Introduction

Emotions are one factor that strongly influence how individuals perceive time. Emotions affect time perception through changes in arousal, which contains related cognitive and physiological components (Gil & Droit-Volet, 2012). The intensity of emotional experience defines the cognitive component of arousal, whereas the intensity of physiological experience defines the physiological component (Bernstein, 2010; Sheldon & Donahue, 2017). The current review thus investigated how inducing specific emotions alters the cognitive and physiological components of arousal.

Basic emotions are adaptations that are universal and innate (Ortony & Turner, 1990). Researchers generally agree upon six basic emotions: anger, sadness, fear, disgust, happiness, and surprise (Ekman, 1992). Scared individuals often overestimate stimulus duration (e.g., Buetti & Lleras, 2012; Tipples et al., 2015). Disgusted, happy, and sad individuals can either overestimate or underestimate duration (e.g., Droit-Volet et al., 2004; Effron et al., 2006; Gil et al., 2009). To the best of my knowledge, no research has examined whether anger or surprise changes time perception. These results suggest that specific basic emotions differ in how they influence time perception.

Arousal is one component of emotion that also affect time perception (Gil & Droit-Volet, 2012; Grewe, Nagel, Kopiez, & Altenmüller, 2007; Mella et al., 2010). Inducing emotions increases arousal through emotional intensity (e.g., Grewe et al., 2007). Regardless of the emotion, in research that manipulates high versus low levels of emotional intensity, individuals generally overestimate the duration of highly arousing stimuli. Similarly, individuals also generally overestimate duration when experiencing high levels of physiological activity relative to low levels (Gil & Droit-Volet, 2012; Mella et al., 2010). However, emotions differ significantly in their physiological response, and physiological activity can influence time perception independent of induced emotional intensity (Kreibig, 2010; Wittmann et al., 2007). Distinguishing between the physiological and emotional components of arousal within emotions could identify unique pathways by which different emotion inductions influence time perception. The current review investigated how specific emotion inductions alter emotional and physiological intensity. 30

Because the current thesis induced anger and surprise, I first reviewed the literature on basic emotion inductions. I wanted to ensure that any emotion inductions in this thesis increased arousal sufficiently to observe effects on time perception. In the following review, I examined the best emotion induction techniques for the six basic emotions. I reviewed a larger subset of emotions than previous reviews (Lench, Flores, & Bench, 2011; Westermann, Spies, Stahl, & Hesse, 1996) as I intended to induce surprise in my thesis, which was not included in previous reviews.

Publication I: A Review of Basic Emotions

Publication I

Siedlecka, E., & Denson, T. F. (2019). Experimental methods for inducing basic

emotions: A qualitative review. Emotion Review, 11(1), 87-97.

https://doi.org/10.1177/1754073917749016

I certify that this publication was a direct result of my research towards this PhD, and that reproduction in this thesis does not breach copyright regulations.

Ewa Siedlecka

Abstract

Experimental emotion inductions provide the strongest causal evidence for the effects of emotions on psychological and physiological outcomes. In the present qualitative review, we evaluated five common experimental emotion induction techniques: visual stimuli, music, autobiographical recall, situational procedures, and imagery. For each technique, we discuss the extent to which they induce six basic emotions: anger, disgust, surprise, happiness, fear, and sadness. For each emotion, we discuss the relative influences of the induction methods on subjective emotional experience and physiological responses (e.g., heart rate, blood pressure). Based on the literature reviewed, we make emotion-specific recommendations for induction methods to use in experiments.

Introduction

Basic Emotion Induction Paradigms

Anger, disgust, surprise, happiness, fear, and sadness are often classified as basic emotions. These emotions are considered adaptations that are innate and universal (Ekman, 1992). Experimentally inducing emotions is the most rigorous means of testing their causal influence on psychological and biological variables. Indeed, there is a rich history of inducing basic emotions in psychology laboratories with numerous methods (e.g., Ax, 1953; Blatz, 1925). However, not all emotion induction methods are equally effective, and researchers are often unsure of which method to use. The aim of this article is to provide research-based suggestions on how to effectively induce six basic emotions (although we note that there is some debate as to whether surprise is truly a basic emotion; e.g., Oatley & Johnson-Laird, 1987).

Although some previous research compared the relative efficacy of emotion induction methods, these articles focused on a few select emotions (e.g., Lench et al., 2011). To date, there is no literature comparing the efficacy of induction techniques across the range of basic emotions. Thus, for many emotions, researchers may often select induction methods based on available resources, convenience, or at random rather than based on efficacy. A review of the literature is necessary to organize these methods into a coherent framework to advise researchers on impactful methods.

Table 2.1 Examples of emotion induction techniques by category

Technique Emotion Illustrative example induced Visual stimuli Surprise Watching a video where a man is startled by birds (Gross & Levenson, 1995) Music Sadness Listening to a slow-tempo, minor-key classical composition (Khalfa, Roy, Rainville, Dalla Bella, & Peretz, 2008) Autobiographical Fear Asking participants questions about a memory recall where they were very afraid (e.g., details of the event; Prkachin et al., 1999) 34

Situational Anger Participants receiving negative feedback on a procedures personal essay (Bushman & Baumeister, 1998) Imagery Happiness Imagining that “It’s your birthday and friends throw you a terrific surprise party” (Mayer et al., 1995)

We broadly classify emotion induction techniques into five specific methods. Visual stimuli can be static images or videos selected to evoke target emotions. Listening to music activates affect via specific types of auditory input (e.g., tempo, melody, lyrics; Krumhansl, 2002). Autobiographical recall involves summoning personal emotional memories to reactivate emotions from the original emotional experience (Prkachin, Williams-Avery, Zwaal, & Mills, 1999). Situational procedures involve creating a social situation that elicits the target emotion. Imagery involves participants creating vivid mental representations of novel emotional events. Imagery can consist of reading vignettes (Mayer, Allen, & Beauregard, 1995), often with guidance from the experimenter (e.g., Grossberg & Wilson, 1968). Table 2.1 provides illustrative examples of these five methods used to induce emotions.

We derived these categories of methods from previous research categories and a meta-analysis, which found that different categories of affect inductions can yield effects of various intensities (e.g., Lench et al., 2011; Westermann et al., 1996). Of these, we excluded some techniques for ineffectiveness and disuse (which we discuss later). We additionally created two new categories: visual stimuli and situational procedures. Visual stimuli merely combined all static and film stimuli, although these are often discussed in our review separately. Situational procedures combined paradigms related to social situations, such as gift giving and behavioural inductions, as these paradigms are similar to social situations people might experience in real life. Additionally, there were not enough studies using situational procedures (e.g., gifts) to justify creating subcategories of this method.

Method

Literature Search

We identified 427 relevant journal articles through PsycInfo and examination of reference lists. Search terms included the individual emotions (e.g., “anger”) paired with 35

individual methodologies (e.g., “music”), “physiology,” “arousal,” “nervous system,” “physiological response,” “autonomic response,” and the broader terms “emotion induction” and “emotion manipulation.” We only included literature that induced the specified target emotion. For brevity’s sake, we limit the number of references for each induction method in the text, but a thorough list and recommended articles are available at https://osf.io/9dxu2/. For each emotion, we provide recommendations for the best methods of induction. We also provide emotion-specific recommendations for the most effective stimuli within each induction category (as determined by effect sizes; these were independently calculated wherever possible).

Efficacy Guidelines

We determined the efficacy of the emotion inductions by assessing the combination of both self-reported and physiological evidence. Physiological evidence is an important criterion of efficacy as emotion inductions influence sympathetic and parasympathetic activity (Kreibig, 2010) and may be less susceptible to social desirability and demand characteristics than self-report. Although some researchers suggest distinct emotions are not associated with a specific physiological footprint (e.g., Cacioppo et al., 2000), a strong physiological response is most likely indicative of strong affect (e.g., Rainville, Bechara, Naqvi, & Damasio, 2006).

Table 2.2

Summary of how each basic emotion influences specific physiological variables within the literature

HR HRV BP ReR SCL/SCR MR ST/FT/BT CO SR MF Anger ↑ ↑ ↑ ↑ ↑ (k = 66) Disgust ↑ ↓- ↓ ↑ ↑ ↑- ↑ - - ↑ ↑ (k = 42) Surprise ↑ ↓- ↑ ↓ ↑- - - (k = 11) Happiness ↑ ↓- - ↑ ↑ ↑ ↓- ↑ ↑- - (k = 66) Fear ↑ ↓- ↓ ↑- ↑ ↓ ↑ ↓ ↓- - ↑ ↓ (k = 104) Sadness ↑ ↓- ↑ ↓ ↑- ↓- ↑ ↓- ↑ ↓ ↑ ↓- - ↓ (k = 67) Note. HR = heart rate; HRV = heart rate variability; BP = blood pressure (either systolic or diastolic); ReR = respiration rate; SCL/SCR = skin conductance level/skin conductance response; MR = muscular response; ST/FT/BT = skin temperature/finger temperature/body temperature; CO = cardiac output; SR = startle response; MF = metabolic function. ↑ = this measure significantly increased; ↓ = this measure significantly decreased; − = no significant changes were found. Blank spaces indicate a lack of data for this emotion and physiological variable.

Although strong physiological evidence seems indicative of an effective emotion induction, studies sometimes report contradictory physiological responses within a single emotion (as noted in Table 2.2). One reason for this contradiction is that emotions may not be unitary phenomena. One example is sadness, which elicits unique physiological responses depending on whether it is approach-related (i.e., a sad film with a positive component related to empathy) or avoidance-related (i.e., a sad film with a negative component related to antipathy; Davydov, Zech, & Luminet, 2011). Similarly, disgust can co-activate both sympathetic and parasympathetic physiological responses depending on whether the disgust is morality-related versus pathogen-related (Ottaviani, Mancini, Petrocchi, Medea, & Couyoumdjian, 2013). Some researchers also 37

suggest that disgust can elicit both sympathetic and parasympathetic responses (e.g., Kreibig, 2010), which might explain the variation in physiological reactions in response to disgust. Surprise evokes different physiological responses depending on whether the surprise has negative or positive valence (Levenson & Ekman, 2002). Overall, our review is consistent with the notion that there may not be specific physiological responses for each basic emotion, at least when induced in the laboratory (see Table 2.2).

Control Groups

Control groups for emotion induction techniques often try to induce neutral affect. Within each induction technique, control groups are largely consistent between different emotions. For experiments using visual stimuli, participants in the control group usually view less evocative still images (e.g., tissues) or movies (e.g., nature documentaries; e.g., Springer, Rosas, McGetrick, & Bowers, 2007). For experiments using music, participants in the control group will often not listen to music, or music that induces a different emotion (e.g., Sharman & Dingle, 2015). Researchers using autobiographical recall often ask participants to think or write about an instance(s) from their past when they experienced an ordinary event (e.g., an interaction with a stranger; Siedlecka, Capper, & Denson, 2015). Control groups for situational procedures usually retain the social aspect of the paradigm, but remove the emotion-eliciting aspect (e.g., writing an essay and receiving neutral, rather than positive or negative feedback; Bushman & Baumeister, 1998). For imagery, participants in the control group typically imagine ordinary situations (e.g., riding a bicycle; Velasco & Bond, 1998). A second type of control group makes use of one or more different emotion inductions. For instance, a researcher might wish to determine whether an anger induction increases aggression more than a fear induction. Creation of adequate control groups is important to consider when evaluating emotion induction techniques as not all emotions can be induced with the same method (see Table 2.3). Thus, comparing an experimental group to an inadequate control group may lead to results stemming from confounding variables, rather than induced emotions.

Table 2.3

Summary of the most effective induction methods for each emotion

Visual Music Autobiographical Situational Imagery methods recall paradigms Anger *** ** *** *** *** (k = 56) (k = 21) (k = 26) (k = 13) (k = 45) Disgust *** *** ** *** (k = 51) (k = 2) (k = 12) (k = 20) (k = 23) Surprise *** ** *** ** (k = 13) (k = 2) (k = 7) (k = 5) (k = 7) Happiness *** *** *** ** *** (k = 55) (k = 10) (k = 23) (k = 15) (k = 28) Fear *** ** *** *** *** (k = 75) (k = 16) (k = 17) (k = 17) (k = 38) Sadness *** *** *** ** *** (k = 64) (k = 12) (k = 23) (k = 6) (k = 25) Note. *** = there is strong (≥ 5 significant references) self-report AND physiological evidence for the successful induction of this emotion via the specified method. This combination of emotion and paradigm is highly recommended. **= there is strong self-report OR physiological evidence for the successful induction of this emotion via the specified method (≥ 5 significant references). *= there is some (at least 1 significant reference but < 5 significant references) self- report AND/OR physiological evidence for the successful induction of this emotion via the specified method. This combination of emotion and paradigm is only recommended if another stronger method cannot be used.

Results

Anger

Visual stimuli. Researchers usually source images and film excerpts from popular culture or prior research. The anger-inducing stimuli typically display scenes of mistreatment, such as domestic violence (Lobbestael, Arntz, & Wiers, 2008). Numerous research articles provided evidence that anger-related static and film stimuli are both effective for inducing subjective anger (e.g., Lench et al., 2011). A review of 26 articles suggested anger-related visual stimuli also increase physiological variables such as 39

blood pressure, startle response, heart rate, and respiration (e.g., Lobbestael et al., 2008).

Music. Numerous studies reported increased self-reported anger in response to music such as rap, heavy metal, and Japanese noise music (e.g., Seidel & Prinz, 2013). Music-induced anger was particularly pronounced for people who favoured the anger- inducing musical genre (Gowensmith & Bloom, 1997). However, there is little physiological evidence to suggest music induces anger effectively. Only a small number of studies reported that angry music increased variables such as heart rate, skin conductance, and blood pressure (e.g., Sharman & Dingle, 2015).

Autobiographical Recall. Researchers often ask participants to think or write about an instance(s) from their past when they experienced anger. The idea is that recall will reactivate the anger, thereby causing participants to relive the anger (e.g., Siedlecka et al., 2015). Numerous studies using autobiographical recall observed significant self- reported anger responses, and significantly increased physiological responses such as heart rate, skin conductance, and systolic blood pressure (e.g., Marci, Glick, Loh, & Dougherty, 2007).

Situational procedures. Situational anger inductions mimic anger provocations that people encounter in real life. Some paradigms challenge participants’ sense of self- worth. Other paradigms manipulate environmental variables, such as raising the room temperature to an uncomfortable level, or asking participants to speak in an angry tone (e.g., Drummond & Han Quah, 2001). Participants may receive insulting feedback on their test performance, a personal essay, or a speech about their life goals (e.g., Bushman & Baumeister, 1998).

Situational procedures increase self-reported anger (e.g., Lench et al., 2011), and physiological variables such as heart rate, systolic and diastolic blood pressure, and skin conductance (e.g., Lobbestael et al., 2008). Socially interactive paradigms are particularly effective at inducing anger as they target personal values and violate norms of polite behaviour. For instance, one study found elevated self-reported anger, blood pressure, heart rate, and skin conductance when participants were socially harassed after receiving negative feedback on a quiz, compared to merely receiving negative feedback (Lobbestael et al., 2008). 40

Imagery. Some researchers instruct participants to imagine scenarios (e.g., reading a vignette involving a norm violation like sexual assault; Salerno & Peter- Hagene, 2013). This technique differs from autobiographical recall as participants do not necessarily recall situations that they have personally experienced. Researchers often use imagery in an interactive way by guiding participants through the imagery process. For example, one study asked participants to read an anger-themed script, then identify bodily sensations they might feel in that situation, to make the mental representation more detailed and vivid (Velasco & Bond, 1998).

Imagining angry situations reliably increases self-reported anger relative to imagining neutral and emotional situations (e.g., Lench et al., 2011). Imagery additionally heightens physiological variables such as heart rate, skin conductance, blood pressure, corrugator muscle activity, and startle response (e.g., Vrana & Rollock, 2002).

Anger summary. Table 2.3 summarizes the efficacy of the five methods for inducing anger. Four methods increase both cardiovascular and self-reported measures: visual stimuli, autobiographical recall, imagery, and situational procedures. There is limited physiological evidence to support using music. A meta-analysis found that visual stimuli have a smaller effect on anger than autobiographical recall, situational procedures, and imagery (Lench et al., 2011). Additional research found that situational procedures are more effective than visual stimuli, but that imagery and recall are more effective than situational procedures (Foster, Smith, & Webster, 1999; Lobbestael et al., 2008). We therefore recommend imagery or recall as the strongest techniques for inducing anger, followed by situational procedures, visual stimuli, and music. Within imagery and/or recall, we recommend methods that ask participants to focus on physiological variables, rather than just imagining/recalling events vividly (Yogo, Hama, Yogo, & Matsuyama, 1995). Imagery vignettes that focus on social scenarios (e.g., people cutting into a queue) appear to be particularly effective (e.g., Vrana & Rollock, 2002; d = 2.82, relative to a neutral control).

Disgust

Visual stimuli. Images from the International Affective Picture System (Lang, Bradley, & Cuthbert, 1997) are frequently used to induce disgust (van Hooff, Devue, Vieweg, & Theeuwes, 2013). Other visual stimuli used to induce disgust include erotic 41

images incongruent with participants’ sexual preferences, bodily mutilations, insect invasions, or disgusted faces (e.g., Sarlo, Buodo, Poli, & Palomba, 2005). Disgust- inducing films have included animal dissection, bodily products (e.g., faeces, vomit), and painful injuries (e.g., Vianna & Tranel, 2006). Many studies using visual induction methods observed increased self-reported disgust relative to neutral films and images (e.g., Vianna & Tranel, 2006). Visual disgust stimuli increased physiological variables such as skin conductance and respiration, and decreased heart rate and skin temperature (e.g., Vianna & Tranel, 2006).

Music. We could not find any research that used music to induce disgust. There is some evidence to suggest that disgusting noises (e.g., burping, flatulence, and vomiting noises) can increase self-reported disgust relative to neutral sounds (Marzillier & Davey, 2005). However, these studies combined disgusting noises with imagery and recall (Marzillier & Davey, 2005).

Autobiographical recall. This method relies on asking participants to recall a time when they experienced a strong feeling of disgust (Lane, Reiman, Ahern, Schwartz, & Davidson, 1997). Recalling disgusting memories effectively induced self- reported disgust (e.g., Lane et al., 1997). Some studies additionally found that recalling disgusting memories increased physiological responses such as blood pressure, skin conductance level, respiration, and decreased stroke volume (e.g., Prkachin et al., 1999).

Situational procedures. There are many situational procedures that induce disgust. These procedures include bad smells (e.g., flatulence sprays), exposure to unsanitary items (e.g., pseudo-faeces, garbage, pseudo-used syringes), or consuming very bitter drinks (e.g., Adolph & Pause, 2012). Situational procedures are effective in increasing self-reported disgust (e.g., Adolph & Pause, 2012). Some research suggests that situational procedures can increase startle blink responses (Adolph & Pause, 2012). However, there is little evidence to suggest that situational procedures alter other physiological reactions often associated with disgust (e.g., decreased heart rate).

Imagery. Researchers using this method asked participants to read guided vignettes. These vignettes involved a violation of purity or fear of contamination, such as describing incest or contact with bodily waste (e.g., Ottaviani et al., 2013). A typical example might be “You go into a public toilet . . . the bowl of the toilet is full of diarrhea” (Marzillier & Davey, 2005). Imagery reliably increases self-reported disgust 42

in comparison to baseline measurements (e.g., Ottaviani et al., 2013). Additionally, imagery increases physiological responses such as startle reflex, salivation, corrugator muscle activity, heart rate, and decreases heart rate variability (e.g., Ottaviani et al., 2013).

Disgust summary. Three methods reliably influence both physiological and self-reported disgust (Table 2.3): visual stimuli, autobiographical recall, and imagery. The physiological evidence for situational procedures is mixed. Evidence suggests visual stimuli are more effective than imagery, and that recall and imagery are equally effective (de Jong, Peters, &, Vanderhallen, 2002; Marzillier & Davey, 2005). We therefore recommend visual stimuli as the strongest technique for inducing disgust, followed by imagery and/or autobiographical recall, and then situational procedures. There is insufficient evidence for using music. Within visual stimuli, images and/or films about bodily mutilation (e.g., amputation) appear to be particularly effective (e.g., Britton, Taylor, Berridge, Mikels, & Liberzon, 2006; d = 2.53, relative to a positive control; for a list of effective standardised images see Stark, Walter, Schienle, & Vaitl, 2005).

Surprise

Visual stimuli. Research with static images typically uses faces with surprised versus neutral expressions (e.g., Collet, Vernet-Maury, Delhomme, & Dittmar, 1997). Some studies also use schema-discrepant visual imagery (e.g., colour inversion of font and background halfway through a task; Meyer, Niepel, Rudolph, & Schützwohl, 1991). Films usually depict situations in which the actor experiences surprise (e.g., being startled by birds; Gross & Levenson, 1995). There is good evidence that both static images and films are effective in increasing self-reported surprise relative to neutral and emotional stimuli (e.g., Gross & Levenson, 1995). However, to our knowledge, only a few studies showed increases in physiological arousal. These studies found an increase in heart rate in children (Anastassiou-Hadjicharalambous & Warden, 2007), and respiratory changes and increased skin conductance, temperature, and blood flow in adults (Collet et al., 1997; Kragel & LaBar, 2013).

Music. We failed to find much evidence that music can induce feelings of surprise. One study did report that music increased self-reported surprise, skin conductance, and altered respiration patterns, but the music was combined with visual 43

stimuli (Kragel & LaBar, 2013). Another study found that inducing fear through music co-induced surprise, but surprise was not analysed (Krumhansl, 1997).

Autobiographical recall. Participants who recalled memories of unexpected events reported more surprise than other emotions (e.g., Levenson, Carstensen, Friesen, & Ekman, 1991). However, there is little evidence to suggest that recalling surprising events can reliably influence physiological responses (e.g., Levenson et al., 1991).

Situational procedures. The directed facial action paradigm is one common method for inducing surprise. Participants form facial expressions that mimic those of a surprised person (e.g., raised eyebrows, dropped jaw; Ekman, 1989). Another example of a situational procedure is the use of odorants. Using vanillin and menthol induced surprise in one study (Alaoui-Ismaïli, Robin, Rada, Dittmar, & Vernet-Maury, 1997). Both manipulations are effective in inducing self-reported surprise relative to other emotions (e.g., Boiten, 1996). Directed facial action is additionally effective at inducing physiological variables such as decreased heart rate, skin conductance, muscle activity, and increased finger temperature (e.g., Levenson, Ekman, & Friesen, 1990). However, there is mixed support for the notion that odorants influence physiological responses (e.g., Alaoui-Ismaïli et al., 1997).

Imagery. Only a few studies used imagery to induce surprise. These studies asked participants to visualize schema-discrepant stimuli (e.g., sexual behaviours out of line with their sexual preference; Mosher & White, 1980). There is some evidence to suggest that imagining schema-discrepant stimuli increases self-reported surprise more than schema-consistent stimuli (e.g., Mosher & White, 1980), but too little evidence to support the use of imagery induced surprise to elicit physiological responses (e.g., Walter & Yeager, 1956).

Surprise summary. Compared to other emotions, surprise is seldom induced in the laboratory. Of these few studies, only a subset examined physiological responses. Situational procedures and visual stimuli are the only methods that increase both self- reported and some physiological correlates of surprise (see Table 2.3). Imagery and autobiographical recall may be effective at inducing surprise, pending further research. We recommend either using situational procedures or visual stimuli, followed by autobiographical recall and/or imagery. There is no support for using music. Within situational procedures, directed facial action is the only stimulus with clear supportive 44

evidence, but the effects are not large (e.g., Boiten, 1996; d = 0.26–0.48, for respiratory variables relative to emotional controls). Within visual stimuli, a scene from Capricorn One where agents unexpectedly burst through a door appears to be particularly effective (e.g., Gross & Levenson, 1995; d = 3.06, relative to a neutral control). We additionally recommend making distinctions between positive and negative surprise as valence can differentially influence both emotional and physiological responses (Levenson & Ekman, 2002).

Happiness

Visual stimuli. Visual stimuli are a popular method for inducing happiness. Researchers often use images from the International Affective Picture System or facial expressions (Ekman, 1992). Films are usually humorous and can include comedy monologues, cartoons, and amusing television segments (e.g., elimination challenges; e.g., Hubert & de Jong-Meyer, 1990). Viewing static images and films reliably increases subjective happiness (e.g., Lench et al., 2011) and influences physiological responses. Visually induced happiness increased physiological variables such as zygomatic muscular response, respiration, heart rate, blood pressure, and skin conductance (e.g., Hubert & de Jong-Meyer, 1990).

Music. Happy music usually features a fast tempo, major harmonies, and a dance-like rhythm (Krumhansl, 2002). Listening to these musical excerpts effectively induces feelings of happiness compared to other emotions (e.g., Etzel, Johnsen, Dickerson, Tranel, & Adolphs, 2006). A small number of studies also found increases in physiological factors such as zygomatic muscular response, respiration, and skin conductance level (e.g., Etzel et al., 2006).

Autobiographical recall. In this paradigm, participants recall experiences where they felt happy. Autobiographical recall of happy experiences is sometimes paired with exercises that enhance vividness. Participants are asked to close their eyes and relive emotions, or read narratives in the second person about their own experiences (e.g., Rainville et al., 2006). Recalling happy experiences is generally effective at increasing feelings of happiness and decreasing negative feelings, relative to recalling neutral events (e.g., Rainville et al., 2006). Recalling happy events additionally increases physiological factors such as heart rate, blood pressure, and galvanic skin 45

response; and decreases factors such as respiratory period and amplitude (e.g., Marci et al., 2007).

Situational procedures. There are several creative situational procedures to induce happiness. They include being given a gift (e.g., a small bag of gummy bears), the use of pleasant odorants (e.g., vanillin), eating enjoyable foods (e.g., chocolate), success in a difficult task, or playing with a device that produces flatulence noises (e.g., Alaoui-Ismaïli et al., 1997). Situational procedures successfully induce self-reported happiness (e.g., Westermann et al., 1996). The evidence for physiological changes is less robust. Some studies found increased galvanic skin responses, skin conductance, and temperature (e.g., Stemmler, 1989). Other studies did not find changes in heart rate, and some found decreases in skin conductance and heart rate (e.g., Alaoui-Ismaïli et al., 1997).

Imagery. Happiness imagery usually involves the participant visualizing specific hypothetical situations. For instance, researchers might ask participants to imagine situations like reading a good book on a quiet afternoon, an attractive stranger walking towards them, or a professor reading their A+ essay out to the class (e.g., Witvliet & Vrana, 1995). Happiness imagery increases self-reported happiness (e.g., Gehricke & Fridlund, 2002). Additionally, this procedure increases galvanic skin responses, skin conductance, heart rate, and body temperature; and decreases inspiratory and expiratory times: a response consistent with the evidence for the proposed physiological correlates of happiness (e.g., Gehricke & Fridlund, 2002).

Happiness summary. Four methods increase both physiological and self- reported outcomes (Table 2.3): visual stimuli, music, autobiographical recall, and imagery. Some researchers have compared the relative effects of multiple techniques on inducing happiness. Of these, visual stimuli seem to be the most effective form of happiness induction (e.g., Lench et al., 2011; Westermann et al., 1996). Similarly, imagery induces more intense happiness than either autobiographical recall or music, and music is more effective at inducing happiness compared to recall (e.g., Lench et al., 2011). Recent research also suggests that the combination of paradigms (e.g., combining imagery or autobiographical recall with music) is more effective at inducing happiness than any single procedure (Zhang, Yu, & Barrett, 2014). Thus, we recommend the use of visual stimuli, followed by imagery, music, and then autobiographical recall, to induce happiness. A combination of methods may be most 46

effective. We tentatively recommend using situational procedures to induce happiness, as we have only found strong supportive self-report evidence. Within visual stimuli, we recommend films over static images. One study compared select films and found a television game segment from Les Trois Frères to be particularly effective (Schaefer, Nils, Sanchez, & Philippot, 2010; ds = 1.62–3.89, relative to neutral controls).

Fear

Visual stimuli. Static images of fearful stimuli are available from established data sets such as the International Affective Picture System (Lang et al., 1997). Images may include phobic and threatening stimuli, such as pictures of spiders, snakes, or aimed weapons (e.g., Sarlo, Palomba, Buodo, Minghetti & Stegagno, 2005). Researchers often show horror movies or show content depicting impending death, anticipation of injury, or home invasions (e.g., Montoya, Campos, & Schandry, 2005; Schofield, Youssef, & Denson, 2017).

Viewing fearful stimuli is effective at increasing feelings of fear relative to neutral and other emotional stimuli (e.g., Montoya et al., 2005). Exposure to static images or films is also effective at inducing physiological responses consistent with fear. These autonomic reactions include increased skin conductance response, heart rate, startle response, respiration rate, and blood pressure (e.g., Sarlo et al., 2005).

Music. Fearful music is often taken from classical music such as Holst’s Mars: The Bringer of War and Liszt’s Mefisto Waltz, film scores, or can even be purposely created for the research (e.g., Krumhansl, 1997). Listening to music reliably increases feelings of fear compared to sadness and happiness (e.g., Etzel et al., 2006). However, listening to such music can co-induce other feelings, such as surprise and anxiety (Krumhansl, 1997). There is some evidence to suggest that listening to music can increase heart rate, blood pressure, and rate of breathing, while simultaneously decreasing breath duration, skin conductance, and skin temperature (e.g., Etzel et al., 2006). Although these altered physiological factors are promising indicators of music- induced fear, they may also represent nonspecific arousal or arousal induced by emotions other than fear.

Autobiographical recall. In this paradigm, participants are asked to recall a time(s) when they felt afraid or were in danger (e.g., Rainville et al., 2006). Recalling scary experiences is effective at inducing feelings of fear compared to other emotions 47

(e.g., Rainville et al., 2006). Autobiographical recall also increases heart rate, blood pressure, and skin conductance; and decreases heart rate variability, stroke volume, respiratory period, and respiratory rate (e.g., Prkachin et al., 1999). However, some evidence suggests that these physiological responses change in comparison to neutral conditions, but not relative to other emotion inductions (Prkachin et al., 1999).

Situational procedures. Situational procedures target social fear or physical fear. Social fear inductions include anticipated failure at an easy task or preparing a speech to be performed and evaluated by others (Gerritsen, Weigant, Bermond, & Frijda, 1996; Pauls & Stemmler, 2003; Schofield et al., 2017). Examples of paradigms that target physical fear include anticipated electric shock, abseiling down the side of a building, or interrogating participants after forcing them to commit a pseudo-crime (e.g., Brooke & Long, 1987). These paradigms are occasionally modified for the sample, such as exposing arachnophobic individuals to real spiders (Castaneda & Segerstrom, 2004). Both social and physical fear inductions are effective at increasing feelings of fear (e.g., Castaneda & Segerstrom, 2004). Additionally, situational procedures increased heart rate, skin conductance response, blood pressure, and decreased metabolic function (e.g., Gerritsen et al., 1996). These autonomic effects occur in comparison to both neutral controls and other emotions (e.g., anger; Pauls & Stemmler, 2003).

Imagery. This paradigm similarly induces both social and physical fears. To induce social fear, participants might imagine performing a speech in front of a group of people, or being unprepared for an exam. Examples of physical fear include imagining being injected with a needle, finding a spider in one’s bed, or being trapped in an elevator (e.g., Vrana & Lang, 1990). Sometimes, researchers cue participants to autonomic or behavioural responses that they could encounter in these scenarios (Vrana & Lang, 1990). Fear imagery is effective at increasing feelings of fear compared to other emotions (e.g., Vrana & Lang, 1990). Imagery also increases physiological variables such as startle response, respiration rate, pulse rate, heart rate, and skin conductance (e.g., Cuthbert et al., 2003).

Fear summary. All five methods influence both autonomic responses and self- reported fear. We recommend the use of situational procedures, followed by imagery, visual stimuli, autobiographical recall, and then music to induce fear. Within situational procedures, we recommend commonly feared authentic stimuli such as spiders or 48

virtual reality exposure to heights (e.g., Castaneda & Segerstrom, 2004; Wilhelm et al., 2005; ds = 2.24 and 3.57, relative to baseline and low-anxiety controls, respectively). Of those studies that directly compared the effects of specific techniques on inducing fear, situational procedures were more effective than imagery. Combining music with visual procedures was more effective in inducing fear than visual exposure alone (e.g., Castaneda & Segerstrom, 2004). Because music alone induces multiple emotions and nonspecific physiological arousal, we recommend combining music with visual stimuli for researchers using these methods.

Sadness

Visual stimuli. Static image compilations include faces with sad facial expressions (e.g., Pictures of Facial Affect, FACES; e.g., Ekman & Friesen, 1976). Stronger facial expressions within these data sets evoke more sadness (Wild, Erb, & Bartels, 2001). Films are usually selected for themes of unjust suffering, loss, and grief (e.g., Sakuragi, Sugiyama, & Takeuchi, 2002). Stimuli are sometimes also self-selected by participants (e.g., tragic videos that make the participant cry; Sakuragi et al., 2002). Static visual stimuli and films are both effective at inducing feelings of sadness relative to neutral and other emotional stimuli (e.g., Wild et al., 2001). However, there is little evidence to suggest static stimuli influence physiological factors, other than decreasing heart rate variability (Lane et al., 2009). Sad films, however, alter physiological variables such as heart rate variability, skin conductance responses, finger temperature, respiration rate, metabolic function, heart rate, and blood pressure (e.g., Fernández et al., 2012).

Music. Music used to induce sadness usually has a slow tempo, low volume, and a minor key (e.g., Khalfa et al., 2008). Some researchers have constructed their own compositions based on these properties (Lundqvist, Carlsson, Hilmersson, & Juslin, 2009). Listening to sad music is effective at inducing sadness and autonomic responses (e.g., Etzel et al., 2006). Music decreases skin conductance, respiration rate, and heart rate, and increases finger temperature, tension in facial corrugator muscles, and blood pressure (e.g., Krumhansl, 1997).

Autobiographical recall. Participants are asked to recall a time when they felt overwhelmingly sad (Marci et al., 2007). Autobiographical recall is effective at increasing feelings of sadness compared to both neutral recall and recall of other 49

emotions (e.g., Marci et al., 2007). Additionally, recalling sad memories increases parasympathetic factors such as heart rate, systolic and diastolic blood pressure, and vascular resistance, and decreases heart rate variability (e.g., Lane et al., 2009).

Situational procedures. Directed facial action is the only situational procedure whereby participants manipulate their facial muscles into an expression of sadness (e.g., extend the lower lip, furrow the brow; Levenson et al., 1990). To our knowledge, no other situational procedures are commonly used to specifically induce sadness. Directed facial action does indeed induce self-reported sadness (e.g., Levenson et al., 1990) and there is some evidence that it increases heart rate, finger temperature, and skin conductance, while decreasing muscular activity, and expiratory and inspiratory time (e.g., Boiten, 1996).

Imagery. Participants might be asked to imagine a funeral service, a homeless person rummaging through a garbage bin, or listening to a young mother tell the participant how she lost her job (e.g., Witvliet & Vrana, 1995). This type of imagery effectively induces sadness relative to other emotions (e.g., Gehricke & Fridlund, 2002). Imagining sad scenarios can additionally increase physiological outcomes such as heart rate, blood pressure, and corrugator muscle activity, and decrease facial muscle activity and skin conductance (e.g., Witvliet & Vrana, 1995).

Sadness summary. All five methods influence both autonomic and self- reported sadness (Table 2.3). Some research has compared the relative efficacy of different methods for inducing sadness. These analyses found that visual stimuli are the most effective method of inducing sadness (Westermann et al., 1996); however, this was only true for film stimuli (Lench et al., 2011). Additionally, imagery is more effective than situational procedures, which are more effective than recall, and recall is more effective than both static visual stimuli and music (Lench et al., 2011). Music may be especially effective when paired with autobiographical recall (van der Does, 2002). Similarly, combining visual stimuli with music is more effective at inducing sadness than static visual stimuli alone (Baumgartner, Esslen, & Jäncke, 2006).

Thus, we recommend visual film stimuli as the strongest paradigm for inducing sadness, followed by imagery, then situational procedures, recall, static visual stimuli paired with music, music paired with recall, and music or static visual stimuli alone. Within film stimuli, scenes depicting grief appear to be particularly effective. These 50

include death-related excerpts from City of Angels and Dangerous Minds (e.g., Schaefer et al., 2010; d = 2.18–2.45, relative to a neutral control).

Discussion

Subjective Emotional Experience

Table 2.3 provides a summary of the five emotion induction methods. Looking down the columns, the most effective induction method for the six basic emotions is visual stimuli. Music is only recommended for happiness, fear, and sadness. Autobiographical recall is effective for anger, happiness, fear, disgust, and sadness, but less so for surprise. Situational procedures also show breadth as they effectively induce anger, surprise, fear, and happiness. Imagery effectively induces anger, happiness, disgust, sadness, and fear, but may be less effective for surprise.

One consideration for using these induction methods is that creating an adequate neutral control or other emotion comparison condition can be challenging, particularly when investigating multiple emotions. As shown in Table 2.3, not all emotions can be induced with the same method. Moreover, even within the same method, certain paradigms utilize dissimilar procedures to induce different emotions. For example, in a situational induction, happiness might be elicited by eating chocolate (Macht & Dettmer, 2006), whereas disgust might be induced by consuming a bitter drink (Adolph & Pause, 2012). However, eating chocolate might induce happiness, but also guilt. Similarly, consuming a bitter beverage might induce surprise in addition to disgust. Thus, some procedures may co-induce more than the target emotion. Although we did not allocate much space to control procedures, researchers should carefully construct them to ensure accurate causal inferences can be made. Ideally, researchers should pilot test their preferred induction to elicit high levels of the target emotion and low levels of other co-induced emotions. We also recommend measuring possible co-induced emotions and accounting for them in statistical analyses, whenever feasible.

A combination of paradigms may be more effective than a single procedure. Certainly, some induction stimuli combine paradigms (e.g., films often use evocative music). Although there is often inconclusive evidence regarding the efficacy of these combinations, we might guess a combination of paradigms would be effective in inducing most emotions (e.g., Zhang et al., 2014). However, we only recommended a combination of methods when the literature suggested it was beneficial for a certain 51

emotion. We do not feel we can make such a recommendation for all emotions based on the limited research available on this topic.

Limitations

This review was limited in some aspects. We did not discuss explicitly self- referent statements, priming, or reading stories. We excluded self-referent statements because 30–50% of participants do not respond to self-referent statements and they are often susceptible to demand characteristics (e.g., Clark, 1983). However, we included priming and reading stories as forms of visual inductions and imagery, respectively. Additionally, we did not divide situational paradigms into subcategories as have other reviews (e.g., Westermann et al., 1996), as some situational paradigms can only induce certain emotions (e.g., gift-giving is not used to induce anger).

Notably, there is evidence to suggest some techniques may also be susceptible to experimenter effects and demand characteristics (Västfjäll, 2002). This research is particularly problematic for techniques that rely on asking the participant to attend to an emotion (e.g., recall). However, it is difficult to gauge the extent to which these effects contribute to the induced emotion. When possible, we recommend obscuring the aim of the emotion induction task from participants.

