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MEASUREMENT OF FORECASTED, EXPERIENCED, AND REMEMBERED

AFFECT IN A SUBSTANCE USE CONTEXT

Maya Annelies Pilin

Hons. B.A., University of Ottawa, 2013

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF ARTS

THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES

Psychological Science

THE UNIVERSITY OF BRITISH COLUMBIA

(Okanagan)

September 2019

The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:

Measurement of Forecasted, Experienced, and Remembered Affect in a Substance Use Context

submitted by Maya A. Pilin in partial fulfillment of the requirements for the degree of Master of Arts in Psychological Science

Examining Committee:

Dr. Marvin Krank

Supervisor

Dr. Paul Davies

Supervisory Committee Member

Dr. Maya Libben

Supervisory Committee Member

Dr. Sarah Dow-Fleisner

External Examiner

Dr. Robert Campbell

Chair

Abstract

Individuals often overestimate the intensity and duration of the happiness they will feel after experiencing a positive event, such as receiving tenure. However, few studies have examined whether individuals will make affective forecasting errors in the context of alcohol and cannabis use. In the current study, we examined whether university students would make affective forecasting errors in a substance use context using experience sampling methodology. A sample of n = 46 university students made predictions about the intensity and duration of five emotions

(happiness, relaxation, fun, sexiness, and excitement) that they may experience when using alcohol or cannabis, as well as completing a set of additional questionnaires regarding their substance use cognitions. When using alcohol or cannabis during the weekend, participants received questionnaires asking how intensely they were experiencing each of the five emotions.

They were also asked the same questions one day after consuming and one week after. Growth curve models and Analyses of Variance demonstrated that while intensity emotions varied significantly over time, participants only overestimated how relaxed they would feel [F(1, 2.11)

= 3.46, p < 0.05] and how much fun they would have consuming substances [F(1, 1) = 4.30, p =

0.05]. In conclusion, preliminary results demonstrate that individuals make relatively few affective forecasting errors in a substance use context, as opposed to other contexts in which such errors have previously been studied, although some errors do occur with particular emotions. Such findings may fit into the puzzle of explaining why maladaptive substance use habits continue despite seemingly negative consequences, such as hangovers.

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Lay Summary

Individuals are sometimes inaccurate at predicting their emotions, often overestimating how happy positive events will make them feel. This study measures whether prediction errors occur when students consume alcohol or cannabis. Forty-six university students predicted how intensely and for how long they would feel five emotions, and then reported the intensity at which they were experiencing these emotions when consuming substances during the weekend.

The following day and week, participants reported how intensely they had felt these emotions when consuming. Analyses showed that participants over-estimated how relaxed they would feel and how much fun they would have consuming substances. Moreover, participants who had large differences between their predictions of how sexy they would feel and how sexy they did feel when consuming were more likely to have alcohol use problems. Future studies should aim to determine why such errors occur and whether their correction will reduce substance use.

Preface

This research was conducted at the University of British Columbia (Okanagan) and supervised by Dr. Marvin Krank. For the present thesis, I was responsible for the original conception and design of the study, filing the research ethics application, the programming of the questionnaires, a portion of the data collection, all data preparation and analyses, and the writing of every section of the thesis. Prior to data collection, this study was reviewed and approved by the Behavioral

Research Ethics Board of the University of British Columbia (Okanagan). The ethics file number for the current study is H13-02327. To date, the results of this study have not been published.

Table of Contents

Abstract ...... iii

Lay Summary ...... iv

Preface ...... v

Table of Contents ...... vi

List of Tables ...... viii

List of Figures ...... ix

Acknowledgements ...... x

Dedication ...... xi

Chapter 1: Introduction ...... 1

1.1. A Brief History of Affective Forecasting ...... 1

1.2. Cognitive Mechanisms of Affective Forecasting Errors ...... 4

1.3. Rosy Hindsight Bias ...... 9

1.4. Substance Use and Forecasting and Remembering Errors ...... 11

1.5. The Importance of Affective Forecasting Interventions in Substance Use...... 12

1.6. Rationale for the Current Study...... 15

1.7. The Current Study...... 16

Chapter 2: Methodology...... 18

2.1 Participants ...... 18

2.2 Measures ...... 19

2.2.1. Substance use frequency questionnaires...... 19

2.2.2. Word associates task...... 19 vi

2.2.3. Outcome expectancy liking task...... 20

2.2.4. Substance use attitudes questionnaire...... 21

2.2.5. Measures of affective forecasting errors...... 22

2.3. Procedure ...... 23

2.4. Data Analysis Plan ...... 24

Chapter 3: Results...... 29

3.1. Descriptive Statistics ...... 29

3.1.1. Forecasts...... 29

3.1.2. Experiences...... 29

3.1.3. Memories...... 29

3.1.4. Cognitions...... 30

3.1.5. Substance Use...... 31

3.2. Comparisons Between Substances ...... 31

3.2.1. Forecasts...... 31

3.2.1.1. Levene's Tests...... 31

3.2.1.2. Analyses of Variance...... 31

3.2.2. Experiences...... 32

3.2.2.1. Levene's Tests...... 32

3.2.2.2. Analyses of Variance...... 32

3.2.3. Memories from the Previous Day...... 32

3.2.3.1. Levene's Tests...... 32

3.2.3.2. Analyses of Variance...... 32

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3.2.4. Memories from the Previous Week...... 33

3.2.4.1. Levene's Tests...... 33

3.2.4.2. Analyses of Variance...... 33

3.3. Repeated Measures Analyses ...... 33

3.3.1. Happiness...... 34

3.3.2. Relaxation...... 34

3.3.3. Fun...... 34

3.3.4. Sexiness...... 35

3.3.5. Excitement...... 35

3.4. Growth Curve Models...... 36

3.4.1. Happiness...... 36

3.4.2. Relaxation...... 36

3.4.3. Fun...... 36

3.4.4. Sexiness...... 37

3.4.5. Excitement...... 37

3.5. Chi Square Analyses ...... 38

3.5.1. Happiness...... 38

3.5.2. Relaxation...... 38

3.5.3. Fun...... 38

3.5.4. Sexiness...... 38

3.5.5. Excitement...... 38

3.6. Polynomial Surface Regressions...... 39

3.6.1. Happiness...... 39 viii

3.6.1.1. AUDIT scores...... 39

3.6.1.2. CUDIT scores...... 39

3.6.2. Relaxation...... 40

3.6.2.1. AUDIT scores...... 40

3.6.2.2. CUDIT scores...... 40

3.6.3. Fun...... 41

3.6.3.1. AUDIT scores...... 41

3.6.3.2. CUDIT scores...... 41

3.6.4. Sexiness...... 42

3.6.4.1. AUDIT scores...... 42

3.6.4.2. CUDIT scores...... 43

3.6.5. Excitement...... 43

3.6.5.1. AUDIT scores...... 43

3.6.5.2. CUDIT scores...... 44

3.7. Mediation Models ...... 45

3.7.1. Happiness...... 45

3.7.1.1. Expectancy Scores...... 45

3.7.1.2. WATs...... 45

3.7.2. Relaxation...... 46

3.7.2.1. Expectancy Scores...... 46

3.7.2.2. WATs...... 47

3.7.3. Fun...... 47

3.7.3.1. Expectancy Scores...... 47

3.7.3.2. WATs...... 48

3.7.4. Sexiness...... 48

3.7.4.1. Expectancy Scores...... 48

3.7.4.2. WATs...... 49

3.7.5. Excitement...... 50

3.7.5.1. Expectancy Scores...... 50

3.7.5.2. WATs...... 50

Chapter 4: Discussion ...... 52

4.1. Substance Use as Unique Context ...... 53

4.2. Relaxation and Fun as Unique Emotions ...... 54

4.3. Scale Construction as a Unique Component of the Study ...... 55

4.4. Study Limitations and Possibilities of Type II Errors...... 56

4.5. Dual-Processes Models of Affective Forecasting...... 57

4.6. Implications...... 58

4.7. Future Directions...... 60

4.8. Conclusion...... 61

Bibliography ...... 62

Tables ...... 74

Figures ...... 88

Appendices ...... 103

Appendix A ...... 103

Appendix B ...... 104 x

Appendix C ...... 105

Appendix D ...... 106

Appendix E ...... 107

Appendix F...... 108

Appendix G ...... 109

List of Tables

Table 1 Demographics ...... 74

Table 2 Sample Sizes at Each Time Point in Analyses ...... 75

Table 3 Total Sample Sizes for Each Analysis Completed in Study ...... 76

Table 4 Descriptive Statistics (Forecasts, Experiences, Memories) ...... 77

Table 5 Polynomial Regression - Happiness (AUDIT) ...... 78

Table 6 Polynomial Regression - Happiness (CUDIT) ...... 79

Table 7 Polynomial Regression - Relaxation (AUDIT) ...... 80

Table 8 Polynomial Regression - Relaxation (CUDIT) ...... 81

Table 9 Polynomial Regression - Fun (AUDIT)...... 82

Table 10 Polynomial Regression - Fun (CUDIT) ...... 83

Table 11 Polynomial Regression - Sexiness (AUDIT) ...... 84

Table 12 Polynomial Regression - Sexiness (CUDIT) ...... 85

Table 13 Polynomial Regression - Excitement (AUDIT)...... 86

Table 14 Polynomial Regression - Excitement (CUDIT) ...... 87

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List of Figures

Figure 1.1. Growth Curve - Intensity of Happiness ...... 88

Figure 1.2. Growth Curve - Intensity of Relaxation ...... 89

Figure 1.3. Growth Curve - Intensity of Fun ...... 90

Figure 1.4. Growth Curve - Intensity of Sexiness ...... 91

Figure 1.5. Growth Curve - Intensity of Excitement ...... 92

Figure 2.1. Surface Analysis - Happiness - AUDIT ...... 93

Figure 2.2. Surface Analysis - Happiness - CUDIT ...... 94

Figure 2.3. Surface Analysis - Relaxation - AUDIT ...... 95

Figure 2.4. Surface Analysis - Relaxation - CUDIT ...... 96

Figure 2.5. Surface Analysis - Fun - AUDIT...... 97

Figure 2.6. Surface Analysis - Fun - CUDIT ...... 98

Figure 2.7. Surface Analysis - Sexiness - AUDIT ...... 99

Figure 2.8. Surface Analysis - Sexiness - CUDIT ...... 100

Figure 2.9. Surface Analysis - Excitement - AUDIT ...... 101

Figure 2.10. Surface Analysis - Excitement - CUDIT ...... 102

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Acknowledgements

I would like to thank Dr. Marvin Krank for giving me the opportunity to pursue a research question that I was passionate about. Your guidance and expertise in cognition has shaped my research and theoretical interests.

I would also like to thank my committee, Dr. Paul Davies and Dr. Maya Libben for your help in shaping this project in its early days. I would like to thank Dr. Derrick Wirtz, Dr. Cynthia

Mathieson, and Dr. Brian O’Connor for help, ideas, and advice on various parts of this thesis.

I would also like to thank the incredible group of student volunteers who ran each of the many participants for this study – this project would never have been possible without your help.

I would like to thank Dora Chen, Jade Cowan, Melissa Fenton, Dunigan Folk, Haylie Gibb,

Shannon Golsof, Sherry Hanna, Helen Hofer, Makenzie Houston, Danielle Klassen, Connie Ku,

Brandy Lynch, Vicky Medeia, Emma Mikkelsen, Isabella Panagos, Donelle Pavey, Ciara Petkau,

Kyle Potter, Jill Robinson, Lauren Rossiter, Tatiana Sanchez, Sarah Sanders, Kaylene Scheil,

Jennica Sedmak, and Ross St George.

Finally, I would like to thank the Social Sciences and Humanities Research Council

(SSHRC) for providing the funding during my Master’s degree that allowed me to conduct this research.