The primary criterion used to determine successful emotion induction in this review was a statistically significant effect. Although effect sizes were considered when determining the most effective stimuli within a paradigm, effect sizes did not determine the most effective induction categories. The studies included in this review likely differed in both levels of statistical power and magnitude of effect size. As such, the results of this review do not allow researchers to plan studies based on a desired effect size. Future researchers could consider conducting a meta-analysis to extend the applications of this review.

Conclusions

Philosophers and psychologists have long concerned themselves with the study of human emotion. This review highlights the diversity and quantity of experimental research that has produced creative and often powerful emotion induction methods. Many of these techniques reliably induce a range of basic emotions and associated physiological responses. Some emotion inductions require initial evidence or more 52

conclusive evidence before researchers may embrace these methods for specific emotions. However, this review suggests that there are numerous impactful options for the researcher to select from when designing their investigations into basic emotions. 53

Chapter Discussion Chapter 2 of this thesis identified the most effective methods of increasing arousal for each basic emotion. As such, this review increased the probability of inducing the target emotions in subsequent studies in this thesis. For example, this review identified that situational paradigms are the best technique for inducing surprise, and that researchers should distinguish between positive and negative surprise. In Experiment 2, I induce surprise using a situational paradigm, and distinguish between positive and negative surprise. Similarly, this review identified that autobiographical recall is one of the most effective methods of anger induction. In Experiment 4, I induce anger using autobiographical recall.

Physiological intensity is one component of emotion that also affects time perception through heightened arousal (Mella et al., 2010). Thus, basic emotions may influence time perception through changes in physiological response. This review was consistent with the notion that basic emotions do not elicit unique physiological profiles (Cacioppo et al., 2000). As such, basic emotions may not exert independent effects on time perception through unique physiological signatures. Because the physiological component of emotions involves changes in sympathetic and parasympathetic activity, emotions may instead influence time perception through changes in autonomic activity (Evans, 2014; Kreibig, 2010). In Experiments 1 and 2 of this thesis, I examine how changes in the two autonomic branches, the sympathetic and parasympathetic branches, change time perception.

CHAPTER 3. Experiment 1: Do Changes in Autonomic Nervous System Activity Influence How People Perceive Time?

Introduction

One component of arousal is the intensity of physiological experience, which can be operationalised as the level of activity in the two branches of the autonomic nervous system (Bernstein, 2010; Gadsdon, 2001; Wann, 1997; Weiten, 2007). These two branches are the sympathetic and parasympathetic branches. Sympathetic activity prepares an organism for action by increasing variables such as heart rate, whereas parasympathetic activity aids in digestion and recovery by increasing heart rate variability (Acharya, Joseph, Kannathal, Lim, & Suri, 2006). Individuals typically overestimate stimulus durations when experiencing heightened sympathetic activity (e.g., during exercise) relative to lower sympathetic activity (e.g., Wittmann et al., 2007). Although a small body of research examined how parasympathetic activity changes the ability to discriminate between durations of time (e.g., Meissner & Wittman, 2011), research examining the effects of physiological intensity on time perception typically ignores the role of parasympathetic activity. Distinguishing between sympathetic and parasympathetic activity may identify unique psychological factors through which arousal affects time perception. The present experiment examined the effects of sympathetic and parasympathetic activity on time perception.

Another component of arousal is the intensity of emotional experience, which is typically measured with self-reports of intensity or manipulated with pre-tested emotional images (Sheldon & Donahue, 2017). People often overestimate the durations of emotionally intense stimuli (Gil & Droit-Volet, 2012). Arousal is characterised by changes in both physiological and emotional intensity (Gil & Droit-Volet, 2012). However, studies examining the effects of arousal on time perception typically manipulate arousal through either emotional or physiological intensity (e.g., Gil & Droit-Volet, 2012). Similarly, these studies generally do not consider how the different components of arousal interact. Distinguishing between emotional and physiological intensity within arousal may identify unique pathways by which arousal affects time perception. The current experiment examined the effects of arousal on time perception by manipulating both emotional and physiological intensity.

Physiological Activity within Arousal and Time Perception

The intensity of physiological experience can be operationalised as the level of activity in the two branches of the autonomic nervous system (Gadsdon, 2001; Wann, 1997; Weiten, 2007). These branches can initially induce compensatory effects, such that increased activity in one branch can concurrently decrease activity in the other (McCorry, 2007). However, to maintain homeostasis, these systems are also complementary, such that increases in one branch are usually followed by increases in the other over time (McCorry, 2007).

Participants generally experience overestimate and underproduce stimulus durations after consuming drugs that heighten sympathetic activity (e.g., amphetamines) relative to placebos and drugs that block sympathetic activity (e.g., Kopell et al., 1969; Lake & Meck, 2013; Lapp et al., 1994; Moore, 2014; Sewell et al., 2012; Tinklenberg et al., 1976; Vachon et al., 1974; Wittmann et al., 2007; for a review of this literature, see pp. 19). Similarly, some research activating the sympathetic nervous system using other paradigms (e.g., electric shocks, physical activity) indicates that individuals overestimate duration during heightened sympathetic activity relative to less sympathetic activity (e.g., Aschoff, 1998; Einhauser, Koch, & Carter, 2010; Falk & Bindra, 1954; Hancock, 1993; Hawkes et al., 1962; Kuriyama et al., 2003; Meissner & Wittmann, 2011; Mella et al., 2010; Pfaff, 1968; Tinklenberg et al., 1976; Vercruyssen et al., 1989; Warm et al., 1967). Overestimated stimuli indicate slower time perception, (Pande & Pati, 2010). This evidence hence indicates that increases in sympathetic activity slow time perception.

To my knowledge, there is no research about how parasympathetic activity changes the speed of perceived time. Parasympathetic activity often decreases initially in response to heightened sympathetic activity (McCorry, 2007). As such, parasympathetic activity may exert the opposite effects on time perception to sympathetic activity, such that individuals underestimate stimulus duration during heightened parasympathetic activity. Supporting this idea, in some psychological research, high physiological activity is operationalised as heightened sympathetic activity, and low physiological activity as heightened parasympathetic activity (e.g., Martínez-Montes, Chobert, & Besson, 2016). Similarly, individuals underestimate 56

stimulus duration during activities that can heighten parasympathetic activity (e.g., relaxation; Gorn et al., 2004). However, to my knowledge, no studies of time perception have operationalised either arousal or physiology in this way. This experiment examined if parasympathetic activity influences time perception in addition to sympathetic activity via the measurement of relevant cardiovascular responses.

Sympathetic and parasympathetic activity do sometimes involve opposite reactions for specific physiological responses, such as sympathetic activity increasing heart rate and parasympathetic activity decreasing heart rate (Lehne, 2013). Although these responses can be complementary, sympathetic and parasympathetic activity are also often independent physiological responses. Sympathetic activity controls ejaculation in the male reproductive system, for example, but parasympathetic activity controls erection (Lehne, 2013). Additionally, sometimes these branches control unique physiological functions, such as sympathetic activity influencing blood vessel dilation (Lehne, 2013). Furthermore, dysfunction in the sympathetic nervous system leads to different physical and mental health outcomes compared to dysfunction in the parasympathetic nervous system (Bolis, Licinio, & Govoni, 2002; Karatinos, 2014). Combined, this evidence suggests that sympathetic activity does not always involve the opposite physiological response to parasympathetic activity. The present experiment examined the independent effects of both sympathetic and parasympathetic activity on time perception.

Sympathetic and parasympathetic activity also often independently influence cognition, rather than exerting opposing reactions. Sympathetic and parasympathetic activity, for example, both enhance executive functions, which are cognitive processes necessary for behavioural control (Barkley, 2012; Hansen, Johnsen, & Thayer, 2003; Murray & Russoniello, 2012). Furthermore, within executive functions, heightened parasympathetic activity improves attentional processes, whereas sympathetic activity improves mental flexibility (Hansen et al., 2003; Murray & Russoniello, 2012). Similarly, sympathetic and parasympathetic activity both enhance memory processes (Nava, Landau, Brody, Linder, & Schächinger, 2004; Quas, Carrick, Alkon, Goldstein, & Boyce, 2006). Moreover, parasympathetic activity enhances long-term memory consolidation and retention, whereas sympathetic activity enhances short-term memory retention (Nava et al., 2004; Quas et al., 2006). In addition to distinct effects on cognitive variables, sympathetic and parasympathetic activity are often associated with 57

different physiological functions, behaviours, and emotions (Arnold, 1945; Bolis et al., 2002; Diamond & Cribbet, 2013; Friedman, 2011; Luft, Takase, & Darby, 2009). As time perception is a form of cognition, sympathetic and parasympathetic activity may not exert opposite effects on time perception. In further support of this hypothesis, individuals overestimate the durations of activities that heighten parasympathetic activity (e.g., meditation) relative to sympathetic activity (Kramer et al., 2006). I tested this hypothesis in the present research.

Emotional Intensity within Arousal and Time Perception

One component of arousal is the intensity of emotional experience (Sheldon & Donahue, 2017). Individuals overestimate stimuli that elicit relatively greater emotional intensity (e.g., live spiders) relative to less emotional intensity (e.g., mushroom images; Bar-Haim et al., 2009; Droit-Volet et al., 2016; Effron et al., 2006; Gil & Droit-Volet, 2011a; 2012; Gil et al., 2007; Grommet et al., 2011; Lee et al., 2011; Mella et al., 2011; Smith et al., 2011; Thayer & Schiff, 1973; Tipples, 2008; Yamada & Kawabe, 2011; see pp. 15 for a review of this literature). In a few experiments, however, individuals underestimated the durations of arousing stimuli relative to neutral stimuli (Gil et al., 2009; Kellaris & Mantel, 1994; Lee et al., 2011). One published experiment additionally reported null effects for arousal on time perception, despite participants reporting increases in arousal (Droit-Volet et al., 2011). Overestimated stimuli indicate slower time perception (Pande & Pati, 2010). Although increased emotional intensity generally slows time perception, these mixed effects suggest that there may be other factors that contribute to or moderate the effects of arousal on time perception.

One factor that could contribute to the mixed literature regarding arousal and time perception is the interaction between the different components of arousal. These components are emotional intensity and physiological activity. Emotions covary with physiological activity in the sympathetic and parasympathetic branches of the autonomic nervous system (Kreibig, 2010), although there is significant variability in autonomic activity between emotions (for reviews, see Kreibig, 2010; Siedlecka & Denson, 2019). Some emotions involve a parasympathetic response (e.g., sadness), whereas other emotions involve a sympathetic response (e.g., fear; Ax, 1953; Rottenberg, Chambers, Allen, & Manber, 2007). Other emotions (e.g., disgust, anger) involve activity in both autonomic branches (Ax, 1953; de Jong et al., 2011). Assessing activity in each autonomic branch could identify unique physiological moderators for 58

the effects of arousal on time perception. This experiment examined whether autonomic activity moderates the effects of emotional intensity on time perception.

The Present Research

This experiment examined how sympathetic and parasympathetic activity influence time perception. Participants completed a laboratory measure of time perception at 4 different time points. The first time point was a baseline measure. During the second time point, participants also submerged their non-dominant hand in extremely cold water to simultaneously activate both increased sympathetic activity and decreased parasympathetic activity (Dishman, Nakamura, Jackson, & Ray, 2003; Porges, 1995; Quigley & Stifter, 2006). During the third and fourth time points, I expected parasympathetic activity to increase and sympathetic activity to decrease due to recovery from both increased sympathetic activity and decreased parasympathetic activity. Parasympathetic activity commonly increases during recovery from painful tasks, such as submerging a limb in extremely cold water (Chen et al., 2011; Nahman- Averbuch, Sprecher, Jacob, & Yarnitsky, 2016; Nazarewicz, Verdejo-Garcia, & Giummarra, 2015). Parasympathetic activity also commonly increases following reduced parasympathetic activity (Gabella, 2012). I included two time points assessing parasympathetic activity to determine if changes in parasympathetic activity proportionately affected time perception. I hypothesized that participants would overestimate stimulus durations during heightened sympathetic activity and underestimate stimulus durations during heightened parasympathetic activity. I also hypothesized that individuals would underestimate stimulus durations as a function of increased parasympathetic activity.

This experiment also examined how emotional intensity affects time perception during sympathetic and parasympathetic activity. The laboratory measure of time perception involved viewing images rated high in emotional intensity (e.g., aimed weapons) and neutral (e.g., mushrooms) images. Images were presented for varying durations and participants were asked to respond whether the images appeared for short or long durations. A short response indicates accurate time perception for shorter durations and a quickening of time for longer durations. A long response indicates accurate time perception for longer durations and a slowing of time for shorter durations. I hypothesized that participants would overestimate the duration of emotional images, or have a larger proportion of ‘long’ responses for emotional images, relative to 59

neutral images. I also hypothesized that physiological activity would moderate the effect of emotional intensity on time perception, such that individuals would have a larger proportion of ‘long’ responses for emotional images relative to neutral images during heightened sympathetic activity relative to heightened parasympathetic activity.

Method

Participants and Design

Participants were 50 community members recruited from the University of New South Wales in exchange for AUD$10. A power analysis using GPower, with a goal to obtain 0.8 power to detect a medium effect size (f = 0.25; Cohen, 1977), with an alpha level of 0.05, determined the sample should include a minimum of 36 participants (Faul, Erdfelder, Buchner, & Lang, 2009). I excluded data from 3 participants for receiving a score of less than 70% in the bisection training phase. I am unaware of any experiments excluding the data of participants receiving a score of less than 70% in the bisection training phase. However, participants usually only complete these training phases when they do not make any errors for a significant number of blocks (e.g., 8 blocks; Rattat & Droit-Volet, 2001). To reduce the number of blocks participants must complete, thus reducing the time demands of the experiment, I instead followed the precedent in other cognitive research of excluding practise data with less than 70% accuracy (e.g., Altmann & Hambrick, 2017). I also excluded the physiological data of 6 additional participants due to poor quality of physiological data (e.g., due to electrodes falling off during the experiment; Nacke, Grimshaw, & Lindley, 2010). However, the final data set included the non-physiological data of these latter participants. Notably, I did not exclude any participant data based on Weber ratios higher than 0.34 (indicating less than half the temporal sensitivity of the average adult; e.g., Droit-Volet, Tourret, & Wearden, 2004; Wearden, 1991). I similarly did not exclude any participant data based on the bisection experimental data failing a residual check (indicating poor model fit; McPherson, 2001).

The final sample of 47 participants included 27 women and 20 men, with a mean age of 22.26 years (SD = 3.48) and 41 with complete physiological data (24 women and 17 men, with a mean age of 22.39 years, SD = 3.62). These individuals were primarily Asian (76.6%), with 17.0% of the sample Caucasian, and the final 6.4% of the sample 60

comprising of other ethnicities. The sample was primarily non-religious (31.92%), with a further 27.7% of the sample Christian, 12.8% Muslim, 12.7% Buddhist, and the remaining 14.9% comprising of other religious affiliations. The most prevalent completed level of education was high school graduation (34.0%), followed by a bachelor’s degree (29.8%), a master’s degree (19.2%), and some college (12.8%), with the remaining 4.2% comprising of other education levels. Majority of the sample did not speak English as a first language (57.5%). For a full list of participant demographics, as well as participant demographics for physiological data, see Appendix A (pp. 244).

All participants provided written informed consent. The Human Research Ethics Advisory Panel at the University of New South Wales approved the research.

In a (2) (images: emotional versus neutral) × (4) (time) within-subjects design, I examined the influence of physiological activity and emotional intensity at 4 different time points. During each of these time points, participants completed a measure of time perception (i.e., the bisection task). During each measure of time perception, participants judged the duration of both emotional and neutral images. During the second time point, participants all concurrently completed the bisection task and a cold pressor task designed to induce sympathetic physiological activity. However, I anticipated parasympathetic activity would increase in response to the cold pressor task. I anticipated parasympathetic activity would increase because parasympathetic activity commonly increases during recovery from painful tasks and following reduced parasympathetic activity (Chen et al., 2011; Gabella, 2012; Nahman-Averbuch et al., 2016; Nazarewicz et al., 2015). Thus, I anticipated the latter 2 blocks or time points in the experiment would likely involve heightened parasympathetic activity. During each bisection task, participants wore an electrocardiograph to measure physiological indices of sympathetic and parasympathetic activation. The primary purpose of the physiological indices was to provide manipulation checks for sympathetic and parasympathetic activity. Before the third and fourth time points, participants respectively watched a 1-minute and 5-minute filler video. I included these filler videos to expand the duration following the cold pressor task to determine if changes in parasympathetic activity proportionately influence time perception.

I programmed this experiment using Inquisit 4.0 software. All physiological data were recorded using PowerLab (ADInstruments, 2011), and analysed using LabChart 8.1.5 software (ADInstruments, 2016). 61

Materials and Procedure

Time perception. Participants completed the bisection task as the primary measure of time perception. The bisection task consists of 2 phases: a training phase and an experimental phase (Church & Deluty, 1977; Wearden, 1991). The training phase included a single block of 30 trials. In the training phase, participants viewed a blue rectangle for either 400 ms or 1600 ms and judged whether the rectangle appeared for either a short or a long time. Participants responded via the key press on the computer keyboard (← for short and → for long). I randomised the durations of the blue rectangle for each participant. Participants received feedback to facilitate learning. The purpose of the training phase was to teach participants to recognise the minimum and maximum presentation durations for images in the experimental phase.

The experimental phase included a single block of 50 trials. Each trial began with a fixation cross displayed for 1000 ms that alerted participants to the commencement of a new trial. A 200 ms mask followed the fixation cross, after which one of 20 neutral or emotional images randomly appeared in the centre of the screen. These images appeared for a randomised duration of 400, 600, 800, 1000, 1200, 1400, or 1600 ms. As such, 1 of 20 random images appeared for 1 of 7 random duration in each trial of the bisection task. Participants indicated whether each image was shown for a duration closer to the short or the long blue rectangle. Participants responded via the key press on the computer keyboard (← for short and → for long). The bisection task is a common standardized measure of perceived time (e.g., Kopec & Brody, 2010).

Participants completed the bisection task before the cold pressor task, during the cold pressor task, after a 1-minute filler video, and after a 5-minute filler video. In total, there were 4 blocks of time perception measurements.

Physiological measures. At the beginning of the experiment, participants were attached to a 3-lead electrocardiograph, which was facilitated by a data acquisition device called PowerLab (ADInstruments, 2018). The electrocardiograph, configured according to manufacturer instructions, recorded physiological measurements during each of 4 bisection tasks (ADInstruments, 2018). The recorded physiological measurements were heart rate and heart rate variability, which generally correspond to respective increases in sympathetic and parasympathetic activity (Acharya et al., 2006). I computed 5 variables to assess heart rate variability: 62

• intervals between consecutive heart beats (R-R intervals) • root mean square of successive differences between R-R intervals (RMSSD) • low frequency power (as a percentage of total power) • high frequency power (as a percentage of total power) • ratio of low to high frequency power (sympathovagal balance; for explanations of these variables, see Shaffer & Ginsberg, 2017; Xhyheri, Manfrini, Mazzolini, Pizzi, & Bugiardini, 2012).

Notably, unlike the other measures of heart rate variability, greater low frequency power and sympathovagal balance are associated with sympathetic activity (Acharya et al., 2006). A set of physiological measurements was recorded during each bisection task, thus creating 4 blocks of physiological data. The primary purpose of these physiological measurements was to provide manipulation checks for sympathetic and parasympathetic activity.

Emotional intensity. Participants viewed both neutral and emotional images in the bisection task experimental phase. I obtained these images and valence and arousal scores from overall adult ratings from the International Affective Picture System and Technical Manual (Lang et al., 1997; Lang, Bradley, & Cuthbert, 2008). Emotional image content examples included aimed weapons and disfigured faces. The emotional images had arousal scores between 6.36 and 7.35 (M = 6.78, SD = 2.17), and valence scores between 1.4 and 2.7 (M = 2.03, SD = 1.373). Neutral image content examples included mushrooms and lightbulbs. The neutral images had arousal scores between 3.21 and 4.93 (M = 3.79, SD = 1.98), and valence scores between 5.03 and 5.21 (M = 5.16, SD =1.36). The arousal and valence scales for these items range between scores of 1 to 9. Arousal scores range from low (1) to high (9), whereas valence scores range from negative (1) to positive (9). Thus, the emotional images were highly arousing and negative, and the neutral images were minimally arousing and neutral. For a list of all emotional and neutral pictures used, see Appendix A (pp. 244).

Cold pressor task. To induce increased sympathetic and decreased parasympathetic activity, all participants submerged their non-dominant hand in a bucket of iced water for up to 2 minutes. I monitored the water temperature to ensure it remained between 0 and 4 degrees Celsius. Participants received instructions to submerge their hand for as long as possible, though to remove their hand if they 63

experienced unbearable pain. The cold pressor task reliably induces sympathetic activity (e.g., Dishman et al., 2003; Quigley & Stifter, 2006). Parasympathetic activity commonly increases in response to heightened sympathetic activity, and following reduced parasympathetic activity (Acharya et al., 2006; Barral & Croiber, 2008; Gabella et al., 2012). Thus, I anticipated that parasympathetic activity would increase during the 2 time points following the cold pressor task.

Filler videos. Prior to completing the latter 2 bisection tasks, participants viewed neutral videos. The first video, shown before the penultimate bisection task, was a 1-minute excerpt from a documentary about cave fish (Prukprakarn, Ringgaard, & Subhabhundu, 2013). The second video, shown before the final bisection task, was a 5- minute excerpt from a documentary about ice worms (National Geographic Channel, 2007). The purpose of these videos was to expand the length of time following the cold pressor task to determine if changes in parasympathetic activity proportionately affect time perception.

I included 2 filler videos to account for potential delays in parasympathetic activity following the cold pressor task. Individuals can experience delays in the parasympathetic response based on individual factors such as health and level of fitness (e.g., Borresen & Lambert, 2008; Sung, Choi, & Park, 2006). For these reasons, researchers often include a recovery period between 3 and 10 minutes in length following exercise before measuring the parasympathetic response (e.g., James, Munson, Maldonado-Martin, & De Ste Croix, 2012; Solberg, Ingjer, Holen, Sundgot- Borgen, Nilsson, & Holme, 2000). I anticipated that some participants could take longer to recover from the cold pressor task than others. As such, another purpose of these videos was to keep these individuals engaged until I could record their parasympathetic response.

Results

Calculating time perception variables. For each stimulus duration, I calculated the proportion of trials in which participants responded ‘long’. The choice of analysing long compared to short responses is arbitrary and mathematically equivalent; thus, I followed the precedent of analysing long responses set by previous research (e.g. Smith et al., 2011). 64

I calculated two further variables to confirm these results: a bisection point (Church & Deluty, 1977; Wearden, 1991) and the Weber ratio (Brown, 1960; Gibbon, 1977; Hobson, 1975). A bisection point is the estimated time interval at which the probability of answering short and long is equal. A lower bisection point indicates overestimating the time the images were presented and hence slower perceived time; a higher bisection point indicates underestimating the time the images were presented and hence faster perceived time (Droit-Volet et al., 2004). The Weber ratio is a measure of temporal sensitivity, or ability to discriminate between millisecond durations, where a higher score indicates less sensitivity (Kopec & Brody, 2010).

I calculated a bisection point and Weber ratio for individual participant responses in the experimental phase of each bisection task. I calculated the bisection point using the regression intercept and slope obtained from individual linear regressions (Wearden, 1991). I used these values to calculate the stimulus duration that yielded 50% of ‘long’ responses in the bisection task (i.e., the bisection point). I calculated the Weber ratio for each participant by calculating time intervals for each the 0.25 and 0.75 probability of answering ‘long’ during the bisection task and subtracting the former numeral from the latter (Droit-Volet & Wearden, 2002). I divided this number by two, then further divided this quotient by the bisection point, to obtain the Weber ratio (Droit-Volet & Wearden, 2002).

Preliminary Analyses

Duration and time perception. I initially analysed these data using a repeated- measures ANOVA to examine whether stimulus duration during the bisection task influenced the probability of answering ‘long’. As Mauchly’s test of sphericity was violated, χ2(20) = 253.95, p < .0001, I used a Greenhouse-Geisser correction. As shown in Figure 2.1, there was a significant main effect of duration across all time points, where participants were more likely to respond ‘long’ as stimulus durations increased,

F(4.06, 755.86) = 899.90, p < .0001, ηp² = .83. The effect size for duration was large. This result suggests that participants could discriminate between the different stimulus durations. All post-hoc pairwise comparisons of least significant differences were significant (all ps < .04). For statistical output regarding pairwise comparisons within duration, see Appendix A (pp. 244). 65

g 1.0 n o l '

g 0.8 n r i e

s w 0.6 n a

o 0.4 y t i l i

b 0.2 a b r o

P 0.0 400 600 800 1000 1200 1400 1600 Stimulus duration (ms)

Figure 2.1. Probability of answering ‘long’ for each stimulus duration. Bars represent standard error of the mean. The probability of answering ‘long’ increased as stimulus duration increased.

Sympathetic activity manipulation check. I conducted multiple repeated- measures ANOVAs to determine whether heart rate, low frequency power, and sympathovagal balance changed across the course of the experiment. Increases in heart rate, low frequency power and sympathovagal balance indicate heightened sympathetic activity (Acharya et al., 2006).

Table 3.1

Changes in sympathetic and parasympathetic activity over the course of Experiment 1

Variable Time 1 Time 2 Time 3 Time 4 N (Cold Pressor) M SE M SE M SE M SE Low 36.45 2.51 34.02 2.75 36.09 2.54 29.64 2.18 41 Frequency Power Sympathovagal 1.76 0.35 1.80 0.27 1.50 0.21 1.67 0.32 41 Balance 66

Heart Rate 95.24 4.49 95.44 7.30 114.14 6.86 124.35 15.73 41 R-R Intervals 722.88 16.53 692.37 13.70 747.58 17.36 759.61 24.91 41 RMSSD 47.77 7.71 65.78 6.74 74.47 8.58 60.50 7.67 41 High 35.68 2.65 31.32 2.84 38.93 3.06 35.08 3.13 41 Frequency Power

* * 40 2.5

2.0 30

1.5 20 1.0 Video 2 Video Video 1 Video Video 2 Video Video 1 Video 10

0.5 Task Low frequency power Cold Pressor Task Pressor Cold Sympathovagal balance Cold Pressor 0 0.0

Time1 Time1 Time 2 Time 3 Time 4 Time 2 Time 3 Time 4 150

100 Video 2 Video 50 1 Video Heart Rate (bpm) Task Cold Pressor 0

Time1 Time 2 Time 3 Time 4

Figure 2.2. Changes in sympathetic activity over the course of the experiment, as indicated by increases in low frequency power, sympathovagal balance and heart rate. Error bars represent standard error of the mean. Percentage of low frequency power marginally decreased following the second video (Time 4) compared to baseline (Time 1) and immediately following the cold pressor task. * p < .05.

As seen in Figure 2.2 and Table 3.1, percentage of low frequency power marginally changed over the course of the experiment, F(3, 120) = 2.28, p = .08, ηp² = .05. Uncorrected post-hoc tests of least significant difference revealed that percentage of low frequency power strongly decreased following the cold pressor task (Time 4), 67

compared to baseline (Time 1; t(40) = -2.29; 95% CI = -12.84, -0.79; p = .03; ηp² = - .45) and immediately following the cold pressor task (Time 3; t(40) = -2.76; 95% CI = -

11.24, -1.67; p = .01; ηp² = -.43). As Mauchly’s test of sphericity was violated for heart rate, χ2(5) = 37.91, p < .0001, I used a Greenhouse-Geisser correction. As seen in Figure 2.2, heart rate did not significantly change over the course of the experiment,

F(1.81, 72.58) = 2.40, p = .103, ηp² = .06. This result suggests that surprise did not influence heart rate in this experiment. However, the moderate effect size for heart rate also indicates a moderate increase in surprise following the surprise manipulation, relative to before the manipulation. As also seen in Figure 2.2, sympathovagal balance did not change significantly over the course of the experiment, F(3, 120) = 0.39, p =

.76, ηp² = .01. Unexpectedly, these results indicate that the cold pressor task likely did not induce increased sympathetic activity.

Parasympathetic activity manipulation check. I conducted multiple repeated- measures ANOVAs to determine whether heart rate variability (as indicated by R-R intervals, RMSSD, and high frequency power) changed across the course of the experiment. Increases in these measures of heart rate variability indicate heightened parasympathetic activity (Acharya et al., 2006). 68

* * * * 1000 *** 80 *** 800 60 600 40 RMSSD Video 2 Video 400 1 Video

R-R Intevals R-R 20 Video 2 Video Video 1 Video 200 0 Cold Pressor Task Cold Pressor Task 0

Time1 Time 2 Time 3 Time 4 Time1 Time 4 Time 2 Time50 3

20 Video 2 Video Video 1 Video

10 Task High frequency power Cold Pressor 0

Time1 Time 2 Time 3 Time 4

Figure 2.3. Changes in parasympathetic activity over the course of the experiment, as indicated by increases in R-R intervals, RMSSD and high frequency power. Error bars represent standard error of the mean. R-R intervals decreased during the cold pressor task (Time 2) relative to baseline (Time 1). R-R intervals increased following the cold pressor task (Times 3 and 4) relative to baseline and during the cold pressor task (Times 1 and 2). RMSSD increased following the cold pressor task (Time 3) compared to baseline measurements (Time 1). * p < .05. *** p < .001.

As Mauchly’s test of sphericity was violated for R-R intervals, χ2(5) = 37.39, p < .0001, I used a Greenhouse-Geisser correction. As seen in Figure 2.3, R-R intervals changed moderately over the course of the experiment, F(1.88, 75.26) = 7.63, p = .001,

ηp² = .16. Uncorrected post-hoc tests of least significant difference revealed that R-R intervals decreased during the cold pressor task (Time 2), compared to baseline (Time 1; t(40) = -2.93; 95% CI = -51.52, -9.49; p = .01; ηp² = -.31). Similarly, R-R intervals increased immediately following the cold pressor task (Time 3), compared to baseline

(Time 1; t(40) = 2.89; 95% CI = 7.44, 41.97; p = .01; ηp² = .23), and during the cold pressor task (Time 2; t(40) = 4.59; 95% CI = 30.92, 79.50; p < .0001; ηp² = .55). Additionally, R-R intervals increased following the cold pressor task (Time 4), 69

compared to baseline (Time 1; t(40) = 2.03; 95% CI = 0.18, 73.28; p = .049; ηp² = .27), and during the cold pressor task (Time 2; t(40) = 3.56; 95% CI = 29.01, 105.46; p <

.001; ηp² = .52). As indicated by the effect sizes and confidence intervals, R-R intervals changed the most between the cold pressor task and adjacent time points (i.e., directly before and after).

As seen in Figure 2.3 and Table 3.1, RMSSD changed significantly over the course of the experiment, F(3, 120) = 2.97, p = .04, ηp² = .07. However, this effect was small. Uncorrected post-hoc tests of least significant difference revealed that RMSSD strongly increased following the cold pressor task (Time 3), compared to baseline measurements (Time 1; t(40) = 2.86; 95% CI = 7.81, 45.58; p = .01; ηp² = .51). As also seen in Figure 2.3, percentage of high frequency power did not change significantly over the course of the experiment, F(3, 120) = 1.90, p = .13, ηp² = .05. There were sufficient increases in most heart rate variability measures to indicate that parasympathetic activity increased following the cold pressor task. These results may also indicate that parasympathetic activity decreased during the cold pressor task.

Primary Analyses

Emotional intensity and perceived time. I conducted a mixed ANOVA on the estimated bisection points to determine if participants perceived time differently while viewing emotional or neutral images in the bisection task.

Emotional Neutral 0.3 1100 t o n i i 1000 t

o 0.2 a P

n r o e i 900 t b c e e 0.1 s i W

B 800

700 0.0 1 2 3 4 1 2 3 4 Ti me Time

Figure 2.4. Interactions between block (i.e., Times 1-4) and stimulus content (i.e., emotional or neutral) for bisection point and Weber ratio. Error bars represent standard error of the mean. Bisection point was different between emotional and neutral stimuli based on experimental 70

block, but only between unrelated data points. Weber ratio did not differ between emotional and neutral stimuli based on experimental block.

As Mauchly’s test of sphericity was violated for bisection point, χ2(5) = 29.83, p < .0001, I used a Greenhouse-Geisser correction. As seen in Figure 2.4, there were no significant differences between the perceived time of emotional and neutral images,

F(1, 91) = 0.48, p = .49, ηp² = .01. As also seen in Figure 2.4, experimental block and stimulus type did not significantly interact to influence time perception, F(2.47, 225.07)

= 0.76, p = .50, ηp² = .01.

Similarly, another mixed ANOVA on the estimated Weber ratios determined if participants differed in temporal sensitivity while viewing emotional or neutral images in the bisection task. As seen in Figure 2.4, there were no significant differences in temporal sensitivity between emotional and neutral images, F(1, 92) = 0.002, p = .96,

ηp² < .0001. There were also no significant interactions between image content and experimental block for temporal sensitivity, F(3, 276) = 1.62, p = .19, ηp² = .02.

Physiological activity and perceived time. As there were no significant differences in time perception between emotional and neutral images, I combined these data for subsequent analyses. I conducted a repeated-measures ANOVA on bisection point to determine whether participants perceived the duration of emotional and neutral images differently across the course of the experiment.

Table 3.2

Changes in bisection point and Weber ratios over the course of Experiment 1

Variable Time 1 Time 2 Time 3 Time 4 N (Cold Pressor) M SE M SE M SE M SE Bisection 999.91 24.33 952.23 31.81 866.87 21.05 833.38 19.92 47 Point Weber 0.16 0.01 0.21 0.02 0.14 0.01 0.14 0.01 47 Ratio

*** *** * ** *** 0.3 * * 1000 *

0.2

500 Video 1 Video Video 2 Video 0.1 Weber ratio Weber Video 2 Video Video 1 Video Bisection Point Bisection Cold Pressor Task Cold Pressor Task Pressor Cold 0 0.0

Time1 Time1 Time 2 Time 3 Time 4 Time 2 Time 3 Time 4

Figure 2.5. Changes in bisection point and Weber ratio of emotional and neutral stimuli over the course of the experiment. Error bars represent standard error of the mean. The bisection points for emotional and neutral stimuli decreased following the cold pressor task (Times 3 and 4) compared to baseline measurements and during the cold pressor task (Times 1, 2 and 3). Weber ratios increased during the cold pressor task (Time 2) compared to baseline (Time 1) and decreased following the cold pressor task (Times 3 and 4) compared to during the cold pressor task (Time 2). * p < .05. ** p < .01 *** p < .001.

As Mauchly’s test of sphericity was violated for bisection point, χ2(5) = 25.16, p < .0001, I used a Greenhouse-Geisser correction. As seen in Figure 2.5 and Table 3.2, the perceived duration of emotional and neutral images differed over the course of the experiment, F(2.28, 104.95) = 12.55, p = .000005, ηp² = .21. Uncorrected post-hoc tests of least significant difference revealed lower bisection points following the cold pressor task (Time 3), compared to both baseline (Time 1; t(46) = -5.24; 95% CI = -183.03, -

83.05; p = .0001; ηp² = -.61) and during the cold pressor task (Time 2; t(46) = -2.81;

95% CI = -145.82, -24.89; p = .01; ηp² = -.33). Similarly, bisection points decreased following the cold pressor task (Time 4), compared to baseline (Time 1; t(46) = -6.29;

95% CI = -217.48, -115.59; p < .0001; ηp² = -.78), during the cold pressor task (Time 2; t(46) = -4.04; 95% CI = -177.25, -60.44; p = .0004; ηp² = -.46), and immediately following the cold pressor task (Time 3; t(46) = -1.66; 95% CI = -70.94, 3.95; p < .02;

ηp² = -.17). Bisection points did not significantly change during the cold pressor task

(Time 2), compared to baseline (Time 1), p = .32, ηp² = .01. Lower bisection points indicate slower time perception (Droit-Volet et al., 2004). These results imply that 72

participants perceived the durations of both neutral and emotional images as slower during blocks with heightened parasympathetic activity, relative to both emotional and neutral images during blocks without heightened parasympathetic activity. Notably, the effect sizes were the largest for comparisons between non-adjacent time points. These results may imply that the cold pressor task did not induce changes in time perception in this experiment.

As there were no significant differences in Weber ratio between emotional and neutral images, I also combined these data for subsequent analyses. I conducted a repeated-measures ANOVA on Weber ratio to determine whether temporal sensitivity changed across the course of the experiment. As Mauchly’s test of sphericity was violated, χ2(5) = 13.97, p = .02, I used a Greenhouse-Geisser correction. As seen in Figure 2.5, Weber ratio differed significantly over the course of the experiment, F(2.44,

112.35) = 4.22, p = .01, ηp² = .08. Uncorrected post-hoc tests of least significant difference revealed that the Weber ratio increased during the cold pressor task (Time 2), compared to baseline (Time 1; t(46) = 3.16; 95% CI = 0.02, 0.09; p = .001; ηp² = .44). Similarly, the Weber ratio was significantly higher during the cold pressor task (Time 2), compared to immediately after the cold pressor task (Time 3; t(46) = -3.79; 95% CI

= -0.11, -0.03; p = .02; ηp² = -.23), and following the cold pressor task (Time 4; t(46) =

-3.50; 95% CI = -0.11, -0.03;p = .03; ηp² = -.28). These results suggest that temporal sensitivity strongly decreased during the cold pressor task, relative to before and after the cold pressor task.

Physiological activity as a moderator between emotional intensity and time perception. Although participants overestimated stimulus duration in blocks with heightened parasympathetic activity, there were no significant effects of stimulus type on time perception. As such, I did not complete a planned moderation analysis assessing if physiological activity moderates the effect of emotional intensity on time perception.

Correlations between perceived time and heart rate variability. There were changes in both time perception variables, as well as some measures of heart rate variability, over the course of the experiment. Hence, I conducted Pearson’s correlations to determine if the bisection point and Weber ratio were correlated with R-R intervals, RMSSD and percentage of low frequency power. I only included these physiological variables as they were the only variables that significantly changed over the course of the experiment. There was a significant, yet small, negative correlation between the 73

Weber ratio and R-R intervals, r(41) = -.21, p = .01. However, there was no significant correlation between bisection point and R-R intervals, r(41) = -.08, p = .30. There were also no significant correlations found between Weber ratio and either RMSSD, r(41) = .07, p = .35, or percentage of low frequency power, r(41) = .02, p = .79. Similarly, there were no significant correlations found between bisection point and either RMSSD, r(41) = .04, p = .66, or percentage of low frequency power, r(41) = .06, p = .42.

Correlations between physiological measures. RMSSD, R-R intervals and percentage of low frequency power changed significantly over the course of the experiment. I conducted Pearson’s correlations to determine if these measures of parasympathetic activity correlated with one another. There was a small, yet significant, negative correlation between RMSSD and percentage of low frequency power, r(41) = - .33, p < .00001. However, there was no significant correlation between RMSSD and R- R intervals, r(41) = .03, p = .75. There was also no significant correlation between percentage of low frequency power and R-R intervals, r(41) = -.12, p = .12. These results suggest that one measure of parasympathetic activity increased as a function of decreased sympathetic activity. These results also suggest that the measures of parasympathetic activity changed independent of one another.

Discussion

Participants overestimated the durations of both neutral and emotional images during blocks with heightened parasympathetic activity, as indicated by increased R-R intervals and RMSSD, relative to blocks with lower parasympathetic activity. These results may suggest that people overestimate duration when they experience heightened parasympathetic activity (Pande & Pati, 2010). Moreover, although previous research suggests that individuals overestimate duration during heightened sympathetic activity (Einhauser et al., 2010; Falk & Bindra, 1954; Lake & Meck, 2013; Moore, 2014; Vachon et al., 1974; Wittmann et al., 2007), this experiment is the first to identify that individuals may also overestimate duration during heightened parasympathetic activity. However, the individual measures of parasympathetic activity did not correlate with changes in time perception. Similarly, I did not successfully induce sympathetic activity in the cold pressor task. As such, it is possible that time perception and autonomic activity changed independent of one another. Overall, the evidence was too weak to determine if parasympathetic activity can change time perception. 74

Participants in this experiment overestimated stimulus duration in blocks with higher parasympathetic activity, and underestimated stimulus duration in blocks with lower parasympathetic activity. Increased R-R intervals and RMSSD indicated higher parasympathetic activity. These results did not support my hypothesis that individuals would underestimate stimulus durations during heightened parasympathetic activity. Similarly, although one measure of sympathetic activity negatively correlated with one measure of parasympathetic activity, sympathetic activity did not change time perception. These results suggest that sympathetic and parasympathetic activity may independently exert similar effects on time perception, rather than opposing effects.

Participants experienced both lower temporal sensitivity and lower parasympathetic activity during the cold pressor task, as indicated by higher Weber ratios and decreased R-R intervals, relative to before and after the cold pressor task. Moreover, R-R intervals and Weber ratios were correlated, such that temporal sensitivity increased with heightened parasympathetic activity. These results imply that heightened parasympathetic activity may improve the ability to discriminate between millisecond durations. This result also replicates and adds to a small body of research showing that heightened parasympathetic activity can increase temporal sensitivity relative to lower levels of activity (Cellini et al., 2015; Meissner & Wittmann, 2011; Pollatos, Yeldesbay, Pikovsky, & Rosenblum, 2014).

Participants did not perceive time differently when viewing emotional or neutral images. This finding contradicts previous research showing that individuals overestimate stimuli of heightened emotional intensity (Gil & Droit-Volet, 2012). I sourced the images from the International Affective Picture System, which provides standardized arousal ratings for each picture (Lang et al., 2008). As such, the emotional images were likely more arousing than the neutral images. However, the task used to induce physiological activity in this experiment is usually both strongly arousing and painful (e.g., Kowalczyk, Evans, Bisaga, Sullivan, & Comer, 2006). Furthermore, individuals often preferentially attend to pain over other arousing stimuli (e.g., social threat) and pain can impair emotional judgments (e.g., emotional decisions; Apkarian et al., 2004; Beck et al., 2011). This evidence may suggest that the effects of physiological activity on time perception overrode the smaller effects of emotional intensity on time perception. This evidence additionally implies that the effects of arousal on time 75

perception in this study were only evident through changes in physiology, and not through changes in emotional intensity.

Implications

The results of the present experiment possibly provided evidence that changes in parasympathetic activity can influence time perception. As such, this is the first experiment to suggest that individuals may overestimate duration during heightened parasympathetic activity. This evidence may have theoretical implications because time perception models typically ignore the role of parasympathetic activity (e.g., Allman et al., 2014). However, the results of the present experiment suggest there is possibly merit to considering the role of parasympathetic activity when examining the effects of physiology on time perception. This evidence also may have methodological implications because some research examining arousal and time perception by manipulating physiology operationalises physiological activity by low and high levels of intensity (e.g., Mella et al., 2010). Moreover, as previous research suggests that individuals also overestimate duration during heightened sympathetic activity, there may be merit to operationalising physiology by parasympathetic and sympathetic activity when examining the effects of arousal on time perception (e.g., Wittmann et al., 2007).

One reason why individuals may overestimate duration during heightened parasympathetic activity is possibly because people have more awareness of the time when they are in a state of heightened parasympathetic activity. When individuals are relaxed, they can experience both heightened parasympathetic activity and greater awareness of their environment. Meditation, for example, is a relaxing activity that increases both an individual’s awareness of their bodily sensations and their levels of parasympathetic activity (Silverstein, Brown, Roth, & Britton, 2011; Wu & Lo, 2008). Moreover, in one experiment, greater awareness of the time was accompanied by slower time perception (Conti, 2001). Parasympathetic activity may slow time perception by optimizing individual awareness of the time. Future researchers may wish to examine how parasympathetic activity changes time awareness because it could identify unique psychological factors by which parasympathetic activity slows time perception.

The results of the present experiment also provided evidence that changes in parasympathetic activity covary with changes in temporal sensitivity. In this 76

experiment, increased R-R intervals and RMSSD indicated heightened parasympathetic activity and lower Weber ratios indicated higher temporal sensitivity. Heightened parasympathetic activity in this experiment correlated with increased temporal sensitivity (i.e., the ability to discriminate between temporal durations). Deficits in the ability to discriminate between temporal durations characterises several psychological disorders, such as schizophrenia and depression (Biermann et al., 2011; Davalos, Kisley, & Ross, 2002; Elvevåg et al., 2003). Some research suggests that stimulating parasympathetic activity can decrease symptoms of these disorders by allowing people to form more accurate representations of duration (Biermann et al., 2011; Meissner & Wittmann, 2011). This explanation could justify why stimulating the vagus nerve, which heightens parasympathetic activity (Levy, 1971), decreases depression symptoms (see Gimm & Bajbouj, 2010 for a review). Activities that stimulate parasympathetic activity in general (e.g., vagus nerve stimulation, meditation) may help reduce symptoms of disorders characterised by lower temporal sensitivity (Gimm & Bajbouj, 2010; Wu & Lo, 2008).

Certain individuals may be less susceptible to the effects of parasympathetic activity on temporal sensitivity. Autonomic flexibility is the capacity of the parasympathetic nervous system to adapt to changes by monitoring and altering arousal, respiration, heart rate, and attention (Friedman & Thayer, 1998). Certain psychological phenomena inhibit autonomic flexibility, such as panic and certain phobias (Friedman & Thayer, 1998). As such, certain (e.g., anxious) individuals may inhibit increases in temporal sensitivity during tasks that heighten parasympathetic activity. The ability to regulate response to stressors through the parasympathetic nervous system may directly influence how parasympathetic activity influences temporal sensitivity.

Some research suggests that parasympathetic activity may increase temporal accuracy by facilitating optimal attention conditions for accurate time perception (e.g., Cellini et al., 2015). Higher parasympathetic activity increases the ability to both regulate and sustain attention (Burg, Wolf, & Michalak, 2012; Hansen et al., 2003). Thus, attention may mediate the effects of parasympathetic activity on time perception. However, in some models of time perception (Allman et al., 2014), individuals overestimate stimulus durations when attending to time relative to not attending to time. As such, it is possible that parasympathetic activity increases the likelihood of attending 77

to time. However, researchers may wish to better investigate how physiological activity and attention interact to influence time perception.

Some research additionally suggests that bodily signals can inform time perception through changes in temporal sensitivity (e.g., Cellini et al., 2015). Interoceptive accuracy is the ability to perceive bodily signals, where higher interoceptive accuracy means a better ability to perceive internal signals like heart rate (Meissner & Wittmann, 2011). Individuals with better interoceptive accuracy perceive time more accurately than individuals with lower interoceptive accuracy (Meissner & Wittmann, 2011). Moreover, individuals with heightened parasympathetic activity can perform better in heartbeat detection tasks (Fairclough & Goodwin, 2007; Schäflein, Sattel, Pollatos, & Sack, 2018). Individuals in a heightened state of parasympathetic activity may thus perceive time more accurately due to heightened interoceptive awareness abilities.

Limitations and Future Research

Participants underestimated image duration during the cold pressor task, relative to after the cold pressor task. Cold pressor tasks are typically painful, and painful tasks tend to increase sympathetic activity (Dishman et al., 2003; Quigley & Stifter, 2006). However, the cold pressor task did not increase sympathetic activity in this experiment. Although parasympathetic activity decreased during the cold pressor task, this is insufficient evidence to conclude that sympathetic activity increased during the task. This result was unexpected, as painful tasks often induce sympathetic activity (Dishman et al., 2003; Quigley & Stifter, 2006).

Although individuals overestimated duration in blocks with heightened parasympathetic activity, time perception did not correlate with the individual measures of heightened parasympathetic activity. However, neither did the measures of parasympathetic activity correlate with one another. Individual measures of heart rate variability do not always correlate with one another. For instance, RMSSD sometimes does not correlate with high frequency power, sympathovagal balance rarely correlates with other measures of heart rate variability, and heart rate variability measures sometimes do not even correlate with recordings of the same measure over different time periods (e.g., 1 minute compared to 5 minutes; Nussinovitch et al., 2011; Shaffer, McCraty, & Zerr, 2014; Wang & Huang, 2012). Similarly, the extent to which 78

sympathetic and parasympathetic activity contribute to different measures of heart rate variability vary with testing conditions (Kember, Fenton, Armour, & Kalyaniwalla, 2001). Additionally, R-R intervals are used to calculate the other measures of heart rate variability and, as such, do not correlate with the other measures (Peltola, 2012). Thus, individual measures of parasympathetic activity may be difficult to correlate with time perception. Although parasympathetic activity did not correlate with time perception, heightened parasympathetic activity still likely influenced time perception.

Another limitation is that I did not directly manipulate increased parasympathetic activity in this experiment. Direct manipulations may prove informative as people may have a different physiological response when their parasympathetic response increases from baseline (e.g., during meditation) compared to during recovery from heightened sympathetic or reduced parasympathetic activity (e.g., from an electric shock). Future research could manipulate increases in both sympathetic and parasympathetic activity directly.

Another possible reason why participants overestimated image duration in experimental blocks with heightened parasympathetic activity may be that participants experienced boredom as the experiment progressed. Participants overestimated the duration of images in the latter half of the experiment, relative to the former half of the experiment. Boredom typically increases when an individual lacks stimulation, such as when repeating a monotonous task (Caldwell, Darling, Payne, & Dowdy, 1999). Because participants completed the bisection task multiple times, they may have found the task less stimulating towards the end of the experiment. There is some evidence to support this idea, as participants with accurate time perception should produce bisection points of around 1000ms, and participants moved farther from this duration as the experiment progressed. Moreover, bored individuals can overestimate stimulus duration, and boredom can increase parasympathetic activity (Danckert & Allman, 2005; Keller, Bless, Blomann, & Kleinbohl, 2011). Participants possibly both experienced heightened parasympathetic activity and overestimated stimulus durations due to increased boredom towards the end of the experiment. Future time perception research could include a manipulation check, or a control condition with more engaging tasks, to determine if boredom influenced time perception during the experiment.

The cold pressor task did not change time perception relative to baseline measurements. However, these results were likely minimized by an inability to 79

concentrate during the cold pressor task. The cold pressor task commonly induces pain and pain commonly interferes with the ability to concentrate (Dishman et al., 2003; Eisenach, Pan, Smiley, Lavand’homme, Landau, & Houle, 2008; Fink, 2000; Quigley & Stifter, 2006). This idea is supported by the result that temporal sensitivity decreased during the cold pressor task. Future research could use paradigms that do not affect ability to concentrate when assessing the effects of physiological activity on time perception.

I also did not include a control condition for the cold pressor task (e.g., a warm bucket of water). As such, the results in this experiment were possibly unrelated to the physiological manipulation. The lack of an appropriate control thus weakens any conclusions about the effects of arousal on time perception in this study. Future research examining the effects of sympathetic and parasympathetic activity could include a control condition to extend the results from this experiment.

Attention possibly confounded the effects of the cold pressor task on time perception. Participants completed the cold pressor task while concurrently completing a bisection task. However, participants completed the other bisection tasks without completing another concurrent task. People completing concurrent tasks often distribute their attentional resources more broadly (Allman et al., 2014). Similarly, people distribute their attentional resources more broadly when they are distracted by stimuli unrelated to their task, such as completing the cold pressor task (Brown, 2008). Individuals underestimate stimulus duration when their attentional distribution is broader relative to narrower (Block et al., 2010). Participants underestimated image duration during the cold pressor task relative to after the cold pressor task. However, this underestimation may be due to the broadening of attentional resources and not decreases in parasympathetic activity.

I also did not include a manipulation check for emotional intensity. Although emotional intensity did not change time perception in this experiment, it is possible the emotion manipulations were unsuccessful. Images from the International Affective Picture System are not commonly used to manipulate emotional intensity in time perception experiments. However, individuals often overestimate the duration of emotionally intense images (e.g., Gil & Droit-Volet, 2012). As such, it is likely the images in this experiment did increase emotional intensity. However, future research 80

could include manipulation checks to ensure that the emotional images increase emotional intensity and arousal in general.

I hypothesised that physiological activity would moderate the effects of emotional intensity on time perception in this experiment. However, I did not complete a planned moderation analysis because the effect of stimulus type on time perception was not significant. I also did not obtain different physiological readings for participants when viewing emotional and neutral images because participants viewed both image types during each bisection task. Thus, I could not assess the physiological differences between emotional and neutral images in each block. These differences in physiology could contribute to how physiology moderates the effects of emotional intensity on time perception because emotional stimuli elicit a stronger physiological response than neutral stimuli (e.g., McManis, Bradley, Berg, Cuthbert, & Lang, 2001). Future research could better isolate the physiological component of emotional intensity when assessing if physiological activity moderates the influence of emotional intensity on time perception.

I used 3 physiological measures to assess changes in each sympathetic and parasympathetic activity. This decision was deliberate, as different measures of physiology are associated with different physiological and psychological outcomes, even within each autonomic branch. For example, elevated heart rate and low frequency heart rate variability both indicate sympathetic activity. However, increased heart rate predicts higher sleep quality, whereas higher sympathovagal balance predicts lower sleep quality (Fernandes et al., 2013; Hall et al., 2004). Additionally, physiological measures differ in sensitivity (Mehler, Reimer, & Coughlin, 2012). One limitation of using multiple dependent variables to measure a single outcome is the potential to inflate the likelihood of obtaining a Type 1 error. In this experiment, changes in sympathetic and parasympathetic activity differed between dependent measures. As such, it is possible that some of the effects in this experiment spuriously indicate a significant effect. Notably, there are corrections that can adjust these effects for an inflated Type I error rate over multiple comparisons. I did not apply these corrections as I wanted to maximize sensitivity for detecting the exploratory effects of autonomic activity on time perception. However, future research could endeavour to assess the replicability of these findings. 81

The bisection task in this experiment consisted of only a single block of 50 trials, within which I additionally manipulated image type and duration. As there were 2 image types and 7 durations, there were only 3.5 trials on average per cell. As image type and duration were randomized, this design additionally implies that the number of trials in a given cell could be lower than this average for a particular participant. This approach limits the conclusions about time perception in this experiment, as 50 trials was likely too low to accurately assess image type for each duration. Future researchers could consider increasing the number of trials in the bisection task to accurately reflect the number of variables being assessed. In time perception research, bisection tasks typically include a single block of 70 trials when the block does not include within-trial manipulations (e.g., Smith et al., 2011). In the subsequent bisection tasks in this thesis, I increased the number of trials to 70.

Conclusions

In this experiment, individuals overestimated duration in experimental blocks with heightened parasympathetic activity. Additionally, heightened parasympathetic activity covaried with increased temporal sensitivity, replicating previous research (Cellini et al., 2015). However, this study lacked an appropriate control. This methodological limitation prevents any firm conclusions about the effects of autonomic activity on time perception.

CHAPTER 4. Experiment 2: How do Autonomic Activity and Emotional Valence Influence Time Perception?

Introduction

Arousal and emotional valence are both factors that significantly determine how people perceive time (Angrilli et al., 1997). Arousal involves two related components: emotional intensity and physiological activity. Arousal is often operationalised in psychological and physiological research as sympathetic and parasympathetic autonomic activity (Gadsdon, 2001; Weiten, 2007). Individuals typically overestimate duration when they experience heightened sympathetic activity (e.g., during exercise) relative to lower sympathetic activity (e.g., Wittmann et al., 2007). Research examining the effects of physiological activity on time perception, however, has not extensively investigated the role of parasympathetic activity. The results of Experiment 1 indicated that individuals may overestimate duration during states of heightened parasympathetic activity. However, Experiment 1 did not manipulate increased parasympathetic activity. The current experiment attempted to replicate and extend these results by manipulating increased activation of the sympathetic and parasympathetic branches.

Valence is the intrinsic hedonic value of a stimulus such as an image or emotion (Anderson & Sobel, 2003). Standard models characterise valence as a one-dimensional bi-polar construct (Briesemeister, Kuchinke, & Jacobs, 2012). However, some research suggests that characterising valence as a two-dimensional construct provides better model fit for emotional constructs (Rubin & Talarico, 2009). As such, in more recent models, valence is characterised along two continua as positive versus negative, and pleasant versus unpleasant (Goldie, 2009). Individuals often overestimate the durations of negative stimuli and underestimate the durations of positive stimuli (e.g., Lambrechts et al., 2011; Tobin & Grondin, 2009). Within the study of time perception, however, some research may suggest that changes in arousal can explain the effects of valence on time perception (e.g., Droit-Volet & Gil, 2009; Droit-Volet & Meck, 2007; Droit-Volet et al., 2011). However, not all valence data are consistent with arousal explanations of valence and time perception, and previous experiments examining valence and time perception often do not control for arousal (e.g., Bailey & Areni, 2006; Tobin & Grondin, 2009). To address these limitations, the second part of the current experiment examined the effects of valence on time perception while controlling for arousal. I controlled for both the cognitive and physiological components of arousal by using 83

positive and negative subcategories of an emotion (i.e., surprise) that induce similar arousal levels (Russell, 1980; Talarico, Berntsen, & Rubin, 2009; Watson & Tellegen, 1985), and also by statistically controlling for arousal levels before the emotion induction.

Arousal, Physiological Activity and Time Perception

Experiment 1 investigated the effects of arousal on time perception by examining changes in parasympathetic activity. In Experiment 1, participants overestimate image duration in experimental blocks with heightened parasympathetic activity relative to blocks with lower parasympathetic activity. These results may imply that individuals overestimate duration during heightened parasympathetic activity. However, parasympathetic activity did not correlate with time perception, and I did not directly induce increased parasympathetic activity in this experiment. I also did not effectively induce sympathetic activity in Experiment 1 with the cold pressor, although previous research suggests that individuals also overestimate duration during heightened sympathetic activity (e.g., Wittmann et al., 2007). To address these limitations, the current experiment examined how directly manipulating both sympathetic and parasympathetic activity changes time perception.

In Experiment 1, relative to before completing a painful task, participants exhibited both lower temporal sensitivity and decreased parasympathetic activity after the painful task. Furthermore, parasympathetic activity correlated with temporal sensitivity, such that higher parasympathetic activity improved the ability to discriminate between millisecond stimulus durations. The current experiment attempted to replicate and extend the result that heightened parasympathetic activity can improve temporal sensitivity.

Valence

Positive and negative emotional valence independently influence time perception. Individuals overestimate the duration of negative stimuli (e.g., harsh lighting, negative sounds, pictures of weapons) compared to positive and neutral stimuli (Bailey & Areni, 2006; Corke et al., 2018; Goldstone et al., 1978; Gorn et al., 2004; Hornik, 1992; Lambrechts et al., 2011; Mella et al., 2010; Noulhiane et al., 2007; Twenge et al., 2003; Yamada & Kawabe, 2011). Conversely, individuals underestimate the duration of positive stimuli (e.g., erotic sounds, videogames) compared to negative 84

and neutral stimuli (Bailey & Areni, 2006; Droit-Volet et al., 2010; Gorn et al., 2004; Hornik, 1992; Kellaris & Kent, 1994; Noulhiane et al., 2007; Tobin & Grondin, 2009; Yalch & Spangenberg, 2000). Overestimated stimuli indicate slower time perception, whereas underestimated stimuli indicate faster time perception (Pande & Pati, 2010). These results indicate that individuals underestimate positive stimuli, and overestimate negative stimuli, relative to neutral stimuli.

Some research may suggest that changes in arousal can explain the effects of valence on time perception (e.g., Droit-Volet & Gil, 2009; Droit-Volet & Meck, 2007; Droit-Volet et al., 2011). The most compelling evidence for this argument is that negative stimuli (e.g., images of snakes) are generally more arousing than positive stimuli (e.g., images of domestic animals; Bradley & Lang, 2007; Bradley, Codispoti, Cuthbert, & Lang, 2001; Noulhiane et al., 2007; Partala, Jokiniemi, & Surakka, 2000). This evidence could explain why individuals overestimate negative stimuli compared to positive stimuli, because individuals also overestimate arousing stimuli (e.g., Droit- Volet & Meck, 2007; Gil & Droit-Volet, 2012). Negative stimuli are likely more arousing than positive stimuli because negative stimuli in experiments are often threat- related and take cognitive precedence over positive stimuli (Estes & Verges, 2008; Williams, Palmer, Liddell, Song, & Gordon, 2006).

Much of the research investigating the effects of valence on time perception utilised images and sounds from the International Affective Picture System and International Affective Digitalized Sounds system, respectively (Bradley & Lang, 1999; Lang et al., 1997; 2008). These images and sounds vary in the arousal that they elicit. Because individuals can overestimate arousing stimuli (Gil & Droit-Volet, 2012), controlling for arousal may distinguish the effects of arousal from valence. Many experiments to date have not controlled for arousal. Therefore, in the present experiment, I manipulated valence while holding arousal constant by inducing similar levels of arousal in the valence conditions.

The limited time perception experiments that directly manipulated both arousal and emotional valence reported mixed results. Some experiments reported main effects of both arousal and valence on time perception (Noulhiane, et al., 2007). Other experiments did not report main effects for emotional valence on time perception, with or without reported main effects for arousal on time perception (Angrilli et al., 1997; Droit-Volet, Ramos, Bueno, & Bigand, 2013). Other experiments reported interactions 85

for time perception based on valence and factors like duration of stimulus length and arousal levels (Angrilli et al., 1997; Buetti & Lleras, 2012; Droit-Volet et al., 2013; Smith et al., 2011; van Volkingburg & Balsam, 2014). Moreover, the pattern of overestimated duration for negative stimuli and underestimated duration for positive stimuli only occurred for highly arousing stimuli in these experiments. Those experiments provided inconclusive evidence for the effects of valence on time perception in the absence of arousal. These inconclusive results suggest a need for more controlled experiments examining the effects of emotional valence dimensions on time perception.

In one influential experiment, participants listened to pleasant or unpleasant music with either a fast or slow tempo (Droit-Volet et al., 2013). This experiment operationalised positive valence as pleasant music and negative valence as unpleasant music. This experiment also operationalised heightened arousal as fast tempo and low arousal as slow tempo. Participants listening to music with a fast tempo underestimated duration relative to participants listening to slower music. Participants listening to pleasant music also underestimated duration relative to participants listening to unpleasant music. However, the effects of pleasantness on time perception disappeared when controlling for tempo. The authors concluded that arousal can explain the effects of valence on perceived time.

An alternative possibility may be that arousal confounded the effects of valence on time perception. Valence can also be operationalised by tempo, such that faster music is considered more pleasant than slower music (Bruner, 1990). Indeed, participants in Droit-Volet et al.’s (2013) experiment reported faster music as more pleasant than slower music. As such, individuals possibly underestimated the duration of faster music due to changes in valence, and not arousal as the authors reported. Furthermore, although Droit-Volet et al. (2013) reported that any effects of pleasantness disappeared when controlling for tempo, the pleasant music was also sometimes more arousing than the unpleasant music (i.e., when the stimulus was presented for a long time, or when the tempo of the music was fast). As individuals overestimate the duration of arousing stimuli (Gil & Droit-Volet, 2012), participants should have overestimated the duration of pleasant, relative to unpleasant, music. However, participants in Droit-Volet et al.’s (2013) experiment overestimated the duration of unpleasant, rather than pleasant, music. Thus, the conclusions regarding arousal in this 86

experiment may be a result of operationalising arousal as tempo, because valence and tempo may produce similar effects on time perception.

Surprise

The current experiment examined the effects of valence by manipulating positive and negative surprise. Positive and negative surprise are two distinct subcategories of one emotion that differ by valence (Prinz, 2004). An individual may feel positive surprise, for example, when their friends throw them an unexpected party (e.g., Talarico et al., 2009). Alternately, an individual may feel negative surprise when they are unexpectedly attacked (Bednarek, 2008). However, positive and negative surprise are usually experienced as similarly arousing and intense (Russell, 1980; Talarico et al., 2009; Watson & Tellegen, 1985). Manipulating positive and negative surprise is one way to manipulate valence while controlling for arousal.

To my knowledge, there is no research investigating how surprise affects time perception. Evidence suggests individuals overestimate duration when they experience some basic emotional states (e.g., fear; Droit-Volet et al., 2011; Tipples, 2008). There is also evidence that other basic emotions influence time perception, but these effects are mixed (e.g., sadness, disgust, happiness; Droit-Volet et al., 2010; Gil et al., 2009; Tipples et al., 2015). In this experiment, I examined the effects of positive and negative surprise on time perception.

The Present Research

This experiment examined the effects of arousal, as indicated by sympathetic and parasympathetic activity, on time perception. This experiment also examined the effects of positive and negative emotional valence on time perception. Participants completed isometric or deep breathing exercises to induce sympathetic or parasympathetic activity, respectively. Isometric exercises involve muscular contractions in which the muscle does not change length and the joints do not move (e.g., planking; Boone, 2014). Participants completed a laboratory measure of time perception similar to that from Experiment 1 to examine the effects of physiological activity on time perception. Participants then completed the isometric or deep breathing exercises again to ensure arousal, as indicated by increased physiological activity, was consistent throughout the experiment. Participants then drank either a sweet or bitter beverage while blindfolded to induce positive or negative surprise. Participants then 87

completed the same laboratory measure of time perception. Participants also completed a self-report measure of arousal to ensure arousal, as indicated by emotional intensity, was consistent between valence conditions.

Method

Participants and Design

Participants were 90 first-year psychology students recruited from the University of New South Wales in exchange for course credit. Sixty-seven additional participants were recruited from a community sample and paid AUD$10 in exchange for participation. A power analysis using GPower, with a goal to obtain 0.8 power to detect a medium effect size (f = 0.25; Cohen, 1977), with an alpha level of 0.05, determined the sample should include a minimum of 76 participants (Faul et al., 2009). The data of 42 participants were excluded for at least one of the following: receiving a score of less than 70 in the bisection training phase (n = 1; see Experiment 1, pp. 59), not following instructions during the arousal or valence manipulations (n = 10), poor quality of all physiological data (e.g., due to electrodes falling off during the experiment; n = 12), having a Weber ratio of over 0.34 (indicating less than half the temporal sensitivity of the average adult; e.g., Droit-Volet et al., 2004; Wearden, 1991; n = 18), and/or the bisection experimental data failing a residual check (indicating poor model fit; McPherson, 2001; n = 9).

The final sample of 115 participants included 44 males and 70 females, with a mean age of 22.00 years (SD = 6.13). Sixty-two of these individuals were recruited for course credit, and 53 from a community sample and paid AUD$10 for participation. These individuals were primarily Asian (65.2%), or Caucasian (27.8%) and the remaining 7.0% of the sample reported identification with other ethnicities. The majority of the sample was non-religious (47.0%), with a further 24.3% Christian, 11.3% Hindu, 7.0% Muslim, and the remaining 10.4% identifying with other religious affiliations. The most prevalent completed level of education was high school graduation (47.8%), followed by a Bachelor’s degree (33.0%), and some college (7.8%), with the remaining 11.4% of other education levels. Eighty-three participants (72.2%) were native English speakers.

I conducted multiple Chi square tests and one-way between-subjects ANOVAs to determine if demographics significantly differed between both the autonomic 88

(sympathetic vs. parasympathetic activity) and valence (positive surprise vs. negative surprise) conditions. The autonomic conditions did not significantly differ based on gender, χ2(2) = 1.97, p = .37, ethnicity, χ2(3) = 1.21, p = .75, education, χ2(8) = 5.41, p = .71, English as a first language, χ2(1) = 2.22, p = .14, religion, χ2(7) = 3.36, p = .85, or age, F(1, 113) = 0.02, p = .90, ηp²= .00. Similarly, the valence conditions did not significantly differ based on gender, χ2(2) = 2.24, p = .33, ethnicity, χ2(3) = 1.08, p = .78, education, χ2(8) = 7.59, p = .47, English as a first language, χ2(1) = 1.07, p = .30, 2 religion, χ (7) = 5.11, p = .65, or age, F(1, 113) = 0.90, p = .35, ηp²= .01. For a full list of participant demographics, see Appendix B (pp. 252).

All participants provided written informed consent. The experiment was approved by the Human Research Ethics Advisory Panel at the University of New South Wales.

In a 2 (arousal: sympathetic vs. parasympathetic activity) × 2 (valence: positive vs. negative surprise) × (2) (time: before surprise manipulation vs. after surprise manipulation) mixed design, I manipulated physiological activity and valence between- subjects. To manipulate arousal, randomized participants completed either isometric (sympathetic activity; n = 59) or deep breathing exercises (parasympathetic activity; n = 56). Participants then completed a standard cognitive measure of perceived time (Church & Deluty, 1977; Wearden, 1991). During each measure of time perception, participants wore an electrocardiograph and finger electrodes to measure physiological indices of sympathetic and parasympathetic activation. The primary purpose of the physiological indices was to provide manipulation checks for sympathetic and parasympathetic activity. Participants then repeated the physiological manipulation to ensure physiological activity remained consistent throughout the valence manipulation. To manipulate valence, randomised participants then received a positive surprise (pleasant drink; n = 52) or a negative surprise (unpleasant drink; n = 63). Participants subsequently completed a second measure of time perception.

I programmed this experiment using Inquisit 4.0 software (Millisecond, 2016). All physiological data were recorded using PowerLab (ADInstruments, 2011), and analysed using LabChart 8.1.5 software (ADInstruments, 2016).

Materials and Procedure 89

Arousal and autonomic activity. I randomly allocated participants to arousal conditions manipulating sympathetic or parasympathetic activity. To induce parasympathetic activity, participants completed a 5-minute session of guided deep breathing exercises, administered by the experimenter. Deep breathing exercises heighten parasympathetic activity (Jerath, Edry, Barnes, & Jerath, 2006; May, Arildsen, & Møller, 1999). To induce sympathetic activity, participants completed full-body isometric exercises in the form of planks, wall sits and static lunges. Isometric exercises involve muscular contractions in which the muscle doesn’t change length and the joints do not move (e.g., Boone, 2014). Participants held each position for as long as possible and cycled through the exercises until 5 minutes elapsed. Isometric exercises heighten sympathetic activity (Nielsen & Mather, 2015; Victor, Secher, Lyson, & Mitchell, 1995).

Time perception. Participants completed the bisection task as the primary measure of perceived time (for a full description of the bisection task, see Experiment 1, pp. 60). Due to a potentially low number of trials per duration in Experiment 1 (pp. 80), I increased the number of trials in this bisection task to 70. In this task, participants viewed neutral images sourced from the International Affective Picture System (Lang et al., 1997). I obtained valence and arousal scores from overall adult ratings in the International Affective Picture System Technical Manual (Lang et al., 2008). These images had neutral content, such as pictures of geckos and pizza. The images had arousal scores between 2.42 and 6.03 (M = 4.17, S.D. = 2.1), and valence scores between 4.5 and 5.98 (M = 5.34, S.D. = 1.58). The arousal and valence scales for these items range between scores of 1 to 9. Arousal scores range from low (1) to high (9), whereas valence scores range from negative (1) to positive (9). As such, the scores for images in this experiment indicate low arousal and neutral valence. In comparison, for example, the mean arousal for emotional images in Experiment 1 (pp. 62) was 6.78, with a mean valence of 2.03. For a list of all images used, see Appendix B (pp. 252). Participants completed the bisection task twice; once after the first autonomic manipulation, and once after the second autonomic and sole valence manipulations.

Physiological measures. As in Experiment 1, a 3-lead electrocardiograph measured parasympathetic and sympathetic activity for each participant (see pp. 61 for a description). An additional two electrodes, however, measured galvanic skin response in this experiment. These electrodes attached to the middle and index fingers of each 90

participant’s non-dominant hand. As also in Experiment 1, the electrocardiograph and finger electrodes measured physiology during each of the 2 bisection tasks. As indices of sympathetic activity, I measured galvanic skin response, heart rate, and a few measures of heart rate variability. To assess parasympathetic activity, I measured several indices of heat rate variability. Notably, unlike the other measures of heart rate variability, greater low frequency power, very low frequency power, and higher sympathovagal balance (i.e., ratio of low to high frequency power) are associated with sympathetic activity (Acharya et al., 2006).

I computed 8 variables to assess heart rate variability:

• intervals between consecutive heart beats (R-R intervals) • root mean square of successive differences between R-R intervals (RMSSD) • low frequency power (as a percentage of total power) • high frequency power (as a percentage of total power) • ratio of low to high frequency power (sympathovagal balance) • standard deviation of the R-R intervals (SDRR) • very low frequency power • the ratio of successive R-R intervals that differ by more than 50ms to R- R intervals (pRR50; for explanations of these variables, see Shaffer & Ginsberg, 2017; Xhyheri et al., 2012).

Valence (surprise). I randomly allocated participants to conditions manipulating positive or negative surprise. To induce positive surprise, participants consumed a 5mL shot of orange Fanta (Coca-Cola South Pacific, North Sydney, Australia) after consuming four 5mL shots of Swedish Bitters (Herbal Supplies Pty Ltd, Ridgehaven, Australia). To induce negative surprise, participants consumed a 5mL shot of Swedish Bitters (Herbal Supplies Pty Ltd, Ridgehaven, Australia) after consuming four 5mL shots of orange Fanta (Coca-Cola South Pacific, North Sydney, Australia). Participants commonly rate sweet drinks as pleasant and positive, and bitter drinks as unpleasant and negative (Desmet & Schifferstein, 2008). The final shot differed to the four prior shots, so that the final beverage was unexpected and induced a positive or negative surprise. Situational procedures are the most effective method of inducing surprise (Siedlecka & Denson, 2019). Moreover, the proximal cause of surprise is 91

violated expectancy, and researchers have successfully induced surprise using mildly arousing unexpected events (e.g., inverting screen and font colour; Meyer et al., 1991; Stiensmeier-Pelster, Martini, & Reisenzein, 1995). In these experiments, researchers first habituated the participants to one stimulus, such that participants would anticipate a similar stimulus. As such, participants would not be able to anticipate a novel stimulus and this violated expectancy would increase surprise. For this reason, in the current experiment, participants first drank 4 shots that differed in taste to the unexpected beverage. I also blindfolded participants to ensure they could not anticipate the surprising beverage. I reobtained verbal consent to blindfold the participant prior to blindfolding them to alleviate anxiety. I told participants they would be asked to consume 5 beverages but obscured the flavour of the beverages. The electrocardiograph and finger electrodes, attached to each participant’s non-dominant hand, commenced recording as they consumed their surprising beverage.

Emotional intensity. Participants subsequently completed a self-report measure of positive and negative affect to assess differences in arousal, as operationalised by emotional intensity, between valence conditions. Participants completed the Positive and Negative Affect Schedule, with 10 items each for positive affect (α = .87) and negative affect (α = .87) (Watson, Clark, & Tellegen, 1988). Participants rated the extent to which they felt each item during the valence manipulation on a 5-point Likert scale. Example items for positive affect included strong, alert, and interested. Example items for negative affect included irritable, jittery, and distressed. Higher scores indicate higher arousal or emotional intensity within each positive and negative affect. As there was no missing data, I used the summed scores for each scale, rather than the average scores. The maximum score for each scale was 50. Participants only completed this scale once at the end of the experiment. For a complete list of scale items, see Appendix B (pp. 252).

Results

overestimation and slower perceived time; a higher bisection point indicates both stimuli underestimation and faster perceived time (Droit-Volet et al., 2004). The Weber ratio is a measure of temporal sensitivity, or ability to discriminate between millisecond durations, where a higher score indicates less sensitivity (Kopec & Brody, 2010). For a description of how to calculate bisection points and Weber ratios, see Experiment 1 (pp. 63).

Preliminary Analyses

Duration and perceived time. I initially analysed these data using a one-way repeated-measures ANOVA to examine whether stimulus duration during the bisection task influenced the probability of answering ‘long’. I used a Greenhouse-Geisser correction as Mauchly’s test of sphericity was violated, χ2(20) = 391.74, p < .0001.

1.0

0.5

0.0

Probability of answering 'long' answering of Probability 400 600 800 1000 1200 1400 1600 Stimulus duration (ms)

Figure 3.1. Probability of answering ‘long’ for each stimulus duration. Error bars represent standard error of the mean. The probability of answering long increased as stimulus duration increased.

As seen in Figure 3.1, there was a significant main effect of duration, in which participants were more likely to respond ‘long’ as stimulus durations increased, F(2.69, 304.24) = 1241.22, p < .0001. This result suggests that participants could discriminate between the different stimulus durations. All Tukey post-hoc pairwise comparisons 93

were significant (all ps < .0001). For statistical output regarding pairwise comparisons within duration, see Appendix B (pp. 252).

Physiological activity manipulation checks. I conducted multiple 2 (arousal: sympathetic vs. parasympathetic activity) x (2) (time: before surprise manipulation vs. after surprise manipulation) mixed ANOVAs to determine if isometric and deep breathing exercises respectively induced sympathetic and parasympathetic activity. Because I measured physiology before and after the surprise manipulation, these analyses also determined how inducing surprise changed sympathetic and parasympathetic activity. Increases in heart rate, galvanic skin response, low frequency power, very low frequency power, and sympathovagal balance all indicate heightened sympathetic activity (Acharya et al., 2006). Increases in the other measures of heart rate variability indicate heightened parasympathetic activity (Acharya et al., 2006).

There were predominantly no significant differences in any physiological measures, or interactions with time or autonomic condition, based on valence conditions (for these analyses, see Appendix B, pp. 252). One 3-way interaction between valence, time, and autonomic activity for heart rate was marginally significant, F(1, 104) = 3.70, p = .06, ηp² = .01. This interaction suggests that there is marginal evidence for a higher heart rate following a negative compared to a positive surprise. However, this difference between valence conditions was only present following the second sympathetic manipulation. Moreover, the effect size for heart rate was very small. Given the weak and isolated evidence for an influence of valence on physiological response, in subsequent analyses I do not report main effects or interactions for valence. The ANOVA table for the significant 3-way interaction, as well as the means and standard error for each condition, are available in Appendix B (pp. 252).

Table 4.1

Mean parasympathetic and sympathetic activity levels between autonomic conditions, before and after the surprise manipulation.