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Dedication

To my grandfather, for telling me he was sitting waiting outside my kindergarten all day when I was too scared to go to alone, then quietly sneaking away. To my grandmother, for teaching my mother to make the food that would sustain me over school breaks. To my parents, for moving across oceans twice to provide every opportunity for their two daughters, only to have them both become psychologists and criticize their parenting. To my big sister, for convincing me that being a chef was not the right career path – without you, I would be miserable at Le Cordon Bleu right now. To my most favourite humans, Mori, Roi, and Mia, because seeing your faces reminds me that there are good things in the world outside of the lab.

Finally, to Barbra Streisand, for obvious reasons.

Chapter 1: Introduction

Psychological science involves measuring clashes between cognition, affect, and behavior. Affective forecasting aims to understand the discordance between expected and experienced affect. Specifically, extant literature on affective forecasting has demonstrated that individuals make mistakes when predicting the intensity and duration of their emotional reactions to negative and positive events (Wilson & Gilbert, 2005). However, the theory of affective forecasting errors has mainly been applied within positive psychology and has rarely been examined within the context of substance use. Moreover, while several descriptions of the cognitive errors inherent in affective forecasting have been elaborated (i.e. Wilson, Meyers, &

Gilbert, 2003), the mechanisms behind such errors remain unclear. The current study will measure whether affective forecasting errors occur in the context of substance use in young adults. Specifically, the mechanisms of affective forecasting will be explored through the lens of dual-processes theory (see Evans, 2008).

1.1. A Brief History of Affective Forecasting

While currently a field of research strictly within psychology, affective forecasting has its earliest roots in philosophy and behavioural economics. In the late 1700s, Jeremy Bentham, a

British philosopher, developed and published his theory of utilitarianism. The concept of utility would later become a buzzword within behavioral economics, often used by the likes of Daniel

Kahneman and Amos Tversky, two of its early founders. In his description of his philosophy,

Bentham noted that individuals are ruled by pain and pleasure and that proportions of pain to pleasure play a central role in decision-making. A decision that maximizes pleasure and minimizes pain would conform to Bentham’s tenants of utilitarianism. Importantly, Bentham noted that the emotions of pain and pleasure vary by their intensity and duration, among several 1

other aspects (Bentham, 1781). Therefore, early philosophers subscribing to utilitarianism were conceptualizing emotion and its ‘measurement’ in the same terms as the affective forecasting experts of the present who measure errors in forecasting the intensity and duration of emotions

(see Wilson & Gilbert, 2005; Wilson, Meyers, & Gilbert, 2003).

A century after Bentham published his views on utilitarianism, William James, a pre- eminent psychologist at Harvard, published his essay What is an Emotion? in 1884. Most notably, James, unlike his predecessors, did not consider humans to be fully rational. As one of the founders of the functionalist movement, he was more interested in discovering how people truly behaved and adapted to the world (Schultz & Schultz, 2007). This contrast between an idealized and rational view of behavior, as opposed to humans’ actual behavior, is familiar, a preview of the later debates between Richard Thaler’s and other behavioral economists’ views of individuals as sometimes irrational and traditional economists’ views of individuals as perfectly rational ‘Econs’ (Thaler, 2016). Most importantly, James noted that emotions were not necessarily logical, rational feelings. In fact, he stated that they might oppose “…the verdict of our deliberate reason…” (James, 1884, p. 190). Notably, Lacasse (2017) pointed out the connection between James’ view of emotions as non-rational and later theories, such as

Kahneman’s dual-processing (2011) and Slovic’s affect heuristic (Slovic, Finucane, Peters, &

MacGregor, 2007). Had James tried to push emotions as being subject to rational rules, the field of affective forecasting, which posits that individuals cannot correctly predict their own emotions, may have had more trouble gaining a foothold.

Through the 20th century, economists viewed individuals through a perspective of rationality, studying what decisions rational individuals make. In the late 1970s, economics underwent a major paradigm shift, resulting in the development of the field of behavioral 2

economics. Much of the credit for the early development of behavioral economics is owed to researchers Daniel Kahneman and Amos Tversky. Their seminal 1979 paper Prospect Theory:

An Analysis of Decision Under Risk stated that most individuals, at least if they are rational, follow expected utility theory when making decisions. In essence, the authors posited that when making decisions, individuals should (and do) choose the option that will result in the greatest utility. However, Kahneman and Tversky noted, in a theme that became common through most of their work, that individuals are subject to violating expected utility theory. In fact, they are prone to biases such as the certainty effect, in which they overweigh certain outcomes over probable ones. These biases result in what all economists other than behavioral ones would consider irrational and sometimes faulty decision-making (Kahneman & Tversky, 1979). For example, Kahneman claimed that when individuals make forecasts of happiness, they will often use their immediate reaction to a specific situation to forecast its long-term effects on their happiness, a “generalization of the analysis offered in prospect theory” (Kahneman, 2000, p. 12).

Later, Kahneman and Snell would go on to further explain their view of decision-making in a

1992 paper, claiming that individuals are poor forecasters of their own predicted utility thus unable to determine what will result in the greatest amount of utility for them. In a study of this concept, Kahneman and Snell (1992) found that participants predicted that their liking of a type of yogurt would decline if they ate it every day for a week straight. In fact, their liking for the yogurt increased, demonstrating that their predicted utility was inaccurate.

As noted above, affective forecasting research is a relatively new field, but is grounded within earlier work in philosophy, psychology, and economics. However, prior to the late 1990s, few experiments of affective forecasting had been conducted. Most importantly, while theories of errors in decision-making existed, few theories explained the mechanisms behind why 3

affective forecasting errors occurred, and even fewer experiments directly tested such mechanisms. Studies in the early 2000s demonstrated evidence for the concept of affective forecasting errors. As will be described below, such studies have shown that individuals make affective forecasting errors in a variety of scenarios. The aim of the current study is to examine the existence of such errors in a context fundamentally different from those used in prior studies.

More importantly, the goal is to take affective forecasting theory forward by a step by including theory-based cognitive mediators that may explain why affective forecasting errors occur and how they might influence behavior.

1.2. Cognitive Mechanisms of Affective Forecasting Errors

Several landmark studies have established the existence of affective forecasting errors within positive events (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998; Wirtz, Kruger,

Scollon, & Diener, 2003). One study compared assistant professors who forecasted their reactions to receiving tenure to professors who did receive tenure. While the forecasters’ long- term estimates of their happiness were relatively accurate, those who had received tenure were less elated in the short-term than forecasters believed they would be (Gilbert et al., 1998).

Another study measured students’ enjoyment of a vacation before, during, and after spring break.

While students expected to feel quite happy, sociable, and calm before their vacation, their online ratings of these emotions during the vacation revealed a different picture, with significantly lower satisfaction ratings than expected. Interestingly, their remembered enjoyment of the vacation as early as 2 to 4 days after they had returned to campus was significantly higher than their on-line enjoyment (Wirtz et al., 2003). Finally, a study conducted by Dunn, Wilson, &

Gilbert (2003) found that students assigned to college dormitories that they had forecasted to be desirable were less happy one year later than they had expected to be. Although there is 4

evidence that the effect of affective forecasting errors is weaker for positive events than for negative ones (Hoerger, Quirk, Lucas, & Carr, 2010), these studies demonstrate that some inaccuracy does occur even with positive events.

Errors in affective forecasting generally affect the individuals’ estimates of the intensity and duration of their emotions after an event, not the valence of the emotion (Gilbert, Driver-

Linn, & Wilson, 2002a). An impact bias refers to errors in estimating the intensity of future emotions (Wilson & Gilbert, 2005). For example, individuals may overestimate the intensity of their negative emotions after a romantic breakup (Eastwick, Finkel, Krishnamurti, &

Loewenstein, 2008). Meanwhile, a durability bias refers to errors in estimating the duration of an emotion (Wilson et al., 2003). For example, individuals whose preferred candidate lost an election were happier than they had expected to be one month after the loss (Gilbert et al., 1998).

While evidence for both impact and durability bias abounds, the mechanisms of these biases remain unclear.

One lens through which such biases may be viewed is dual-process theory (see Evans,

2008, for a review). Most accounts of dual-processes theory have posited that there are two cognitive systems, often referred to as System 1 and System 2. System 1 is fast, automatic, and mainly works below our level of awareness. Several accounts of dual-process theory have claimed that System 1 is associative, such that the activation of one concept may lead to the memory of another concept (Strack & Deutsch, 2004). Crucially, the speed of System 1 results in efficiency, but not necessarily accuracy. Therefore, thinking with System 1 can lead to cognitive errors and biases (see Tversky & Kahneman, 1974, for a review). Meanwhile, System 2 is rule- based and logical, and mainly works at the level of awareness (Smith & DeCoster, 2000). For example, Kahneman (2011) noted that a complicated double or triple-digit multiplication 5

equation would require System 2 processing, such that an individual would be aware of the steps required to solve the equation. Several theories have been proposed regarding the relationships of these two systems, however, a common theory is the default-interventionist model. In this model,

System 1 is always active and functioning in the background, while System 2 may override

System 1 processing when an individual has enough cognitive resources and motivation to do so

(Kahneman & Frederick, 2002, 2005). Therefore, it is important to note that the two systems do not function completely independently but influence one another. While dual-processes theory has been examined within affective forecasting (i.e. Wilson et al., 2003), there is still an ongoing debate as to the roles of Systems 1 and 2 in forecasting errors.

Several explanations of the role of dual-processes theory in affective forecasting have been proposed, but the theory most relevant to the current study was elaborated by Wilson and colleagues (2003). The authors explained that valence of emotional reactions (i.e. whether the event was positive or negative) is encoded implicitly (i.e. within System 1), while explicit memories (i.e. System 2) are often used as guides in decision-making, particularly to determine the intensity and duration of positive or negative emotional reactions. The problem lies in the many System 1 biases that can infect explicit memories of emotions. For example, the ‘peaks- and-ends’ rule states that individuals are influenced by the last and the most intense emotion they experienced, as opposed to an average of their emotions throughout the event (Kahneman,

Fredrickson, Schreiber, & Redelmeier, 1993). These cognitive biases affect recall of past events and predictions of future affect (Wilson et al., 2003).

Another interesting perspective on the roles of dual-processes theory in affective forecasting was posited by Gilbert, Gill, and Wilson (2002) whose paper noted that in predicting reactions to future events individuals use ‘proxies’, imagining the event and their emotional 6

reaction to it. Such mental images might be tagged with a negative or positive affect, which may act as a cue when making decisions (Finucane, Alhakami, Slovic, & Johnson, 2000). However, yet again Gilbert and colleagues (2002) noted that proxies can be ‘contaminated’ by current affect. For example, an individual who feels upset when making a forecast may find it difficult to accurately imagine their future level of happiness after a positive event. Moreover, such proxies are atemporal (i.e. such that an affective forecast for an event that supposedly takes place at

Christmas versus Easter would be the same). The authors posited that making temporal corrections to affective forecasts requires explicit processing and thus more cognitive resources.

In their study, cognitively busy participants were unable to correct their affective forecasts by taking time into account. Thus, both System 1 and System 2 processes seem to play a role in affective forecasting.

Another role of System 1 and 2 processing within affective forecasting errors is in the effect of implicit theories on forecasts. Ross (1989) defined implicit theories as being ‘rarely discussed’ beliefs about the stability of attributes. Importantly, he went on to note that “an implicit theory may thus serve to organize memories into a coherent pattern of information that is consistent with the theory” (p. 342). In their study of affective forecasting for spring breaks,

Wirtz and colleagues (2003) noted that students may have overestimated their enjoyment of spring break because they hold an ‘implicit theory’ of it as a fun activity. Therefore, students may have ignored all the neutral or unpleasant parts of spring break when forecasting their enjoyment. Furthermore, Meyvis, Ratner, and Levav (2010) noted that individuals may hold

‘intuitive theories’ of experiences. The authors noted that “even when people are able to accurately recall their affective experiences, they often fail to adjust their intuitive theories to reflect those experiences” (p. 580). Therefore, the authors suggested that implicit theories are 7

held at the System 1 level and that System 2 fails to adjust the theories to reflect true experiences. Nonetheless, it is unclear whether implicit theories function at the level of System 1 for everybody and for every topic. For example, it is possible that an individual is conscious of their implicit theory regarding spring break but unconscious of their implicit theory regarding binge-drinking with friends. Furthermore, to our knowledge no studies have specifically examined the role of implicit theories in affective forecasting. Nonetheless, such theories may have a role to play in forecasting errors.

Finally, MacInnis and Patrick (2006) provided a contrasting view on the role of dual- processes theory in affective forecasting. Specifically, the authors modified Strack, Werth, &

Deutsch’s (2006) theory of the reflective (i.e. System 2) and impulsive (i.e. System 1) cognitive systems. According to Strack and colleagues (2006), affect is a trigger for the impulsive system.

MacInnis and Patrick modified the model to demonstrate that affective forecasting is a precursor to self-regulation. In this model, an individual may have an impulse that results in a lack of self- regulation or their impulse may lead to reflective processing and affective forecasting of the positive and negative consequences of self-regulation. Therefore, the MacInnis and Patrick model sees the action of affective forecasting as a result of System 2 processes but does not make a claim as to the origin of forecasting errors. Notably, this model mentions consumption impulses, such as the impulse to consume alcohol. MacInnis and Patrick noted that the activation of an impulse (i.e. to drink) may result in the anticipation of positive emotions, such as joy. Here, an individual might use System 2 to forecast positive emotions from controlling the impulse to drink, thus leading to a healthier lifestyle, or might give in to the impulse by simply forecasting the joy resulting from indulging.

1.3. Rosy Hindsight Bias

Several studies of affective forecasting have included both prospective evaluations (i.e. forecasts) as well as retrospective evaluations (i.e. post-event memories). Both types of evaluations are inextricably linked, and both have the potential to contribute to decision-making errors. The studies that have examined affective forecasts and retrospective evaluations have found fairly consistent results: individuals have the tendency to report inaccurate forecasts (i.e. overestimate how good a positive event will make them feel) and inaccurate retrospective evaluations (i.e. mistakenly remember that a positive event was more enjoyable than it really was). One of the most prolific studies examining this concept was conducted by Mitchell and colleagues (1997). In this study, the researchers were able to obtain measures of enjoyment of a bicycle trip before, during, and after the trip. Analyses revealed a quadratic pattern: participants overestimated how much they would enjoy the trip yet remembered enjoying it significantly more than they actually had (see also Wirtz et al., 2003; Wilson & Klaaren, 1992).

Early studies on the inaccuracy of retrospective evaluations were conducted within the framework of dual-processes theory. For example, in a study conducted by Kahneman and colleagues (1993), participants placed their hand in uncomfortably cold water for 60 seconds.

Seven minutes later, they placed their hand in cold water again, for another 90 seconds.

However, during the final 30 seconds of this trial, the temperature of the water increased by one degree, rendering it marginally less painful. When asked which trial they would prefer to repeat, significantly more participants preferred to repeat the second trial, despite their awareness that the trial was longer. Moreover, participants remembered the long trial as being significantly less uncomfortable than the short trial. In response to the results of this study, Kahneman and colleagues concluded that cognitions are represented not by their duration, but by “transitions 9

and singular moments” (p. 404; see also Conway, 2009) and defined the peak-end effect as a cognitive bias that occurs when individuals’ retrospective judgements of positive and negative experiences are biased towards the peak of the experience as well as its ending. As previously mentioned, such cognitive biases originate in System 1.

Inaccurate retrospective evaluations are often conceptualized as having been made by an

‘experiencing’ and ‘remembering’ self. Specifically, Kahneman and Riis (2005) noted that “the remembering self is sometimes simply wrong” (p. 286) and that such discrepancies can often lead to poor decision-making. In line with this reasoning, Kahneman and colleagues’ 1993 study noted that while retrospective evaluations guide future decisions, they are often inaccurate.

Moreover, such inaccuracies have been demonstrated to play a role in health-related decisions.

For example, Kent (1985) stated that discrepancies in remembered and experienced pain might affect the amount of pain patients expect to experience at their next appointment (see also

Redelmeier & Kahneman, 1996).

Importantly, several theories within memory frameworks have also discussed the discrepancies between experienced and remembered affect, such as Accessibility Model of

Emotional Self-Report (AM; Robinson & Clore, 2002). The basic proposition of the AM is that there are differences in the types of memory that individuals access when reporting on emotions during various durations of time. When experiencing an event, individuals report on the emotion that they are currently experiencing. When completing a retrospective self-report, individuals access episodic memory if the event took place recently (see Geng, Chen, Lam, & Zheng, 2013 for exact time durations). If the event took place a longer time ago, they access semantic memory, specifically situation-specific or identity-specific beliefs. In describing the peak-end effect, Robinson and Clore (2002) posited that the inaccuracies in participants’ retrospective 10

reports of pain in Kahneman and colleagues’ study (1993) were due to systematic biases in episodic memory. Had participants been asked to report on their emotions during Kahneman’s cold-water task several months later, they would have accessed semantic memories about how that situation should make them feel (i.e. “cold-water tasks are painful”) or about their identities

(“I’m a person who is very sensitive to pain”) (Robinson & Clore, 2002). Therefore, System 1 biases may infect episodic memories, leading to poor decision-making; moreover, implicit theories embedded in semantic memory may sometimes also lead to poor decisions, when the implicit theories are unhealthy (i.e. an implicit theory that binge-drinking should be fun).

Finally, memory rehearsal may affect the discrepancies between experienced and remembered affect, particularly within substance use. According to Walker and colleagues

(2009), positive events are more likely to be rehearsed in memory and such rehearsal results in less fading of emotions. Generally, the fading affect bias is an adaptive feature of human memory that states that memories of pleasant emotions last longer than memories of negative emotions (Skowronski, Gibbons, Vogl, & Walker, 2004); importantly, the bias begins within the first 24 hours after an event (Gibbons et al., 2011). In the context of alcohol use, a study by

Gibbons and colleagues (2013) suggested that the fading affect bias may be maladaptive.

Specifically, the authors found that unpleasant memories from alcohol-related events were quickly forgotten by high-frequency drinkers, which they speculated might lead to more alcohol use. While the authors did not discuss fading affect bias within the context of dual-processes theory, it is likely related to System 1, as are other cognitive biases.

1.4. Substance Use and Forecasting and Remembering Errors

Very few studies have attempted to measure or modify substance-related affective forecasts. One notable exception in the measurement domain is O’Hara and colleagues (2011), 11

who measured affective forecasts in heavy drinking behaviors within the context of the prototype-willingness model (see Gerrard, Gibbons, Houlihan, Stock, & Pomery, 2008). They found that intention (System 2) was a mediator between anticipated positive affect and changes in substance use behavior. Moreover, intention but not willingness to drink varied as a function of cognitive load when making affective forecasts. Within intervention research, Murgraff and colleagues (1999) measured affect towards drinking in two groups of participants: one group was asked to make an affective forecast regarding how they would feel after a potential drinking scenario while a second group was simply asked about the feelings they had about the drinking scenario. Participants who were in the affective forecasting condition reported more negative affect than the other group. However, even though the affective forecasting group made negative forecasts, they did not reduce their drinking behavior two weeks after reading the scenario.

1.5. The Importance of Affective Forecasting Interventions in Substance Use and Abuse

Some authors have made a case against correcting affective forecasting errors. Such errors can in fact be adaptive and motivational (Miloyan & Suddendorf, 2015). When preparing for an exam, it may be motivational to be believe that we would feel horrible if we failed said exam, even though the existing affective forecasting literature claims we would not feel nearly as bad as we think we would (Miloyan & Suddendorf, 2015). However, forecasting errors are not adaptive within the context of substance use; the belief that a drink will increase positive affect may result in increased drinking behavior (see Murgraff, McDermott, White, & Phillips, 1999).

Evidence for this claim comes from expectancy models of substance use. According to such models, positive alcohol outcome expectancies increase motivation to drink (Jones, Corbin, &

Fromme, 2001); in the case of alcohol use, individuals can learn that their positive expectancies are mistaken and that negative consequences of alcohol use may occur. However, negative 12

consequences such as hangovers were not significant predictors of time to next drink in a multivariate model (Epler et al., 2014). The role of outcome expectancies as predictors of substance use has been explored numerous times (see Katz, Fromme, & D’Amico, 2000; Wood,

Read, Palfai, & Stevenson, 2001); notably, there is evidence that priming affects outcome expectancies, pointing to the role of System 1 in such processes (Krank, Ames, Grenard,

Schoenfeld, & Stacy, 2010). Though affective forecasts have not to our knowledge been explicitly discussed within the context of expectancy theory, such forecasts fit neatly within this theory. Just as outcome expectancies are consequences that individuals believe may occur after substance use, affective forecasts are expectations specifically regarding the emotions in which substance use will result. In fact, although expectancy scales such as the Brief Comprehensive

Effects of Alcohol Scale (Addictive Behaviors Research Centre, 1997) do not exclusively focus on emotional expectancies, emotions are included in many expectancy measures as well as in descriptions of expectancy theory. For example, the Marijuana Effects Expectancy Questionnaire

(Torrealday et al., 2008) asks about expectations regarding the effects of cannabis on the feeling of relaxation. Moreover, in Morean, Corbin, and Treat (2012), alcohol expectancies are described as the belief that “drinking will enhance a positive emotional state or improve a negative emotional state” (p. 1). However, the role of affective forecasting errors in outcome expectancy judgements remains unresolved.

Affective forecasts are used to make health decisions (Halpern & Arnold, 2008) therefore, it is crucial that we measure such errors in the context of substance use. Drinking, and binge-drinking in particular, is exceedingly common on Canadian university campuses. A recent study found that 83.1% of students surveyed at a large Canadian university reported drinking in the past year and 69.7% of these students reported binge-drinking in the past month. Most of the 13

students who were binge-drinking reported doing so two to three times in the past month, and the problem was equally common in males and females (Edkins, Edgerton, & Roberts, 2017). The seriousness of the binge-drinking problem in young adults must be clearly understood: its risks include vehicular accidents, increased risk of future alcohol dependence, and health effects (i.e. liver disease) (Centers for Disease Control and Prevention, n.d.). Meanwhile, the recent legalization of cannabis may create another substance use problem on Canadian campuses. The prevalence rate of cannabis use for young adults (ages 20 to 24) was 26% in 2013 (Canadian

Tobacco Alcohol and Drugs, 2013). Cannabis use in young adults also brings up a host of issues.

For example, there is some evidence that cannabis can lead to increased risk of vehicular accidents (National Institute on Drug Abuse, n.d.) and a variety of evidence that heavy use affects neurocognition even in adults (Nader & Sanchez, 2018).

The evidence above demonstrates that young adults could benefit from a better ability to make affective forecasts about substance use. Decision affect theory has stated that individuals make decisions by the future emotions they expect to experience and the chances of those outcomes occurring into account. While this theory presumes that the decisions that we make are rational (controlled by System 2), even its authors conceded that cognitive biases (System 1 errors) creep into our forecasting of future emotions (Mellers & McGraw, 2001). It is widely accepted in the literature that affective forecasting plays some role in decision-making. For example, affective forecasting plays a role when choosing to pursue or not to pursue a treatment for a serious health condition, and mistakes may occur if patients forgo treatment because they inaccurately forecast their future emotions (Halpern & Arnold, 2008).

Moreover, the motivational model of alcohol use (Cox & Klinger, 1988) theorized that the decision to drink or not to drink is affected by distal factors (i.e. sociocultural environment, 14

biological predisposition) and current factors, which notably include anticipated positive and negative changes in affect. The motivational model is closely linked to affective forecasting errors, particularly as the authors conceded that anticipated changes in affect are subject to error and are influenced by both System 1 and 2 factors (Cox & Klinger, 2011). Prior to attempting to modify affective forecasting errors, it is essential to examine whether such errors exist within the context of substance use. Furthermore, in order to effectively correct forecasting errors, we must examine their mechanisms and potential moderators.