Before Surprise Manipulation After Surprise Manipulation Variable Isometric Deep Breathing Isometric Deep Breathing M (SE) N M (SE) N M (SE) N M (SE) N 94

Heart Rate 84.99 56 87.94 53 96.70 58 98.38 55 (27.12) (2.32) (3.81) (2.64) Galvanic Skin 13.32 59 12.64 53 4.52 58 1.10 55 Response (0.95) (1.19) (1.37) (0.67) Low Frequency 21.74 59 27.85 53 28.80 58 28.71 55 Power (1.26) (1.67) (1.39) (1.32) Sympathovagal 0.94 59 1.35 53 1.52 58 1.31 55 Balance (0.09) (0.18) (0.18) (0.12) Very low 42.67 59 41.05 53 35.69 58 40.95 55 frequency power (2.51) (2.41) (2.39) (1.75) R-R Intervals 736.61 59 775.63 53 691.30 58 765.58 55 (14.97) (14.18) (12.00) (11.87) RMSSD 64.57 59 66.38 53 129.05 58 97.84 55 (5.69) (6.54) (21.00) (10.60) SDRR 64.74 59 74.33 53 111.38 58 102.39 55 (3.74) (4.54) (14.23) (7.27) High Frequency 32.59 59 29.25 53 31.53 58 28.16 55 Power (2.22) (2.04) (2.44) (1.66) pRR50 18.85 59 20.25 53 17.37 58 26.53 55 (2.25) (2.12) (2.17) (2.12)

Isometric Deep Breathing

800 30 s l a 750 20 v 0 r 5 e t R n R I

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Figure 3.2. Parasympathetic activity between autonomic conditions, before and after the surprise manipulation, as indicated by R-R intervals, pRR50, RMSSD, high frequency power and SDRR. Error bars represent the standard error of the mean. R-R intervals and pRR50 were higher for participants completing deep breathing relative to isometric exercises. There was a larger difference in R-R intervals between participants who completed deep breathing exercises and participants who completed isometric exercises following the surprise manipulation relative to before the surprise manipulation. pRR50 differed between autonomic conditions following the surprise manipulation, but not before the surprise manipulation. R-R intervals were lower following the surprise manipulation, relative to before the surprise manipulation. pRR50, RMSSD, and SDRR were higher following the surprise manipulation relative to before the surprise manipulation. 96

There were several main effects of arousal showing that deep breathing exercises increased parasympathetic activity relative to isometric exercises. Increases in R-R intervals and pRR50 indicated higher parasympathetic activity. These results may suggest that both autonomic manipulations were effective. However, there was a smaller difference in R-R intervals, and no differences in pRR50, between autonomic conditions before relative to after the surprise manipulation. As such, it is possible that only the second set of breathing exercises successfully induced parasympathetic activity.

As seen in Figure 3.2 and Table 4.1, a main effect of arousal showed that participants completing deep breathing exercises had higher R-R intervals relative to participants who completed isometric exercises, F(1, 109) = 11.01, 95% CI = [42.98,

110.82], p = .001, ηp² = .09. This effect size was moderate. As also seen in Figure 3.2, a main effect of time showed that participants had lower R-R intervals following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 31.04,

95% CI = [-2.69, 51.81], p < .00001, ηp² = -.22. As also seen in Figure 3.2, a significant interaction showed a larger difference in R-R intervals between participants who completed deep breathing exercises and participants who completed isometric exercises following the surprise manipulation relative to before the surprise manipulation, F(1,

109) = 14.71, p = .0002, ηp² = .12.

As seen in Figure 3.2 and Table 4.1, a main effect of arousal showed that participants who completed deep breathing exercises also had higher pRR50 relative to participants who completed isometric exercises, F(1, 109) = 4.39, 95% CI = [8.42,

10.06], p = .04, ηp² = .04. However, this effect was very small. As also seen in Figure 3.2, a main effect of time showed that participants had higher pRR50 following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 5.15, 95%

CI = [-1.56, 7.04], p = .03, ηp² = -.05. As also seen in Figure 3.2, a significant interaction showed that pRR50 differed between participants who completed deep breathing exercises and participants who completed isometric exercises following the surprise manipulation, but not before the surprise manipulation, F(1, 109) = 7.37, p =

.008, ηp² = .06. These results indicate that participants who completed deep breathing exercises experienced greater parasympathetic activity relative to participants who completed isometric exercises, as evidenced by increased R-R intervals and pRR50. 97

However, the effect sizes for some of these effects were small. These results also indicate that surprise both increased and decreased parasympathetic activity.

Isometric Deep Breathing

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) 100 m p b ( 95 e t a R

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Figure 3.3. Sympathetic activity between autonomic conditions, before and after the surprise manipulation, as indicated galvanic skin response, low frequency power, sympathovagal balance, very low frequency power, and heart rate. Error bars represent standard error of the mean. Galvanic skin response was lower for participants completing deep breathing relative to isometric exercises. Low frequency power was higher for participants completing deep breathing relative to isometric exercises. Low frequency power and sympathovagal balance differed between autonomic conditions before the surprise manipulation, but not after the surprise manipulation. Deep breathing exercises increased very low frequency power relative to isometric exercises, but only following the surprise manipulation. Galvanic skin response and very low frequency power were lower following the surprise manipulation relative to before the 98

manipulation. Participants had higher low frequency power, sympathovagal balance, and heart rate following the surprise manipulation relative to before the surprise manipulation.

There was a single main effect of arousal showing that isometric exercises increased sympathetic activity relative to deep breathing exercises. Increased galvanic skin response indicated higher sympathetic activity. This result may suggest that both autonomic manipulations were effective. However, isometric exercises also decreased sympathetic activity relative to deep breathing exercises, as evidenced by changes in low frequency power. Moreover, deep breathing exercises increased low frequency power and sympathovagal balance before the surprise manipulation, but not after the manipulation. Additionally, the effect size for galvanic skin response was very small. Deep breathing exercises also increased very low frequency power relative to isometric exercises, but only following the surprise manipulation. Combined, these results suggest that isometric exercises did not increase sympathetic activity following either autonomic manipulation.

As seen in Figure 3.3 and Table 4.1, a main effect of arousal showed that participants who completed isometric exercises had higher galvanic skin response relative to participants who completed deep breathing exercises, F(1, 109) = 4.00, 95%

CI = [-4.15, -3.33], p = .048, ηp² = .035. However, this effect was small. As also seen in Figure 3.3, a main effect of time showed that participants had lower galvanic skin response following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 90.85, 95% CI = [-8.08, -12.41], p < .00001, ηp² = -.46. However, there was no significant interaction between time (i.e., before the surprise manipulation vs. after the surprise manipulation) and autonomic condition for galvanic skin response, F(1, 109) = 2.02, p = .16, ηp² = .02.

As seen in Figure 3.3 and Table 4.1, a main effect of arousal showed that participants who completed isometric exercises had reduced low frequency power relative to participants who completed deep breathing exercises, F(1, 109) = 4.30, 95%

CI = [-0.36, 0.67], p = .04, ηp² = .038. However, the effect size for low frequency power was small. As also seen in Figure 3.3, a main effect of time showed that participants had higher low frequency power following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 12.50, 95% CI = [7.23, 1.56], p

= .001, ηp² = .41. As also seen in Figure 3.3, a significant interaction showed that participants who completed isometric exercises had reduced low frequency power 99

relative to participants who completed deep breathing exercises, but only before the surprise manipulation, F(1, 109) = 6.87, p = .01, ηp² = .06.

As seen in Figure 3.3, a main effect of time showed that participants had marginally lower very low frequency power following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 3.81, 95% CI = [-0.72, 8.41], p

= .053, ηp² = .03. As also seen in Figure 3.3, a significant interaction showed that deep breathing exercises increased very low frequency power relative to isometric exercises, but only following the surprise manipulation, F(1, 109) = 4.14, p = .04, ηp² = .04. However, there was no significant main effect of autonomic condition on very low frequency power, F(1, 109) = 0.40, p = .53, ηp² = .004.

As seen in Figure 3.3, a main effect of time showed that participants had a larger sympathovagal balance following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 5.44, 95% CI = [0.01, 0.59], p = .02, ηp² = .05. As also seen in Figure 3.3, a significant interaction showed a smaller difference in sympathovagal balance between participants who completed deep breathing exercises and participants who completed isometric exercises following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 5.67, p = .02, ηp² = .05. However, there was no significant main effect of autonomic condition on sympathovagal balance, F(1, 109) = 0.48, p = .49, ηp² = .004. These results indicate that participants who completed isometric exercises experienced greater sympathetic activity relative to participants who completed deep breathing exercises, as evidenced by increased galvanic skin response. However, participants who completed isometric exercises also experienced reduced sympathetic activity relative to participants who completed deep breathing exercises, as evidenced by decreased low frequency power. These results also indicate that surprise both increases and decreases sympathetic activity.

As seen in Figures 3.2 and 3.3, there were additionally no significant differences in heart rate, SDRR, RMSSD, or high frequency power between participants who completed deep breathing exercises and participants who completed isometric exercises, ps = .16 - .91. Furthermore, there were no significant interactions between time (i.e., first physiological manipulation vs. second physiological manipulation) and autonomic condition for heart rate, SDRR, RMSSD, or high frequency power, ps = .12 - .42. 100

Table 4.2 Changes in physiological measures following the surprise manipulation relative to before the surprise manipulation

Physiological Measure Corresponding Autonomic After Surprise Branch Manipulation Heart rate Sympathetic ↑ Galvanic skin response Sympathetic ↓ Low frequency power Sympathetic ↑ Sympathovagal balance Sympathetic ↑ Very low frequency power Sympathetic ↓ R-R intervals Parasympathetic ↓ RMSSD Parasympathetic ↑ SDRR Parasympathetic ↑ High frequency power Parasympathetic - pRR50 Parasympathetic ↑ Note. ↑ = increased following the manipulation; ↓ = decreased following the manipulation; - = did not change following the manipulation

Table 4.3 Means for physiological measures before and after the surprise manipulation

Before Surprise Manipulation After Surprise Manipulation Variable M SE N M SE N Heart Rate 86.42 2.17 105 97.52 2.33 113 Galvanic Skin 13.00 0.75 112 2.86 0.79 113 Response Low 24.63 1.07 112 28.76 0.95 113 Frequency Power Sympathovagal 1.13 0.10 112 1.42 0.11 113 Balance Very low 41.90 1.74 112 38.25 1.51 113 frequency power R-R Intervals 755.07 10.47 112 727.46 9.11 113 101

RMSSD 65.43 4.29 112 113.86 11.98 113 SDRR 69.28 2.94 112 107.00 8.09 113 High 31.01 1.52 112 29.89 1.49 113 Frequency Power pRR50 19.51 1.55 112 21.83 1.57 113

Participants experienced significant differences in other measures of physiology following the surprise manipulation relative to before the surprise manipulation. Tables 4.2 and 4.3 summarise the effects of surprise on the physiological measures in this experiment. Participants had a higher heart rate following the surprise manipulation relative to before the surprise manipulation, F(1, 106) = 33.37, 95% CI = [5.86, 18.34], p < .00001, ηp² = .24. Additionally, participants had higher SDRR following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 25.27, 95% CI =

[21.37, 55.79], p < .00001, ηp² = .59. Participants also had higher RMSSD following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 18.31,

95% CI = [23.91, 74.81], p < .00001, ηp² = .51. However, high frequency power did not significantly change following the surprise manipulation relative to before the surprise manipulation, F(1, 109) = 0.49, p = .49, ηp² = .004. The effect sizes for physiology before compared to after the surprise manipulation were large. These results indicate that surprise involves a strong physiological response that activates both sympathetic and parasympathetic activity.

Emotional intensity (arousal) manipulation check. I conducted a 2 (arousal: sympathetic vs. parasympathetic activity) × 2 (valence: positive vs. negative surprise) between-subjects ANOVA to determine if arousal, as induced by autonomic activity, and valence influenced positive and negative emotional intensity. I only measured emotional intensity once and, as such, did not include an interaction assessing emotional intensity over time. Individuals overestimate emotional intense stimuli (Gil & Droit- Volet, 2012), so differences in overall emotional intensity between conditions could influence time perception. Moreover, I wanted to ensure that positive surprise increased positive emotional intensity and negative surprise increased negative emotional intensity.

Table 4.4 102

Levels of positive, negative, and overall emotional intensity for autonomic and valence conditions

Isometric Deep Breathing Positive Negative Surprise Surprise M (SE) N M (SE) N M (SE) N M (SE) N Positive Arousal 43.32 59 39.50 56 40.03 52 42.64 63 (1.17) (1.37) (1.42) (1.17) Negative 22.52 59 20.84 56 23.25 52 20.43 63 Arousal (1.35) (1.30) (1.57) (1.10) Overall Arousal 23.75 59 27.42 56 24.29 52 24.17 63 (1.11) (1.12) (1.12) (1.04)

As seen in Table 4.4, overall emotional intensity did not differ between participants receiving a positive or negative surprise, F(1, 111) = 0.01, p = .92, ηp² = .00. Nor did positive emotional intensity differ between participants receiving a positive or negative surprise, F(1, 111) = 2.02, p = .16, ηp² = .02. Similarly, negative emotional intensity did not differ between participants receiving a positive or negative surprise,

F(1, 111) = 2.29, p = .13, ηp² = .02. This result may imply the valence manipulation was ineffective. However, positive and negative surprise often do not differ in levels of intensity and arousal (Russell, 1980; Talarico et al., 2009). Moreover, participants rated the levels of emotional intensity they felt during the entire valence manipulation, and not just when imbibing the unexpected beverage. These mixed findings suggest there is insufficient evidence to conclude if the valence manipulation was effective.

Unexpectedly, as seen in Table 4.4, there was a main effect of autonomic condition on positive emotional intensity, such that participants who completed isometric exercises reported more positive emotional intensity than participants who completed deep breathing exercises, F(1, 111) = 4.85, 95% CI = [-7.38, -0.27], p = .03,

ηp² = .04. Participants who completed isometric exercises also reported more overall emotional intensity relative to participants who completed deep breathing exercises,

F(1, 111) = 4.53, p = .04, ηp² = .04. However, these effects were small. There was no main effect of autonomic condition on negative emotional intensity, F(1, 111) = 0.97, p

= .33, ηp² = .01. 103

Primary Analyses

Time perception. I conducted a 2 (arousal: sympathetic vs. parasympathetic activity) x 2 (valence: positive vs. negative surprise) x (2) (time: before surprise manipulation vs. after surprise manipulation) mixed ANOVA on bisection point to determine if time perception differed between the autonomic conditions, the valence conditions, and following the surprise manipulation. Table 4.5 displays the full ANOVA table for this analysis.

Table 4.5 Summary table for 2 (arousal: sympathetic vs. parasympathetic activity) x 2 (valence: positive vs. negative surprise) x (2) (time: before surprise manipulation vs. after surprise manipulation) mixed ANOVA on time perception

Sum of Df Mean F Sig. Squares square Time 172625.41 1 172625.41 10.31 .002 Valence 229344.54 1 229344.54 4.00 .048 Arousal 2771.22 1 2771.22 0.05 .83 Time * Valence 32765.84 1 32765.84 2.00 .17 Time * Arousal 1133.76 1 1133.76 0.07 .80 Time * Valence * 18292.97 1 18292.97 1.09 .30 Arousal Between-subjects 6307112.89 110 57337.39 error Within-subjects 1841698.59 110 16742.71 error 104

Positive Surprise Negative Surprise 0.20 1000

t 950

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o 0.10 e t i b c

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Figure 3.4. The effects of valence (positive vs. negative surprise) and time (before surprise manipulation vs. after surprise manipulation) on bisection points and Weber ratios. Error bars represent standard error of the mean. A lower bisection point indicates slower time perception, whereas a lower Weber ratio indicates higher temporal sensitivity. Participants who received a positive surprise underestimated images in the bisection task relative to participants who received a negative surprise. Participants overestimated images in the bisection task after the surprise manipulation relative to before the surprise manipulation. Overestimated stimulus durations indicate slower time perception, whereas underestimated stimulus durations indicate faster time perception (Droit-Volet et al., 2004).

Isometric Deep Breathing 0.20 950 t n

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Figure 3.5. The effects of autonomic arousal (sympathetic vs. parasympathetic) and time (before emotion manipulation vs. after emotion manipulation) on bisection points and Weber ratios. Error bars represent standard error of the mean. There were no significant main effects or interactions. 105

Table 4.6 Bisection points and Weber ratios between valence conditions, before and after the surprise manipulation

Before Surprise Manipulation After Surprise Manipulation Variable Positive Negative Positive Negative Surprise Surprise Surprise Surprise

M (SE) N M (SE) N M (SE) N M (SE) N Bisection Point 917.38 52 877.84 63 886.17 52 800.96 63 (25.07) (18.84) (32.61) (24.71) Weber Ratio 0.15 52 0.15 63 0.16 52 0.11 63 (0.01) (0.01) (0.01) (0.06)

As seen in Table 4.5, Table 4.6 and Figure 3.4, participants underestimated images in the bisection task after receiving a positive surprise relative to a negative surprise when averaging over the physiological and emotional measurements of arousal,

F(1, 110) = 4.00, 95% CI = [-99.28, -77.92], p = .048, ηp² = .04. Notably, this effect was very small, but representative of the effect sizes for emotion on bisection point in the time perception literature (e.g., Tipples, 2008). Because there was only one valence manipulation, I likewise conducted a 2 (arousal: sympathetic vs. parasympathetic activity) x 2 (valence: positive vs. negative surprise) between-subjects ANOVA on bisection point to determine if time perception changed between valence conditions following the surprise manipulation. Results revealed that participants underestimated time in the bisection task after receiving a positive surprise relative to a negative surprise when averaging across level of physiological activity, F(1, 110) = 4.35, p =

.039, ηp² = .04. Underestimated stimulus durations indicate faster perceived time (Droit- Volet et al., 2004). As also seen in Table 4.5 and Figure 3.4, participants overestimated images in the bisection task following the surprise manipulation relative to before the surprise manipulation when averaging across valence and arousal, F(1, 110) = 10.31,

95% CI = [7.45, 108.32], p = .002, ηp² = .30. Overestimated stimulus durations indicate slower time perception (Droit-Volet et al., 2004). However, as seen in Figure 3.5, time perception did not significantly change depending on whether participants completed deep breathing or isometric exercises, F(1, 110) = 0.05, p = .83. There were similarly no 106

significant interactions for time perception between time and either valence or autonomic condition, ps = .17 - .80.

I also conducted a 2 (arousal: sympathetic vs. parasympathetic activity) x 2 (valence: positive vs. negative surprise) x (2) (time: before surprise manipulation vs. after surprise manipulation) mixed ANOVA on Weber ratio to determine if the arousal condition, valence condition, and the surprise manipulation changed temporal sensitivity (i.e., the Weber ratio). As seen in Figures 3.4 and 3.5, temporal sensitivity did not significantly change between autonomic conditions, valence conditions, or before relative to after the surprise manipulation, ps = .24 - .69. As also seen in Figures 3.4 and 3.5, there were similarly no significant interactions for temporal sensitivity between time and either valence or autonomic condition, ps = .26 - .51.

I conducted multiple Pearson’s correlations to determine if any physiological measures covaried with the Weber ratio (i.e., temporal sensitivity).

Figure 3.6. Pearson’s correlations between Weber ratio and low and high frequency power before the surprise manipulation. Dots represent individual data points and line represents the line of best fit. A lower Weber ratio indicates higher sensitivity, increased low frequency power indicates heightened sympathetic activity, and increased high frequency power indicates heightened parasympathetic activity. Low frequency power positively correlated with temporal sensitivity, whereas high frequency power negatively correlated with temporal sensitivity.

As seen in Figure 3.6, there was a marginally significant positive correlation between low frequency power and Weber ratio in the first bisection task, r(111) = .19, p = .05. There was also a small significant positive correlation between sympathovagal 107

balance and Weber ratio in the first bisection task, r(111) = .31, p = .001. As also seen in Figure 3.6, there was a significant negative correlation between high frequency power and Weber ratio in the first bisection task, r(111) = -.20, p = .03. Increased high frequency power indicates heightened parasympathetic activity, whereas increased low frequency power and sympathovagal balance indicate heightened sympathetic activity (Acharya et al., 2006). Higher Weber ratios indicate lower temporal sensitivity (Kopec & Brody, 2010). As such, individuals with higher sympathetic activity and lower parasympathetic activity experienced less temporal sensitivity in the first bisection task regardless of which autonomic condition they were in. Physiological measures did not correlate with temporal sensitivity in the second bisection task (ps = .12 - .92).

Time perception and temporal sensitivity. I conducted linear regressions on bisection point and Weber ratio to determine if time perception and temporal sensitivity covaried over time in this experiment. Bisection points in the first bisection task significantly predicted bisection points in the second bisection task, F(1, 112) = 54.81, b = 0.76, R2= .33, p < .00001. Participants who overestimated durations before the surprise manipulation were more likely to overestimate durations following the surprise manipulation. Weber ratios in the first bisection task significantly predicted Weber ratios in the second bisection task, F(1, 112) = 19.99, b = -2.31, R2= .39, p < .00002. Lower Weber ratios indicate better temporal sensitivity (Kopec & Brody, 2010). Participants with better abilities to discriminate between durations before the surprise manipulation were less able to discriminate between durations following the surprise manipulation. I also conducted Pearson’s correlations between bisection points and Weber ratios to determine if time perception and temporal sensitivity covaried either before or after the surprise manipulation. Bisection points during the second bisection task positively correlated with Weber ratios in the second bisection task, r(113) = .46, p < .00001. As such, participants who could discriminate better between temporal durations were more likely to overestimate duration, but only following the surprise manipulation.

Discussion

Participants did not perceive time differently based on whether they completed exercises designed to induce sympathetic or parasympathetic activity in the current experiment. Thus, this experiment did not find evidence that sympathetic and parasympathetic activity influence time perception. In general, this experiment revealed 108

that manipulations aimed at changing autonomic activity are difficult, and the results are noisy. For these reasons, it is difficult to conclude if and how autonomic activity changes time perception based on the data from this experiment.

Participants overestimated duration following the surprise induction, relative to before the induction. This result may suggest that surprise slows time perception. Participants also underestimated duration in this experiment when they received a positive surprise relative to a negative surprise when averaging across levels of arousal. Moreover, positive and negative surprise mostly induced statistically indistinguishable levels of arousal. This experiment may suggest that valence influences time perception independent of arousal. However, as this effect was small and valence did not interact with time, it is also possible that surprise slows time perception regardless of valence. On this account, any weak effects of valence on time perception that were observed here may reflect sampling biases between conditions. Overall, the data from this study were too weak to allow a confident conclusion of whether valence influences time perception independent of arousal.

Time perception and temporal sensitivity did not differ between participants who completed isometric and deep breathing exercises in this experiment. Although the first autonomic manipulation was possibly ineffective, the second autonomic manipulation was effective. Participants who completed isometric exercises experienced more sympathetic activity following the second autonomic manipulation than participants who completed deep breathing exercises. Increases in galvanic skin response indicated increased sympathetic activity. Similarly, participants who completed deep breathing exercises experienced heightened parasympathetic activity relative to participants who completed isometric exercises. Increases in R-R intervals, pRR50 and RMSSD indicated increased parasympathetic activity. Thus, this experiment did not provide evidence that sympathetic and parasympathetic activity change time perception or temporal sensitivity. Similarly, this experiment failed to replicate the result from Experiment 1 suggesting that individuals may overestimate duration during heightened parasympathetic activity. This experiment also failed to replicate previous research suggesting that individuals overestimate duration during heightened sympathetic activity (e.g., Wittmann et al., 2007).

Heightened parasympathetic activity before the surprise manipulation, as indicated by increased high frequency power, correlated with improved temporal 109

sensitivity. Lower Weber ratios indicated improved temporal sentivity. This finding replicates previous research showing that heightened parasympathetic activity improves the ability to discriminate between temporal durations (Cellini et al., 2015; Meissner & Wittmann, 2011; Pollatos et al., 2014). Moreover, heightened sympathetic activity before the surprise manipulation, as indicated by increased low frequency power and sympathovagal balance, correlated with reduced temporal sensitivity. As such, this study uniquely suggests that heightened sympathetic activity may impair the ability to discriminate between temporal durations. As the first autonomic manipulation was likely ineffective, these results may suggest that individual differences in sympathetic and parasympathetic activity covary with changes in temporal sensitivity. Sympathetic and parasympathetic activity following the effective autonomic manipulation did not correlate with temporal sensitivity, however. This result may imply that inducing heightened sympathetic and parasympathetic activity may not change temporal sensitivity. Similarly, this result may imply that solely individual differences in autonomic activity covary with changes in temporal sensitivity.

Although induced autonomic activity did not change time perception in this experiment, this experiment provided evidence that sympathetic and parasympathetic activity may change time perception indirectly through covarying changes in temporal sensitivity. Following the effective autonomic manipulation, participants overestimated stimulus duration. Moreover, participants who overestimated duration also experienced heightened temporal sensitivity. Overestimated stimuli indicate slower time perception (Pande & Panti, 2010). As heightened parasympathetic activity can improve temporal sensitivity (Cellini et al., 2015), heightened parasympathetic activity may indirectly slow time perception through improved temporal sensitivity. Additionally, temporal sensitivity and time perception did not correlate before the surprise manipulation. As such, improved temporal sensitivity may only covary with overestimated duration during heightened emotional states. These results also may imply that emotions facilitate slower time perception and improved temporal sensitivity.

Participants overestimated duration following the surprise manipulation, relative to before the surprise manipulation, when averaging across valence and physiological activity. These results suggest that individuals overestimate duration when in a state of both positive and negative surprise. This result replicates findings that individuals overestimate duration during heightened emotional states (Gil & Droit-Volet, 2012). 110

However, this is the first experiment to identify that individuals overestimate duration after inducing positive and negative surprise. Additionally, temporal sensitivity did not change following the surprise manipulation. This result suggests that surprise does not influence temporal sensitivity.

Surprise is understudied compared to the other basic emotions (Siedlecka & Denson, 2019). There is little research in particular regarding the physiological response of surprise (Kreibig, 2010; Siedlecka & Denson, 2019). As such, this experiment contributes to a small body of research evaluating the physiological component of surprise (e.g., Alaoui-Ismaïli et al., 1997; Kragel & LaBar, 2013; Levenson et al., 1990). The results of the current experiment suggest that surprise decreases galvanic skin response, R-R intervals, and very low frequency power. These findings replicate previous research showing that surprise decreases R-R intervals and skin conductance (Levenson et al., 1990; Ramachandra, Depalma, & Lisiewski, 2009; Stephens, Christie, & Friedman, 2010). However, these findings also contradict previous research showing that surprise increases skin conductance (Jang, Park, Park, Kim, & Sohn, 2015; Kragel & LaBar, 2013). The results of this experiment also suggest that surprise increases heat rate, SDRR, RMSSD, low frequency power, sympathovagal balance, and pRR50. These findings replicate previous research showing that surprise increases heart rate (Boiten, 1998; Ekman, Levenson, & Friesen, 1983; Jang et al., 2015). However, these findings also contradict previous research showing that surprise decreases heart rate (Levenson et al., 1990). These results indicate that surprise can increase parasympathetic activity (e.g., SDRR, RMSSD) and decrease sympathetic activity (e.g., galvanic skin response). However, these results also indicate that surprise can increase sympathetic activity (e.g., heart rate) and decrease parasympathetic activity (i.e., R-R intervals). Thus, surprise involves an autonomic response that changes both parasympathetic and sympathetic activity. Other basic emotions (e.g., anger, disgust) similarly activate both the sympathetic and parasympathetic autonomic branches (Ax, 1953; de Jong et al., 2011).

Unexpectedly, higher temporal sensitivity before the effective emotion and autonomic manipulations predicted lower temporal sensitivity after the effective emotion and autonomic manipulations. That is, participants with lower Weber ratios at baseline exhibited higher Weber ratios following the effective autonomic and surprise manipulations. However, inducing an arousing emotional state, sympathetic and parasympathetic physiological activity, and positive and negative valence did not alter 111

temporal sensitivity. This result may imply that arousal impedes temporal sensitivity more for individuals with better temporal sensitivity. Future researchers could investigate how arousal interacts with baseline temporal sensitivity to influence temporal sensitivity following heightened arousal.

Implications

The results of this experiment imply that autonomic activity only influences temporal sensitivity through individual differences in sympathetic and parasympathetic activity and not through induced autonomic activity. These results may imply that individual differences in baseline sympathetic and parasympathetic activity alter temporal sensitivity. However, individuals may not be able to improve or impede temporal sensitivity simply by completing tasks that induce parasympathetic or sympathetic activity, respectively. Tasks such as meditation may not be effective at improving temporal sensitivity and activities like exercise may not reduce temporal sensitivity. There is some support for this argument. Highly stressed individuals, for example, experience both heightened sympathetic activation and reduced temporal sensitivity (Kim, Cheon, Bai, Lee, & Koo, 2018; Yao, Wu, Zhou, Zhang, & Zhang, 2015). Similarly, individuals who meditate regularly experience both heightened parasympathetic activity and improved temporal sensitivity (Droit-Volet, Fanget, & Dambrun, 2015; Wu & Lo, 2008). However, although novice meditators may experience heightened parasympathetic activity following meditation, their temporal sensitivity does not improve (Droit-Volet et al., 2015; Fennell, Benau, & Atchley, 2016). Thus, solely individual differences in baseline sympathetic and parasympathetic activity may alter temporal sensitivity.

Similarly, individual differences in the ability to detect and control sympathetic and parasympathetic activity may change temporal sensitivity. Skills such as interoceptive accuracy and autonomic flexibility improve an individual’s ability to detect and control changes in physiology. Interoceptive accuracy is the ability to detect one’s bodily signals like changes in heart rate, whereas autonomic flexibility is the ability to control parasympathetic activity in response to stressors (Friedman & Thayer, 1998; Meissner & Wittmann, 2011). Moreover, individuals with better interoceptive accuracy perceive time more accurately than individuals with lower interoceptive accuracy (Meissner & Wittmann, 2011). Improved temporal accuracy can imply improved temporal sensitivity (e.g., Droit-Volet, Clément, & Fayol, 2008). Improving 112

the ability to detect and control changes in sympathetic and parasympathetic activity may be an effective strategy for improving temporal sensitivity.

Participants overestimated stimulus durations following the surprise manipulation in this experiment. Moreover, participants who overestimated durations also experienced heightened temporal sensitivity following the surprise manipulation. Overestimated stimuli indicate slower time perception (Pande & Pati, 2010). Hence, the results of this experiment also may imply that emotions facilitate slower time perception and improved temporal sensitivity. There is some evidence to support this idea as individuals often overestimate duration during heightened emotional states (Gil & Droit-Volet, 2012), and participants in the present experiment overestimated duration following the surprise induction. Moreover, overestimating duration and improved temporal sensitivity may optimise the ability detect time accurately (e.g., Cellini et al., 2015). Emotions may facilitate more accurate time perception in general.

Certain emotions are also often associated with specific functions, such as fear motivating individuals to avoid danger (Tamir & Ford, 2009). As the immediate cause of surprise is violated expectancy (Stiensmeier-Pelster et al., 1995), surprise may motivate individuals to detect and manage changes in expectancy. Individuals can overestimate duration when they have a higher expectation of an aversive stimulus (Droit-Volet et al., 2010). As such, surprised individuals may overestimate duration due to an increased motivation to detect and manage changes in expectancy.

Limitations and Future Research

The first arousal manipulation likely did not induce sympathetic and parasympathetic activity, although the second arousal manipulation was effective. One reason why only the second manipulation was effective may be that participants completing deep breathing exercises were not comfortable during the first manipulation. As participants completed deep breathing exercises while they were closely supervised and attached to electrodes, participants possibly needed more time to feel comfortable fully engaging with the task. There is some evidence to suggest this, as participants who completed deep breathing exercises had a higher sympathetic response relative to participants who completed isometric exercises during the first autonomic manipulation. As exercise can induce positive benefits, another possibility is that people enjoyed the isometric exercises (Klusmann, Evers, Schwarzer, & Heuser, 2012). There is some 113

evidence to support this argument, as participants who completed isomeric exercises reported more positive emotional intensity relative to participants who completed deep breathing exercises. Future research could consider these factors when attempting to induce sympathetic and parasympathetic activity.

Participants who received a positive surprise did not experience different levels of emotional intensity relative to participants who received a negative surprise. Although this result may seem to imply the valence manipulation was ineffective, positive and negative surprise often do not differ in levels of intensity and arousal (Russell, 1980; Talarico et al., 2009). Furthermore, participants rated the levels of emotional intensity they felt during the entire valence manipulation, in which they consumed both pleasant and unpleasant shots. Thus, participants likely experienced both positive affect from consuming pleasant shots and negative affect from consuming unpleasant shots. However, future research could include better manipulation checks for positive and negative emotional intensity when examining the effects of valence on time perception.

Similarly, the effects of valence may be difficult to isolate from the effects of arousal, as the two concepts are related. The effects of valence on time perception observed in this study were potentially caused by changes in arousal, rather than valence. That is, receiving a negative surprise was possibly more arousing than receiving a positive surprise. This possibility seems unlikely as there were no significant differences in emotional intensity, or in physiological measures, between valence conditions. However, future research could aim to address this limitation by including more sensitive manipulation checks for both valence and arousal when examining their effects on time perception.

I did not include a control condition for the valence manipulation. As such, it is difficult to determine if positive and negative surprise changed time perception relative to a neutral condition, or only relative to each other. However, I included measures of time perception both before and after the valence manipulation. Individuals generally seemed to overestimate duration following the surprise induction, whereas they overestimated duration following the negative surprise relative to the positive surprise. Future research could include a neutral control condition to further expand how valence changes time perception when controlling for emotional and physiological activity. 114

I did not include a manipulation check for the surprise manipulation. Although the Positive and Negative Affect Schedule measured changes in emotional intensity between valence conditions, this scale did not include a question assessing surprise. I did not include a question assessing surprise as I expected that participants receiving a positive surprise would experience similar levels of surprise relative to participants receiving a negative surprise (e.g., Russell, 1980; Talarico et al., 2009). Similarly, I did not want to measure surprise levels before the surprise manipulation to reduce demand characteristics. Moreover, situational procedures (such as surprising beverages) are an effective method of surprise induction (Siedlecka & Denson, 2019). However, future research could include manipulation checks to ensure the surprise manipulation effectively induced surprise.

I also did not include a baseline measurement of positive and negative affect, although I did assess affect between conditions. Individuals who completed isometric exercises reported more positive emotional intensity relative to individuals who completed isometric exercises. However, as I did not include a baseline measure, it is possible these differences reflect sampling biases between conditions.

I used 5 physiological measures to assess changes in each sympathetic and parasympathetic activity. This decision was deliberate, as one limitation of Experiment 1 was that the measures used to assess physiology were possibly not sensitive enough to detect changes in sympathetic activity. This limitation reflects previous research in which physiological measures differed in sensitivity and reliability (Mehler et al., 2012; Nussinovitch et al., 2011). Additionally, different measures of physiology are associated with different physiological and psychological outcomes, even within subtype of autonomic activity. For example, elevated sympathovagal balance and very low frequency heart rate variability both indicate sympathetic activity. However, solely changes in very low frequency power predict patient outcome following a stroke (Gujjar, Sathyaprabha, Nagaraja, Thennarasu, & Pradhan, 2004). One limitation of using multiple dependent variables to measure a single outcome is the potential to inflate the likelihood of obtaining a Type 1 error. In this experiment, changes in sympathetic and parasympathetic activity differed between dependent measures. Some measures of autonomic activity change independent of measures assessing similar activity (e.g., sympathovagal balance; Billman, 2013). However, this limitation raises the possibility that some of the effects observed in this experiment may have been false 115

positives. Future research could endeavour to assess the reliability of these findings. Notably, there are corrections that can adjust these effects for an inflated Type I error rate over multiple comparisons. I did not apply these corrections as I wanted to maximize sensitivity for detecting the exploratory effects of autonomic activity on time perception. As such, future research could also endeavour to assess the replicability of these findings.

Conclusions

This experiment examined the influence of surprise, arousal, and valence on time perception. Participants overestimated stimulus durations following a surprise induction. Individuals underestimated stimuli with a positive valence relative to negative valence, even when averaging across changes in arousal. I additionally controlled for arousal by using positive and negative subcategories of an emotion that induce similar levels of both emotional intensity and physiological activity (Russell, 1980; Talarico et al., 2009; Watson & Tellegen, 1985). Additionally, heightened parasympathetic activity correlated with improved temporal sensitivity and heightened sympathetic activity correlated with reduced temporal sensitivity. However, inducing sympathetic and parasympathetic activity did not change time perception or temporal sensitivity. These results imply that inducing arousal and valence can change time perception, but that only individual differences in the autonomic response covary with temporal sensitivity.

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CHAPTER 5. Experiment 3: Attending to Temporal and Non-Temporal Cues Disrupts Flow State during Videogame Play, but Only Attending to Temporal Cues Changes Time Perception

Introduction

Attention and Temporal Cues

Some models of time perception, such as the pacemaker-accumulator model, posit that attention is a key determinant of time perception (Allman et al., 2014; Treisman, 1963; 1984). In this model, when attention is directed away from direct temporal cues, people underestimate duration relative to when attention is focused on temporal cues (Allman et al., 2014; Treisman, 1963; 1984). Temporal cues provide direct information about timing and can be internal (i.e., monitoring real time through an internal clock) or external (e.g., monitoring real time by observing a wristwatch; Treisman, 1963). An alternative explanation for the effects of attention on time perception is that individuals underestimate duration when attention is directed away from both temporal (direct) and non-temporal cues (indirect; e.g., light, social cues; e.g., Honma, Honma, Nakamura, Sasaki, Endo, & Takahashi, 1995). Distinguishing between these explanations may identify unique psychological factors by which attention affects time perception. To accomplish this aim, the current experiment examined how attending to temporal and non-temporal cues changes time perception.

Although temporal cues provide optimal information about timing, individuals may also infer timing from non-temporal cues. For example, individuals infer the time of day from changes in light, temperature, social cues (e.g., social contact), music and fatigue (Aschoff, Fatranská, Giedke, Doerr, Stamm, & Wisser, 1971; Ferguson, Paech, Sargent, Darwent, Kennaway, & Roach, 2012; Honma et al., 1995; Liu, Merrow, Loros, & Dunlap, 1998; Noseworthy & Finlay, 2009). Similarly, individuals can infer shorter durations from internal bodily cues (Grossman, Gueta, Pesin, Malach, & Landau, 2019; Meissner & Wittmann, 2011). These findings suggest that time perception is more likely computed from a range of cues, rather than solely from direct information about timing. Rather than direct information about timing, time perception is likely influenced primarily by one’s available attentional resources. Thus, people may underestimate duration due to a broader distribution of attentional resources in general. Attentional resources are distributed more broadly under higher cognitive load or when completing 117

concurrent tasks. When attentional resources are more broadly distributed, the information individuals may use to infer timing is more difficult to process (Tse et al., 2004). The present experiment examined how non-temporal cues affect time perception relative to direct information about timing.