1.6. Rationale for the Current Study

As previously mentioned, affective forecasting has been studied in several positive event contexts, such as spring vacations (Wirtz et al., 2003) and dormitory selection (Dunn et al.,

2003). However, it has rarely been studied within the context of substance use, which differs from other positive events in several ways. Firstly, alcohol and cannabis use influences memory processes (i.e. Schreiber Compo et al., 2011; Solowij & Battisti, 2008), which play an important role in forming retrospective evaluations as well as affective forecasts. Furthermore, the popularity of alcohol use in the student community is likely to result in students holding implicit theories about their potential enjoyment of alcohol use. As discussed above, such implicit theories may also influence affective forecasts and retrospective evaluations. Finally, there is a rich background of literature on the short-term negative effects of alcohol use (i.e. vomiting, headaches, slurred speech) which provides a unique component to the substance use context that is not present in most other positive events. As it is evident that heavy alcohol use may cause seemingly negative consequences upon its ingestion, it is all the more interesting to determine whether individuals mis-predict and mis-remember such negative effects.

Moreover, the current study improves methodologically upon past studies in the affective forecasting and retrospection literature. In a 2012 article, Levine, Lench, Kaplan, and Safer posited that errors in affective forecasting may be methodological artifacts. Specifically, the authors claimed that when making affective forecasts, individuals respond to how they will feel about a specific event. When reporting on the event, individuals respond to how they feel in general, potentially resulting in inaccurate reports of current affect. In a series of studies, Levine and colleagues demonstrated that when questions are modified so that individuals respond to how they currently feel about the specific event, then they are able to respond accurately. The current study has taken care to phrase questions in such a way that participants respond to how they feel about the substance-use event in question. Finally, extant studies have used between- subjects designs, where one set of participants is asked to predict how they will feel in the future while another set of participants respond to how they feel after experiencing the event. As recommended by Kahneman and Riis (2005), the current study uses experience-sampling methodology to obtain measures of how individuals currently feel as they are experiencing the event. Such within-subjects designs allow for more statistical power as well as a more valid assessment of current affect.

1.7. The Current Study

As is evident from the literature reviewed above, while much research has been completed on affective forecasting, many of its details and mechanisms remain unclear. The current study will measure affective forecasting errors in order to reduce substance use behaviors. The study asks the following research question: Do young adults overestimate the intensity and duration of their enjoyment of substance use and does this affect their substance use problems? More specifically, the study aims to determine whether: 16

a. Given the differing biological effects of cannabis and alcohol, do affective

forecasting errors differ between the two substances? Specifically, do young

adults overestimate their enjoyment of cannabis more than alcohol, or vice versa,

when making forecasts? b. Are affective forecasting errors mediated by measures of outcome expectancies or

by emotional associate measures? Specifically, is System 1 or System 2 more

influential in causing affective forecasting errors?

Chapter 2: Methodology

2.1. Participants

Participants were recruited during the fall and winter of the 2018/2019 academic year through SONA, an online system where students registered in certain psychology courses could participate in research studies in exchange for course credit. Eligibility criteria for the study included being 19 years old or older, being an enrolled student at the University of British

Columbia, and planning to drink alcohol or use cannabis on the weekend following participation in the in-lab session of the study. As compensation for participation in the full study, participants received 1.5 SONA credits to be used towards their course grade. The participants who opted out of receiving SONA credits were eligible for an entry into a draw for a $10 Tim Horton’s gift card. On average, participants were 21.86 years old (SD = 5.75) and in their third year of university (SD = 1.04). Participants were primarily female (76.1%). Other descriptive and demographic information is available in Table 1.

A sample of n = 607 participants completed the initial pre-screening survey (Part 1; affective forecasts, questionnaires, demographics). Out of this sample, n = 54 participants were eligible and interested in participating in the experience sampling portion of the study (Part 2; affective experiences, retrospectives). Due to technical errors, n = 48 participants were sent Part

2 questionnaires to complete and only n = 46 were included in the dataset. Due to high attrition, a large proportion of data was missing at each time point. Table 2 describes the sample size for each time point and each substance while Table 3 describes the sample size per analysis. A total of n = 17 participants had full data for each time point.

2.2. Measures

2.2.1. Substance use frequency questionnaires. Participants completed the Alcohol Use

Disorder Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fuente, & Grant, 1993) and the Cannabis Use Disorders Identification Test (CUDIT; Adamson & Sellman, 2003) if they reported having had a drink of alcohol in the past month or having used cannabis in the past month. The AUDIT and CUDIT included 10 questions each to probe participants about potential problems they may be experiencing with their alcohol or cannabis use (i.e. ‘how often over the past year have you had a feeling of guilt or remorse after drinking?’). Questions on both measures ranged in scale from a binary Yes/No scale to a 5-pt scale measuring frequency (Never

– Daily or Almost Daily). The scores for the AUDIT could range from 0 to 46 while scores for the CUDIT could range from 0 to 52, with higher scores for each scale indicating higher likelihood of a substance use disorder. Question #1 of each questionnaire probed participants about their frequency of alcohol or cannabis use. If participants answered Never when asked about the frequency of their use, they were directed to skip the remainder of the AUDIT or

CUDIT questions. A score from 16 to 19 on the AUDIT indicates high-risk or harmful level drinking behavior (Australian Government Department of Veterans’ Affairs, n.d.) while a score of 8 or higher on the CUDIT indicates potential cannabis use disorder (Adamson & Sellman,

2003) with acceptable sensitivity and predictive value. Due to differences in scoring of scales, cut-off points were not used in the current analyses. AUDIT and CUDIT questions were summed to obtain a separate AUDIT sum score and CUDIT sum score. See Appendices A and B for copies of the AUDIT and CUDIT.

2.2.2. Word Associates Task (WAT). Participants were presented with cue phrases (i.e. When

I feel happy I will...) and were asked to type the first action that came to mind when reading the 19

cue (i.e. ‘drink alcohol’). Participants then used a self-coding procedure in order to sort their responses into categories, including ‘Marijuana,’ ‘Alcohol,’ ‘Leisure,’ and so forth. This self- coding procedure is intended to reduce the ambiguity inherent in researchers’ coding of participants’ potentially ambiguous responses. The fast-response style of this task is meant to act as a measure of System 1 substance use associates. In order to obtain a cannabis WAT score, each time that a participant had coded an ambiguous word into the cannabis category was summed. The same procedure was followed with the alcohol categories in order to obtain an alcohol WAT score. The maximum possible score for each measure was 20, indicating that participants responded with a cannabis or alcohol-related response to each cue phrase.

Participant with higher scores would be considered to have more alcohol and cannabis use associates in System 1. See Appendix C for a copy of the WAT.

2.2.3. Outcome Expectancy Liking Task (OEL). Participants were asked to list four things they believed would happen if they used a moderate amount of alcohol. After listing each expectation, participants rated whether they would like each outcome on a scale of Not Like a

Lot to Like a Lot. Participants then completed the same task with ‘moderate amount of cannabis’ as the cue. Scores on the OELs could range from -2 to 2, with positive scores indicating overall higher liking of alcohol or cannabis use expectancies. A previous study noted that the OEL total score demonstrates criterion validity as higher scores are related to earlier initiation of substance use and faster escalation of substance use (Fulton, Krank, & Stewart, 2012). The slower-response style of this task is meant to act as a measure of System 2 substance use cognitions. See

Appendix D for a copy of the OEL.

2.2.4. Substance use attitudes questionnaires. Participants completed the Brief

Comprehensive Effects of Alcohol scale (B-CEOA; Addictive Behaviors Research Centre, 1997) 20

as a measure of their attitudes towards substance use. The B-CEOA is a 15-item scale that evaluates individuals’ expectancies of several positive and negative effects of alcohol (i.e. the likelihood of these events occurring) as well as their valuations of these effects (i.e. whether they would be desirable or not). The B-CEOA was adapted from the original 38-item scale (Fromme,

Stroot, & Kaplan, 1993). A psychometric analysis indicated that the B-CEOA demonstrates concurrent validity with drinking problems and weekly drinking levels. The factors on the scale include Sociability, Self-Perception, and Risk/Aggression (Ham, Stewart, Norton, & Hope,

2005). A participant scoring 42 on the B-CEOA (the maximum score taking into account reverse-scoring) would be considered to have overall positive expectancies of alcohol use. In order to assess their attitudes towards cannabis, participants completed the Marijuana Effect

Expectancy Questionnaire – Brief (MEEQ-B; Torrealday et al., 2008). This is a 6-item scale with two factors: negative and positive expectancies. The MEEQ-B demonstrated poor test-retest reliability in past studies; however, the authors noted that this was assessed in a sample of adolescents in treatment. Moreover, there were significant negative correlations between negative expectancies on the MEEQ-B and cannabis use (Torrealday et al., 2008). A participant scoring 22 (the maximum score taking into account reverse-scoring) on the MEEQ-B would be considered to have overall positive expectancies of cannabis use. Both the B-CEOA and the

MEEQ-B were used as questionnaires in order to target System 2 cognitions regarding substance use due to the slower-response style of the questionnaires. Responses to the MEEQ and the B-

CEOA were averaged to obtain a mean MEEQ score and a mean B-CEOA score, after accounting for reverse-coded questions. See Appendices E and F for copies of the B-CEOA and

MEEQ, respectively.

2.2.5. Measures of affective forecasting errors. The measure of affective forecasting errors in this study was modelled on measures of substance use outcome expectancies conducted in prior studies, which have been strong predictors of substance use behaviors (Fulton et al., 2012).

Participants were presented with five emotions (happiness, relaxation, having fun, feeling sexy, and excitement). The emotions were selected based on research interest and due to the researchers’ hypotheses that these emotions would be most relevant to substance use experiences. The participants then rated the predicted intensity (on a scale of 0-100) and duration

(on a scale of I don’t expect to experience this emotion to I expect to experience this emotion longer than one week) of these emotions. Predicted intensity of 100 for each emotion indicated that participants expected to experience the particular emotion very intensely. Such questions were meant to assess both the impact and the durability bias. Participants completed these measures at 7 time points in total, before, during, and after drinking or using cannabis. The first measures were completed in person on a computer at the laboratory, whereas the following measures were completed via an online survey on the participants’ phones in whatever location the participants chose. The language of the phone survey measures was modified slightly, from

“how intense do you expect X emotion to be” to “as you are thinking about drinking right now, how intensely are you experiencing X emotion?” Participants were also asked to indicate whether they were experiencing each of the five emotions on a dichotomous (Yes/No) scale as well as to indicate how many drinks or portions of cannabis they had consumed. Participants who were not currently consuming either substance had the option to select “I am not currently consuming” on each survey. See Appendix G for copies of each affective forecasting questionnaire.

2.3. Procedure

In order to measure affective forecasting errors, participants participated in a one-hour in- lab session on a weekday. After signing a consent form, participants completed all the questionnaires listed above (i.e. outcome expectancy task, demographics, substance use behaviors, and affective forecasts) during this session. At the end of the survey, all participants were asked whether they currently have plans to consume alcohol or cannabis over the upcoming weekend (the Friday, Saturday, or Sunday of the weekend following the week they participated in the in-lab session). Participants who indicated that they currently had plans to consume either substance were asked whether they were interested in participating in Part 2 of the study after being provided with a brief description. Those who were interested in participating signed a second consent form and confirmed that they were 19 years old or older. Afterwards, participants completed a second brief questionnaire providing information about the time, date, and context that they planned to consume as well as contact information. Participant were asked to provide either an e-mail address or a cellphone number and all were reminded to keep their cellphone and notifications on during the time that they planned to consume. Participants received an extra 0.5

SONA credits or an entry into a draw for completing at least half of the questionnaires that they were sent for Part 2.

During the date and time that participants indicated they would be consuming, they were sent up to a total of four questionnaires, 45 minutes apart each. The first questionnaire was sent

45-minutes after the start of the time participants planned to consume. If participants were consuming for less than three hours, they would receive fewer questionnaires (i.e. one questionnaire if they were only consuming for one hour). Each questionnaire was received through e-mail or text. Participants first indicated which substance they were consuming 23

(Alcohol/Cannabis/Neither). If they indicated that they were not consuming either substance, the questionnaire automatically ended and thanked them for their participation. However, the following questionnaires would still be sent. Once they indicated which substance they were consuming, participants indicated whether they were experiencing each emotion (Yes/No) and how intensely. Additional details regarding the questionnaires are available in section 2.2.5. At

12pm the day after they indicated they would be consuming, participants were sent another questionnaire with the same format but modified wording, asking them to answer the same duration and intensity questions regarding the previous day. Finally, at 12pm one week following consuming, participants were again sent the same questionnaire with modified wording to complete.