Participants with direct information about timing generally overestimate stimulus duration (for a review, see Buhusi & Meck, 2005). Participants also underestimate duration when completing non-temporal tasks (e.g., counting animal names), compared to when completing tasks directly monitoring timing (e.g., monitoring the duration of an interval; Brown, 1997; 2008; 2010; Lui et al., 2011; Macar et al., 1994; Matthews & Meck, 2016; McClain, 1983; Zakay, 1992; 1998). In the pacemaker-accumulator model, these results are explained by the idea that individuals completing non-temporal tasks have fewer available temporal cues than individuals monitoring timing (Allman et al., 2014; Treisman, 1963). According to this model, individuals completing non-temporal tasks have fewer available temporal cues because temporal information is lost when an ‘attentional gate’ or switch closes (Block & Zakay, 1995). This gate or switch closes when individuals do not focus directly on timing (Block & Zakay, 1995). As such, fewer temporal cues accumulate over a specific duration when participants complete non-temporal tasks. Because participants use consistent temporal cues to monitor a duration, the duration seems shorter when there are fewer temporal cues (Allman et al., 2014). Similarly, people underestimate duration when engaging in concurrent tasks, compared to when completing tasks under a lower cognitive load (e.g., Block et al., 2010; Gautier & Droit-Volet, 2002; Matthews & Meck, 2016). Furthermore, the extent to which individuals underestimate duration increases proportionally with increased cognitive demands (Block et al., 2010; Burle & Casini, 2001; Casini & Macar, 1997; Fasolo et al., 2009; Macar et al., 1994). In the pacemaker-accumulator model, these results are explained by the notion that concurrent tasks deplete attentional resources by dividing attention between tasks (Allman et al., 2014; Treisman, 1963).

Previous experiments examining the effects of attention on time perception primarily utilised prospective paradigms, in which participants were asked to monitor the duration of a specific upcoming interval (Brown, 1985). In prospective paradigms, participants deliberately attend to internal temporal cues in all conditions. However, participants differ in the task they complete while they monitor the specified duration 118

(Macar et al., 1994). For example, participants might solely monitor timing or do so while concurrently completing a non-temporal task (e.g., while counting animal names) for a specific duration (Macar et al., 1994). According to the pacemaker-accumulator model, individuals should overestimate the duration of a greater number of temporal cues (Allman et al., 2014; Treisman, 1963).

One potential confound in prospective paradigms is that participants co- completing non-temporal tasks may distribute their attentional resources more broadly than participants solely monitoring timing. Completing concurrent tasks requires a broader distribution of attentional resources, and individuals underestimate stimulus duration when their attentional resources are broader (Block et al., 2010). Individuals may underestimate duration when completing non-temporal tasks in prospective paradigms due to broader attentional resource distribution, and not decreased amount of available temporal cues.

One way to examine the influence of temporal and non-temporal cues on time perception is to use retrospective paradigms. Retrospective paradigms ask participants to estimate the durations of previously observed intervals (Brown, 1985). However, participants are not instructed to monitor the duration of the interval before it is presented (Brown, 1985). For example, participants may listen to a sound and then verbally estimate the duration of the sound (e.g., Noulhiane et al., 2007). Contrary to prospective paradigms, participants in retrospective paradigms are unaware of the temporal nature of the experiment (Brown, 1985). As such, participants are unlikely to spontaneously use chronometric counting or attend to internal temporal cues during the experiment. Participants are also unlikely to spontaneously use chronometric counting when monitoring durations in the millisecond range (Poynter, 1989). Retrospective paradigms allow researchers to directly manipulate the number of cues, and amount of information about timing, available to participants. The ability to manipulate the amounts of available temporal and non-temporal cues allows researchers to control how individuals allocate attentional resources. Thus, retrospective paradigms eliminate confounds such as differences in cognitive load between conditions, boredom and anticipation. The current experiment used a retrospective paradigm to examine the effects of temporal and non-temporal cues on time perception. That is, I manipulated temporal and non-temporal cues in a retrospective design. Using this approach, I could determine if the effects of attention on time perception are due to general changes in the 119

distribution of attentional resources. Similarly, I could determine if the effects of attention on time perception are alternatively due to the amount of specific information about timing available.

Flow State and Time Perception

Flow state is an optimal state of enjoyment in which individuals are fully immersed in an activity (IJsselsteijn, de Kort, Poels, Jurgelionis, & Bellotti, 2007). Flow states occur along a spectrum, such that people can experience higher or lower levels of flow (Jackson & Marsh, 1999). Being in a heightened flow state speeds up time perception and one component of flow state is perceived loss of time (Jin, 2011; 2012; Rau, Peng, & Yang, 2006; Sherry, 2004; Tobin & Grondin, 2009; Wood, Griffiths, & Parkes, 2007).

Flow state is commonly experienced during videogame play, and videogames commonly intensify flow state relative to other activities (Chou & Ting, 2003; Cowley, Charles, Black, & Hickey, 2008; Hsu & Lu, 2004; Khang, Kim, & Kim, 2013; Mauri, Cipresso, Balgera, Villamira, & Riva, 2011). Being in a heightened flow state speeds up time perception. In one experiment, participants underestimated the time while playing a videogame, compared to participants reading a neutral book (Tobin & Grondin, 2009). Participants with more gaming experience also underestimated duration during videogame play relative to participants with less gaming experience (Tobin & Grondin, 2009). Underestimated stimuli indicate faster time perception (Pande & Pati, 2010). Highly experienced gamers generally experience less accurate and faster time perception relative to less experienced players (Rau et al., 2006; Schleifer, 2005). Changes in flow state possibly explain these results, because experts generally experience higher flow state compared to amateurs. However, novices and experts are both subject to the effects of faster time perception during flow state (Rau et al., 2006; Sinnamon, Moran, & O’Connell, 2012; Tobin & Grondin, 2009). This effect of faster time perception during heightened flow states is also evident in self-reports of general internet and media users (Agarwal & Karahanna, 2000; Pace, 2004). In this experiment, I examined the relationship between flow state and time perception in videogames.

By manipulating attentional resources, I examined flow state in participants playing videogames. Broader attentional resources and increased cognitive load reduce the level of experienced flow state, although these effects may be bidirectional (e.g., 120

Chang et al., 2017; Jin, 2011; 2012; Kalyuga, Chandler, & Sweller, 1999; Reed, Burton, & Kelly, 1985; Sherry, 2004). For example, amateur videogame players experience less intense flow states than experienced players during high-challenge tasks (Jin, 2011; 2012; Rau et al., 2006; Schleifer, 2005). Amateur players likely distribute their attentional resources more broadly and experience greater cognitive load during high- challenge tasks compared to more experienced players (Sinammon et al., 2012). Flow state may also influence attention. That is, individuals absorbed in a task may be less likely to attend to cues unrelated to the task, including information about timing. Individuals may be less likely to attend to these cues as they have less awareness of these factors during heightened flow states (Jackson & Marsh, 1996). Narrowing attentional resources increases experienced flow, and people underestimate duration during more intense flow states (Rau et al., 2006; Sherry, 2004). As such, flow state may moderate the effects of broader attentional distribution on time perception. This experiment examined if flow state moderates the effects of broader attentional distribution on time perception.

The Present Research

This experiment tested the effects of temporal and non-temporal cues on time perception. Specifically, this experiment examined if the attentional effects on time perception are driven by changes in attention allocation in general or whether solely temporal cues provide information individuals use to monitor timing. Similarly, this experiment examined if people use non-temporal cues to monitor timing. Participants played a strategy videogame (Age of Empires II: HD Edition, Ensemble Studios, TX), while either instructed to attend to other cues (i.e., encouraging distraction from the game) or without further instructions (i.e., encouraging immersion in the game). In the two distraction conditions, I instructed participants to attend to either temporal or non- temporal cues. In the immersion condition, I merely instructed participants to play the game until my return. Participants then completed a retrospective laboratory paradigm of time perception, and self-report measures of flow state and game engagement. I hypothesized that participants in the immersion condition would overestimate durations relative to participants distracted during the game. However, I expected similar differences in time perception for participants in the temporal and non-temporal cue conditions (i.e., the distraction conditions) as their attentional resources would be broadly distributed in both conditions. I also hypothesized that participants in the 121

immersion condition would report higher levels of flow and game engagement than participants in the distraction conditions. However, I expected similar differences in flow state between individuals in the temporal and non-temporal cue conditions. I additionally hypothesized that flow state would moderate the effects of attention on time perception such that, as flow state decreased, participants in the immersion condition would overestimate stimulus duration compared to participants in the two distraction conditions.

Method

Participants and Design

The participants were 184 first-year psychology students recruited from the University of New South Wales in exchange for course credit. I recruited 14 additional participants from a community sample in exchange for AUD$20. A power analysis using GPower, with a goal to obtain 0.8 power to detect a medium effect size (f = 0.25; Cohen, 1977), with an alpha level of 0.05, determined the sample should include a minimum of 159 participants (Faul et al., 2009). I excluded the data from 29 participants for at least one of the following: receiving a score of less than 70% in the bisection training phase (n = 3; see Experiment 1, pp. 59), failing the attention check (n = 13), not following instructions (n = 4), having a Weber ratio of over 0.35 (indicating less than half the temporal sensitivity of the average adult; e.g., Droit-Volet et al., 2004; Wearden, 1991; n = 12) and/or the bisection experimental data failing a residual check (indicating poor model fit; McPherson, 2001; n = 2).

The final sample of 169 participants included 92 men and 77 women, with a mean age of 19.57 years (S.D. = 3.65). One hundred and fifty-nine of these individuals were students and 10 were from the community. Participants were primarily Asian (56.8%), with 33.7% of the sample Caucasian, and the final 9.5% of the sample comprising of other ethnicities. The majority of the sample (46.2%) was non-religious, 31.4% Christian, 6.5% Buddhist, 5.9% Hindu, and the remaining 10.1% reported other religious affiliations. The most prevalent completed level of education was high school graduation (70.4%), followed by some college (16.0%), and a bachelor’s degree (10.7%), with the remaining 3.0% of other education levels. One hundred and forty-two participants (84.0%) were native English speakers. 122

I conducted multiple Chi square tests and a one-way between-subjects ANOVA to determine if demographics significantly differed between attention conditions. Attention conditions did not significantly differ based on gender, χ2(2) = 0.42, p = .81, 2 age, F(2, 166) = 0.38, p = .68, ηp² = .01, ethnicity, χ (10) = 11.13, p = .35, education, χ2(8) = 7.47, p = .49, religion, χ2(14) = 21.11, p = .11, or English as a first language, χ2(2) = 0.75, p = .69. For a full list of participant demographics, see Appendix C (pp. 267).

All participants provided written informed consent and the research was approved by the Human Research Ethics Advisory Panel at the University of New South Wales.

In a between-subjects design, I randomly allocated participants to 1 of 3 conditions manipulating attention. In the immersion condition participants played the videogame with no distractions (n = 56). In two other conditions, participants were distracted from gameplay by a series of tones which signalled participants to attend to either temporal or non-temporal cues. In the non-temporal cue condition, participants wrote down the amount of wood they had collected in the game whenever they heard the tone (n = 56). In the temporal cue condition, participants wrote down the amount of time they had been playing the videogame whenever they heard the tone (n = 57). Participants then completed laboratory measures assessing time perception, flow state, and game engagement.

I programmed this experiment using Inquisit 5.0 software (Millisecond, 2018).

Materials and Procedure

Attention manipulation. Each participant completed as many training modules as they could in 17 minutes for a videogame called Age of Empires II: HD Edition (Ensemble Studios, Texas, 2013). Age of Empires II: HD Edition is a competitive strategy-based game, in which participants collect resources (e.g., wood, gold) and use those resources to build military units to expand their empire. Participants completed as many training modules as they could, up to a maximum of 4. All participants completed at least 2.5 modules. Participants then played the videogame until the experimenter returned. 123

I randomly allocated participants to 1 of 3 conditions manipulating attention while they played a videogame. In the immersion condition, participants received no instructions other than to play the game normally. Participants could also hear the sounds in the game to increase immersion. Participants in the other conditions were distracted while they were playing by a series of loud randomized tones, played on average every 3 seconds. Each tone lasted 1 second and I ensured that the number of tones was identical in both distraction conditions. Participants listened to these tones in an audiofile that played while they engaged with the videogame. I muted the sound in the game, which primarily consisted of music, in these conditions to increase the salience of the distracting tones. Participants in the non-temporal cue condition wrote down the amount of resources they had collected each time they heard the tone. Participants in the temporal cue condition wrote down the amount of time they had been playing each time they heard the tone. Participants could only see the timer in the temporal cue condition. I intentionally obscured the timer in the other conditions to both obscure temporal cues and minimise the use of counting strategies. Each participant played Age of Empires II: HD Edition for a total of 25 minutes, not including the training modules.

Time perception. Participants completed the bisection task as the primary measure of subjective time (for a full description of the bisection task, see Experiment 1, pp. 60). Due to limitations in Experiment 1 (pp. 80), I increased the number of trials in the bisection task in this experiment to 70. Because contextual change can influence both perceived time and what individuals attend to, I attempted to minimize contextual change by using images sourced from Age of Empires II: HD Edition (e.g., Brown, 2008; Grondin, 2010; Zakay & Block, 1996). I created 25 colour stills of gameplay, each approximately 5.5 × 2.5 cm. For a list of all images used, see https://osf.io/rq5k3/ (for example images, see Appendix C, pp. 267).

Participants completed the bisection task directly after playing the videogame. This decision was deliberate, as I did not want to increase the number of temporal cues available to participants by increasing awareness of the experimental aims. As such, I assumed that the amount of temporal information participants received during gameplay would influence how participants perceived time directly after the videogame. This approach reflects the design of other retrospective time perception experiments (e.g., Cahoon & Edmonds, 1980). 124

Attention check. To ensure participants were paying attention throughout the experiment, I asked participants to identify the last picture they saw in the bisection task. This picture was of a medium McDonald’s chips. I selected an image unrelated to Age of Empires II: HD Edition to increase the salience of the attention check. I accepted any answer involving chips, fries, or McDonald’s and excluded the data of all participants who failed the attention check.

Flow state. Each participant completed the Flow State Scale (Jackson & Marsh, 1995). The Flow State Scale is a 36-item questionnaire assessing flow state through 9 individual dimensions. These dimensions are challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration on task at hand, sense of control, loss of self-consciousness, transformation of time, and autotelic experience (Jackson & Marsh, 1995). Thus, there were 4 items assessing each dimension. Participants rated how much they agreed or disagreed with statements, in the context of playing Age of Empires II: HD Edition, on a bipolar 5-point Likert scale. Higher scores for each subscale indicate higher identification with the statement. Because another scale (i.e., the Game Engagement Questionnaire) in this experiment also includes a measure of flow, I refer to flow state in this scale as overall flow.

The following are example items for each dimension:

• Challenge-skill balance (α = .85): “My abilities matched the high challenge of the situation”. • Action-awareness merging (α = .82): “I performed automatically”. • Clear goals (α = .85): “I knew what I wanted to achieve”. • Unambiguous feedback (α = .89): “I was aware of how well I was performing”. • Concentration on task at hand (α = .91): “I had total concentration”. • Sense of control (α = .87): “I felt like I could control what I was doing”. • Loss of self-consciousness (α = .84): “I was not concerned with what others may have been thinking of me”. • Transformation of time (α = .71): “The way time passed seemed to be different from normal”. • Autotelic experience (α = .88): “I found the experience extremely rewarding”. 125

For a full list of items in the Flow State Scale, see Appendix C (pp. 267).

Game engagement. Each participant completed the Game Engagement Questionnaire (Brockmyer, Fox, Curtiss, McBroom, Burkhart, & Pidruzny, 2009). The Game Engagement Questionnaire was developed in the context of videogames specifically. This 19-item questionnaire assesses 4 dimensions of game engagement. These dimensions are flow, immersion, absorption, and presence. Nine items in this questionnaire assess flow, 1 assesses immersion, 4 assess absorption, and 4 assess presence. Participants rated how much they agreed or disagreed with statements, in the context of playing Age of Empires II: HD Edition, on a bipolar 7-point Likert scale. Higher scores for each subscale indicate higher identification with the statement. Because another scale (i.e., the Flow State Scale) in this experiment also includes a measure of flow, I refer to flow state in this scale as game-engagement flow.

The following are example items for each dimension:

• Flow (α = .78): “I played without thinking how to play”. • Immersion: “I really got into the game”. • Absorption (α = .70): “Time seemed to stand still or stop”. • Presence (α = .61): “Things seemed to happen automatically”.

For a full list of items in the Game Engagement Questionnaire, see Appendix C (pp. 267).

Previous gaming experience. I included 3 questions assessing previous gaming experience as gamers experience greater flow when game difficulty is more closely aligned with their skill level (Jin, 2011; 2012; Rau et al., 2006; Schleifer, 2005). These questions instructed participants to report the number of hours they play videogames during an average week, and the number of years they have played videogames (e.g., Sanders & Cairns, 2010). An additional question asked participants to indicate previous experience with Age of Empires II: HD Edition specifically.

Reliability analyses for flow state and game engagement. I conducted reliability analyses to assess whether individual items in the Flow State Scale and Game Engagement Questionnaire were consistent with both overall scores and individual subscale scores on these scales. A reliability analysis of the items in the individual subscales of the Flow State Scale revealed high consistency for clear goals (α = .85) and 126

autotelic experience (α = .88). A reliability analysis of the items revealed 1 item whose removal would increase the reliability of each the challenge-skill balance, action- awareness merging, unambiguous feedback, concentration, perceived control, loss of self-consciousness, and transformation of time subscales. A reliability analysis of the items for the Flow State Scale total score revealed 2 items (i.e., questions 2 and 4 in the transformation of time subscale) whose removal would increase the reliability of the Flow State Scale from .92 to .93. For individual item statistics, deleted items, and the reliability analyses, see Appendix C, pp. 267.

A reliability analysis of the items in the individual subscales of the Game Engagement Questionnaire revealed relatively lower consistency for game-engagement flow (α = .78), presence (α = .61), and absorption (α = .70). A reliability analysis of the items for overall game engagement revealed 1 item (i.e., question 1 in the absorption subscale) whose removal would increase the reliability of the Game Engagement Questionnaire total score from .87 to .88. For individual item statistics, deleted items, and the reliability analyses, see Appendix C, pp. 267.

Results

Calculating time perception variables. As in Experiment 1, I calculated the proportion of trials in which participants responded ‘long’ for each stimulus duration. I calculated two further variables to confirm these results: a bisection point (Church & Deluty, 1977; Wearden, 1991) and the Weber ratio (Brown, 1960; Gibbon, 1977; Hobson, 1975). A bisection point is the time interval at which the probability of answering short and long is equal. A lower bisection point indicates both stimuli overestimation and slower perceived time; a higher bisection point indicates both stimuli underestimation and faster perceived time (Droit-Volet et al., 2004). The Weber ratio is a measure of temporal sensitivity, where a higher score indicates a reduced ability to discriminate between millisecond durations (Kopec & Brody, 2010). For a description of how to calculate these variables, see Experiment 1 (pp. 63).

Preliminary Analyses

Duration and perceived time. I initially analysed these data using a repeated- measures ANOVA to examine whether stimulus duration during the bisection task influenced the probability of answering ‘long’. As Mauchly’s test of sphericity was violated, χ2(20) = 337.00, p < .0001, I used a Greenhouse-Geisser correction. As shown 127

in Figure 4.1, there was a significant main effect of duration, such that participants were more likely to respond ‘long’ as stimulus durations increased, F(3.67, 616.01) = 977.61, p < .0001. All Tukey pairwise comparisons were significant (all ps < .0001). For statistical output regarding pairwise comparisons within duration, see Appendix C (pp. 267).

Figure 4.1. Probability of answering ‘long’ for each stimulus duration. Bars represent standard error of the mean. The probability of answering ‘long’ increased as stimulus duration increased.

Primary Analyses

Effects of attention conditions on perceived time. I conducted a one-way between-subjects ANOVA on bisection point to determine the effects of attention conditions on time perception. Time perception significantly differed between attention conditions, F(2, 166) = 3.11, p = .047, ηp² = .04. As seen in Figure 4.2 and Table 5.1, post-hoc Tukey tests revealed that participants in the immersion condition significantly overestimated stimulus durations compared to participants in the temporal cue condition, t(110) = -2.49; 95% CI = [-163.59, -18.63], p = .01, ηp² = .48. Figure 4.3 likewise represents this underestimation by a shift of the bisection point to the left, as outlined in previous research (e.g., Droit-Volet et al., 2004). However, time perception did not significantly differ between participants in the immersion condition and participants in the non-temporal cue condition, p = .15. Similarly, time perception did 128

not significantly differ between participants in the temporal and non-temporal cue conditions, p = .30.

Table 5.1

Mean bisection points and Weber ratios for attention conditions in Experiment 3

Immersed Non-Temporal Temporal Cues Cues

M (SE) N M (SE) N M (SE) N

Bisection Point 904.34 56 957.57 56 995.45 57 (21.11) (25.91) (29.87) Weber Ratio 0.19 56 0.19 56 0.19 57

(0.01) (0.01) (0.01)

Figure 4.2. Changes in perceived time for individuals in the immersion condition, temporal cue condition, and non-temporal cue condition. Bars represent standard error of the mean. Participants in the temporal cues condition underestimated stimulus duration relative to individuals in the immersion condition. * p < .05.

129

I also conducted a one-way between-subjects ANOVA on Weber ratio to determine the effects of attention conditions on temporal sensitivity. Temporal sensitivity did not significantly differ between attention conditions, F(2, 166) = 0.06, p

= .95, ηp² = .001.

Figure 4.3. Probability of answering ‘long’ for each stimulus duration for attention conditions. Bars represent standard error of the mean. Participants in the temporal cue condition had a lower probability of answering ‘long’ for stimulus durations compared to participants in the immersion condition. ' g

n 1.0 o l

' Immersed

n Non-temporal i r

e Temporal w s n

a 0.5

f o

t y i l i b a b o r 0.0 P 400 600 800 1000 1200 1400 1600 Stimulus duration (ms)

Effects of attention conditions on flow state. I conducted a one-way analysis of covariance (ANCOVA) on the average of all responses in the Flow State Scale. This analysis determined the effects of attention condition on overall flow state. As the 3 questions assessing previous videogame experience correlated with overall flow state (r(167) = .16 - .24, ps = .001 - .03), I included these items as covariates (for the correlation matrix, see Appendix C, pp. 267). Overall flow state differed significantly between conditions when controlling for previous gaming experience, F(2, 163) = 4.54, p = .012, ηp² = .05. As seen in Figure 4.4and Table 5.2, post-hoc tests using a Sidak correction showed that participants in the immersion condition reported higher levels of overall flow state compared to participants in both the non-temporal (t(111) = 2.95;

95% CI = 0.07, 0.48; p = .02; ηp² = .53) and temporal cue conditions (t(110) = 2.73;

95% CI = 0.05, 0.46; p = .04; ηp² = .48), when controlling for previous gaming experience. Overall flow state similarly differed significantly between conditions 130

without controlling for previous gaming experience, F(2, 166) = 5.03, p = .008, ηp² = .06. Tukey post-hoc tests similarly revealed that participants in the immersion condition reported higher levels of overall flow state compared to participants in both the non- temporal (p = .015) and temporal cue conditions (p = .022), without controlling for previous gaming experience.

Table 5.2

Flow state and flow state subscale scores for attention conditions in Experiment 3

Immersed Non-Temporal Temporal Cues Cues

M (SE) N M (SE) N M (SE) N

Clear Goals 3.49 56 3.15 56 3.33 57 (0.13) (0.12) (0.13) Autotelic 3.55 56 3.25 56 3.46 57 Experience (0.11) (0.11) (0.11) Challenge-Skill 3.26 56 3.07 56 2.95 57 Balance (0.12) (0.12) (0.12) Action- 3.45 56 3.08 56 2.92 57 Awareness (0.12) (0.12) (0.12) Merging Unambiguous 3.23 56 3.14 56 3.17 57 Feedback (0.14) (0.13) (0.14) Concentration 4.11 56 3.44 56 3.36 57 (0.13) (0.13) (0.13) Perceived 3.79 56 3.33 56 3.20 57 Control (0.12) (0.12) (0.12) Loss of Self- 3.70 56 3.80 56 3.60 57 Consciousness (0.13) (0.13) (0.13) Transformation 3.36 56 3.25 56 3.62 57 of Time (0.10) (0.10) (0.10) Total Flow State 3.52 56 3.24 56 3.27 57 (0.07) (0.07) (0.07) 131

* 4 *

2 Flow State Flow 1

Temporal Immersion Non-Temporal

Figure 4.4. Overall flow state scores for attention conditions. Bars represent standard error of the mean. Participants in both the non-temporal and temporal cue conditions experienced lower overall flow state compared to participants in the immersion condition. * p < .05.

I conducted further multiple one-way ANOVAs to determine the effects of the experimental manipulation on the individual flow state subscales within overall flow state (i.e., from the Flow State Scale). Action-awareness merging differed significantly between attention conditions, F(2, 166) = 4.90, p = .009, ηp² = .06. Transformed time also differed significantly between conditions, F(2, 166) = 3.38, p = .04, ηp² = .04. I conducted post-hoc Tukey tests to determine the effects of attention condition on action-awareness merging and transformed time. As seen in Figure 4.5, participants in the immersion condition reported more action-awareness merging than participants in the temporal cue condition (t(110) = 3.09; 95% CI = 0.12, 0.93; p = .008; ηp² = .59). As also seen in Figure 4.5, participants in the temporal cue condition reported greater time transformation than participants in the non-temporal cue condition (t(111) = 2.69; 95%

CI = 0.03, 0.71; p = .03; ηp² = .49).

132

Figure 4.5. Action-awareness merging and transformation of time scores for attention conditions. Bars represent standard error of the mean. Participants in the temporal cue condition experienced less action-awareness merging compared to participants in the immersion condition. Participants in the temporal cue condition experienced higher transformation of time relative to participants in the non-temporal cue condition. * p < .05. ** p < .01.

Concentration on task at hand differed significantly between conditions, F(2,

101.91) = 14.40, p < .0001, ηp² = .11. Perceived control likewise differed significantly between conditions, F(2, 109.03) = 7.78, p = .001, ηp² = .07. Levene’s test of equality indicated unequal variance for the effects of attention on both the perceived control subscale, F(2, 166) = 4.96, p = .01, and the concentration on task at hand subscale, F(2, 166) = 14.00, p < .0001. As such, I analysed the effects of the experimental manipulation on these subscales using Welch’s test. I conducted post-hoc Games- Howell tests to determine the effects of attention condition on concentration and perceived control as I used Welch’s tests to initially analyse these data. As seen in Figure 4.6, participants in the immersion condition reported higher concentration than participants in both the non-temporal (t(111) = 4.17; 95% CI = 0.28, 1.05; p = .0002;

ηp² = .69) and temporal (t(110) = 4.34; 95% CI = 0.34, 1.16; p = .0001; ηp² = .77) cue conditions. As also seen in Figure 4.6, participants in the immersion condition reported higher perceived control than participants in both the non-temporal (t(111) = 2.80; 95%

CI = 0.07, 0.85; p = .02; ηp² = .47) and temporal (t(110) = 3.60; 95% CI = 0.21, 0.99; p 133

= .001; ηp² = .66) cue conditions. As indicated by the effect sizes, the effects of attention on the flow state subscales were all large. There were no significant effects of the manipulation on the other subscales of the Flow State Scale (i.e., challenge-skill balance, clear goals, unambiguous feedback, loss of self-consciousness, or autotelic experience), all ps > .05.

Figure 4.6. Concentration on task at hand and perceived control scores for attention conditions. Bars represent standard error of the mean. Participants in both the non-temporal and temporal cue conditions experienced lower concentration and perceived control compared to participants in the immersion condition. * p < .05. ** p < .01. * p < .001.

Effects of attention conditions on game engagement. I conducted a one-way ANOVA on the average of all response in the Game Engagement Questionnaire to determine the effects of the experimental manipulation on game engagement. There was a significant effect of attention condition on game engagement, F(2, 166) = 3.28, p =

.04, ηp² = .04. However, this effect was small. As seen in Figure 4.7 and Table 5.3, a post-hoc Tukey test showed that participants in the non-temporal cue condition reported lower levels of game engagement compared to participants in the temporal cue condition (t(111) = 2.76; 95% CI = 0.11, 0.77; p = .03; ηp² = .49).

Table 5.3

Game engagement and game engagement subscale scores for attention conditions in Experiment 3 134

Immersed Non-Temporal Temporal Cues Cues

M (SE) N M (SE) N M (SE) N

Absorption 3.22 56 3.05 56 3.47 57 (0.15) (0.15) (0.15) Flow 3.48 56 3.22 56 3.71 57

(0.13) (0.13) (0.13) Presence 4.23 56 4.17 56 4.58 57

(0.14) (0.14) (0.14) Immersion 5.32 56 5.16 56 5.46 57

(0.20) (0.20) (0.20) Total Game 3.66 56 3.48 56 3.91 57 Engagement (0.12) (0.12) (0.12)

Figure 4.7. Game engagement scores for attention conditions. Bars represent standard error of the mean. Participants in the temporal cue condition experienced higher game engagement relative to participants in the non-temporal cue condition. * p < .05.

I conducted further multiple one-way ANOVAs to determine the effects of the attention manipulation on scores in the individual game engagement subscales. Game- engagement flow differed significantly between attention conditions, F(2, 166) = 3.39, p 135

= .04, ηp² = .04. Notably, this effect was small. However, there were no significant effects of attention condition on the other subscales of the Game Engagement Questionnaire (i.e., presence, immersion or absorption), ps = .09 - .38. As seen in Figure 4.8, a post-hoc Tukey test showed that participants in the non-temporal cue condition reported lower levels of game-engagement flow compared to participants in the temporal cue condition (t(111) = 2.81; 95% CI = 0.04, 0.92; p = .03; ηp² = .50).

Figure 4.8. Game-engagement flow scores for attention conditions. Bars represent standard error of the mean. Participants in the temporal cue condition experienced higher game- engagement flow compared to participants in the non-temporal cue condition. * p < .05.

The moderating effects of overall flow state and game engagement. Bisection point did not significantly correlate with overall flow state, game engagement, or any of the overall flow state or game engagement subscales (rs(167) = .02 - .15, ps = .07 - .99). As this result violates the assumptions for a moderation analysis, I did not conduct planned analyses examining if overall flow state or game-engagement flow state moderates the effect of broader attentional distribution on time perception (Baron & Kenny, 1986; Dearing & Hamilton, 2006).

Discussion 136

The present research examined how attending to temporal and non-temporal cues changes time perception relative to immersion within the context of a videogame. In one model of time perception, attention influences time perception when attentional resources are allocated toward or away from direct information about timing (Allman et al., 2014; Treisman, 1963). My results supported this notion as participants in the temporal cue condition underestimated stimulus durations relative to participants in the immersion condition. Similarly, participants who attended to temporal cues had more accurate time perception than immersed participants, as evidenced by bisection points closer to 1000ms (i.e., the true bisection point). Although I did not analyse this result, this result suggests that temporal cues provided information that individuals used to accurately monitor the time. Participants in the two distraction conditions also experienced lower levels of overall flow and game engagement compared to participants in the immersion condition. This result confirmed my hypothesis that broader attentional distribution would decrease levels of experienced overall flow and game engagement (e.g., Chang et al., 2017).

The current experiment did not directly find differences in time perception between participants in the non-temporal cue condition and participants in the immersion condition. Time perception for participants in the non-temporal cue condition was statistically indistinguishable from the other two conditions. This result may suggest that temporal cues provide better information about timing than non- temporal cues. This result is sensible, as a timer in a videogame provides optimal information about the speed of real time, and individuals would have to infer timing from how much wood they collected. Although this result was not significant, it possibly provides evidence that non-temporal cues provided information that individuals used to accurately monitor the time. There is further evidence to support this idea. Although I did not analyse this result, participants who attended to non-temporal cues produced bisection points closer to 1000ms than those induced to immersion. Participants with accurate time perception should produce bisection points of around 1000ms.

Cues that increase linearly may also provide better information about timing than nonlinear cues that fluctuate over time. Time, as displayed by the timer in the game, increased linearly over the course of the game. The amount of wood a participant collected, however, changed over the course of the game because participants both 137

collected wood and used the collected wood to build military units. Because people can perceive real time as a linear construct, a linear cue may provide better information for monitoring a linear construct than a non-linear cue (e.g., Mellor, 1998). Nonlinear cues can provide conflicting information for timing decisions, such as increases and decreases in wood over time (Liebhaber, Kobus, & Feher, 2002). Furthermore, people assign less weight to conflicting information when they make decisions (Hammond, Keeney, & Raiffa, 1998; Liebhaber et al., 2002). People may preferentially use linear cues over nonlinear cues when making decisions about timing because nonlinear cues may provide less reliable information. However, further research may be required to investigate how linear and non-linear cues influence time perception.

The results in the present experiment did not support the hypothesis that participants in the non-temporal cue condition would underestimate stimulus durations relative to participants in the immersion condition. However, there is evidence to suggest that non-temporal cues may provide information that individuals can use to infer timing. Although the pacemaker-accumulator model posits the existence of a specific timing receptor, specific sensory receptors only exist for a few senses (Allman et al., 2014; Nevid, 2012; Treisman, 1963). Examples of senses with specific sensory receptors include smell, warmth, pain, and taste (Nevid, 2012). Generally, however, the perception of most senses and experiences is computed and integrated from a range of cues, rather than a direct sensory receptor (Trommershauser, Kording, & Landy, 2011). Examples of senses and experiences that are computed from a range of cues include inferring depth from texture and motion cues, and moisture from temperature and tactile cues (Filingeri, Fournet, Hodder, & Havenith, 2014; Rogers & Graham, 1979; Young, Landy, & Maloney, 1993). Individuals do not possess specific sensory receptors, however, for either depth or moisture (Filingeri et al., 2014; Young et al., 1993). Furthermore, although researchers speculate there may be a neural receptor specifically for processing temporal cues, there are many neural areas associated with processing timing (e.g., Buhusi & Meck, 2005; Eagleman, Tse, Buonomano, Janssen, Nobre, & Holcombe, 2005). As such, time perception is likely computed from a range of cues.

Cues and Overall Flow State and Game Engagement

In the present experiment, participants in both the temporal and non-temporal cue conditions experienced less intense overall flow than participants in the immersion condition (Chang et al., 2017; Jin, 2011; 2012; Kalyuga et al., 1999; Rau et al., 2006; 138

Reed et al., 1985; Sherry, 2004; Sinammon et al., 2012). Completing concurrent tasks, such as monitoring temporal and non-temporal cues while playing a videogame, broadens attentional resources (Sweller, 1988; Sweller, Ayres, & Kalyuga, 2011). This finding replicates previous work showing that broader attentional distribution decreases levels of flow (Chang et al., 2017). This finding confirms my hypotheses that participants in the immersion condition would report higher levels of overall flow and game engagement than participants in both distraction conditions. This finding confirms my hypothesis that participants in the temporal cue condition would report similar overall flow states to participants in the non-temporal cue condition.

Two components in particular appear to drive the differences in overall flow between participants in the distraction and immersion conditions. These components are concentration on the task at hand and perceived control. Participants in the temporal and non-temporal cue conditions reported less concentration and perceived control relative to participants in the immersion conditions. Participants also reported less action- awareness merging in the temporal cue condition compared to participants in the immersion condition. As such, decreases in action-awareness merging may contribute to less intense flow when attending to temporal cues. Action-awareness merging occurs when an individual loses awareness of their external environment and gains automation of task-relevant behaviours (DiPaola & Forsyth, 2011; Fullagar & Delle Fave, 2017). As participants knew they would stop playing the game at some point, a timer may have increased awareness of the external laboratory environment. In contrast, participants in the non-temporal cue condition may have been able to maintain action-awareness merging because they attended to cues more strongly related to the videogame (i.e., resources used to play the game).

Participants in different attention conditions did not report different levels of challenge-skill balance, clear goals, unambiguous feedback, autotelic experience, or loss of self-consciousness. Attending to cues while playing a videogame could plausibly affect any of these components of overall flow. When distracted during an enjoyable videogame, participants could plausibly feel more frustrated, perform worse, feel more self-conscious, and have less ability to form clear goals or respond to feedback, relative to being immersed in the game. However, these components of overall flow state did not change based on whether participants were in the immersion or distraction conditions in this experiment. Thus, the effects of attention on overall flow state do not 139

appear to be driven by changes in challenge-skill balance, clear goals, unambiguous feedback, autotelic experience, or loss of self-consciousness.

In the present experiment, participants in the temporal cue condition reported greater transformation of time relative to participants in the non-temporal cue condition. These findings were unexpected, particularly as differences in time perception between participants in the temporal and non-temporal cue conditions were not significant. These findings possibly reflect increased awareness of time transformation for participants attending to temporal cues. Exposure to stimuli related to a specific concept (e.g., clocks) increases awareness of that concept (e.g., time). For example, tourists exposed to pro-environmental practises when staying at resorts reported more awareness of environmental issues after their departure relative to before their arrival (Lee & Moscardo, 2005). Exposure to a timer may hence increase temporal awareness. Although participants distracted by temporal and non-temporal cues exhibited similar time perception, participants in the temporal cue condition were possibly more aware of these changes.

Furthermore, participants in the temporal cue condition reported greater game engagement and overall flow on the Game Engagement Questionnaire relative to participants in the non-temporal cue condition. This result was unexpected because overall flow state, as measured by the Flow State Scale (Jackson & Marsh, 1996), did not differ between these conditions, and flow is one component of game engagement (Brockmyer et al., 2009). However, the Game Engagement Questionnaire was developed to identify susceptibility to engaging in violent videogames (Norman, 2013). In addition, there may be little research supporting the validity of the Game Engagement Questionnaire (Denisova, Nordin, & Cairns, 2016; Lenhart et al., 2008; Wiebe, Lamb, Hardy, & Sharek, 2013). The Game Engagement Questionnaire may thus not assess the same construct as the Flow State Scale and may additionally not be a valid tool for assessing engagement in non-violent videogames.

Flow State and Time Perception

Overall flow state did not significantly correlate with time perception in the current experiment. This finding contradicts previous literature showing that individuals underestimate duration when they are in a heightened state of flow (Rau et al., 2006; Schleifer, 2005; Sinnamon et al., 2011; Tobin & Grondin, 2009). One possibility I did 140

not observe this correlation is that the effects of attention on time perception took precedence over the effects of flow state on time perception. Distracted individuals typically experience less flow state, but also underestimate duration, relative to immersion, whereas individuals overestimate duration during weaker flow states (Jin, 2011; 2012; Rau et al., 2006; Schleifer, 2005). Participants in the immersion condition overestimated duration relative to participants in one distraction condition. However, participants in the immersion condition also reported stronger overall flow. If flow state were the primary driving factor for changes in time perception in the present experiment, participants in the immersion condition would have underestimated duration relative to participants in this distraction condition. This result suggests the influence of attention likely took precedence over the effects of overall flow state in determining the speed of time perception. Thus, underestimated stimulus duration in this experiment was likely primarily determined by broader attentional resource distribution.

Implications

In the current experiment, participants in the temporal cue condition underestimated stimulus duration relative to participants in the immersion condition. These data provide some support for the internal clock component of the pacemaker- accumulator model. This model specifies that information about real time, derived from an internal clock, provides the base rate for human time perception (Allman et al., 2014; Treisman, 1963). However, participants in the non-temporal cue condition did not differ in time perception relative to participants in either the temporal cue or immersion conditions. These equivocal results suggest that participants in the non-temporal cue condition may have used non-temporal cues to infer timing. However, future research may need to confirm if attending to non-temporal cues can change time perception relative to immersion.