2.4. Data Analysis Plan

The time that each participant completed each experience questionnaire was examined to prepare the data. Participants’ data was retained as long as questionnaires were completed the night that they had used substances (i.e. participants who chose to answer the questionnaires the next day were removed). Due a lack of questionnaires completed within the initially set 45 - minute timeline, only these late submissions were removed while all other responses were retained in the dataset.

After calculating means and standard deviations for all descriptive statistics, analyses of variance were used to make the initial comparisons between average forecasts, experiences, and memories of intensity of emotion for cannabis and alcohol users. The cannabis-specific forecasts were used for the participants who had used primarily cannabis and the alcohol-specific forecasts were used for participants who had primarily used alcohol. Levene’s tests were conducted to test for homogeneity of variances between alcohol and cannabis users. If the tests were significant, 24

subsequent comparisons used Welch’s F-test. If Levene’s tests were not significant, subsequent comparisons used traditional analyses of variance (ANOVAs). All Levene’s tests, Welch’s F- tests, and ANOVAs were conducted using the car package on R (Fox & Weisberg, 2011).

Growth curve modelling was used to determine whether the affect intensity data fit the expected pattern and whether the participants exhibited an impact bias. The data was initially restructured into a ‘long’ format using the SPSS Restructure function. All other analyses were conducted using the nlme package on R (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team,

2019). All analyses followed the same order: firstly, a baseline model, a random intercept model, a model with Time as a predictor, and a random slopes model (with Time as a predictor) were created. An ANOVA was used to compare the five models. The model that had the best fit for the data (as demonstrated by a p-value less than .05) was then updated to add a quadratic and a cubic term that would allow us to determine whether the data was better represented by either of these terms than by a linear model. The best-fitting model from the linear models was compared to the quadratic and cubic models using an ANOVA. Finally, the results from the best-fitting model (linear, cubic, or quadratic) were reported. More detailed analyses were completed using repeated-measures general linear models on SPSS Version 25 (IBM Corp, 2017).

Chi-square analyses were used to determine whether the participants exhibited a durability bias. A variable was added to the model to code for how long the participants had felt each emotion. The last time the participant had marked that they felt a particular emotion was used to determine overall length. For example, if a participant had indicated that they did feel happy when drinking alcohol, that they did not remember feeling happy the day after, and that they did remember feeling happy the next week, they were coded as having experienced the emotion for one week. Participants who forecasted experiencing an emotion for longer than one 25

week were recoded to having predicted a one-week duration in order to allow for proper comparisons. Chi-square analyses were used to compare forecasted durations for each emotion to length of experienced emotions. Comparisons were split by substance, such that the alcohol- specific forecasts of participants who had used alcohol over the weekend were compared to the experiences of these participants, while the cannabis-specific forecasts of participants who had used cannabis over the weekend were compared to their experiences.

Polynomial regression with response surface analysis (PRRSA) was used to determine whether the discrepancies between participants’ forecasted and experienced emotions predicted their overall substance use as well as their substance-use problems. PRRSA is a recent method of examining data with difference scores. Shanock and colleagues (2010) explain that PRRSA can be used to answer how agreement, degree of difference, and direction of difference between scores (i.e. forecasts and experiences of emotions) relates to an outcome variable. Edwards

(2002) explains that difference scores, which are often used in several fields of psychological research, may cause methodological problems in decreasing reliability as well as reduce meaningfulness of findings by using one score to represent separate constructs. PPRSA, as evidenced by its name, involves two parts. Firstly, polynomial regression is used to investigate non-linear data (i.e. data with a potentially quadratic or cubic form) (Dalal & Zickar, 2012).

Instead of using a single difference score as a predictor, polynomial regression allows us to see the congruence between two scores as “correspondence between the component measures in a two-dimensional space” (Edwards, 2002, p. 360). Then, response surface analysis allows for the visualization of how congruence between two predictors relates to an outcome as well as significance testing of the congruence slopes (Edwards, 2002; see also, Shanock, 2010).

In order to prepare the data, the predictor variables (forecasts and experienced emotions) were centered around their midpoints (i.e. fifty was subtracted from each score). Then, three new variables were created: the square of the centered forecast, the square of the centered experience, and the cross-product of forecast and experience scores. After running the polynomial regressions using SPSS syntax available through Shanock and colleagues (2010), we entered the unstandardized beta values for each variable (forecast, experience, forecast squared, experience squared, and cross-product) as well as their standard errors and covariances into an Excel sheet provided by Shanock and colleagues (2010). The Excel formulas allowed for the calculation of the surface values (a1 through a4). A significant a1 represented a “linear additive relationship along the line of perfect agreement” (Shanock, 2010, p. 549) between the predictor variables as it related to each outcome. Essentially, as both predictor variables would increase, so would the outcome. A significant a2 represented a “non-linear slope of the line of perfect agreement”

(Shanock, 2010, p. 549), indicating a non-linear relationship between the congruence of the two predictors and the outcome variable. a3 represented the direction of the discrepancy and its effect on the outcome (i.e. whether the outcome variable value is larger when experience is greater than forecast or vice-versa) and a4 represented the degree of discrepancy and its effect on the outcome (i.e. how the outcome variable is affected as the discrepancy between forecast and experience increases or decreases) (Shanock, 2010). All surface values were analyzed in these analyses, although a4 values were particularly related to the current study’s research questions.

Finally, in order to determine whether cognitive variables (i.e. WAT scores) mediated the relationship between forecast-experience discrepancy scores and alcohol/cannabis use, the SPSS

Process macro was used (Hayes, 2013). Firstly, a discrepancy score was calculated by subtracting experienced affect for each emotion from forecasted affect. The forecast chosen for 27

each participant was specific to the substance they used. For example, if participants had used alcohol over the weekend, their experienced affect was subtracted from their alcohol use-specific forecast, not from their cannabis-use forecast or from a mean forecast. Bootstrapping (5000 samples) and confidence intervals were used to determine whether there were significant indirect effects.

Chapter 3: Results

3.1. Descriptive Statistics

3.1.1. Forecasts. Affect intensity forecasts ranged from M = 47.24 (SD = 35.77) to M =

82.42 (SD = 21.01) across all substances. When using alcohol, participants expected to feel the least sexy of all the emotions listed (M = 55.27, SD = 34.32) and to have the most fun and happiness (M = 85.25, SDfun = 21.30, SDhappy = 8.35). When using cannabis, participants expected to feel the least sexy (M = 34.09, SD = 35.69) and the most happy (M = 82.58, SD =

16.78). For both alcohol and cannabis, participants mainly expected to feel each emotion only while using the substance, on average (Mode: 1.00 [‘Only While Using’]). However, for cannabis use, participants did not expect to feel sexy and for alcohol use, they expected to feel relaxed until the next day.

3.1.2. Experiences. Participants’ experienced affects ranged from M = 45.95 (SD =

26.46) to M = 75.28 (SD = 18.04). On average, participants experienced almost identical average affect intensities for both alcohol (M = 67.22, SD = 14.12) and cannabis (M = 66.79, SD =

11.76). For both alcohol and cannabis, participants experienced the lowest intensities of sexiness

(Malcohol = 44.34, SD = 29.61; Mcannabis = 48.89, SD = 20.51). For alcohol, participants experienced the highest intensities of fun (M = 78.69, SD = 18.36), while for cannabis, they experienced the highest intensities of happiness (M = 78.58, SD = 8.01). On average, participants experienced the most intense emotions at Time 3 (M = 74.27, SD = 15.31).

3.1.3. Memories. The average remembered intensity of affect from the previous day was 69.70 (SD = 14.92) across all emotions. For alcohol memories, participants remembered having the most fun (M = 85.29, SD = 8.38) and feeling the least sexy (M = 40.00, SD = 27.16) the previous day. For cannabis memories, participants remembered feeling the most happiness 29

(M = 84.50, SD = 11.41) and the least sexiness (M = 47.56, SD = 29.18) the previous day. On a binary scale, participants on average remembered having experienced fun, relaxation, and happiness while using alcohol the previous day, but not excitement or sexiness. The same pattern persisted for cannabis users.

The average remembered intensity of affect from the previous week was 66.65 (SD =

14.83) across all emotions. For alcohol memories, participants remembered feeling the most happiness (M = 79.12, SD = 13.61) and feeling the least sexy (M = 38.24, SD = 31.07) the previous week. For cannabis memories, participants remembered feeling the most relaxation (M

= 78.89, SD = 25.83) and the least sexiness (M = 43.89, SD = 27.81) the previous week. On a binary scale, participants on average remembered having experienced fun, relaxation, excitement, and happiness while using alcohol the previous week, but not sexiness. A similar pattern persisted for cannabis users, although cannabis users did not remember feeling excitement along with sexiness the previous week and did remember feeling the other three emotions listed. Summary statistics of affective forecasts, experiences, and memories are available in Table 4.

3.1.4. Cognitions. Participants on average associated 3.14 (SD = 4.13) out of a total of

19 ambiguous cues with cannabis and 6.21 (SD = 2.39) out of a total of 19 ambiguous cues with alcohol. Participants’ average alcohol outcome expectancy liking score was 1.24 (SD = 0.70) and their average cannabis outcome expectancy liking score was 0.78 (SD = 0.89). Accordingly, participants also had an average score of 2.86 (SD = 0.38) on the B-CEOA and an average score of 3.52 (SD = 0.43) on the MEEQ. Therefore, participants expected fairly positive outcomes to occur from drinking alcohol and using cannabis.

3.1.5. Substance Use. Participants received an average score of 8.67 (SD = 4.25, Range:

2-20) on the AUDIT and an average score of 6.14 (SD = 5.62, Range: 0-22) on the CUDIT.

Therefore, the frequency of alcohol use problems was relatively high in the current sample while the frequency of cannabis use problems was low.

3.2. Comparisons Between Substances

3.2.1. Forecasts.

3.2.1.2 Levene’s Tests. Levene’s tests were conducted to test for homogeneity of variances between alcohol and cannabis users. A significant difference was revealed between variances for happiness forecasts (F = 8.13, p < 0.01) and excitement forecasts (F = 4.31, p <

0.05) for cannabis and alcohol users. Levene’s tests were not significant for relaxation (F =

0.002, p = 0.96), fun (F = 0.33, p = 0.57), or sexiness (F = 0.02, p = 0.88) forecasts.

3.2.1.3 Analyses of Variance. Analyses of Variance (ANOVAs) were conducted to determine whether average forecast intensities differed between individuals who had consumed cannabis versus alcohol. As Levene’s tests were significant for happiness and excitement forecasts, Welch’s F tests were applied to the data in order to adjust for differences in variance.

Welch’s F-tests revealed no significant differences in intensity of happiness [F(1, 11.15) = 0.88, p = 0.37] in alcohol and cannabis users but did reveal significant differences in excitement forecasts [F(1, 15.83) = 7.52, p < 0.05]. For the remainder of the analyses, traditional one-way

ANOVAs were conducted as Levene’s tests were not significant. Forecast intensities for fun

[F(1, 29) = 1.02, p = 0.32] and sexiness [F(1, 27) = 2.53, p = 0.12] did not differ significantly between alcohol and cannabis users; however, there was a significant difference between users of the two substances for relaxation forecasts [F(1, 28) = 9.19, p = < 0.01].

3.2.2. Experiences.

3.2.2.1. Levene’s Tests. Levene’s tests were conducted to test for homogeneity of variances between alcohol and cannabis users. Levene’s tests were not significant for happiness

[F(1, 32) = 0.68, p = 0.42], relaxation [F(1, 32) = 0.00, p = 0.95], fun [F(1, 29) = 0.08, p =

0.77], sexiness [F(1, 29) = 2.42, p = 0.13], or excitement [F(1, 28) = 0.41, p = 0.53] experiences between alcohol and cannabis users.