Research suggests that people use non-temporal cues to infer timing, such as internal bodily cues, social cues, and external environmental changes (Aschoff et al., 1971; Ferguson et al., 2012; Honma et al., 1995; Liu et al., 1998; Meissner & Wittmann, 2011; Noseworthy & Finlay, 2009). Moreover, people still perceive changes in time in the absence of external temporal cues (e.g., clocks; Miller, Vieweg, Kruize, & McLea, 2010). As such, people may not need an internal method of keeping track of real time and the internal clock component of the pacemaker-accumulator model may 141

need revision. With more conclusive evidence, this model might be updated by proposing that the distribution of attentional resources toward both temporal and non- temporal cues can speed up time perception. An updated model would specify that people experience faster time due to difficulty in processing cues in general when attention is more broadly distributed. Any updated model would hence likely involve removing the internal clock component of the pacemaker-accumulator model. However, further research is required to confirm that attending to non-temporal cues can speed up time perception before considering such revisions.

Pathological gaming is associated with poor mental health (Gentile et al., 2011). A meta-analysis estimated that the prevalence of pathological gaming in the general population is 3.1% (Ferguson, Coulson, & Barnett, 2011). Individual studies, however, report large variation in prevalence rates between countries, such as 0.6% in Norway and 14.6% in Britain (Lopez-Fernandez, Honrubia-Serrano, Baguley, & Griffiths, 2014; Mentzoni et al., 2011). Moreover, pathological gaming is associated with comorbidity of mental health issues (particularly anxiety and depression), as well as lower life satisfaction and social and academic deficits (Ferguson et al., 2011; Mentzoni et al., 2011).

Faster time perception and heightened flow state may both maintain addiction to videogames. Flow state can maintain addiction in pathological gaming by motivating individuals to play videogames (Chou & Ting, 2003; Hsu & Lu, 2004; Khang et al., 2013; Mauri et al., 2011). Time loss is a common, perhaps unwanted consequence of playing videogames, and more time is lost when time perception is faster (Wood et al., 2007). Faster time perception may cause people to overuse videogames, because people lose track of how long they have been playing during fast time perception. As more experienced videogame players experience faster time perception compared to less experienced players, more experienced players are more likely to experience time loss during videogames (Rau et al., 2006; Schleifer, 2005). As such, more experienced players may also be more likely to overuse videogames compared to less experienced players. Experience playing videogames may be a risk factor for pathological gaming due to faster time perception and higher flow state.

Forty-nine percent of individuals frequently report losing track of time while playing videogames, 33% always experience time loss, and 17% occasionally experience time loss (Wood et al., 2007). Almost half of these individuals deliberately 142

use strategies to minimize time lost playing videogames, the most popular of which is regularly attending to a clock or timer (Wood et al., 2007). The results of the present experiment suggest that attending to concurrent cues, especially timers, while playing videogames is an ineffective strategy for minimizing time loss. Attending to concurrent cues can instead speed up time perception, and potentially contribute to further time lost, relative to immersion in a videogame. However, there is at least one example of people using non-temporal cues (i.e., music) to both infer timing and minimize time lost gambling (Noseworthy & Finlay, 2009). Loss of time can also motivate individuals to engage in activities high in flow state, such as videogames (Hsu & Lu, 2004; Tobin & Grondin, 2009). The results of the present experiment suggest that disrupting flow state (e.g., by monitoring a clock) in videogames may further speed up time perception and contribute to loss of time, relative to just playing the videogame. These results may also explain why using videogames as distractors from undesired physical sensations (e.g., pain) is effective at reducing those sensations (Pegelow, 1992; Phillips, 1991; Vasterling, Jenkins, Tope, & Burish, 1993).

Limitations and Future Research

One limitation of the current experiment is that I measured time perception after participants had finished playing the videogame. This decision was deliberate, as I did not want participants to be aware the experiment examined time perception. I assumed that the amount of temporal information participants received during gameplay would influence how participants perceived time directly after the videogame. This approach reflects the design of other retrospective time perception experiments (e.g., Cahoon & Edmonds, 1980). However, future research could examine time perception within videogame play to augment further evidence for the results from this experiment.

I included the Game Engagement Questionnaire because it was developed specifically for videogames. Thus, I thought this scale might assess flow state in videogames more sensitively than the Flow State Scale. However, these two measures yielded different patterns of results. Furthermore, although the Game Engagement Questionnaire was developed for videogames, it was developed to identify susceptibility to engaging in violent videogames (Norman, 2013). In addition, the Game Engagement Questionnaire may be invalid (Denisova et al., 2016; Lenhart et al., 2008; Wiebe et al., 2013). This evidence may suggest that the Game Engagement Questionnaire may not assess the same construct as the Flow State Scale. This evidence may additionally 143

suggest that the Game Engagement Questionnaire does not assess engagement in non- violent videogames well.

One potential confound in this experiment was the inclusion of videogame music for participants who played the game without attending to concurrent cues. In the other conditions, the music was muted while participants played the game. Listening to music is commonly used as an emotion induction paradigm that can also be used in conjunction with other induction paradigms to increase their efficacy (Siedlecka & Denson, 2019). Moreover, emotion intensity influences time perception (Gil & Droit- Volet, 2012). However, attention conditions did not influence autotelic experience in this experiment. This result suggests that music did not increase positive emotions or the enjoyability of the videogame for participants in the immersion condition. As such, it seems unlikely that music confounded the effects of attention in this experiment.

Conclusions

This experiment showed that individuals underestimated duration when attending to temporal cues during a videogame relative to being immersed in the videogame. This result provides evidence for the internal clock component of the pacemaker-accumulator model. However, attending to non-temporal cues during the videogame did not change time perception relative to either attending to temporal cues during the game or immersion in the game. As such, people may use non-temporal cues to infer timing, although further research is required to confirm this relationship. Additionally, although the current experiment replicated previous work showing that broader attentional distribution decreases levels of flow, overall flow state did not correlate with time perception (Chang et al., 2017). However, researchers could endeavour to explore the mediating role of flow state in future experiments because flow state generally does speed up time perception (e.g., Rau et al., 2006).

144

CHAPTER 6. Experiment 4: Do Individual Differences in How People Think about Time Alter the Subjective Passage of Time?

Introduction

Accurate time perception is crucial for a range of everyday behaviours (e.g., driving) and general survival (Buhusi & Meck, 2005; Wittmann, 2013). However, human time perception is imperfect and there is little consensus about precisely which variables cause individuals to misperceive time (e.g., Grondin, 2010; Ivry & Schlerf, 2008). Some research may suggest that certain personality dimensions help determine the speed of time perception (e.g., Hogan, 1978). The current study examined whether the specific personality dimension of time perspective influences time perception.

Personality and Time Perception

Models of time perception typically ignore the role of personality dimensions. In one exception, Hogan’s (1978) model proposed that stimulus complexity and introversion interact to alter time perception. In Hogan’s (1978) model, individuals overestimate duration when stimulus complexity increases because more complex stimuli are more difficult to process. Furthermore, highly introverted individuals can tolerate less stimulus complexity than more extraverted individuals (Eysenck, 1967; Ludvigh & Happ, 1974). As such, when working on a complex task, highly introverted individuals might overestimate duration relative to more extraverted individuals. This model only provided one specific example of how a single personality dimension may inform time perception. Although certain personality dimensions may help determine the speed of time perception, there is little understanding of the process by which they may do so.

To our knowledge, only two experiments examined the relationships between personality dimensions and time perception, neither of which examined time perspective. In these experiments, highly extraverted individuals overestimated duration and perceived time less accurately compared to more introverted individuals (e.g., Rammsayer, 1997; Zakay et al., 1984). Another experiment examined several other personality dimensions such as social desirability, extraversion, neuroticism, and psychoticism (Rammsayer, 1997). In this experiment, there was only a positive correlation between high psychoticism and overestimated duration (Rammsayer, 1997). Although these experiments provided preliminary evidence that personality dimensions 145

can alter perceived time, the precise mechanisms underlying these relationships are unclear.

Research also examined the relationship between mental time travel and personality dimensions. Mental time travel is the ability to mentally project forwards or backwards in time to imagine future possibilities or to recall past experiences (Suddendorf & Corballis, 1997). In some experiments, an increased ability to mentally project into the past negatively correlated with personality dimensions such as extraversion, resilience, optimism, agreeableness, and conscientiousness, and positively correlated with personality dimensions such as neuroticism, cynicism, anxiety, and depression (Fortunato & Furey, 2009; 2011; Quoidbach, Hansenne, & Mottet, 2008). Increased ability to mentally project into the future positively correlated with personality dimensions such as extraversion, openness, resilience, and optimism, and negatively correlated with neuroticism and depression (Fortunato & Furey, 2009; 2011). These experiments did not directly examine the effects of personality dimensions on time perception. However, these experiments provided evidence that personality dimensions are related to a preference and ability to mentally “spend time” in the past or future.

Individual Differences in Time Perspective: The Role of Incongruence

Time perspective is a personality dimension that describes the extent to which individuals tend to focus on the past, present, and/or future in their everyday lives. A predominantly past-oriented individual may frequently focus on re-experiencing memories, whereas a future-oriented individual may frequently plan ahead for desired goals. Present-oriented individuals focus on living in the current moment.

Certain time perspectives can also vary as a function of valence, such that individuals can think about the past and present time orientations in a positive or negative manner. A positive past-oriented individual will tend to concentrate on re- experiencing positive memories, whereas a negative past-oriented individual will tend to focus on re-experiencing negative memories. Similarly, a positive present individual (called a present-hedonist) can make choices to optimise hedonism, whereas a negative present individual (called a present-fatalist) may feel like they are trapped in the current moment. Future perspective is always considered positive, as research has not yet identified a reliable future negative dimension (e.g., Zimbardo & Boyd, 1999). 146

Time perspective dimensions do correlate with behaviour related to time perception. Individuals high in the past-negative dimension, for example, are more likely to experience depression (Anagnostopoulos & Griva, 2012; Oyanadel & Buela- Casal, 2014; Zimbardo & Boyd, 2008). Slowed time perception is a characteristic of depression, such that depressed individuals may feel like their symptoms will never end (for a meta-analysis, see Thönes & Oberfeld, 2015). Similarly, individuals high in present-fatalism are both more impulsive and at higher risk for substance abuse issues than individuals low in present-fatalism (Keough, Zimbardo, & Boyd, 1999; Wills, Sandy, & Yaeger, 2001; Zimbardo & Boyd, 2008). Slowed time perception is also a characteristic of substance abuse and impulsivity (Jokic, Zakay, & Wittmann, 2018; Wittmann, Leland, Churan, & Paulus, 2007). These data indicate that time perspective could inform how individuals perceive time, although these experiments did not directly link time perspective dimensions to changes in time perception. These data may also signify that a moderator is required to observe the effects of time perspective on time perception.

One study examined how time perspective dimensions relate to time awareness (Wittmann, Rudolph, Gutierrez, & Winkler, 2015). Time awareness in this study was the perceived change of long periods of time, such as weeks and months. In their study, high identification with present-hedonism correlated positively with faster perceived passage of the previous week. Similarly, high identification with the past-negative dimension correlated positively with slower perceived passage of the last ten years. However, the results in this experiment were largely inconsistent; for example, high identification with the past-negative dimension did not generally correlate with slower time perception. Additionally, this study measured time awareness, and not time perception. Although time awareness does influence time perception, the two concepts are distinct (Droit-Volet, 2013; Lamotte, Izaute, & Droit-Volet, 2012). This literature indicates a need to directly link time perspective dimensions to a controlled laboratory measure of time perception.

One potential way to determine if time perspective influences time perception is by testing whether incongruence between time perspective dimensions and task features affects time perception. Specifically, when dominant features of a task (e.g., recalling an event in the past) are incongruent with a specific time perspective dimension (e.g., a personality low in past-orientation), individuals may overestimate stimulus durations. 147

Overestimated stimuli indicate slower time perception (Pande & Pati, 2010). Individuals may overestimate the duration of incongruent information because incongruent information is more difficult to process than congruent information, or because incongruent information increases arousal relative to congruent information (Davis & Matthews, 1996; Finnegan, Oakhill, & Garnham, 2015). One experiment examined incongruence between the personality dimensions of instrumentality and expression and features of a task that varied in these same dimensions (Davis & Matthews, 1996). Instrumental individuals focus highly on achieving goals for a group whereas expressive individuals focus highly on the social dynamics of a group. The instrumental task involved convincing another participant of their own view during a discussion, whereas the expressive task involved understanding and empathising with another participant during a discussion. This experiment reported increased blood pressure for participants who completed tasks incongruent with their personality, such as highly instrumental individuals completing the expressive task. These results relate to time perception because individuals overestimate stimulus duration when they experience increased arousal (e.g., increased blood pressure; e.g., Carrasco, Redolat, & Simón, 1998; Gil & Droit-Volet, 2012; Mella et al., 2010). Thus, there is also some evidence to suggest that individuals overestimate stimulus durations when their personality dimensions are incongruent with task features. Similarly, this experiment might suggest that personality dimensions alter perceived time through incongruence between personality dimensions and dominant task features.

Incongruence may also explain the data showing that highly extraverted individuals overestimated duration relative to more introverted individuals (Rammsayer, 1997; Zakay et al., 1984). Participants high in extraversion only overestimated the durations of low and medium complexity stimuli (Zakay et al., 1984). Highly extraverted individuals, however, also possess a higher need for stimulation than more introverted individuals (e.g., Eysenck, 1967; Ludvigh & Happ, 1974). Low and medium complexity stimuli may not provide sufficient stimulation for highly extraverted individuals, and so may be considered incongruent with the extraversion personality dimension. Thus, people high in extraversion possibly overestimated low and medium complexity stimuli due to incongruence between personality dimension (i.e., extraversion) and task features (i.e., low and medium complexity). In this way, Hogan’s (1978) model of time perception may also illustrate just one example of incongruence 148

between personality dimensions and task features. This interpretation of these results might suggest that incongruence could generally explain the effects of personality dimensions on time perception.

The Present Research

This study examined how incongruence between time perspective dimension and recall of a past event influenced time perception. This study also examined how incongruence between valence of time perspective dimension and recall of a negative event influenced time perception. Online participants completed a time perspective questionnaire, recalled an angry or neutral event from the past, and completed a laboratory measure of time perception. I hypothesised that past-oriented individuals would underestimate duration following the recall task relative to individuals that were not past-oriented (i.e., a temporal congruence effect). I also hypothesised that negatively-oriented individuals would underestimate duration after recalling an angry event compared to individuals who were not negatively-oriented (i.e., a valence congruence effect).

Method

Participants and Design

Two hundred and sixty-six participants recruited from Amazon’s Mechanical Turk completed the study in exchange for USD$2.50. A power analysis using GPower, with a goal to obtain 0.8 power to detect a medium effect size (f = 0.25; Cohen, 1977), with an alpha level of 0.05, determined the sample should include a minimum of 180 participants (Faul et al., 2009). The data of 53 participants were excluded for at least one of the following: not following instructions in the writing task (n = 15), receiving a score of less than 70% in the bisection training phase (n = 29; see Experiment 1, pp. 59), having a Weber ratio of over 0.34 (indicating less than half the temporal sensitivity of the average adult; e.g., Droit-Volet et al., 2004; Wearden, 1991; n = 25) and/or the bisection experimental data failing a residual check (indicating poor model fit; McPherson, 2001; n = 17).

The final sample of 213 participants included 116 men and 97 women, with a mean age of 38.62 years (SD = 11.49). These individuals were primarily Caucasian (81.2%), with 7% of the sample Asian, 4.2% Hispanic, and 4.2% African American, 149

with the final 3.4% of the sample comprising of other ethnicities. The majority of the sample (97.7%) spoke English as a first language. The sample was primarily non- religious (41.7%), with another 42.7% of the sample Christian, and the remainder comprising of other religious affiliations. The most prevalent completed level of education was a bachelor’s degree (34.7%), followed by some college (26.3%), an associate degree (13.1%), graduating high school (12.2%), and a master’s degree (7.5%), with the remaining 6.2% of other education levels.

I conducted multiple Chi square tests and one-way between-subjects ANOVAs to determine if demographics significantly differed between both the emotion (anger vs. neutral) and congruence (high in past personality dimensions vs. low in past personality dimensions) conditions. The emotion conditions did not significantly differ based on gender, χ2(1) = 0.77, p = .38, ethnicity, χ2(5) = 3.74, p = .59, education, χ2(9) = 7.42, p = .59, English as a first language, χ2(1) = 5.27, p = .22, religion, χ2(5) = 3.40, p = .64, or age, F(1, 211) = 0.60, p = .44, ηp²= .00. Similarly, the congruence conditions did not significantly differ based on gender, χ2(1) = 0.15, p = .70, ethnicity, χ2(5) = 9.29, p = .11, education, χ2(9) = 2.65, p = .98, English as a first language, χ2(1) = 0.78, p = .38, 2 religion, χ (5) = 5.15, p = .40, or age, F(1, 211) = 0.64, p = .42, ηp²= .00. For a full list of participant demographics, see Appendix D (pp. 290).

All participants provided written informed consent. The Human Research Ethics Advisory Panel at the University of New South Wales approved the study.

In a 2 (emotion: anger vs. neutral) × 2 (congruence: high in past/negative personality dimensions vs. low in past/negative personality dimensions) between- subjects design, I randomly allocated participants to 1 of 2 conditions manipulating emotion. Participants completed a time perspective personality measure and wrote about either an anger-inducing (n = 108) or a neutral event (n = 105) that occurred in the last 6 months. I averaged participant scores on the past subscales (i.e., past-positive and past- negative subscales) of the time perspective personality measure and coded these scores for incongruence (n = 98) or congruence (n = 115) with past recall. Within participants who wrote about anger-inducing events, I likewise averaged participant scores on the negative subscales (i.e., past-negative and present-fatalistic subscales) of the time perspective personality measure. I coded these scores for incongruence (n = 26) or congruence (n = 82) with negative valence. Participants then completed a standard cognitive measure of perceived time (Church & Deluty, 1977; Wearden, 1991). The 150

experiment was programmed using Inquisit 5.0 software (Millisecond, 2018), and hosted on the Millisecond and Qualtrics websites.

Materials and Procedure

Zimbardo Time Perspective Inventory. The Zimbardo Time Perspective Inventory is a 56-item questionnaire that provides a mean score for each of 5 subscales that differ in combined dimensions of direction (i.e., past, present, and future) and valence (i.e., positive and negative). Time perception literature refers to these subscales as past-positive, past-negative, present-hedonistic, present-fatalistic, and future subscales (Zimbardo & Boyd, 1999). Time perspective is a robust personality measure with good test-retest reliability and internal consistency (e.g., Carelli, Wiberg, & Wiberg, 2011; Zhang, Howell, & Bowerman, 2013; Zimbardo & Boyd, 1999). Participants rated how much they agreed or disagreed with statements on a bipolar 5- point Likert scale. Higher scores for each subscale indicate higher identification with that time perspective dimension.

The following are example items for each dimension:

• Past-Positive (α = .86): “Familiar childhood sights, sounds, and smells often bring back a flood of wonderful memories”. • Past-Negative (α = .85): “The past has too many unpleasant memories that I prefer not to think about”. • Present-Hedonistic (α = .81): “I often follow my heart more than my head”. • Present-Fatalistic (α = .82): “My life path is controlled by forces I cannot influence”. • Future (α = .81): “Meeting tomorrow’s deadlines and doing other necessary work comes before tonight’s play”.

For a full list of items in the Zimbardo Time Perspective Inventory, see Appendix D (pp. 290).

Writing task. Randomly assigned participants wrote about an event that involved either a neutral interaction with a stranger, or extreme anger (e.g., an argument with a close friend) within the last 6 months. The instructions encouraged participants to include details such as what happened, who they were with, how they felt, where it 151

occurred and when it happened. Previous research suggests that recalling anger- inducing autobiographical events effectively induces anger (for a review, see Chapter 2).

Time perception. Participants completed the bisection task as the primary measure of perceived time (for a full description of the bisection task, see Experiment 1, pp. 60). Due to limitations in Experiment 1 (pp. 80), I increased the number of trials in the bisection task in this experiment to 70. In this task, participants viewed neutral images sourced from the International Affective Picture System (Lang et al., 1997). I obtained valence and arousal scores from overall adult ratings in the International Affective Picture System Technical Manual (Lang et al., 2008). These images had neutral content, such as pictures of pastries and caves. The images had arousal scores between 2.42 and 6.03 (M = 4.17, SD = 2.1), and valence scores between 4.5 and 5.98 (M = 5.34, SD = 1.58). The arousal and valence scales for these items range between scores of 1 to 9. Arousal scores range from low (1) to high (9), whereas valence scores range from negative (1) to positive (9). As such, the scores for images in this experiment indicate low arousal and neutral valence. In comparison, for example, the mean arousal for emotional images in Experiment 1 (pp. 62) was 6.78, with a mean valence of 2.03. For a list of all images used see Appendix D (pp. 290). Participants completed the bisection task directly after completing the writing task.

Creation of past subscale. To assess congruence with recall direction, I created an overall past subscale. The past subscale combined scores from the past-positive and past-negative subscales of the Zimbardo Time Perspective Inventory. A reliability analysis of the items revealed 4 items (i.e., items 3, 5, 6 and 8 from the past-positive subscale) whose removal would increase scale reliability. These items were removed, increasing the reliability of the past subscale from .67 to .77 (for individual item statistics, deleted items, the reliability analyses, and analyses without excluded items, see Appendix D, pp. 290). I also created a negative subscale based on combined scores from the past-negative and present-fatalistic subscales (α = .87). However, I did not use this scale due to the ineffective anger manipulation.

Results

Calculating time perception variables. As in Experiment 1, I calculated the proportion of trials in which participants responded ‘long’ for each stimulus duration. I 152

calculated two further variables to confirm these results: a bisection point (Church & Deluty, 1977; Wearden, 1991) and the Weber ratio (Brown, 1960; Gibbon, 1977; Hobson, 1975). A bisection point is the time interval at which the probability of answering short and long is equal. A lower bisection point indicates both stimuli overestimation and slower perceived time; a higher bisection point indicates both stimuli underestimation and faster perceived time (Droit-Volet et al., 2004). The Weber ratio is a measure of temporal sensitivity, or ability to discriminate between millisecond durations, where a higher score indicates less sensitivity (Kopec & Brody, 2010). For a description of how to calculate bisection points and Weber ratios, see Experiment 1 (pp. 63).

Figure 5.1. Probability of answering ‘long’ for each stimulus duration. Bars represent standard error of the mean. The probability of answering ‘long’ increased as stimulus duration increased.

Preliminary Analyses

as Mauchly’s test of sphericity was violated, χ2(20) = 625.81, p < .0001. As shown in Figure 5.1, there was a significant main effect of duration, where participants were more likely to respond ‘long’ as stimulus durations increased, F(2.195, 617.98) = 1520.60, p < .0001. This result suggests that participants could discriminate between the different stimulus durations. All Tukey pairwise comparisons were significant (all ps < .0001). For statistical output regarding pairwise comparisons within duration, see Appendix D (pp. 290).

Anger manipulation check. I conducted a one-way between-subjects ANOVA on bisection point to determine the effects of the writing task on perceived time. There were no significant differences in perceived time between participants who wrote about angry and neutral past events, F(1, 211) = 0.57, p = .45, ηp² = .003. I also conducted a one-way between-subjects ANOVA on Weber ratio to determine the effects of the writing task on temporal sensitivity. There were no significant differences in temporal sensitivity between participants who wrote about angry and neutral past events, F(1,

211) = 0.49, p = .49, ηp² = .002.

Table 6.1

Past subscale and bisection point means for participants who were and were not past-oriented

Past Past Bisection Bisection N Subscale Subscale Point Mean Point Std. Mean Std. Error Error Past-Oriented 3.64 .03 923.80 15.60 115 Not Past- 2.79 .03 863.55 16.90 98 Oriented

Primary Analyses

Coding congruence. As the anger manipulation was unsuccessful, I combined all participant data to assess congruence with recall direction. According to the authors, a score of 3.26 is a moderately high score on any Zimbardo Time Perspective Inventory subscale (Zimbardo & Boyd, 1999). I determined that a moderately high score on the past-subscale could be coded as congruent with recall direction; scores less than this were coded as incongruent by default. I refer to participants who had a past-subscale 154

score congruent with the direction of recall as past-oriented in this paper. Similarly, I refer to participants who had a past-subscale score incongruent with the direction of recall as not past-oriented. The mean past-subscale scores of participants who were and were not past-oriented can be seen in Table 6.1.

Figure 5.2. Bisection points for participants who were and were not past-oriented. Bars represent standard error of the mean. Participants who were past oriented underestimated duration relative to participants who were not past-oriented. ** p < .01. ' g

n 1.0 o l '

g n i r e w s n

a 0.5

f Past-Oriented o

t y Not Past-Oriented i l i b a b o

r 0.0 P 400 600 800 1000 1200 1400 1600 Stimulus duration

155

Figure 5.3. Probability of answering ‘long’ for each stimulus duration for past-oriented and not past-oriented participants. Bars represent standard error of the mean. Participants who were past oriented were less likely to answer ‘long’ than participants who were not past-oriented as stimulus duration increased.

Congruence and perceived time. I conducted a one-way between-subjects ANOVA to determine if congruence between past time perspective and past recall direction influenced time perception, as measured by bisection point, compared to incongruence. As seen in Figure 5.2, past-oriented participants (M = 923.80, SE = 15.63) significantly underestimated temporal intervals compared to participants who were not past-oriented, M = 863.55, SE = 16.87, F(1, 211) = 6.86, 95% CI = [14.91,

105.59], p = .009, ηp² = .03. Figure 5.3 likewise represents this underestimation by a shift of the bisection point to the right (e.g., Droit-Volet et al., 2004). These results imply significantly faster perceived time for past-oriented individuals, relative to participants who were not past-oriented. However, this effect was very small. Table 6.1 illustrates the mean bisection point scores of participants who were and were not past- oriented. A one-way ANOVA on the Weber ratio indicated that participants who were past-oriented did not differ significantly in temporal sensitivity from participants who were not past-oriented, F(1, 211) = 0.54, p = .47, ηp² = .003.

I also analysed the effects of congruence between past recall direction, and the individual past-positive and past-negative subscales, on perceived time. I performed these analyses to determine if either the individual past-positive or past-negative subscale was driving the effects of congruence on time perception. Individuals high in the past-negative subscale underestimated temporal intervals compared to individuals low in the past-negative subscale, though the effect was only marginally significant,

F(1, 211) = 3.37, p = .07, ηp² = .02. This result possibly implies that there is faster perceived time for individuals high in the past-negative subscale, compared to individuals low in the past-negative subscale. There were no significant differences in time perception between individuals high in the post-positive subscale compared to individuals low in the past-positive subscale, F(1, 211) = 0.24, p = .62, ηp² = .001. As such, the effects of congruence on perceived time were only present for overall past- orientation.

Discussion 156

The current study uniquely contributes to the time perception literature by identifying that individual differences in how people think about time can alter the speed of perceived time. Prior work reported that certain personality dimensions such as extraversion can influence time perception (Rammsayer, 1997). However, this study was the first to demonstrate how individual differences in time perspective influence time perception. Moreover, this study suggested that incongruence may be the mechanism through which time perspective, and possibly personality dimensions in general, influences time perception. Specifically, individuals may overestimate duration due to incongruence between specific personality dimensions (e.g., past-oriented time perspective) and task features (e.g., past recall).

Relative to participants who were past-oriented, participants who were not past- oriented overestimated stimulus durations after recalling an event from the past. This finding supports my hypothesis that individuals would overestimate durations due to incongruence between the past time perspective dimension and the past autobiographical recall task. This finding may hence conceptually replicate previous research showing that individuals overestimate duration due to incongruence between extraversion and task features. For instance, in one study, participants high in extraversion overestimated the durations of low and medium complexity stimuli (Zakay et al., 1984). Low and medium complexity stimuli may not provide sufficient stimulation for highly extraverted individuals and may be incongruent with the extraversion dimension (e.g., Eysenck, 1967; Ludvigh & Happ, 1974). Thus, people high in extraversion possibly overestimate the durations of low and medium complexity stimuli due to incongruence between personality dimension (i.e., extraversion) and dominant task features (i.e., low and medium complexity). The current study hence adds to evidence suggesting that individuals can overestimate duration due to incongruence between personality dimensions and task features.

One way in which incongruence between personality dimensions and task features may lead to individuals overestimating duration is by increasing arousal, as indicated by changes in physiological activity. Incongruent stimuli can heighten arousal relative to congruent stimuli by increasing skin conductance and blood pressure (Davis & Matthews, 1996; Kobayashi, Yoshino, Takahashi, & Nomura, 2007). Some research may suggest that encountering incongruent information elicits cognitive conflict which subsequently catalyses an increase in physiological activity (Verguts & Notebaert, 157

2008; 2009). To illustrate how personality may increase arousal via incongruence, one study examined incongruence between task features and the personality dimensions of instrumentality and expressiveness (Davis & Matthews, 1996). Instrumental individuals focus on group goal achievement whereas expressive individuals focus on group social dynamics. In the instrumental task, participants convinced another participant of their own view during a discussion. In the expressive task, participants empathised with another participant during a discussion. In this experiment, participants who completed tasks incongruent with their personality experienced heightened blood pressure. Although this experiment did not examine time perception, people overestimate durations when they are in a heightened state of arousal (e.g., Mella et al., 2010). Heightened arousal could explain why individuals overestimate duration when there is incongruence between their personality dimensions and dominant task features.

An alternative explanation may be that information that is incongruent with certain personality dimensions is more difficult to process than congruent information. Information type is one example of a task feature. In one experiment, Koreans who viewed information incongruent with their cultural identity (i.e., a Korean website with American advertisements) reported exerting more effort to process the information than Koreans who viewed congruent information (i.e., a Korean website with Korean advertisements; Ko, Seo, & Jung, 2015). In another experiment, participants exposed to information incongruent with stereotypes experienced greater processing difficulty relative to participants exposed to stereotype-congruent information (Sherman, Lee, Bessenoff, & Frost, 1998). Because incongruent information takes more effort and time to process than congruent information, incongruent information is more densely and widely interconnected in memory (Anderson & Bower, 1979; Sherman & Frost, 2000). Thus, incongruent information is also easier to recall than congruent information (e.g., Hastie & Kumar, 1979; Rojahn & Pettigrew, 1992; Sherman & Frost, 2000). People overestimate the duration of stimuli that are easier to recall (e.g., Zakay & Feldman, 1993). As such, people may overestimate the duration of incongruent information compared to congruent information because incongruent information is more difficult to process and also easier to recall. There was some evidence for this notion in this experiment. That is, participants who were not past-oriented (M = 156.37, SE = 6.41) wrote marginally more words in the writing task than participants who were past- oriented, M = 140.44, SE = 6.08, F(1, 211) = 3.24, p = .07, ηp² = .02. This result may 158

imply that individuals who were not past-oriented could recall more information than individuals who were past-oriented during the recall task.

An alternative explanation for these results may be that incongruent information is more complex than congruent information and, as such, requires more abstract thinking. Incongruent information is more complex than congruent information because it involves a higher degree of integrating unrelated concepts (Erisen, Redlawsk, & Erisen, 2018). Furthermore, complex information involves more abstract concepts, whereas simple information involves more concrete concepts (Claunch, 1964). Abstract thinking also helps individuals integrate information into a larger perspective, such as when integrating incongruent information (Henderson & Trope, 2009; Trope & Liberman, 2003; 2010). Individuals overestimate stimulus duration when thinking abstractly relative to thinking concretely (Hansen & Trope, 2013). Thus, increased abstract thinking when integrating incongruent concepts may explain the effects of incongruence between time perspective and dominant task features on time perception.

Implications

In this experiment, people overestimated stimulus durations when their dispositional tendencies toward time perspectives were incongruent with dominant features of the task they completed. These findings suggest that individuals high in certain time perspective dimensions may be predisposed to overestimating or underestimating the time when completing incongruent tasks. Individuals high in the past-negative time perspective dimension, for example, are more likely to experience depression (Anagnostopoulos & Griva, 2012; Oyanadel & Buela-Casal, 2014; Zimbardo & Boyd, 2008). One characteristic of depression is slow time perception (Thönes & Oberfeld, 2015). As such, past-negative individuals may feel more depressed and experience slower time perception when anticipating the future. Similarly, playing videogames involves anticipating the future and also speeds up time perception (Atkins, 2006; Tobin & Grondin, 2009). This evidence suggests that individuals high in future time perspective may be more susceptible to the effects of pathological gaming. These examples are just a few ways in which specific time perspective dimensions can identify risk factors for feeling the effects of misperceived time.

Limitations and Future Research 159

Time perception did not differ in this experiment between participants who wrote about prior angry and neutral experiences. To the best of my knowledge, this study was the first to attempt to examine the effects of anger on time perception. These findings thus possibly indicate that anger does not influence time perception. However, as I did not measure self-reported anger in this study, I cannot confirm if participants who wrote about angry events experienced anger. It is possible the anger manipulation failed. Previous reviews suggest that autobiographical recall is an effective anger induction method (e.g., Siedlecka & Denson, 2019).

An alternative possibility is that anger itself is congruent with the past and, as such, it is difficult to alter time perception by recalling anger. In one experiment, inducing anger elicited a past temporal focus (Kelber, Lickel, & Denson, 2019). Similarly, inducing a past temporal focus increased anger in the experiment. These results suggest that anger is congruent with the past. Future research examining the effects of anger on time perception could use present (e.g., viewing angry faces in the bisection task) or future focused paradigms (e.g., imagining a future anger-eliciting event; Gil & Droit-Volet, 2011b; Keltner, Ellsworth, & Edwards, 1993).

Participants who were not past-oriented in the present study overestimated the duration of images after recalling an event from the past, relative to participants who were past-oriented. Thus, the current study only provided one example of how individuals overestimate duration due to incongruence between specific time perspective dimensions and specific task features. Future research may need to replicate and extend this finding to other temporal directions (i.e., present and future). Future research also may need to investigate if individuals likewise overestimate duration due to incongruence between the valence of time perspective (i.e., positive or negative) and relevant task features.

In this experiment, I operationalised congruence as a dichotomous variable. This approach adopts the methodology in previous research examining the effects of congruence (e.g., Sherman et al., 1998). However, this approach was limited in that participant numbers were uneven between conditions, and some conditions contained low numbers of participants. For instance, there were only 26 individuals with personality dimensions incongruent with the created negative time perspective. To address these statistical limitations, future researchers could consider analysing congruence as a continuous variable. 160

Conclusions

This study was the first to report that individuals overestimate the duration of images when certain time perspectives are incongruent with temporal task features. This finding may help identify altered time perception in individuals who differ in their temporal orientation when completing tasks that are congruent or incongruent with their disposition. Future research could focus on replicating and extending these results for time perspective dimensions characterised by other temporal directions (e.g., future) and also by emotional valence (e.g., positive).

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CHAPTER 7: General Discussion

Overview

A central aim of this thesis was to examine how emotions influence time perception and, by doing so, identify ways of updating existing models of time perception. In this thesis, I provided evidence that several psychological factors influence time perception. These factors were attention, arousal, autonomic activity, emotional valence, and personality. Individuals overestimated duration when they experienced heightened arousal, attentional resources were more narrowly distributed, personality dimensions were incongruent with task features, and individuals experienced negative stimuli. In contrast, individuals underestimated duration when they experienced low levels of arousal, attentional resources were more broadly distributed, personality dimensions were congruent with task features, and individuals experienced positive stimuli. These factors are either components of emotion (e.g., arousal) or relate to emotional phenomena (e.g., attention).

The second chapter in this thesis reviewed the efficacy of different emotion induction techniques. In that chapter, I reviewed how effectively six different emotion- induction paradigms induced six basic emotions (i.e., happiness, surprise, sadness, anger, fear, and disgust). The review identified which paradigms effectively heighten the emotional and physiological components of arousal. The review was also consistent with the notion that basic emotions do not elicit unique physiological profiles (Cacioppo et al., 2000). This review thus informed the designs of Experiments 1 and 2, which examined how the physiological components of emotion (i.e., parasympathetic and sympathetic activity) change and covary with time perception.

Experiments 1 and 2 examined the effects of arousal, as measured by changes in both physiological activity and emotion intensity, and emotional valence on time perception. Viewing emotional images (e.g., mutilated body) did not influence time perception relative to viewing neutral images (e.g., a gecko) in Experiment 1. However, in Experiment 2, participants overestimated stimulus duration after a surprise induction relative to before the induction. Notably, the effect of surprise on time perception was very small. As reviewed in Chapter 2, inducing emotions and viewing emotional images heightens arousal. As such, the result from Experiment 1 failed to replicate previous research (e.g., Angrilli et al., 1997; Droit-Volet et al., 2016; Effron et al., 2006; 162

Grommet et al., 2011). These results suggest that individuals overestimate stimulus duration during heightened arousal, as indicated by heightened emotional intensity, although this evidence was mixed.

In Experiment 1, I also examined how changes in arousal, as indicated by sympathetic and parasympathetic activity, covary with changes in time perception. In this experiment, participants overestimated image duration in experimental blocks with heightened parasympathetic activity. Although the arousal manipulation successfully reduced parasympathetic activity in Experiment 1, the arousal manipulation did not increase sympathetic activity. As such, I could not make any conclusions about sympathetic activity in this experiment. In Experiment 2, I attempted to extend the results of Experiment 1 by directly manipulating parasympathetic and sympathetic activity. Despite one successful autonomic manipulation, arousal did not change time perception in Experiment 2. Although there was some evidence that individuals overestimate stimulus duration during heightened parasympathetic activity in this thesis, this evidence was both weak and mixed.

In Experiment 2, participants also received a positive or negative surprise to determine how valence influences time perception. Participants who received a positive surprise overestimated stimulus durations relative to participants who received a negative surprise. However, this experiment lacked a neutral control group for positive and negative surprise. As such, I could not conclude if positive or negative surprise altered time perception in this experiment.

Experiment 3 examined how attending to temporal and non-temporal cues whilst playing a videogame changes time perception relative to immersion in the videogame. In this experiment, participants played a videogame normally or while regularly reporting either the amount of time they had been playing (i.e., a temporal cue) or the amount of a particular resource they had collected so far (i.e., a non-temporal cue). Previous research shows that individuals underestimated stimulus durations when completing concurrent tasks (Gautier & Droit-Volet, 2002). However, only participants attending to temporal cues underestimated stimulus durations relative to immersed participants in Experiment 3. I concluded that individuals only underestimate duration while completing concurrent tasks when the concurrent task provides relevant temporal information. 163

Experiment 4 examined the effects of time perspective, a personality dimension assessing how individuals view time in their everyday lives, on time perception. In this experiment, individuals overestimated stimulus durations when their time perspective dimension was incongruent with the features of the task they completed. That is, individuals who were low in past-orientation overestimated stimulus durations relative to individuals who were high in past-orientation following a past-oriented memory recall task. However, time perception was similar for participants who recalled an angry event relative to those who recalled a neutral event. As such, this thesis uniquely identified that time perspective personality dimensions influence time perception. However, this experiment did not provide evidence that arousal influences time perception.

The Role of Arousal in Time Perception

This thesis provided mixed evidence for the effects of arousal, as measured by both emotional intensity and physiological activity, on time perception. In Experiment 1, participants overestimated stimulus durations in experimental blocks with heightened parasympathetic activity, relative to blocks with lower parasympathetic activity. Increased R-R intervals and RMSSD indicated higher parasympathetic activity. In Experiment 2, participants overestimated stimulus durations after an emotion induction manipulating surprise, relative to before the emotion induction. These results replicate previous research in which individuals overestimated stimulus durations during heightened arousal relative to lower arousal (e.g., Droit-Volet & Gil, 2009; Gil & Droit- Volet, 2012).

In Experiment 1, however, viewing emotional images did not change time perception relative to viewing neutral images. Similarly, in Experiment 4, time perception was similar between participants who recalled angry memories (e.g., an argument with a close friend) and those who recalled neutral memories (e.g., a neutral interaction with a stranger). Additionally, in Experiment 2, participants with heightened parasympathetic activity experienced similar time perception to participants with heightened sympathetic activity. Increased R-R intervals and pRR50 indicated higher parasympathetic activity, while increased galvanic skin response indicated heightened sympathetic activity. Negative stimuli are more arousing than neutral stimuli and inducing negative emotions and autonomic activity heightens arousal (Lang et al., 1997; Siedlecka & Denson, 2019). These unexpected null results were thus inconsistent with previous research in which 164

individuals overestimated stimulus durations during heightened arousal states (Droit- Volet & Meck, 2007; Gil & Droit-Volet, 2012).

The null results regarding arousal, as measured by emotional intensity, may stem from methodological limitations. In Experiment 1, participants also completed a task that typically heightens both pain and physiological activity (Demaree & Harrison, 1997; Mitchell et al., 2004). It is possible that physiological activity overrode the relatively more subtle influence of emotional intensity on time perception in Experiment 1. Physiological activity and emotional intensity are both components of arousal (Cappo & Holmes, 1984). In Experiment 4, it is possible that the anger manipulation was ineffective, as recalling angry memories reliably increases anger (e.g., Lench et al., 2011; Siedlecka & Denson, 2019). This evidence suggests that individuals possibly did not overestimate the durations of emotional stimuli in Experiments 1 and 4 for methodological reasons, although I did not include manipulation checks in these experiments to confirm this idea.

An alternative explanation for these mixed effects of arousal on time perception may be that individuals in some experiments distributed their attentional resources more broadly. Experiments 1 and 4 used aversive negative stimuli which participants were potentially motivated to avoid. In contrast, Experiment 2 manipulated surprise, which induces lower levels of arousal than other basic emotions (e.g., anger; Adolph & Alpers, 2010). When individuals avoid aversive stimuli, they may distribute their attentional resources more broadly as they are not fully attending to the aversive stimulus. Individuals overestimate stimulus durations during heightened arousal, whereas individuals underestimate duration when their attentional resources are more broadly distributed (Droit-Volet & Gil, 2009; Matthews & Meck, 2016). As such, broader distribution of attention possibly obscured the effects of heightened arousal on time perception in these experiments. Exploring how emotional stimuli influence attentional distribution could illustrate one way of updating extant time perception models.

Another potential explanation for these mixed results is that individuals with a greater awareness of the time are able to regulate their time perception. Experiments 1 and 4 utilized negative stimuli that typically induce moderate to high levels of arousal (Lang et al., 1997; 2008). In contrast, Experiment 2 manipulated surprise, which typically induces lower levels of arousal than other basic emotions (e.g., anger; Adolph & Alpers, 2010). Higher negative arousal may increase awareness of the time as 165

individuals may wish for time to pass quickly to improve their mood (Hornik, 1992). Greater awareness of time typically slows time perception (Conti, 2001). However, in one experiment, increasing awareness of the time suppressed the effects of heightened arousal on time perception (Droit-Volet, Lamotte, & Izaute, 2015). Moreover, increased self-regulation, such as when inhibiting an emotional response, can induce effects on time perception in the opposite direction to heightened arousal (Marshall & Wilsoncroft, 1989). Awareness of time thus possibly obscured the effects of heightened arousal on time perception in this thesis. Future researchers may wish to investigate how arousal influences time awareness.

In this thesis, there was evidence that arousal may influence time perception through changes in autonomic activity. However, autonomic activity only changed time perception when I did not manipulate degree and type of arousal. I did not manipulate the physiological component of arousal in Experiment 1, although I manipulated this component of arousal in Experiment 2. In Experiment 1, participants with heightened parasympathetic activity overestimated stimulus durations. In Experiment 2, participants with heightened parasympathetic activity experienced similar time perception to participants with heightened sympathetic activity. These mixed results may imply that individuals overestimated stimulus durations due to individual differences in arousal, but not induced arousal. There is some evidence to support this notion, as an increased ability to accurately perceive bodily signals correlated with overestimated duration in one study (Di Lernia et al., 2018). To confirm this hypothesis, future research could examine how individual differences in parasympathetic activity change time perception relative to induced parasympathetic activity.

Another potential explanation for these mixed results is that individuals use physiological cues to infer duration. One model of time perception hypothesized that individuals use the duration of limb movements to infer the duration of other events (Addyman et al., 2011). That is, physiological movements act as a reference memory for duration. As such, physiological movements and sensations may provide temporal information with which individuals can infer the time. Some research suggests that individuals may infer millisecond durations from internal bodily cues (e.g., blinking; Di Lernia et al., 2018; Grossman et al., 2019; Meissner & Wittmann, 2011). There was also some support for this hypothesis in this thesis, as solely individual differences in 166

autonomic activity altered time perception. Participants in this thesis thus possibly used their individual physiological sensations to estimate the time.

Overall, the results of this thesis supported the inclusion of both arousal and autonomic activity within time perception models (e.g., Lui et al., 2011). Although arousal is a component of some existing models of time perception, these models may be able to incorporate arousal more directly. In the pacemaker-accumulator model, for instance, heightened arousal slows time perception by increasing the rate of the pacemaker in the internal clock (Lui et al., 2011). However, there is little evidence for an internal structure in the human body that keeps track of real time, like an internal clock (e.g., Ivry & Schlerf, 2008). Because arousal influences time perception directly, components that explain the role of arousal indirectly (e.g., an internal clock) may be unnecessary. Additionally, the results of this thesis supported distinguishing between different subcategories of arousal (i.e., emotional vs. physiological vs. autonomic) when examining effects on time perception. These approaches may outline constructive ways to update and expand existing models of time perception.

This thesis also provided evidence that arousal changes the ability to discriminate between millisecond-length durations (i.e., temporal sensitivity). In Experiment 1, participants experienced improved temporal sensitivity in experimental blocks with heightened parasympathetic activity. Increased R-R intervals and RMSSD indicated higher parasympathetic activity, and lower Weber ratios indicate improved temporal sensitivity. In Experiment 2, heightened parasympathetic activity likewise positively correlated with improved temporal sensitivity. In Experiment 2, increased R- R intervals and pRR50 indicated higher parasympathetic activity. These results replicated previous research in which heightened parasympathetic activity improved the ability to discriminate between temporal durations (Cellini et al., 2015; Meissner & Wittmann, 2011; Pollatos et al., 2014). Furthermore, the results of Experiments 1 and 2 suggest that this relationship may be bidirectional, such that lower parasympathetic activtiy also impedes the ability to discriminate between temporal durations. These results may additionally help explain how people perceive time because, in Experiment 2, improved temporal sensitivity also positively correlated with overestimated stimulus durations. Thus, individuals may also overestimate arousing stimuli due to improved temporal sensitivity. 167

In this thesis, autonomic activity also only changed temporal sensitivity when I did not manipulate degree and type of arousal. Participants in Experiment 2 experienced similar temporal sensitivity, as indicated by Weber ratio, following the parasympathetic induction relative to the sympathetic induction. However, participants experienced improved temporal sensitivity when their individual levels of parasympathetic activity increased, regardless of which autonomic manipulation they completed. Increased R-R intervals indicated higher parasympathetic activity. These mixed results may imply that individual differences in arousal covary with changes in temporal sensitivity but induced arousal does not change temporal sensitivity. There is additional support for this hypothesis from Experiment 1, in which I also did not induce parasympathetic activity. In that experiment, participants experienced improved temporal sensitivity during experimental blocks with heightened parasympathetic activity, as evidenced by increased R-R intervals and RMSSD.

Additional support for this hypothesis derives from research about the individual ability to control and monitor physiological responses. Individuals experience better temporal sensitivity when they have a more accurate perception of their individual parasympathetic response (Meissner & Wittmann, 2011). Additionally, individuals with higher parasympathetic activity possess both improved temporal sensitivity and a greater ability to perceive and control their autonomic responses (Cellini et al., 2015; Fairclough & Goodwin, 2007; Friedman & Thayer, 1998; Schäflein et al., 2018). These results suggest that a heightened ability to perceive and control the parasympathetic response may increase temporal sensitivity. However, individuals may not be able to improve temporal sensitivity simply by completing tasks that heighten parasympathetic activity.

Temporal sensitivity is a component of some existing models of time perception. In Experiment 2, improved temporal sensitivity positively correlated with overestimated stimulus durations, as evidenced by lower Weber ratios and bisection points. Moreover, factors such as heightened parasympathetic activity can both improve temporal sensitivity (e.g., Cellini et al., 2011) and alter time perception. Hence, attention, arousal, autonomic activity, and valence may also change time perception through covarying temporal sensitivity. The results of this thesis supported the inclusion of temporal sensitivity within models of time perception. However, future research could examine 168

how temporal sensitivity changes time perception to further improve the theoretical understanding of how people perceive time.

The Role of Valence in Time Perception

Experiment 2 did not provide conclusive evidence that emotional valence influences time perception. In Experiment 2, participants who received a positive underestimated stimulus duration relative to participants who received a negative surprise. These results replicated previous research in which individuals underestimated positive stimuli relative to neutral and negative stimuli (e.g., Delay & Richardson, 1981; Droit-Volet et al., 2010; Goldstone et al., 1978). However, I did not include a neutral control condition or a manipulation check for positive and negative surprise. Moreover, valence did not interact with time in this experiment. Due to these limitations, this experiment was unable to draw meaningful conclusions regarding the effects of positive and negative surprise on valence.

Some research may be taken to suggest that changes in attention and arousal can explain how negative and positive valence affect time perception (e.g., Droit-Volet & Gil, 2009; Droit-Volet & Meck, 2007; Droit-Volet et al., 2011). However, there is a lack of controlled experimental research examining if valence influences time perception independent of arousal. Unfortunately, methodological limitations of Experiment 2 meant this experiment did not provide conclusive evidence that emotional valence influences time perception. Future research could address these limitations in order to better examine how valence influences time perception in the absence of arousal.

The Role of Attention in Time Perception

Experiment 3 provided evidence that attention influences time perception. In Experiment 3, participants underestimated stimulus duration when attending to a timer while playing a videogame relative to playing the videogame without a concurrent task. When people complete concurrent tasks, they both distribute their attentional resources more broadly and experience higher cognitive load. The results of this thesis replicate previous research in which individuals underestimated stimulus duration under a higher cognitive load and during broader attentional distribution (e.g., Gil et al., 2009; Matthews & Meck, 2016; McClain, 1983; Zakay, 1992; 1998). 169

Participants in Experiment 3 only underestimated stimulus duration when they attended to concurrent cues that provided direct timing information. Time perception for participants attending to concurrent non-temporal cues was statistically indistinguishable from time perception for participants playing the videogame without a concurrent task. Similarly, time perception for participants attending to a concurrent non-temporal cue was statistically indistinguishable from time perception for participants attending to a concurrent timer. This result may provide some supportive evidence for the internal clock component of one model of time perception: the pacemaker-accumulator model. In this model, attention only influences time perception when attentional resources are allocated toward or away from direct information about timing (Allman et al., 2014; Treisman, 1963).

Alternatively, this result may suggest that temporal cues merely provide better information about timing than non-temporal cues. This research could expand existing models of time perception by supporting the inclusion of attention to non-temporal cues. Future research could examine how the effects of attention on time perception differ based on what information individuals attend to.

Attention may also be an indirect mechanism for the effects of context on time perception. Although individuals underestimate duration during broader attentional distribution, context can determine how individuals allocate their attentional resources (Brown, 2008). Variable contexts, for example, impair time perception compared to fixed contexts because variable contexts impede the ability to attend to a target stimulus (Spencer, Karmarkar, & Ivry, 2009). A fixed context is one that is predictable, such as consistently presenting an interval for 100ms across an experimental block. A variable context is one that changes, such as presenting an interval for varying millisecond durations across an experimental block (Karmarkar & Buonomano, 2007). Similarly, the results of this thesis replicate previous research suggesting that time perception estimations vary based on what information individuals attend to (e.g., Zakay, 1992). Researchers may wish to consider whether changes in context are a more proximal factor for determining time perception than changes in attentional distribution.

Attention is a component in some models of time perception (e.g., Lui et al., 2011). The results of this thesis generally supported the inclusion of attention within time perception models. In some models of time perception, attention influences the transfer rate of temporal units between the pacemaker and the accumulator in the 170

internal clock (Lejeune, 1998). However, because attention influences time perception directly, there may not be a need to explain the effects of attention indirectly through an internal clock. Rather, researchers could consider incorporating the role of attention more directly within existing models of time perception.

The Role of Personality in Time Perception

Experiment 4 provided preliminary evidence that personality may influence time perception within the context of anger recall. Specifically, Experiment 4 demonstrated that one specific personality dimension, called time perspective, influences time perception. In Experiment 4, participants who were low in the past-orientation time perspective overestimated stimulus durations when recalling past events relative to participants who were high in past-orientation. Overestimated stimuli indicate slower time perception (Pande & Pati, 2010). For these reasons, I concluded that individuals overestimate duration when their time perspective is incongruent with the dominant features of a task (such as an individual who is not past-oriented recalling a past event). These results replicated previous research in which other personality dimensions correlated with overestimated duration when dominant task features were incongruent with these dimensions (Davis & Matthews, 1996; Zakay et al., 1984). Additionally, this thesis provided the first evidence that time perspective influences time perception.

To the best of my knowledge, only a single time perception model included the role of a relevant personality dimension. Moreover, Hogan’s (1978) model only provided one specific example of how a single personality dimension may inform time perception. Thus, although personality dimensions in general may play a role in time perception, existing models provide little theoretical explanation regarding how they may do so. The results of this thesis expanded Hogan’s (1978) model by suggesting that other personality dimensions, such as time perspective, also influence time perception. Future research could consider expanding models of time perception that include personality dimensions (e.g., Hogan, 1978) by incorporating the role of incongruence. Similarly, future research could consider incorporating the role of personality into models of time perception that do not currently consider the influence of personality (e.g., Treisman, 1963).

This thesis expanded existing knowledge about time perception by providing evidence for novel psychological factors that influence time perception. Moreover, this 171

thesis provided evidence that psychological factors from various independent models influence time perception. However, I did not directly examine if any of these factors mediated how emotion influences time perception. Examining if these factors mediate the effects of emotion on time perception could strengthen the evidence that these factors underlie how emotions influence time perception. Future research could focus on how factors like attention, personality and valence interact to change time perception, and consider building more parsimonious models of time perception based on such interactions.

Implications

Everyday behaviour. The results of this thesis suggested that factors like arousal, personality dimensions and attention can cause individuals to misperceive time. These psychological factors may be risk factors for behavioural consequences of misperceived time.

Driving is one complex behaviour that requires accurate time perception to be completed safely. In 2017, speeding was the primary factor in 40% of driving fatalities in Australia, and 26% in the United States of America (National Highway Traffic Safety Administration, 2019; Roads and Maritime Service, 2018). If individuals perceive time as faster than it is, they may underestimate their driving speed and be more likely to exceed the speed limit. Similarly, if individuals perceive time as slower than it is, they may overestimate the amount of time they have to complete complex driving manoeuvres (e.g., crossing a gap on a busy road). Some factors that influence time perception, such as alcohol and fatigue, contribute significantly to driving errors and accidents (e.g., Summala & Mikkola, 1994). Consistent with my hypothesis, for example, alcohol both speeds up time perception and increases the likelihood of speeding while driving (Bogstrand, Larsson, Holtan, Staff, Vindenes, & Gjerde, 2015; Tinklenberg et al., 1976). Identifying which factors and stimuli alter time perception may identify unique risk factors for driving errors and accidents.

Emotions aid and motivate cognitive and behavioural decisions, such as those about timing (Naqvi, Shiv, & Beehara, 2006; Zeelenberg, Nelissen, Breugelmans, & Pieters, 2008). However, specific emotions motivate decisions based on the emotion’s underlying function (Zeelenberg et al., 2008). As such, individuals may overestimate or underestimate duration when experiencing certain emotions based on their function. 172

Fear aids individuals in overcoming negative obstacles. Scared individuals may overestimate durations because slower time perception is more effective for accurately monitoring an aversive obstacle (e.g., Noyes & Kletti, 1972). The results of Experiment 2 supported this hypothesis as individuals overestimated negative stimuli relative to positive stimuli. Overestimated duration also positively correlated with improved temporal sensitivity following the emotion induction. Similarly, one function of happiness may be to increase the likelihood of goal acquisition. Thus, happiness may quicken time perception to decrease the perceived amount of time needed to accomplish a goal (Gable & Poole, 2012). However, happiness may not require more accurate time perception because there may be little need to effectively monitor pleasant stimuli. Future research could examine if the evolutionary functions of emotions shed light on how emotions influence time perception.

Clinical implications. There are several clinical disorders that are characterised by deficits in processing and perceiving time. These disorders include schizophrenia, Parkinson’s disease, mood disorders (e.g., depression), attention-deficit hyperactive disorder, and autism (Allman & Meck, 2011; Droit-Volet, 2013; Tysk, 1984). Many of these disorders are also characterised by poor emotion regulation, such as autism, depression, and attention-deficit hyperactive disorder (Barkley, 2011; Joormann & Gotlib, 2010; Mazefsky et al., 2013). Individuals with poor emotion regulation experience difficulty controlling their emotional response during events that elicit strong emotions (e.g., Ochsner & Gross, 2005). Thus, these individuals may be more susceptible to experiencing misperceived time when they experience heightened emotions relative to individuals with better emotion regulation. Examining the pathways by which emotion regulation influences time perception may identify unique strategies for correcting misperceived time in certain clinical disorders. For example, slower time perception in certain disorders (e.g., depression) might be corrected by stimuli and phenomena that speed up time perception (e.g., positive stimuli, broader attentional distribution).

Similarly, deficits in time perception may also aid in maintaining the symptomology of certain disorders. More experienced videogame players, for example, experience faster time perception during game play relative to more amateur players (Rau et al., 2006). Additionally, individuals experience greater time loss when time perception is faster. Loss of time is a symptom of pathological videogame use, and one 173

unwanted consequence of videogame use in general (Griffiths, Kuss, & King, 2012; Wood et al., 2007). Faster time perception may aid in maintaining time loss in pathological gaming because pathological gamers are more likely to experience faster time perception. Similarly, depressed individuals experience slower time perception relative to less depressed individuals. Thus, depressed individuals may be more likely to feel as if their symptoms will never end, which may maintain their depression symptoms (Thönes & Oberfeld, 2015). Identifying which factors influence time perception may identify novel strategies for reducing symptoms in clinical disorders characterised by deficits in time perception.

An alternative strategy for reducing the symptoms of clinical disorders characterised by deficits in time perception may be to change the way individuals think about time. For example, developing more effective time management skills can reduce the symptoms of pathological videogame use (Tolchinsky & Jefferson, 2011). Moreover, effective time management skills predict more accurate time perception in general (Francis-Smythe & Robertson, 1999). In other experiments, teaching war veterans to think about time in a more balanced way (e.g., by thinking more about the future, and less about past negative experiences) reduced self-reported depression and symptoms of post-traumatic stress disorder (Sword, Sword, & Brunskill, 2015; Sword, Sword, Brunskill, & Zimbardo, 2014). Post-traumatic stress disorder is characterised by deficits in time perception, such that individuals can feel as if they are reliving past events in the present moment (Speckens, Ehlers, Hackmann, & Clark, 2006). Furthermore, a more balanced perspective about time decreased the likelihood of developing post-traumatic stress disorder after a vehicle accident in another experiment (Stolarski & Cyniak-Cieciura, 2016). Attempting to change the speed of time perception in a specific direction may not be the only way to reduce unwanted consequences of misperceived time. Individuals may also be able to reduce these consequences by fostering a more balanced awareness of time in general. Future research could examine how thinking about time changes time perception.

Limitations and Future Research

Effects of emotion on time perception. In some experiments in this thesis, I was unable to successfully use emotional stimuli such as images to alter time perception. For instance, in Experiment 2, participants perceived the duration of negative arousing images similarly to neutral images. One possible explanation is that 174

the effects of emotion on time perception are not as robust as they appear in the literature. The size of the effect of emotion on time perception is typically only small to medium (d = 0.3 - 0.45 or ηp² = .03 - .04; e.g., Droit-Volet et al., 2004; Tipples, 2008). In this thesis, the magnitude of the effect for emotion on time perception reflected effect sizes in the time perception literature. For instance, the effect size for the influence of surprise on time perception in Experiment 2 was ηp² = .04. However, these effect sizes are not large, raising the possibility that any influence of emotions on time perception may only be observed under specific controlled circumstances. These limitations may in turn suggest that any effect of emotion on time perception does not have serious implications for everyday behaviours.

Moreover, within time perception research, emotion is primarily induced using emotional facial expressions (for a review, see Droit-Volet & Meck, 2007). In this thesis, I tried to use the most effective method of emotion induction available for each emotion. I did not use facial expressions, as in previous time perception research, to induce emotion as facial expressions are not the most effective way of inducing emotions (Siedlecka & Denson, 2019). However, I also did not observe changes in time perception following some emotion manipulations. One possibility for these null findings is that facial expressions do not influence time perception through increased subjective emotional experience. Individuals may alternatively experience changes in time perception in response to another individual’s emotional expression. For instance, a person viewing a scared facial expression may experience altered time perception due to broader attentional distribution while searching for a threat. However, this person may not be experiencing altered time perception due to increased fear. Overall, there is a need to replicate the effects of emotion on time perception using stimuli other than facial expressions.

Sample selection. I recruited participants from university samples of first year psychology students, community samples surrounding the university, and an online sample of American research participants. Moreover, I randomly allocated participants to experimental conditions. As such, any group differences in the participant samples in this thesis were likely randomly distributed across recruitment sources and experimental conditions. However, I did not match the groups based on gender, education levels, or age. In previous experiments, the accuracy of time perception judgments and perceived speed of time differed based on age and gender, and possibly on education level (Block, 175

Hancock, & Zakay, 2000; Block, Zakay, & Hancock, 1998; Siu, Lam, Le, & Przepiorka, 2014). Similarly, more educated individuals are more oriented towards thinking about future time, and future-oriented individuals generally perceive time as passing more quickly (Trommsdorff, 1986; Wittmann et al., 2015). Hence, more educated individuals may generally perceive time as faster. Future research could endeavour to replicate the experiments in this thesis using samples matched for age, gender, and education level.

Time perception measurement. In this thesis, I used the bisection task to measure time perception in every experiment. This decision was deliberate, as I wanted to compare time perception data across experiments. However, participants in at least one other experiment demonstrated different changes in time perception depending on which task was used to measure time perception (Gil & Droit-Volet, 2011b). The results of this thesis may hence only apply to time perception as measured by the bisection task. Future research could examine why time perception changes based on how time perception is measured.

Additionally, the bisection task is a retrospective time perception paradigm, meaning participants judged time intervals following their presentation and while unaware of the temporal nature of the experiment (Brown, 1985). By contrast, prospective timing involves judging time intervals during their presentation and while completely aware of the temporal nature of the experiment (Brown, 1985). Retrospective timing and prospective timing may involve different processes (Zakay & Block, 1995). The results from this thesis may thus not generalise to conclusions about prospective time perception.

I did not enquire if participants used counting strategies in any of the experiments. The amount of temporal information an individual possesses influences time perception (e.g., Block et al., 1980). As such, using counting strategies could increase the amount of available temporal information. However, I confiscated any time keeping devices at the start of the experiment (excluding Experiment 4, which was administered online) to prevent the use of counting strategies. Additionally, some research suggests that participants are unlikely to use chronometric counting strategies for stimulus durations in the millisecond range (Brown, McCormack, Smith, & Stewart, 2005). As such, I further minimized the use of counting strategies by presenting stimuli for durations in the millisecond range in the bisection tasks. Moreover, one benefit of 176

retrospective paradigms, such as the bisection task used in this thesis, is that participants are unaware of the temporal nature of the experiment (Brown, 1985). My results supported this idea, as participants did not correctly identify the aims of any experiment in this thesis. This finding indicates that participants likely did not use counting strategies in the experiments in this thesis. However, future research could confirm if counting strategies influence time perception in retrospective paradigms and control for strategy use in statistical analyses if necessary.

Manipulation checks. I did not include manipulation checks to ensure I induced the correct emotions in several experiments in this thesis. Thus, I could not determine if participants felt more negative emotions when viewing negative pictures in Experiment 1, felt greater surprise following the surprise manipulation in Experiment 2, or felt greater anger during the anger manipulation in Experiment 4. Future research could endeavour to include manipulation checks to ensure that both the correct emotions are induced, and the emotion manipulations are successful.

Similarly, I did not always include manipulation checks to ensure that participants attended to the stimuli and tasks in the experiments. In Experiment 1, for example, participants viewed both negative and neutral images in the bisection task. The negative images were highly arousing and confronting, such as images of burned and mutilated bodies (Lang et al., 1997). Due to the confronting nature of these images, participants may have been motivated to avoid looking at such stimuli. As such, participants possibly did not attend to the target stimuli in some experiments. Future research could include manipulation checks or eye tracking to ensure that participants attended to the stimuli and tasks in all experiments.

Furthermore, I did not pilot the novel manipulations in this thesis to ensure they induced the correct response. Although I ensured there was sufficient research to suggest these manipulations could induce the desired response, I did not confirm that these manipulations would be successful. For instance, I did not confirm that isometric exercises would increase sympathetic activity in Experiment 2. Similarly, I did not confirm that drinking an unexpected beverage would induce surprise in Experiment 2. Future research could pilot any novel manipulations to ensure that these manipulations increase the desired dependent variables in the intended direction. 177

Thesis approach. This thesis broadly examined the effects of several different factors on time perception rather than extensively investigating any one of these factors. However, this approach limited the results of this thesis because I did not investigate each factor over multiple experiments. Consequently, there remain some unanswered questions in this thesis about certain factors. For example, although I determined that individual differences in arousal may influence time perception differently to induced arousal, I did not investigate this hypothesis experimentally. Future research could investigate some of the factors implicated in time perception in this thesis in greater detail.

Replication. This thesis also provided evidence for several novel effects. Despite providing preliminary support for these novel effects, future research could garner further support for these psychological factors by replicating these effects.

Conclusions

The central aim of this thesis was to examine how psychological factors related to emotions influence time perception. This approach allowed me to evaluate the plausibility of explaining how emotion-relevant phenomena influence time perception using psychological factors. With further research, this thesis could constructively contribute to a parsimonious and updated model of time perception.

In this thesis, I replicated previous research suggesting that attention, arousal, autonomic activity, valence, and personality influence time perception. This thesis identified parasympathetic activity and time perspective as novel influences on time perception. However, the evidence for some of these variables (e.g., arousal) was mixed. The results of this thesis may suggest a greater need to replicate the effects of emotion on time perception using stimuli other than emotional facial expressions. The results of this thesis also may have implications for behavioural consequences of misperceived time (e.g., driving accidents) and clinical disorders characterised by misperceived time (e.g., depression). 178

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244

Appendix A

Participant Demographics

Table A1.1

Participant age statistics in Experiment 1after exclusions based on poor practise data

N Minimum Maximum Mean Standard

Deviation

Age 47 18.0 34.0 22.26 3.48

Valid N 47

Table A1.2

Participant gender statistics in Experiment 1 after exclusions based on poor practise data

Gender Frequency Percent Valid Cumulative

Percent Percent

Male 20 42.55 42.55 42.55

Female 27 57.45 57.45 100.0

Total 47 100.0 100.0

Table A1.3

Participant ethnicity statistics in Experiment 1 after exclusions based on poor practise data

Ethnicity Frequency Percent Valid Cumulative

Percent Percent

Caucasian 8 17.02 17.02 17.02

Asian 36 76.60 76.60 93.61 245

Middle Eastern 1 2.13 2.13 95.74

Hispanic 1 2.13 2.13 97.87

Other 1 2.13 2.13 100.0

Total 47 100.0 100.0

Table A1.4

Participants speaking English as a First Language (EFL) in Experiment 1 after exclusions based on poor practise data

EFL Frequency Percent Valid Cumulative

Percent Percent

Yes 20 42.55 42.55 42.55

No 27 57.45 57.45 100.0

Total 47 100.0 100.0

Table A1.5

Participant religion statistics in Experiment 1 after exclusions based on poor practise data

Religion Frequency Percent Valid Cumulative

Percent Percent

Atheist 15 31.92 31.92 31.92

Christian 13 27.66 27.66 59.58

Islamic 6 12.77 12.77 72.35

Hindu 5 10.64 10.64 82.98

Buddhist 6 12.77 12.77 95.75

Other 2 4.26 4.26 100.0 246

Total 47 100.0 100.0

Table A1.6

Participant education levels in Experiment 1 after exclusions based on poor practise data

Highest completed level Frequency Percent Valid Cumulative

of education Percent Percent

High school graduate 16 34.04 34.04 34.04

Some college 6 12.77 12.77 46.82

Bachelor's degree 14 29.79 29.79 76.59

Master's degree 9 19.15 19.15 95.74

Ph.D. 1 2.13 2.13 97.87

Other advanced degree 1 2.13 2.13 100.0

Total 47 100.0 100.0

Table A2.1

Participant age statistics in Experiment 1

N Minimum Maximum Mean Standard

Deviation

Age 41 18.0 34.0 22.39 3.62

Valid N 41

Table A2.2

Participant gender statistics in Experiment 1 after all exclusions 247

Gender Frequency Percent Valid Cumulative

Percent Percent

Male 17 41.5 41.5 41.5

Female 24 58.5 58.5 100.0

Total 41 100.0 100.0

Table A2.3

Participant ethnicity statistics in Experiment 1 after all exclusions

Ethnicity Frequency Percent Valid Cumulative

Percent Percent

Caucasian 8 19.5 19.5 19.5

Asian 30 73.2 73.2 92.7

Middle Eastern 1 2.4 2.4 95.1

Hispanic 1 2.4 2.4 97.6

Other 1 2.4 2.4 100.0

Total 41 100.0 100.0

Table A2.4

Participants speaking English as a First Language (EFL) in Experiment 1 after all exclusions

EFL Frequency Percent Valid Cumulative

Percent Percent

Yes 20 48.8 48.8 48.8

No 21 51.2 51.2 100.0

Total 41 100.0 100.0 248

Table A2.5

Participant religion statistics in Experiment 1 after all exclusions

Religion Frequency Percent Valid Cumulative

Percent Percent

Atheist 14 34.1 34.1 34.1

Christian 9 22.0 22.0 56.1

Islamic 6 14.6 14.6 70.7

Hindu 5 12.2 12.2 82.9

Buddhist 5 12.2 12.2 95.1

Other 2 4.9 4.9 100.0

Total 41 100.0 100.0

Table A2.6

Participant education levels in Experiment 1 after all exclusions

Highest completed level Frequency Percent Valid Cumulative

of education Percent Percent

High school graduate 14 34.1 34.1 34.1

Some college 4 9.8 9.8 43.9

Bachelor's degree 13 31.7 31.7 75.6

Master's degree 8 19.5 19.5 95.1

Ph.D. 1 2.4 2.4 97.6

Other advanced degree 1 2.4 2.4 100.0

Total 41 100.0 100.0

249

Materials for Chapter 3

Images in bisection task. The neutral images from the International Affective Picture System (Lang et al., 1997; 2008) used in the Experiment 2 bisection task were: 1616, 2220, 2385, 2487, 2514, 5532, 6150, 7170, 7182, 7202.

250

Additional statistics for Chapter 3

Table A3.1

Post-hoc Tukey tests for the effect of duration on probability of answering ‘long’ in the bisection task in Experiment 1

I J Mean Std. Sig 95% Confidence

(Duration) (Duration) Difference Error Interval for

(I-J) Difference

Lower Upper

bound bound

400 600 -.11* .01 .000 -.14 -.088

800 -.43* .02 .000 -.48 -.39

1000 -.72* .02 .000 -.76 -.68

1200 -.81* .02 .000 -.84 -.78

1400 -.88* .01 .000 -.90 -.85

1600 -.90* .01 .000 -.92 -.88

600 800 -.32* .02 .000 -.36 -.28

1000 -.61* .02 .000 -.65 -.57

1200 -.70* .02 .000 -.73 -.66

1400 -.76* .02 .000 -.80 -.73

1600 -.79* .02 .000 -.82 -.76

800 1000 -.29* .02 .000 -.33 -.25

1200 -.38* .02 .000 -.42 -.34

1400 -.44* .02 .000 -.48 -.40

1600 -.47* .02 .000 -.51 -.42

1000 1200 -.09* .02 .000 -.12 -.05 251

1400 -.16* .02 .000 -.19 -.12

1600 -.18* .02 .000 -.21 -.14

1200 1400 -.07* .01 .000 -.09 -.04

1600 -.09* .01 .000 -.12 -.07

1400 1600 -.02* .01 .040 -.05 -.001

Note: * = p < .05

252

Appendix B

Participant Demographics

Table B1.1

Recruitment source statistics for Experiment 2 after exclusions

Recruitment Frequency Percent Valid Cumulative

source Percent Percent

SONA-1* 62 53.9 53.9 53.9

SONA-P** 53 46.1 46.1 100.0

Total 115 100.0 100.0

* SONA-1 is a recruitment website run by the University of New South Wales. First year psychology students participated in this experiment for course credit. ** SONA-P is a recruitment website run by the University of New South Wales. Community members participated in the experiments for $10AUD.

Table B1.2

Allocation to arousal condition in Experiment 2 after exclusions

Condition Frequency Percent Valid Cumulative

Percent Percent

Sympathetic 59 51.3 51.3 51.3

Parasympathetic 56 48.7 48.7 100.0

Total 115 100.0 100.0

Table B1.3

Allocation to valence condition in Experiment 2 after exclusions

Condition Frequency Percent Valid Cumulative

Percent Percent

Positive surprise 52 45.2 45.2 45.2 253

Negative surprise 63 54.8 54.8 100.0

Total 115 100.0 100.0

Table B1.4

Participant age statistics in Experiment 2 after exclusions

N Minimum Maximum Mean Standard

Deviation

Age 115 18.0 54.0 22.00 6.13

Valid N 115

Table B1.5

Participant gender statistics in Experiment 2 after exclusions

Gender Frequency Percent Valid Cumulative

Percent Percent

Male 44 38.3 38.3 38.3

Female 70 60.9 60.9 99.1

Undisclosed 1 .9 .9 100.0

Total 115 100.0 100.0

Table B1.6

Participant ethnicity statistics in Experiment 2 after exclusions

Ethnicity Frequency Percent Valid Cumulative

Percent Percent

Caucasian 32 27.8 27.8 27.8

Asian 75 65.2 65.2 93.0 254

Middle Eastern 2 1.7 1.7 94.8

Other 6 5.2 5.2 100.0

Total 115 100.0 100.0

Table B1.7

Participants speaking English as a First Language (EFL) in Experiment 2

EFL Frequency Percent Valid Cumulative

Percent Percent

Yes 83 72.2 72.2 76.2

No 32 27.8 27.8 100.0

Total 115 100.0 100.0

Table B1.8

Participant religion statistics in Experiment 2 after exclusions

Religion Frequency Percent Valid Cumulative

Percent Percent

Atheist 54 47.0 47.0 47.0

Christian 28 24.3 24.3 71.3

Islamic 8 7.0 7.0 78.3

Hindu 13 11.3 11.3 89.6

Buddhist 7 6.1 6.1 95.7

Jewish 1 .9 .9 96.5

Other 2 1.7 1.7 98.3

Rather not 2 1.7 1.7 100.0 255

say

Total 115 100.0 100.0

Table B1.9

Participant education statistics in Experiment 2 after exclusions

Highest completed Frequency Percent Valid Cumulative

education Percent Percent

Some high school 1 .9 .9 .9

High school graduate 55 47.8 47.8 48.7

Some college 9 7.8 7.8 56.5

Associate degree 1 .9 .9 57.4

Bachelor's degree 38 33.0 33.0 90.4

Some graduate school 2 1.7 1.7 92.2

Master’s degree 6 5.2 5.2 97.4

M.B.A 1 .9 .9 98.3

Ph.D. 2 1.7 1.7 100.0

Total 115 100.0 100.0

256

Materials for Chapter 4

Images in bisection task. The neutral images from the International Affective Picture System (Lang et al., 1997; 2008) used in the Experiment 3 bisection task were: 1121, 1390, 1945, 1947, 2191, 2272, 2410, 2485, 2616, 5390, 5535, 5661, 5731, 5900, 7000, 7038, 7351, 7402, 7640, 8211.

257

Positive and Negative Affect Schedule.

Read each item and then mark the appropriate answer in the space provided. Please indicate to what extent you felt this way WHILE BLINDFOLDED.

|______|______|______|______|

Extremely Moderately Very Slightly /Not at all

1. Afraid (N1) 2. Jittery (N2) 3. Active (P1) 4. Scared (N3) 5. Alert (P2) 6. Nervous (N4) 7. Strong (P3) 8. Distressed (N5) 9. Attentive (P4) 10. Determined (P5) 11. Irritable (N6) 12. Enthusiastic (P6) 13. Hostile (N7) 14. Excited (P7) 15. Guilty (N8) 16. Inspired (P8) 17. Interested (P9) 18. Ashamed (N9) 19. Proud (P10) 20. Upset (N10)

List of positive items: 3 (Active), 5 (Alert), 7 (Strong), 9 (Attentive), 10 (Determined), 12 (Enthusiastic), 14 (Excited), 16 (Inspired), 17 (Interested), 19 (Proud)

List of negative items: 1 (Afraid), 2 (Jittery), 4 (Scared), 6 (Nervous), 8 (Distressed), 11(Irritable), 13 (Hostile), 15 (Guilty), 18 (Ashamed), 20 (Upset)

258

Additional statistics for Chapter 4

Table B2.1

Post-hoc Tukey tests for the effect of duration on probability of answering ‘long’ in the bisection tasks in Experiment 2

I (Duration) J (Duration) Mean Std. Sig 95% Confidence

Difference Error Interval for

(I-J) Difference

Lower Upper

bound bound

400 600 -.12* .01 .000 -.14 -.09

800 -.46* .02 .000 -.50 -.41

1000 -.72* .02 .000 -.76 -.68

1200 -.85* .02 .000 -.88 -.82

1400 -.92* .01 .000 -.94 -.90

1600 -.94* .01 .000 -.96 -.93

600 800 -.34* .02 .000 -.37 -.30

1000 -.60* .02 .000 -.63 -.56

1200 -.73* .02 .000 -.76 -.70

1400 -.80* .01 .000 -.82 -.77

1600 -.83* .01 .000 -.85 -.80

800 1000 -.26* .01 .000 -.29 -.24

1200 -.40* .02 .000 -.43 -.36

1400 -.46* .02 .000 -.50 -.43

1600 -.49* .02 .000 -.53 -.45

1000 1200 -.13* .01 .000 -.16 -.11 259

1400 -.20* .02 .000 -.23 -.17

1600 -.23* .02 .000 -.26 -.19

1200 1400 -.07* .01 .000 -.09 -.05

1600 -.09* .01 .000 -.12 -.07

1400 1600 -.03* .01 .000 -.04 -.02

Note: * = p < .0001

Table B2.2.0

Descriptive statistics for valence (positive vs. negative surprise), autonomic arousal (sympathetic vs. parasympathetic) and time (before surprise manipulation vs. after surprise manipulation) conditions and heart rate in Experiment 2

Autonomic Valence Time Mean Std. 95% Confidence Condition Condition Error Interval Lower Upper Bound Bound Sympathetic Positive 1 85.45 4.69 76.15 94.76 Surprise 2 91.00 4.81 81.47 100.54 Negative 1 85.28 4.13 77.09 93.46 Surprise 2 105.40 4.23 97.01 113.79 Parasympathetic Positive 1 88.18 4.60 79.06 97.29 Surprise 2 99.21 4.71 89.87 108.55 Negative 1 87.73 4.34 79.12 96.34 Surprise 2 97.51 4.45 88.68 106.34 Note: Time 1 refers to pre-surprise manipulation; Time 2 refers to post-surprise manipulation

Table B2.2.1

Summary table for 2 (valence: positive vs. negative surprise) x 2 (autonomic arousal: sympathetic vs. parasympathetic) x (2) (time: before surprise manipulation vs. after surprise manipulation) mixed ANOVA on heart rate in Experiment 2 260

Sum of Df Mean F Sig.