3.2.2.2. Analyses of Variance. Analyses of Variance (ANOVAs) were conducted to determine whether average experience intensities differed between individuals who had consumed cannabis versus alcohol. As Levene’s tests were not significant, traditional one-way

ANOVAs were conducted. Intensity of experiences for happiness [F(1, 32) = 0.0005, p = 0.98], relaxation [F(1, 32) = 0.54, p = 0.47], fun [F(1, 29) = 2.08, p = 0.16], sexiness [F(1, 29) = 0.20, p = 0.65], and excitement [F(1, 28) = 0.06, p = 0.81] did not differ significantly in intensity between alcohol and cannabis users.

3.2.3. Memories from the Previous Day.

3.2.3.1. Levene’s Tests. Levene’s tests were conducted to test for homogeneity of variances between alcohol and cannabis users. Levene’s tests were not significant for happiness

[F(1, 25) = 0.01, p = 0.93], relaxation [F(1, 26) = 1.19, p = 0.28], fun [F(1, 26) = 3.52, p =

0.07], sexiness [F(1, 24) = 0.22, p = 0.65], or excitement [F(1, 24) = 0.07, p = 0.79] memories of the previous day between alcohol and cannabis users.

3.2.3.2. Analyses of Variance. Analyses of Variance (ANOVAs) were conducted to determine whether average memory intensities differed between individuals who had consumed cannabis versus alcohol the previous day. As Levene’s tests were not significant, traditional one- way ANOVAs were conducted. Intensity of memories for happiness [F(1, 25) = 0.04, p = 0.84], 32

relaxation [F(1, 26) = 2.47, p = 0.13], sexiness [F(1, 24) = 0.43, p = 0.52], and excitement

[F(1, 24) = 1.96, p = 0.17] did not differ significantly in intensity between participants who had consumed alcohol and cannabis the previous day. However, intensity of memories for fun were marginally significant in differing between alcohol and cannabis users [F(1, 24) = 3.80, p =

0.06].

3.2.4. Memories from the Previous Week.

3.2.4.1. Levene’s Tests. Levene’s tests were conducted to test for homogeneity of variances between alcohol and cannabis users. Levene’s tests were not significant for differences in variance for happiness [F(1, 24) = 3.14, p = 0.09], relaxation [F(1, 24) = 0.22, p = 0.65], fun

[F(1, 24) = 2.27, p = 0.15], sexiness [F(1, 24) = 0.22, p = 0.65], or excitement [F(1, 24) = 0.38, p = 0.54] memories of the previous week for alcohol and cannabis users.

3.2.4.2. Analyses of Variance. Analyses of Variance (ANOVAs) were conducted to determine whether average memory intensities differed between individuals who had consumed cannabis versus alcohol the previous week. As Levene’s tests were not significant, traditional one-way ANOVAs were conducted. Intensity of memories for happiness [F(1, 24) = 0.98, p =

0.33], relaxation [F(1, 24) = 0.15, p = 0.70], fun [F(1, 24) = 3.00, p = 0.10], sexiness [F(1, 24)

= 0.21, p = 0.65], and excitement [F(1, 24) = 0.24, p = 0.63] did not differ significantly in intensity between participants who had consumed alcohol and cannabis the previous week.

3.3. Repeated Measures Analyses

3.3.1. Happiness. A repeated measures ANOVA with a Greenhouse-Geisser correction determined that happiness intensity was marginally significant in varying between time points

[F(1, 4.40) = 2.34, p = 0.07]. Furthermore, there was no significant interaction between Time and Primary Substance used [F(1, 4.40) = 1.39, p = 0.25]. Detailed analyses revealed that there 33

was no significant difference between participants’ forecasts and averaged experiences [F(1, 1) =

0.64, p = 0.43]. There were significant differences between happiness intensities at the four experience time points [F(1, 2.76) = 4.86, p < 0.05] and between happiness experiences and happiness retrospective time point intensities [F(1, 1.86) = 3.57, p < 0.05]. However, in post-hoc pairwise comparisons, this difference was observed to lie between participants’ retrospective forecasts from the day before and the week before, such that participants tended to remember feeling happier during their substance use experience one day after consuming than one week after consuming (Mean Difference: 7.50 (SE = 2.70), p < 0.05). Therefore, we can conclude that there was not a significant rosy hindsight bias and that participants did not make affective forecasting errors for happiness.

3.3.2. Relaxation. A repeated measures ANOVA with a Greenhouse-Geisser correction determined that relaxation intensity did not vary significantly between time points [F(1, 2.67) =

1.11, p = 0.36]. Furthermore, there was no significant interaction between Time and Primary

Substance used [F(1, 4.40) = 0.64, p = 0.58]. Detailed analyses revealed that there was a significant difference between participants’ forecasted and averaged experiences [F(1, 1) = 7.23, p < 0.05]. Moreover, there were significant differences between relaxation intensities at the four experience time points [F(1, 2.11) = 3.46, p < 0.05], but not between relaxation forecasts and relaxation retrospective time point intensities [F(1, 1.79) = 0.82, p = 0.44]. Therefore, we can conclude that there was not a significant rosy hindsight bias but that participants made affective forecasting errors for relaxation.

3.3.3. Fun. A repeated measures ANOVA with a Greenhouse-Geisser correction determined that fun intensity varied significantly between time points [F(1, 3.08) = 3.01, p <

0.05]. Furthermore, there was no significant interaction between Time and Primary Substance 34

used [F(1, 3.08) = 0.93, p = 0.44]. Detailed analyses revealed that there was a marginally significant difference between participants’ forecasted and averaged experiences [F(1, 1) = 4.30, p = 0.05]. Moreover, there were significant differences between fun intensities at the four experience time points [F(1, 2.13) = 5.03, p < 0.05] and marginally significant differences between fun experiences and fun retrospective time point intensities [F(1, 1.43) = 3.50, p =

0.06]. In post-hoc pairwise comparisons, it was observed that there was a significant difference between participants’ fun intensity memories one day and one week post-consumption, such that participants remembered having significantly more fun one day after consuming than one week after (Mean Difference: 7.21 (SE = 2.04), p < 0.05). Therefore, we can conclude that there was not a significant rosy hindsight bias but that participants made affective forecasting errors for fun.

3.3.4. Sexiness. A repeated measures ANOVA with a Greenhouse-Geisser correction determined that sexiness intensity did not vary significantly between time points [F(1, 2.30) =

1.61, p = 0.22]. Furthermore, there was a marginally significant interaction between Time and

Primary Substance used [F(1, 2.30) = 3.03, p = 0.06]. Detailed analyses revealed that there was no significant difference between participants’ forecasted and averaged experiences [F(1, 1) =

0.15, p = 0.70]. Moreover, there were significant differences between sexiness intensities at the four experience time points [F(1, 2.10) = 3.85, p < 0.05], but not between sexiness experiences and sexiness retrospective time point intensities [F(1, 1.68) = 1.32, p = 0.28]. Therefore, we can conclude that there was not a significant rosy hindsight bias and that participants did not make affective forecasting errors for sexiness.

3.3.5. Excitement. A repeated measures ANOVA with a Greenhouse-Geisser correction determined that excitement intensity did not vary significantly between time points [F(1, 2.67) = 35

0.67, p = 0.56]. However, there was a significant interaction between Time and Primary

Substance used [F(1, 2.67) = 4.38, p < 0.05]. Detailed analyses revealed that there was no significant difference between participants’ forecasted and averaged experiences [F(1, 1) = 0.07, p = 0.79]. Moreover, there were no significant differences between excitement intensities at the four experience time points [F(1, 2.41) = 2.13, p = 0.13] or between excitement experiences and excitement retrospective time point intensities [F(1, 1.56) = 0.35, p = 0.65]. Therefore, we can conclude that there was not a significant rosy hindsight bias and that participants did not make affective forecasting errors for sexiness.

3.4. Growth Curve Models

3.4.1. Happiness. The relationship between time and intensity of happiness ratings showed significant variance in intercepts across participants, SD = 9.65 (95% CI: 6.94, 13.44),

χ2(3) = 30.93, p < .0001, but not in slopes χ2 (6) = 2.76, p = 0.25. A cubic model fit the data significantly better than a linear model or quadratic model [χ2 (6) = 8.56, p < 0.01]. In the cubic model, time significantly predicted intensity of happiness (b = -0.48, t(147) = -2.92, p < 0.01). A graph of the growth curve model is demonstrated in Figure 1.1.

3.4.2. Relaxation. The relationship between time and intensity of relaxation ratings showed significant variance in intercepts across participants, SD = 1.98 (95% CI: 1.02, 3.85),

χ2(3) = 53.38, p < .0001, and in slopes χ2 (6) = 9.47, p < 0.05. A cubic model fit the data significantly better than a linear model or quadratic model [χ2 (8) = 4.95, p < 0.05]. As relaxation forecasts differed between cannabis and alcohol users, an interaction between Time and Primary Substance Used was also added into the model, but did not improve the cubic model significantly, χ2 (9) = 0.58, p = 0.44. In the cubic model, time significantly predicted intensity of

happiness (b = -0.465, t(137) = -3.92, p < 0.0001). A graph of the growth curve model is demonstrated in Figure 1.2.

3.4.3. Fun. The relationship between time and intensity of fun ratings showed significant variance in intercepts across participants, SD = 2.46 (95% CI: 1.45, 4.20), χ2(3) =

39.81, p < .0001, and in slopes χ2 (6) = 6.19, p = 0.05. A cubic model fit the data significantly better than a linear model or quadratic model [χ2 (8) = 14.67, p < 0.0001]. In the cubic model, time significantly predicted intensity of fun (b = -0.64, t(95) = -3.92, p < 0.0001). A graph of the growth curve model is demonstrated in Figure 1.3.

3.4.4. Sexiness. The relationship between time and intensity of sexiness ratings showed significant variance in intercepts across participants, SD = 27.74 (95% CI: 20.07, 38.33), χ2(3) =

80.33, p < .0001, and in slopes χ2 (6) = 14.26, p < 0.01. A cubic model fit the data significantly better than a linear model or quadratic model [χ2 (8) = 4.34, p < 0.05]. In the cubic model, time significantly predicted intensity of sexiness (b = -0.47, t(135) = -2.08, p < 0.05). A graph of the growth curve model is demonstrated in Figure 1.4.

3.4.5. Excitement. The relationship between time and intensity of excitement ratings showed significant variance in intercepts across participants, SD =20.30 (95% CI: 13.50, 30.52),

χ2(3) = 39.42, p < .0001, and in slopes χ2 (6) = 11.23, p < 0.01. Neither a quadratic [χ2 (7) =

0.07, p = 0.80] nor a cubic model [χ2 (8) = 0.37, p = 0.54] fit the data better than a linear model with random intercepts and slopes. Furthermore, an interaction term between Time and Primary

Substance also did not significantly improve the model, χ2 (7) = 0.66, p < 0.42. In the linear model, time did not significantly predict intensity of excitement (b = 0.39, t(137) = 0.38, p =

0.70). A graph of the growth curve model is demonstrated in Figure 1.5.

3.5. Chi-Square Analyses

3.5.1. Happiness. A chi-square test of independence was performed to determine the relationship between forecasted durations of happiness and experienced durations. Neither alcohol X2 (6, N = 31) = 7.31, p = 0.29 nor cannabis users’ X2 (4, N = 10) = 0.83, p = 0.93 forecasts differed significantly from their experiences.

3.5.2. Relaxation. A chi-square test of independence was performed to determine the relationship between forecasted durations of relaxation and experienced durations. Neither alcohol X2 (9, N = 31) = 9.08, p = 0.43 nor cannabis users’ X2 (4, N = 27) = 5.96, p = 0.43 forecasts differed significantly from their experiences.

3.5.3. Fun. A chi-square test of independence was performed to determine the relationship between forecasted durations of fun and experienced durations. Neither alcohol X2

(9, N = 28) = 8.74, p = 0.46 nor cannabis users’ X2 (6, N = 24) = 5.00, p = 0.54 forecasts differed significantly from their experiences.

3.5.4. Sexiness. A chi-square test of independence was performed to determine the relationship between forecasted durations of sexiness and experienced durations. Neither alcohol

X2 (9, N = 28) = 10.16, p = 0.34 nor cannabis users’ X2 (3, N = 24) = 2.25, p = 0.52 forecasts differed significantly from their experiences.

3.5.5. Excitement. A chi-square test of independence was performed to determine the relationship between forecasted durations of excitement and experienced durations. Neither alcohol X2 (9, N = 27) = 9.69, p = 0.38 nor cannabis users’ X2 (4, N = 23) = 2.91, p = 0.57 forecasts differed significantly from their experiences.‘

3.6. Polynomial Surface Regression Analyses

3.6.1. Happiness.

3.6.1.1. AUDIT scores. Polynomial surface regressions were performed to determine whether the discrepancy between happiness forecasts and happiness experiences predicted

AUDIT and CUDIT scores. Both happiness forecast scores (b = 0.10, SE = 0.12, p = 0.40), experience scores (b = -0.13, SE = 0.14, p = 0.38), as well as their interactions (b = 0.003, SE =

0.004, p = 0.44), did not significantly predict AUDIT scores. There was no significant linear additive relationship between happiness forecasts and experiences as they related to AUDIT scores (b = -0.02, SE = 0.23, p = 0.92). Furthermore, there was no significant non-linear slope of the line of perfect agreement between happiness forecasts and experiences as they related to

AUDIT scores (b < 0.001, SE = 0.004, p = 1.00). The degree of discrepancy between happiness forecasts and experiences did not significantly predict a change in AUDIT scores (b = -0.01, SE

= 0.005, p = 0.22). Finally, the direction of the degree of discrepancy did not significantly predict a change in AUDIT scores (b = 0.23, SE = 0.13, p = 0.08). Happiness forecast and experience discrepancies did not explain a significant proportion of variance in AUDIT scores (R2 = 0.19).

See Table 5 for more details and Figure 2.1. for the surface analysis graph.

3.6.1.2. CUDIT scores. Both happiness forecast scores (b = -0.10, SE = 0.12, p = 0.47), experience scores (b = -0.17, SE = 0.14, p = 0.25), as well as their interactions (b = -0.00, SE =

0.00, p = 0.71), did not significantly predict CUDIT scores. There was no significant linear additive relationship between happiness forecasts and experiences as they related to CUDIT scores (b = -0.40, SE = 0.25, p = 0.12). Furthermore, there was a marginally significant non- linear slope of the line of perfect agreement between happiness forecasts and experiences as they related to CUDIT scores (b = 0.01, SE = 0.01, p = 0.06). The degree of discrepancy between 39

happiness forecasts and experiences did not significantly predict a change in CUDIT scores (b =

0.001, SE = 0.005, p = 0.83). Finally, the direction of the degree of discrepancy did not significantly predict a change in CUDIT scores (b = 0.20, SE = 0.13, p = 0.14). Happiness forecast and experience discrepancies explained a significant proportion of variance in CUDIT scores (R2 = 0.44). See Table 6 for more details and Figure 2.2. for the surface analysis graph.

3.6.2. Relaxation.

3.6.2.1. AUDIT scores. Polynomial surface regressions were performed to determine whether the discrepancy between relaxation forecasts and relaxation experiences predicted

AUDIT and CUDIT scores. Both relaxation forecast scores (b = -0.09, SE = 0.27, p = 0.74), experience scores (b = 0.03, SE = 0.07, p = 0.64), as well as their interactions (b = -0.003, SE =

0.003, p = 0.32), did not significantly predict AUDIT scores. There was no significant linear additive relationship between relaxation forecasts and experiences as they related to AUDIT scores (b = -0.06, SE = 0.27, p = 0.84). Furthermore, there was no significant non-linear slope of the line of perfect agreement between relaxation forecasts and experiences as they related to

AUDIT scores (b = 0.002, SE = 0.003, p = 0.57). The degree of discrepancy between relaxation forecasts and experiences did not significantly predict a change in AUDIT scores (b = 0.01, SE =

0.005, p = 0.11). Finally, the direction of the degree of discrepancy did not significantly predict a change in AUDIT scores (b = -0.12, SE = 0.29, p = 0.67). Relaxation forecast and experience discrepancies did not explain a significant proportion of variance in AUDIT scores (R2 = 0.10).

See Table 7 for more details and Figure 2.3. for the surface analysis graph.

3.6.2.2. CUDIT scores. Both relaxation forecast (b = 0.18, SE = 0.24, p = 0.47) and experience scores did not significantly predict CUDIT scores. However, their interaction did predict CUDIT scores (b = -0.01, SE = 0.003, p < 0.05). There was no significant linear additive 40

relationship between relaxation forecasts and experiences as they related to CUDIT scores (b =

0.24, SE = 0.24, p = 0.34). Furthermore, there was no significant non-linear slope of the line of perfect agreement between relaxation forecasts and experiences as they related to CUDIT scores

(b < 0.0001, SE = 0.004, p = 1.00). The degree of discrepancy between relaxation forecasts and experiences did significantly predict a change in CUDIT scores (b = 0.01, SE = 0.005, p < 0.05).

Specifically, as the degree of discrepancy between relaxation forecasts and experiences increased, so did CUDIT scores. Finally, the direction of the degree of discrepancy did not significantly predict a change in CUDIT scores (b = 0.12, SE = 0.25, p = 0.65). Relaxation forecast and experience discrepancies explained a significant proportion of variance in CUDIT scores (R2 = 0.52). See Table 8 for more details and Figure 2.4. for the surface analysis graph.

3.6.3. Fun.

3.6.3.1. AUDIT scores. Polynomial surface regressions were performed to determine whether the discrepancy between fun forecasts and fun experiences predicted AUDIT, CUDIT, drinking frequency, and cannabis use frequency scores. Both fun forecast scores (b = 0.06, SE =

0.13, p = 0.66), experience scores (b = 0.09, SE = 0.20, p = 0.67), as well as their interactions (b

= -0.003, SE = 0.01, p = 0.66), did not significantly predict AUDIT scores. There was no significant linear additive relationship between fun forecasts and experiences as they related to

AUDIT scores (b = 0.14, SE = 0.32, p = 0.65). Furthermore, there was no significant non-linear slope of the line of perfect agreement between fun forecasts and experiences as they related to

AUDIT scores (b = -0.002, SE = 0.01, p = 0.72). The degree of discrepancy between fun forecasts and experiences did not significantly predict a change in AUDIT scores (b = 0.004, SE

= 0.01, p = 0.50). Finally, the direction of the degree of discrepancy did not significantly predict a change in AUDIT scores (b = -0.03, SE = 0.11, p = 0.80). Fun forecast and experience 41

discrepancies did not explain a significant proportion of variance in AUDIT scores (R2 = 0.15).

See Table 9 for more details and Figure 2.5. for the surface analysis graph.

3.6.3.2. CUDIT scores. Neither fun forecast scores (b = -0.03, SE = 0.27, p = 0.92) nor experience scores (b = 0.20, SE = 0.23, p = 0.39) or their interaction (b = -0.01, SE = 0.01, p =

0.16) significantly predicted CUDIT scores. There was no significant linear additive relationship between fun forecasts and experiences as they related to CUDIT scores (b = 0.17, SE = 0.34, p =

0.61). Furthermore, there was no significant non-linear slope of the line of perfect agreement between fun forecasts and experiences as they related to CUDIT scores (b = -0.003, SE = 0.01, p

= 0.68). The degree of discrepancy between fun forecasts and experiences did not significantly predict change in CUDIT scores (b = 0.02, SE = 0.01, p = 0.15). Finally, the direction of the degree of discrepancy did not significantly predict a change in CUDIT scores (b = -0.23, SE =

0.37, p = 0.55). Fun forecast and experience discrepancies did not explain a significant amount of variance in CUDIT scores (R2 = 0.33). See Table 10 for more details and Figure 2.6. for the surface analysis graph.

3.6.4. Sexiness.

3.6.4.1. AUDIT scores. Polynomial surface regressions were performed to determine whether the discrepancy between sexiness forecasts and sexiness experiences predicted AUDIT,

CUDIT, drinking frequency, and cannabis use frequency scores. Both sexiness forecast scores (b

= 0.02, SE = 0.03, p = 0.47), experience scores (b = 0.05, SE = 0.05, p = 0.31), as well as their interactions (b = -0.001, SE = 0.001, p = 0.66), did not significantly predict AUDIT scores. There was a marginally significant linear additive relationship between sexiness forecasts and experiences as they related to AUDIT scores (b = 0.08, SE = 0.04, p = 0.07). Specifically, as sexiness forecasts and experiences increased in intensity, AUDIT scores increased. Furthermore, 42

there was no significant non-linear slope of the line of perfect agreement between sexiness forecasts and experiences as they related to AUDIT scores (b = 0.001, SE = 0.001, p = 0.26). The degree of discrepancy between sexiness forecasts and experiences significantly predicted a change in AUDIT scores (b = 0.003, SE = 0.001, p < 0.05). Specifically, as the degree of discrepancy between sexiness forecasts and experiences increased, AUDIT scores also increased.

Finally, the direction of the degree of discrepancy did not significantly predict a change in

AUDIT scores (b = -0.03, SE = 0.08, p = 0.70). Sexiness forecast and experience discrepancies did not explain a significant proportion of variance in AUDIT scores (R2 = 0.18). See Table 11 for more details and Figure 2.7. for the surface analysis graph.

3.6.4.2. CUDIT scores. Sexiness forecast scores (b = 0.01, SE = 0.04, p = 0.74) and experience scores (b = 0.06, SE = 0.06, p = 0.32) did not significantly predict CUDIT scores; however, their interaction did predict CUDIT scores (b = -0.01, SE = 0.002, p < 0.05). There was no significant linear additive relationship between sexiness forecasts and experiences as they related to CUDIT scores (b = 0.07, SE = 0.05, p = 0.18). Furthermore, there was a significant non-linear slope of the line of perfect agreement between sexiness forecasts and experiences as they related to CUDIT scores (b = -0.01, SE = 0.003, p < 0.05). The degree of discrepancy between sexiness forecasts and experiences did not significantly predict a change in CUDIT scores (b = 0.004, SE = 0.003, p = 0.17). Finally, the direction of the degree of discrepancy did not significantly predict a change in CUDIT scores (b = -0.05, SE = 0.08, p = 0.55). Sexiness forecast and experience discrepancies did not explain a significant proportion of variance in

CUDIT scores (R2 = 0.34). See Table 12 for more details and Figure 2.8. for the surface analysis graph.

3.6.5. Excitement.

3.6.5.1. AUDIT scores. Polynomial surface regressions were performed to determine whether the discrepancy between excitement forecasts and excitement experiences predicted

AUDIT, CUDIT, drinking frequency, and cannabis use frequency scores. Both excitement forecast scores (b = 0.03, SE = 0.04, p = 0.51), experience scores (b = 0.06, SE = 0.05, p = 0.27), as well as their interactions (b = -0.002, SE = 0.002, p = 0.24), did not significantly predict

AUDIT scores. There was no significant linear additive relationship between excitement forecasts and experiences as they related to AUDIT scores (b = 0.08, SE = 0.08, p = 0.30).

Furthermore, there was no significant non-linear slope of the line of perfect agreement between excitement forecasts and experiences as they related to AUDIT scores (b = -0.001, SE = 0.002, p

= 0.67). The degree of discrepancy between excitement forecasts and experiences did not significantly predict a change in AUDIT scores (b = 0.003, SE = 0.003, p = 0.29). Finally, the direction of the degree of discrepancy did not significantly predict a change in AUDIT scores (b

= -0.03, SE = 0.05, p = 0.50). Excitement forecast and experience discrepancies did not explain a significant proportion of variance in AUDIT scores (R2 = 0.09). See Table 13 for more details and Figure 2.9. for the surface analysis graph.