Squares square

Valence 487.68 1 487.68 0.57 .45

Valence * Time 593.44 1 593.44 2.62 .11

Valence * 896.00 1 896.00 1.05 .31

Autonomic arousal

Valence * Time * 837.33 1 837.33 3.70 .06

Autonomic arousal

Between-subjects 89097.57 104 856.71

error

Within-subjects 23527.04 104 226.22

error

Table B2.3

Sum of Df Mean F Sig.

Squares square

Valence 55.05 1 55.05 0.82 .37

Valence * Time 3.52 1 3.52 0.05 .82

Valence * 2.67 1 2.67 0.04 .84

Autonomic arousal 261

Valence * Time * 15.38 1 15.38 0.23 .63

Autonomic arousal

Between-subjects 7223.80 107 67.51

error

Within-subjects 7045.81 107 65.85

error

Table B2.4

Sum of Df Mean F Sig.

Squares square

Valence 2621.07 1 2621.07 0.14 .71

Valence * Time 578.69 1 578.69 0.56 .45

Valence * 10861.50 1 10861.50 0.58 .45

Autonomic arousal

Valence * Time * 48.83 1 48.83 0.06 .83

Autonomic arousal

Between-subjects 1993235.67 107 18628.37

error

Within-subjects 109756.21 107 1025.76

error

Table B2.5 262

Sum of Df Mean F Sig.

Squares square

Valence 4847.13 1 4847.13 0.91 .34

Valence * Time 8817.78 1 8817.78 2.84 .10

Valence * 342.13 1 342.13 0.06 .80

Autonomic arousal

Valence * Time * 5617.21 1 5617.21 1.81 .18

Autonomic arousal

Between-subjects 573247.19 107 5357.45

error

Within-subjects 332628.41 107 3108.68

error

Table B2.6

Sum of Df Mean F Sig.

Squares square

Valence 9495.89 1 9495.89 0.83 .36

Valence * Time 7792.13 1 7792.13 1.10 .30

Valence * 100.26 1 100.26 0.01 .93

Autonomic arousal 263

Valence * Time * 13455.34 1 13455.34 1.91 .17

Autonomic arousal

Between-subjects 1220643.20 107 11407.88

error

Within-subjects 755729.54 107 7062.89

error

Table B2.7

Sum of Df Mean F Sig.

Squares square

Valence 543.10 1 543.10 1.34 .25

Valence * Time 17.84 1 17.84 0.08 .78

Valence * 58.86 1 58.86 0.15 .70

Autonomic arousal

Valence * Time * 0.59 1 0.59 0.002 .96

Autonomic arousal

Between-subjects 43268.77 107 404.38

error

Within-subjects 25197.66 107 235.49

error

Table B2.8 264

Sum of Df Mean F Sig.

Squares square

Valence 534.73 1 534.73 1.62 .21

Valence * Time 0.53 1 0.53 0.003 .96

Valence * 36.07 1 36.07 0.11 .74

Autonomic arousal

Valence * Time * 27.61 1 27.61 0.15 .70

Autonomic arousal

Between-subjects 35393.02 107 330.78

error

Within-subjects 19754.67 107 184.62

error

Table B2.9

Sum of Df Mean F Sig.

Squares square

Valence 4.21 1 4.21 2.69 .10

Valence * Time 0.04 1 0.04 0.05 .83 265

Valence * 0.05 1 0.05 0.03 .87

Autonomic arousal

Valence * Time * 0.17 1 0.17 0.20 .66

Autonomic arousal

Between-subjects 167.41 107 1.57

error

Within-subjects 89.69 107 .84

error

Table B2.10

Sum of Df Mean F Sig.

Squares square

Valence 163.17 1 163.17 0.41 .52

Valence * Time 24.19 1 24.19 0.12 .73

Valence * 280.40 1 280.40 0.70 .40

Autonomic arousal

Valence * Time * 81.81 1 81.81 0.41 .52

Autonomic arousal

Between-subjects 42807.56 107 400.07

error

Within-subjects 21219.03 107 198.31

error 266

Table B2.11

Sum of Df Mean F Sig.

Squares square

Valence 401.35 1 401.35 0.95 .33

Valence * Time 138.46 1 138.46 1.56 .22

Valence * 4.82 1 4.82 0.01 .92

Autonomic arousal

Valence * Time * 151.71 1 151.71 1.70 .20

Autonomic arousal

Between-subjects 45463.58 107 424.89

error

Within-subjects 9525.02 107 89.02

error

267

Appendix C

Participant Demographics

Table C1.1

Recruitment source statistics for Experiment 3 after exclusions

Recruitment Frequency Percent Valid Cumulative

source Percent Percent

SONA-1* 159 94.1 94.1 94.1

SONA-P** 10 5.9 5.9 100.0

Total 169 100.0 100.0

Table C1.2

Allocation to attention condition in Experiment 3 after exclusions

Condition Frequency Percent Valid Cumulative

Percent Percent

Full immersion 56 33.1 33.1 33.1

Distraction- 57 33.7 33.7 66.9

Temporal

Distraction - 56 33.1 33.1 100.0

Non-temporal

Total 169 100.0 100.0

Table C1.3

Participant age statistics in Experiment 3 after exclusions 268

N Minimum Maximum Mean Standard

Deviation

Age 169 18 59 19.57 3.65

Valid N 169

Table C1.4

Participant gender statistics in Experiment 3 after exclusions

Gender Frequency Percent Valid Cumulative

Percent Percent

Male 92 54.4 54.4 54.4

Female 77 45.6 45.6 100.0

Total 169 100.0 100.0

Table C1.5

Participant ethnicity statistics in Experiment 3 after exclusions

Ethnicity Frequency Percent Valid Cumulative

Percent Percent

Caucasian 57 33.7 33.7 33.7

Asian 96 56.8 56.8 90.5

African- 2 1.2 1.2 91.7

American

Middle Eastern 2 1.2 1.2 92.9

Other 12 7.1 7.1 100.0

Total 169 100.0 100.0

269

Table C1.6

Participants speaking English as a First Language (EFL) in Experiment 3

EFL Frequency Percent Valid Cumulative

Percent Percent

Yes 142 84.0 84.0 84.0

No 27 16.0 16.0 100.0

Total 169 100.0 100.0

Table C1.7

Participant religion statistics in Experiment 3 after exclusions

Religion Frequency Percent Valid Cumulative

Percent Percent

Atheist 78 46.2 46.2 46.2

Christian 53 31.4 31.4 77.5

Islamic 8 4.7 4.7 82.2

Hindu 10 5.9 5.9 88.2

Buddhist 11 6.5 6.5 94.7

Jewish 2 1.2 1.2 95.9

Other 3 1.8 1.8 97.6

Undisclosed 4 2.4 2.4 100.0

Total 169 100.0 100.0

270

Table C1.8

Participant education levels in Experiment 3 after exclusions

Highest completed Frequency Percent Valid Cumulative

education Percent Percent

Some high school 1 .6 .6 .6

High school graduate 119 70.4 70.4 71.0

Some college 27 16.0 16.0 87.0

Bachelor's degree 18 10.7 10.7 97.6

Some graduate school 1 .6 .6 98.2

Master's degree 3 1.8 1.8 100.0

Total 169 100.0 100.0

271

Materials for Chapter 5

Images in bisection task. The neutral stills used in the Experiment 3 bisection task can be found in the folder ‘Images from Age of Empires II’ at https://osf.io/rq5k3/

Some example images from Experiment 3 are:

272

Flow State Scale.

Please answer the following questions in relation to your experience PLAYING THE VIDEOGAME. These questions relate to the thoughts and feelings you may have experienced while playing this game. Think about how you felt while you were playing, and answer the questions using the rating scale below.

|______|______|______|______|

Strongly Disagree Neither Agree Agree Strongly Disagree nor Disagree Agree

1. I was challenged, but I believed my skills would allow me to meet the challenge (CS1) 2. I made the correct movements without thinking about trying to do so (AAM1) 3. I knew clearly what I wanted to do (CG1) 4. It was really clear to me that I was doing well (UAF1) 5. My attention was focussed entirely on what I was doing (CON1) 6. I felt in total control of what I was doing (POC1) 7. I was not concerned with what others may have been thinking of me (LOS1) 8. Time seemed to alter (either slowed down or sped up) (TOT1) 9. I really enjoyed the experience (AUTO1) 10. My abilities matched the high challenge of the situation. (CS2) 11. Things just seemed to be happening automatically (AAM2) 12. I had a strong sense of what I wanted to do (CG2) 13. I was aware of how well I was performing (UAF2) 14. It was no effort to keep my mind on what was happening (CON2) 15. I felt like I could control what I was doing (POC2) 16. I was not worried about my performance during the game (LOS2) 17. The way time passed seemed to be different from normal (TOT2) 18. I loved the feeling of that performance and want to capture it again (AUTO2) 19. I felt I was competent enough to meet the high demands of the situation (CS3) 20. I performed automatically (AAM3) 21. I knew what I wanted to achieve (CG3) 22. I had a good idea while I was performing about how well I was doing (UAF3) 23. I had total concentration (CON3) 24. I had a feeling of total control (POC3) 25. I was not concerned with how I was presenting myself (LOS3) 26. It felt like time stopped while I was performing (TOT3) 27. The experience left me feeling great (AUTO3) 28. The challenge and my skills were at an equally high level (CS4) 29. I did things spontaneously and automatically without having to think (AAM4) 30. My goals were clearly defined (CG4) 273

31. I could tell by the way I was performing how well I was doing (UAF4) 32. I was completely focused on the task at hand (CON4) 33. I felt in total control of my body (POC4) 34. I was not worried about what others may have been thinking of me (LOS4) 35. At times, it almost seemed like things were happening in slow motion (TOT4) 36. I found the experience extremely rewarding (AUTO4)

Challenge-Skill Balance (CS) Items: 1, 10, 19, 28

Action-Awareness Merging (AAM) Items: 2, 11, 20, 29

Clear Goals (CG) Items: 3, 12, 21, 30

Unambiguous Immediate Feedback (UAF) Items: 4, 13, 22, 31

Concentration on the Task at Hand (CON) Items: 5, 14, 23, 32

Perceived Control (POC) Items: 6, 15, 24, 33

Loss of Self-consciousness (LOS) Items: 7, 16, 25, 34

Transformation of Time (TOT) Items: 8, 17, 26, 35

Autotelic Experience (AUTO) Items: 9, 18, 27, 36

Game Engagement Questionnaire.

Read each item and then mark the appropriate answer in the space provided. Please indicate to what extent you agreed with this statement WHILE PLAYING THE VIDEOGAME. Use the following scale to record your answers.

|______|______|______|______|______|______|

Very Slightly Moderately Extremely /Not at all

1. Things seemed to happen automatically (P1) 2. I really got into the game (I) 3. I felt scared (A1) 4. I felt different (A2) 5. The game felt real (F1) 6. I played longer than I meant to (P2) 7. Playing seemed automatic (F2) 274

8. I felt spaced out (A3) 9. I wouldn't answer if someone spoke to me (F3) 10. Playing made me feel calm (F4) 11. My thoughts went fast (P3) 12. I lost track of time (P4) 13. If someone spoke to me, I wouldn't hear them (F5) 14. I played without thinking how to play (F6) 15. I lost track of where I am (A4) 16. I got wound up (F7) 17. I couldn't tell if I was getting tired (F8) 18. Time seemed to kind of stand still or stop (A5) 19. I felt like I just couldn't stop playing (F9)

Flow (F) Items: 5, 7, 9, 10, 13, 14, 16, 17, 19

Immersion (I) Item: 2

Absorption (A) Items: 3, 4, 8, 15, 18

Presence (P) Items: 1, 6, 11, 12

275

Additional statistics for Chapter 5

Table C2.1

Descriptive statistics for subscales of the Flow State Scale (FSS) in Experiment 3

FSS Subscale Number Mean Standard Cronbach’s Variance

of items Deviation alpha for scale

Challenge-Skill 3 3.09 .17 .85 7.23

Balance*

Action-Awareness 3 3.15 .15 .82 7.81

Merging*

Clear Goals 4 3.33 .18 .85 14.32

Unambiguous 3 3.18 .05 .89 9.18

Feedback*

Concentration* 3 3.64 .10 .91 9.19

Sense of Control* 3 3.44 .26 .87 7.90

Loss of Self- 3 3.70 .06 .84 8.36

consciousness*

Transformation of 3 3.41 .45 .71 5.49

Time*

Autotelic Experience 4 3.42 .38 .88 11.54

Total** 34 3.34 .32 .93 383.82

*Deleted items from individual scales: Challenge-Skill Balance Item 1 (CS1), Action- Awareness Merging Item 2 (AAM2), Unambiguous Feedback Item 1 (UAF1), Concentration Item 2 (CON2), Sense of Control Item 4 (POC4), Loss of Self- consciousness Item 2 (LOS2), Transformation of Time Item 4 (TOT4); **Deleted items from total scale: Transformation of Time Item 2 (TOT2), Transformation of Time Item 4 (TOT4) 276

Table C2.2

Deleted items from the Flow State Scale in Experiment 3

Subscale Deleted Deleted Question Question Number Challenge-Skill Balance CS1 I was challenged, but I believed my skills would allow me to meet the challenge Action-Awareness Merging AAM2 Things just seemed to be happening automatically Unambiguous Feedback UAF1 It was really clear to me that I was doing well Concentration CON2 It was no effort to keep my mind on what was happening Sense of Control POC4 I felt in total control of my body Loss of Self-Consciousness LOS2 I was not worried about my performance during the game Transformation of Time TOT2 The way time passed seemed to be different from normal Transformation of Time TOT4 At times, it almost seemed like things were happening in slow motion

Table C2.3

Reliability analysis for challenge-skill balance subscale in the Flow State Scale with all items in Experiment 3

Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .833 .833 4

Table C2.4

Reliability analysis for individual items in the challenge-skill balance subscale in the Flow State Scale in Experiment 3

277

Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted CS1 9.28 7.23 .52 .31 .85 CS2 9.83 5.97 .76 .59 .74 CS3 9.67 6.52 .64 .47 .81 CS4 10.02 6.30 .75 .57 .75

Table C2.5

Reliability analysis for adjusted challenge-skill balance subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .849 .850 3

Table C2.6

Reliability analysis for action-awareness merging subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .808 .809 4

Table C2.7

Reliability analysis for individual items in the action-awareness merging subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted AAM1 9.67 7.79 .60 .42 .77 AAM2 9.44 7.81 .51 .28 .82 AAM3 9.41 6.99 .73 .55 .71 AAM4 9.44 6.90 .69 .48 .73 278

Table C2.8

Reliability analysis for adjusted action-awareness merging subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .817 .818 3

Table C2.9

Reliability analysis for clear goals subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .854 .856 4

Table C2.10

Reliability analysis for unambiguous feedback subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .848 .843 4

Table C2.11

Reliability analysis for individual items in unambiguous feedback subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted UAF1 9.54 9.18 .47 .22 .89 UAF2 8.84 6.92 .73 .57 .79 279

UAF3 8.85 6.88 .77 .64 .77 UAF4 8.93 6.72 .79 .66 .76

Table C2.12

Reliability analysis for adjusted unambiguous feedback subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .887 .888 3

Table C2.13

Reliability analysis for concentration at task at hand feedback subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .873 .876 4

Table C2.14

Reliability analysis for individual items in the concentration at task at hand subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted CON1 10.54 8.24 .73 .60 .84 CON2 10.91 9.19 .55 .34 .91 CON3 10.70 7.77 .85 .76 .79 CON4 10.50 8.60 .81 .74 .81

Table C2.15

Reliability analysis for adjusted concentration at task at hand feedback subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items 280

on Standardized Items .908 .911 3

Table C2.16

Reliability analysis for sense of control subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .808 .803 4

Table C2.17

Reliability analysis for individual items in the sense of control subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted POC1 10.84 5.15 .73 .59 .71 POC2 10.40 6.12 .74 .58 .71 POC3 10.87 5.50 .73 .59 .70 POC4 10.31 7.90 .34 .12 .87

Table C2.18

Reliability analysis for adjusted sense of control subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .872 .876 3

281

Table C2.19

Reliability analysis for loss of self-consciousness subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .774 .781 4

Table C2.20

Reliability analysis for individual items in the loss of self-consciousness subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted LOS1 10.30 7.19 .62 .43 .70 LOS2 11.09 8.36 .36 .14 .84 LOS3 10.41 6.98 .68 .54 .67 LOS4 10.33 7.27 .69 .59 .66

Table C2.21

Reliability analysis for adjusted loss of self-consciousness subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .835 .837 3

Table C2.22

Reliability analysis for transformation of time subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items 282

.689 .696 4

Table C2.23

Reliability analysis for individual items in the transformation of time subscale in the Flow State Scale in Experiment 3 Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted TOT1 9.24 5.41 .51 .41 .61 TOT2 9.43 5.09 .56 .43 .57 TOT3 10.10 4.85 .51 .26 .60 TOT4 10.23 5.49 .34 .16 .71

Table C2.24

Reliability analysis for adjusted transformation of time subscale in the Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .710 .719 3

Table C2.25

Reliability analysis for autotelic experience subscale in the Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .883 .883 4

Table C2.26

Reliability analysis for total Flow State Scale with all items in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on 283

Standardized Items .923 .923 36

Table C2.27

Reliability analysis for individual items in the overall Flow State Scale in Experiment 3

Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted AUTO1 116.05 376.25 .55 .63 .92 AUTO2 116.82 369.72 .65 .66 .92 AUTO3 116.75 371.72 .65 .72 .92 AUTO4 116.82 371.44 .61 .72 .92 CS1 116.37 381.85 .36 .50 .92 AAM1 117.04 369.87 .65 .62 .92 CG1 116.86 367.19 .60 .61 .92 UAF1 117.51 375.32 .58 .57 .92 CON1 116.36 375.93 .43 .69 .92 POC1 116.73 368.38 .61 .71 .92 LOS1 116.28 381.17 .32 .59 .92 TOT1 116.27 388.59 .20 .56 .92 CS2 116.93 370.50 .62 .70 .92 AAM2 116.82 379.92 .35 .47 .92 CG2 116.69 368.25 .64 .66 .92 UAF2 116.82 377.70 .40 .66 .92 CON2 116.72 370.32 .56 .62 .92 POC2 116.29 372.51 .66 .67 .92 LOS2 117.08 379.68 .33 .35 .92 TOT2* 116.46 390.55 .14 .55 .92 CS3 116.77 370.80 .62 .62 .92 AAM3 116.79 369.86 .62 .64 .92 CG3 116.46 372.33 .55 .60 .92 UAF3 116.82 374.19 .50 .71 .92 CON3 116.52 373.41 .50 .80 .92 POC3 116.76 365.73 .73 .75 .92 LOS3 116.40 378.93 .37 .62 .92 TOT3 117.13 385.32 .24 .44 .92 CS4 117.11 374.80 .55 .69 .92 AAM4 116.81 374.77 .47 .59 .92 CG4 116.81 369.68 .60 .64 .92 284

UAF4 116.91 373.13 .52 .74 .92 CON4 116.31 379.21 .42 .79 .92 POC4 116.20 387.50 .25 .33 .92 LOS4 116.31 381.73 .34 .70 .92 TOT4* 117.26 389.42 .14 .32 .93 *Deleted items from total scale: Transformation of Time Item 2 (TOT2), Transformation of Time Item 4 (TOT4)

Table C2.28

Deleted items from the total Flow State Scale in Experiment 3

Subscale Deleted Deleted Question Question Number Transformation of Time TOT2 The way time passed seemed to be different from normal Transformation of Time TOT4 At times, it almost seemed like things were happening in slow motion

Table C2.29

Reliability analysis total adjusted Flow State Scale in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .926 .927 34

Table C2.30

Statistics for subscales of the Game Engagement Questionnaire (GEQ) in Experiment 3

GEQ Subscale Number Mean Standard Cronbach’s Variance

of items Deviation alpha for scale

Flow 9 3.47 1.00 .78 80.80

Immersion 1 5.31 1.52 - - 285

Absorption 5 3.24 1.11 .70 30.65

Presence 4 4.32 1.06 .61 18.10

Total* 18 3.79 .70 .88 282.79

*A1 items deleted; i.e., “I felt scared”

Table C2.31

Reliability analysis for flow subscale in the Game Engagement Questionnaire in Experiment 3

Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .781 .781 9

Table C2.32

Reliability analysis for absorption subscale in the Game Engagement Questionnaire in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .703 .703 5

Table C2.33

Reliability analysis for presence subscale in the Game Engagement Questionnaire in Experiment 3

Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .613 .614 4

Table C2.34 286

Statistics for subscales of the Game Engagement Questionnaire (GEQ) in Experiment 3

GEQ Subscale Number Mean Standard Cronbach’s Variance

of items Deviation alpha for scale

Flow 9 3.47 1.00 .78 80.80

Immersion 1 5.31 1.52 - -

Absorption 5 3.24 1.11 .70 30.65

Presence 4 4.32 1.06 .61 18.10

Total* 18 3.79 .70 .88 282.79

*A1 items deleted; i.e., “I felt scared”

Table C2.35

Deleted items from the Game Engagement Questionnaire in Experiment 3

Subscale Deleted Question Number Deleted Question Absorption A1 I felt scared

Table C2.36

Reliability analysis for all items in the Game Engagement Questionnaire in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .874 .872 19

Table C2.37

Reliability analysis for individual items in the Game Engagement Questionnaire (GEQ) in Experiment 3 GEQ Item Scale Scale Corrected Squared Cronbach's Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted F1 67.33 267.15 .52 .40 .87 F2 65.63 267.06 .52 .60 .87 287

F3 67.10 266.20 .53 .73 .87 F4 66.24 276.35 .34 .55 .87 F5 67.18 267.25 .52 .72 .87 F6 66.06 273.00 .39 .42 .87 F7 66.70 268.24 .45 .36 .87 F8 66.46 262.17 .53 .35 .87 F9 66.41 256.19 .63 .55 .87 P1 65.72 270.05 .49 .53 .87 Immersion 64.72 270.00 .48 .50 .87 A1 68.14 282.79 .26 .33 .88 A2 66.80 269.00 .46 .41 .87 P2 66.38 265.06 .53 .34 .87 A3 65.89 266.60 .47 .36 .87 P3 65.51 272.23 .45 .33 .87 P4 65.24 268.22 .49 .37 .87 A4 66.76 258.15 .63 .51 .87 A5 66.37 263.75 .55 .39 .87 *A1 items deleted; i.e., “I felt scared”

Table C2.38

Reliability analysis for adjusted items in the Game Engagement Questionnaire in Experiment 3 Cronbach's Cronbach's N of Alpha Alpha Based Items on Standardized Items .878 .877 18

Table C2.39

Post-hoc Tukey tests for the effect of duration on probability of answering ‘long’ in the bisection tasks in Experiment 3

I (Duration) J (Duration) Mean Std. Sig 95% Confidence

Difference Error Interval for

(I-J) Difference 288

Lower Upper

bound bound

400 600 -.07* .01 .000 -.09 -.05

800 -.36* .02 .000 -.40 -.32

1000 -.62* .02 .000 -.66 -.58

1200 -.75* .02 .000 -.80 -.71

1400 -.86* .01 .000 -.89 -.84

1600 -.90* .01 .000 -.92 -.87

600 800 -.29* .02 .000 -.33 -.25

1000 -.55* .02 .000 -.59 -.52

1200 -.68* .02 .000 -.72 -.65

1400 -.79* .01 .000 -.82 -.77

1600 -.83* .01 .000 -.85 -.81

800 1000 -.26* .02 .000 -.30 -.23

1200 -.39* .02 .000 -.43 -.35

1400 -.50* .02 .000 -.54 -.46

1600 -.54* .02 .000 -.58 -.50

1000 1200 -.13* .02 .000 -.16 -.10

1400 -.24* .02 .000 -.27 -.21

1600 -.28* .02 .000 -.31 -.24

1200 1400 -.11* .01 .000 -.14 -.08

1600 -.15* .02 .000 -.18 -.12

1400 1600 -.04* .01 .000 -.05 -.02

289

Table C2.40

Correlations between previous videogame experience and Flow State in Experiment 3 Flow VG1*** VG2*** VG3*** State Flow Pearson Correlation 1 .164* .244** .228** State Sig. (2-tailed) .033 .001 .003 N 169 169 169 169 VG1** Pearson Correlation .164* 1 .485** .230** * Sig. (2-tailed) .033 .000 .003 N 169 169 169 169 VG2** Pearson Correlation .244** .485** 1 .242** * Sig. (2-tailed) .001 .000 .002 N 169 169 169 169 VG3** Pearson Correlation .228** .230** .242** 1 * Sig. (2-tailed) .003 .003 .002 N 169 169 169 169 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). ***. VG1 = Weekly hours of videogame play; VG2 = Years of videogame experience; VG3 = Previous experience with Age of Empires II

290

Appendix D

Participant Demographics

Table D1.1

Allocation to writing task condition in Experiment 4 after exclusions

Condition Frequency Percent Valid Cumulative

Percent Percent

Anger 108 50.7 50.7 50.7

Neutral 105 49.3 49.3 100.0

Total 213 100.0 100.0

Table D1.2

Number of participants with past personality scores congruent and incongruent with the writing task in Experiment 4 after exclusions

Condition Frequency Percent Valid Cumulative

Percent Percent

Congruent 115 54.0 54.0 54.0

Incongruent 98 46.0 46.0 100.0

Total 213 100.0

Table D1.3

Participant age statistics in Experiment 4 after exclusions

N Minimum Maximum Mean Standard

Deviation

Age 213 18 71 38.62 11.49

Valid N 213 291

Table D1.4

Participant gender in Experiment 4 after exclusions

Gender Frequency Percent Valid Cumulative

Percent Percent

Male 116 54.5 54.5 54.5

Female 97 45.5 45.5 100.0

Total 213 100.0 100.0

Table D1.5

Participant ethnicity in Experiment 4 after exclusions

Ethnicity Frequency Percent Valid Cumulative

Percent Percent

Caucasian 173 81.2 81.2 81.2

Asian 15 7.0 7.0 88.3

African- 9 4.2 4.2 92.5

American

Middle Eastern 1 .5 .5 93.0

Hispanic 9 4.2 4.2 97.2

Other 6 2.8 2.8 100.0

Total 213 100.0

Table D1.6

Participants speaking English as a first language (EFL) in Experiment 4 292

EFL Frequency Percent Valid Cumulative

Percent Percent

Yes 208 97.7 97.7 97.7

No 5 2.3 2.3 100.0

Total 213 100.0 100.0

Table D1.7

Participant religion in Experiment 4 after exclusions

Religion Frequency Percent Valid Cumulative

Percent Percent

Atheist 91 42.7 42.7 42.7

Christian 91 42.7 42.7 85.4

Islamic 7 3.3 3.3 88.7

Jewish 3 1.4 1.4 90.1

Other 17 8.0 8.0 98.1

Undisclosed 4 1.9 1.9 100.0

Total 213 100.0

293

Table D1.8

Participant education levels in Experiment 4 after exclusions

Highest completed level Frequency Percent Valid Cumulative

of education Percent Percent

Some high school 1 .5 .5 .5

High school graduate 26 12.2 12.2 12.7

Some college 56 26.3 26.3 39.0

Associate degree 28 13.1 13.1 52.1

Bachelor’s degree 74 34.7 34.7 86.9

Some graduate school 5 2.3 2.3 89.2

Master's degree 16 7.5 7.5 96.7

M.B.A. 3 1.4 1.4 98.1

J.D. 3 1.4 1.4 99.5

Ph.D. 1 .5 .5 100.0

Total 213 100.0 100.0

294

Materials for Chapter 6

Images in bisection task. The neutral images from the International Affective Picture System (Lang et al., 1997; 2008) used in the Experiment 5 bisection task were: 1121, 1390, 1945, 1947, 2191, 2272, 2410, 2485, 2616, 5390, 5535, 5661, 5731, 5900, 7000, 7038, 7351, 7402, 7640, 8211.

Zimbardo Time Perspective Inventory.

Read each item and, as honestly as you can, answer the question: 'How characteristic or true is this of me?' Use the following scale to record your answers.

|______|______|______|______|

Very Slightly Moderately Extremely /Not at all

1. I believe that getting together with one's friends to party is one of life's important pleasures (PH1) 2. Familiar childhood sights, sounds, and smells often bring back a flood of wonderful memories (PP1) 3. Fate determines much in my life (PF1) 4. I often think of what I should have done differently in my life (PN1) 5. My decisions are mostly influenced by people and things around me (PN2) 6. I believe that a person's day should be planned ahead each morning (F1) 7. It gives me pleasure to think about my past (PP2) 8. I do things impulsively (PH2) 9. If things don't get done on time, I don't worry about it* (F2) 10. When I want to achieve something, I set goals and consider specific means for reaching those goals (F3) 11. On balance, there is much more good to recall than bad in my past (PP3) 12. When listening to my favourite music, I often lose all track of time (PH3) 13. Meeting tomorrow's deadlines and doing other necessary work comes before tonight's play (F4) 14. Since whatever will be will be, it doesn't really matter what I do (PF2) 15. I enjoy stories about how things used to be in the 'good old times' (PP4) 16. Painful past experiences keep being replayed in my mind. (PN3) 17. I try to live my life as fully as possible, one day at a time (PH4) 18. It upsets me to be late for appointments (F5) 19. Ideally, I would live each day as if it were my last (PH5) 20. Happy memories of good times spring readily to mind (PP5) 21. I meet my obligations to friends and authorities on time (F6) 22. I've taken my share of abuse and rejection in the past (PN4) 295

23. I make decisions on the spur of the moment (PH6) 24. I take each day as it is, rather than try to plan it out* (F7) 25. The past has too many unpleasant memories that I prefer not to think about* (PP6) 26. It is important to put excitement in my life (PH7) 27. I've made mistakes in my past that I wish I could undo (PN5) 28. I feel that it's more important to enjoy what you're doing than to get work done on time (PH8) 29. I get nostalgic about my childhood (PP7) 30. Before making a decision, I weigh the costs against the benefits (F8) 31. Taking risks keeps my life from becoming boring (PH9) 32. It is more important for me to enjoy life's journey than to focus only on the destination (PH10) 33. Things rarely work out as I expected (PN6) 34. It's hard for me to forget unpleasant images of my youth (PN7) 35. It takes joy out of the process and flow of my activities, if I have to think about goals, outcomes, and products (PF3) 36. Even when I am enjoying the present, I am drawn back to comparisons with similar past experiences (PN8) 37. You can't really plan for the future because things change so much (PF4) 38. My life path is controlled by forces I cannot influence (PF5) 39. It doesn't make any sense to worry about the future, since there is nothing I can do about it anyway (PF6) 40. I complete projects on time by making steady progress (F9) 41. I find myself tuning out when family members talk about the way things used to be* (PP8) 42. I take risks to put excitement in my life (PH11) 43. I make lists of things to do (F10) 44. I often follow my heart more than my head (PH12) 45. I am able to resist temptations when I know that there is work to be done (F11) 46. I find myself getting swept up in the excitement of the moment (PH13) 47. Life today is too complicated; I would prefer the simpler life of the past (PF7) 48. I prefer friends who are spontaneous rather than predictable (PH14) 49. I like family rituals and traditions that are regularly repeated (PP9) 50. I think about the bad things that have happened to me in the past (PN9) 51. I keep working at difficult, uninteresting tasks if they will help me to get ahead (F12) 52. Spending what I earn on pleasures today is better than saving for tomorrow's security (PF8) 53. Often luck pays off better than hard work (PF9) 54. I often think about the good things that I have missed out on in my life (PN10) 55. I like my close relationships to be passionate (PH15) 56. There will always be time to catch up on my work* (F13) * reverse scored items

296

Past Positive (PP) items: 2, 7, 11, 15, 20, 25, 29, 41, 49

Past Negative (PN) items: 4, 5, 16, 22, 27, 33, 34, 36, 50, 54

Present Hedonistic (PH) items: 1, 8, 12, 17, 19, 23, 26, 28, 31, 32, 42, 44, 46, 48, 55

Present Fatalistic (PF) items: 3, 14, 35, 37, 38, 39, 47, 52, 53

Future (F) items: 6, 9*, 10, 13, 18, 21, 24*, 30, 40, 43, 45, 51, 56*

297

Additional statistics for Chapter 6

Table D2.1

Statistics for subscales of Zimbardo Time Perspective Inventory (ZTPI) in Experiment 4

ZTPI Subscale Number Mean Standard Cronbach’s Variance

of items Deviation alpha for scale

Past Positive 9 3.59 .73 .855 43.66

Past Negative 10 3.08 .73 .846 52.58

Present Hedonistic 15 2.96 .56 .813 70.18

Present Fatalistic 9 2.40 .68 .815 37.35

Future 13 3.86 .52 .814 45.09

Past Combined* 15 3.25 .53 .766 64.25

Negative Combined 19 2.75 .60 .870 129.62

*items deleted from scale were PP3, PP5, PP6, and PP8

Table D2.2

Reliability analysis for the created ‘past’ subscale with all items (including excluded items) in Experiment 4 Cronbach's Cronbach's N of Alpha Alpha Items Based on Standardize d Items .672 .678 19

Table D2.3

Reliability analysis for individual items in the created ‘past’ subscale in Experiment 4 Item Scale Scale Corrected Cronbach's Mean if Variance if Item-Total Alpha if Item Item Correlation Item Deleted Deleted Deleted PP1 59.09 56.28 .41 .64 PN1 59.63 55.40 .39 .64 298

PN2 60.19 56.72 .35 .65 PP2 59.73 56.88 .32 .65 PP3 59.41 61.88 .03 .68 PP4 59.62 54.98 .44 .64 PN3 60.40 58.02 .20 .667 PP5 59.38 60.58 .11 .674 PN4 59.46 59.96 .15 .671 PP6 59.64 65.24 -.17 .71 PN5 59.12 59.43 .24 .66 PP7 59.48 53.29 .54 .63 PN6 60.43 59.46 .17 .669 PN7 60.33 58.46 .17 .671 PP8 59.44 60.85 .08 .68 PP9 59.40 56.37 .38 .65 PN9 60.18 56.43 .31 .65 PN10 60.00 56.90 .28 .66 PN8 60.01 53.19 .59 .62

Table D2.4

Reliability analysis for the created ‘negative’ subscale with all items in Experiment 4 Cronbach's Cronbach's N of Alpha Alpha Items Based on Standardize d Items .870 .872 19

Table D2.5

Reliability analysis for individual items in the created ‘negative’ subscale in Experiment 4 Scale Scale Scale Corrected Squared Cronbach's Item Mean if Variance if Item-Total Multiple Alpha if Item Item Correlation Correlation Item Deleted Deleted Deleted PN1 48.92 115.23 .53 .53 .86 PN2 49.48 120.83 .33 .23 .87 PN3 49.69 111.40 .63 .64 .86 PN4 48.75 119.08 .42 .35 .87 PN5 48.41 123.02 .31 .36 .87 PN6 49.72 115.08 .58 .43 .86 PN7 49.62 112.06 .59 .61 .86 299

PN9 49.47 112.65 .62 .65 .86 PN10 49.29 113.74 .57 .53 .86 PN8 49.30 120.86 .33 .25 .87 PF1 49.69 119.03 .39 .40 .87 PF2 50.41 118.98 .47 .39 .86 PF3 49.91 117.01 .48 .43 .86 PF4 49.91 116.38 .55 .50 .86 PF5 49.96 115.08 .57 .53 .86 PF6 50.19 118.94 .44 .45 .87 PF7 49.42 117.36 .39 .25 .87 PF8 50.16 121.58 .32 .35 .87 PF9 49.87 118.15 .45 .34 .86

Table D2.6

Analysis of Variance for the effect of past congruence on bisection point with all past items (i.e., before individual item exclusions) Source Type III Df Mean F Sig. Partial

Sum of square Eta

Squares Squared

Condition Hypothesis 70073.76 1 70073.76 2.45 .119 .01

Error 6028559.19 211 28571.37

Table D2.7

Analysis of Variance for the effect of past congruence on weber ratio (WR) with all past items (i.e., before individual item exclusions) Source Type III df Mean F Sig. Partial

Sum of square Eta

Squares Squared

Condition Hypothesis .001 1 .001 .24 .62 .001

Error 1.25 211 .006

300

Table D2.8

Post-hoc Tukey tests for the effect of duration on probability of answering ‘long’ in the bisection tasks in Experiment 4 I (Duration) J (Duration) Mean Std. Sigb 95% Confidence

Difference Error Interval for

(I-J) Differenceb

Lower Upper

bound bound

400 600 -.08* .01 .000 -.10 -.06

800 -.38* .02 .000 -.42 -.34

1000 -.67* .02 .000 -.71 -.63

1200 -.85* .01 .000 -.87 -.82

1400 -.92* .01 .000 -.93 -.90

1600 -.94* .01 .000 -.95 -.92

600 800 -.31* .02 .000 -.34 -.27

1000 -.59* .02 .000 -.63 -.56

1200 -.77* .01 .000 -.80 -.75

1400 -.84* .01 .000 -.86 -.81

1600 -.86* .01 .000 -.88 -.84

800 1000 -.29* .02 .000 -.32 -.26

1200 -.47* .02 .000 -.50 -.43

1400 -.53* .02 .000 -.57 -.49

1600 -.55* .02 .000 -.59 -.51

1000 1200 -.18* .01 .000 -.21 -.15

1400 -.25* .02 .000 -.28 -.22 301

1600 -.27* .02 .000 -.30 -.23

1200 1400 -.07* .01 .000 -.09 -.05

1600 -.09* .01 .000 -.11 -.07

1400 1600 -.02* .01 .000 -.03 -.07