3.6.5.2. CUDIT scores. Both excitement forecast scores (b = 0.02, SE = 0.04, p = 0.74), experience scores (b = 0.04, SE = 0.06, p = 0.53), as well as their interactions (b = -0.004, SE =

0.002, p = 0.09), did not significantly predict CUDIT scores. There was no significant linear additive relationship between excitement forecasts and experiences as they related to CUDIT scores (b = 0.06, SE = 0.08, p = 0.47). Furthermore, there was not a significant non-linear slope of the line of perfect agreement between excitement forecasts and experiences as they related to

CUDIT scores (b = -0.003, SE = 0.002, p = 0.15). The degree of discrepancy between excitement 44

forecasts and experiences did not significantly predict a change in CUDIT scores (b = 0.01, SE =

0.004, p = 0.22). Finally, the direction of the degree of discrepancy did not significantly predict a change in CUDIT scores (b = -0.03, SE = 0.08, p = 0.73). Excitement forecast and experience discrepancies did not explain a significant proportion of variance in CUDIT scores (R2 = 0.22).

See Table 14 for more details and Figure 2.10. for the surface analysis graph.

3.7. Mediation Models

3.7.1. Happiness.

3.7.1.1. Expectancy Scores. Mediation models through the SPSS Process Macro (Hayes,

2013) were used to determine the direct and indirect effects of happiness forecast and experience discrepancy scores on substance use problems. In a model with happiness discrepancy scores as the independent variable, AUDIT scores as the outcome variable, and AOEL and B-CEA scores as mediators, happiness discrepancy scores did not significantly predict mean AOEL scores (R2 =

0.06, F(1, 29) = 1.93, p = 0.18) or mean B-CEA scores (R2 = 0.08, F(1, 29) = 2.52, p = 0.12).

Moreover, AOEL scores (95% CI: -0.04, 0.02) and B-CEA scores (95% CI: -0.01, 0.07) did not have significant indirect effects on AUDIT scores, and happiness discrepancy scores did not have significant direct effects on AUDIT scores (95% CI: -0.02, 0.14). In a model with happiness discrepancy scores as the independent variable, CUDIT as the outcome variable, and

COEL and MEEQ scores as mediators, happiness discrepancy scores did not significantly predict mean COEL scores (R2 < 0.00001, F(1, 25) = 0.004, p = 0.99) or mean MEEQ scores (R2 = 0.06,

F(1, 25) = 1.47, p = 0.24). Moreover, COEL scores (95% CI: -0.04, 0.03) did not have significant indirect effects, while MEEQ scores (95% CI: -0.01, 0.05) had marginally significant indirect effects, and happiness discrepancy scores had marginally significant direct effects on

CUDIT scores (95% CI: -0.001, 0.17). 45

3.7.1.2. WATs. Mediation models were used to determine the direct and indirect effects of happiness forecast and experience discrepancy scores on substance use problems. In a model with happiness discrepancy scores as the independent variable, AUDIT scores as the outcome, and alcohol WATs as the mediator, happiness discrepancy scores did not significantly predict mean alcohol WAT scores (R2 = 0.05, F(1, 29) = 1.45, p = 0.24). In mediation models, happiness discrepancy scores did not have significant indirect effects on AUDIT scores (95% CI: -0.01,

0.05) and happiness discrepancy scores did not have significant direct effects on AUDIT scores

(95% CI: -0.01, 0.15) In a model with happiness discrepancy scores as the independent variable,

CUDIT as the outcome, and cannabis WATs as the mediator, happiness discrepancy scores did not significantly predict mean cannabis WAT scores (R2 = 0.05, F(1, 31) = 1.60, p = 0.22). In mediation models, happiness discrepancy scores did not have significant indirect (95% CI: -0.02,

0.09) or direct effects on the CUDIT (95% CI: -0.09, 0.09).

3.7.2. Relaxation.

3.7.2.1. Expectancy Scores. Mediation models through the SPSS Process Macro (Hayes,

2013) were used to determine the direct and indirect effects of relaxation forecast and experience discrepancy scores on substance use problems. In a model with relaxation discrepancy scores as the independent variable, AUDIT scores as the outcome variable, and AOEL and B-CEA scores as mediators, relaxation discrepancy scores did not significantly predict mean AOEL scores (R2

= 0.001, F(1, 29) = 0.02, p = 0.90) or mean B-CEA scores (R2 = 0.02, F(1, 29) = 0.70, p = 0.41).

Moreover, AOEL scores (95% CI: -0.02, 0.02) and B-CEA scores (95% CI: -0.03, 0.06) did not have significant indirect effects on AUDIT scores and relaxation discrepancy scores did not have significant direct effects on AUDIT scores (95% CI: -0.07, 0.08). In a model with relaxation discrepancy scores as the independent variable, CUDIT as the outcome variable, and COEL and 46

MEEQ scores as mediators, relaxation discrepancy scores did not significantly predict mean

COEL scores (R2 = 0.02, F(1, 25) = 0.45, p = 0.51) or mean MEEQ scores (R2 = 0.03, F(1, 25) =

0.89, p = 0.36). Moreover, COEL scores (95% CI: -0.07, 0.02) and MEEQ scores (95% CI:

-0.01, 0.03) did not have significant indirect effects on CUDIT, while relaxation discrepancy scores did have significant direct effects on CUDIT scores (95% CI: 0.01, 0.17).

3.7.2.2. WATs. Mediation models were used to determine the direct and indirect effects of relaxation forecast and experience discrepancy scores on substance use problems. In a model with relaxation discrepancy scores as the independent variable, AUDIT scores as the outcome, and alcohol WATs as the mediator, relaxation discrepancy scores did not significantly predict mean alcohol WAT scores (R2 = < 0.00001, F(1, 29) = 0.003, p = 0.995). In mediation models, relaxation discrepancy scores did not have significant indirect effects on AUDIT scores (95% CI:

-0.03, 0.02) or significant direct effects on AUDIT scores (95% CI: -0.06, 0.10). In a model with relaxation discrepancy scores as the independent variable, CUDIT as the outcome, and cannabis

WATs as the mediator, relaxation discrepancy scores significantly predicted mean cannabis

WAT scores (R2 = 0.19, F(1, 30) = 6.89, p < 0.05). In mediation models, relaxation discrepancy scores had significant indirect effects on CUDIT scores (95% CI: 0.01, 0.13) but not significant direct effects on CUDIT scores (95% CI: -0.02, 0.20).

3.7.3. Fun.

3.7.3.1. Expectancy Scores. Mediation models through the SPSS Process Macro (Hayes,

2013) were used to determine the direct and indirect effects of fun forecast and experience discrepancy scores on substance use problems. In a model with fun discrepancy scores as the independent variable, AUDIT scores as the outcome variable, and AOEL and B-CEA scores as mediators, fun discrepancy scores did not significantly predict mean AOEL scores (R2 = 0.001, 47

F(1, 26) = 0.01, p = 0.91) or mean B-CEA scores (R2 = 0.03, F(1, 26) = 0.08, p = 0.78).

Moreover, AOEL scores (95% CI: -0.03, 0.02) and B-CEA scores (95% CI: -0.04, 0.02) did not have significant indirect effects on AUDIT scores and fun discrepancy scores did not have significant direct effects on AUDIT scores (95% CI: -0.06, 0.09). In a model with fun discrepancy scores as the independent variable, CUDIT as the outcome variable, and COEL and

MEEQ scores as mediators, fun discrepancy scores did not significantly predict mean COEL scores (R2 = 0.03, F(1, 22) = 0.61, p = 0.44) or mean MEEQ scores (R2 = 0.03, F(1, 22) = 0.67, p

= 0.42). Moreover, both COEL scores (95% CI: -0.02, 0.03) and MEEQ scores (95% CI: -0.01,

0.06) did not have significant indirect effects on CUDIT, and fun discrepancy scores had significant direct effects on CUDIT scores (95% CI: 0.01, 0.21).

3.7.3.1. WATs. Mediation models were used to determine the direct and indirect effects of fun forecast and experience discrepancy scores on substance use problems. In a model with fun discrepancy scores as the independent variable, AUDIT scores as the outcome, and alcohol

WATs as the mediator, fun discrepancy scores did not significantly predict mean alcohol WAT scores (R2 = 0.02, F(1, 26) = 0.57, p = 0.46). In mediation models, fun discrepancy scores did not have significant indirect effects on AUDIT scores (95% CI: -0.02, 0.08) or direct effects on

AUDIT scores (95% CI: -0.08, 0.08). In a model with fun discrepancy scores as the independent variable, CUDIT as the outcome, and cannabis WATs as the mediator, fun discrepancy scores did not significantly predict mean cannabis WAT scores (R2 = 0.04, F(1, 31) = 1.23, p = 0.28). In mediation models, fun discrepancy scores did not have significant indirect effects on CUDIT

(95% CI: -0.02 0.09) or significant direct effects on CUDIT (95% CI: -0.11, 0.17).

3.7.4. Sexiness.

3.7.4.1. Expectancy Scores. Mediation models through the SPSS Process Macro (Hayes,

2013) were used to determine the direct and indirect effects of sexiness forecast and experience discrepancy scores on substance use problems. In a model with sexiness discrepancy scores as the independent variable, AUDIT scores as the outcome variable, and AOEL and B-CEA scores as mediators, sexiness discrepancy were marginally significant in predicting mean AOEL scores

(R2 = 0.14, F(1, 25) = 4.13, p = 0.05) but not mean B-CEA scores (R2 = 0.10, F(1, 25) = 2.68, p =

0.11). Moreover, AOEL scores (95% CI: -0.03, 0.02) did not have significant indirect effects on

AUDIT scores while B-CEA scores (95% CI: -0.004, 0.06) had marginally significant indirect effects. Sexiness discrepancy scores did not have significant direct effects on AUDIT scores

(95% CI: -0.08, 0.02). In a model with sexiness discrepancy scores as the independent variable,

CUDIT as the outcome variable, and COEL and MEEQ scores as mediators, sexiness discrepancy scores did not significantly predict mean COEL scores (R2 = 0.05, F(1, 22) = 1.09, p

= 0.31) or mean MEEQ scores (R2 = 0.10, F(1, 22) = 2.54, p = 0.13). Moreover, COEL scores

(95% CI: -0.02, 0.01) did not have significant indirect effects while MEEQ scores (95% CI: -

0.05, 0.01) had marginally significant indirect effects on CUDIT, and sexiness discrepancy scores did not have significant direct effects on CUDIT scores (95% CI: -0.06, 0.05).

3.7.4.2. WATs. Mediation models were used to determine the direct and indirect effects of sexiness forecast and experience discrepancy scores on substance use problems. In a model with sexiness discrepancy scores as the independent variable, AUDIT scores as the outcome, and alcohol WATs as the mediator, sexiness discrepancy scores did not significantly predict mean alcohol WAT scores (R2 = 0.04, F(1, 25) = 1.07, p = 0.31). In mediation models, sexiness discrepancy scores did not have significant indirect effects on AUDIT scores (95% CI: -0.01, 49

0.03) or direct effects on AUDIT scores (95% CI: -0.07, 0.03). In a model with sexiness discrepancy scores as the independent variable, CUDIT as the outcome, and cannabis WATs as the mediator, sexiness discrepancy scores did not significantly predict mean cannabis WAT scores (R2 = 0.01, F(1, 29) = 0.34, p = 0.57). In mediation models, sexiness discrepancy scores did not have significant indirect effects on CUDIT (95% CI: -0.05, 0.03) or direct effects on

CUDIT scores (95% CI: -0.05, 0.06).

3.7.5. Excitement.

3.7.5.1. Expectancy Scores. Mediation models through the SPSS Process Macro (Hayes,

2013) were used to determine the direct and indirect effects of excitement forecast and experience discrepancy scores on substance use problems. In a model with excitement discrepancy scores as the independent variable, AUDIT scores as the outcome variable, and

AOEL and B-CEA scores as mediators, excitement discrepancy scores did not significantly predict mean AOEL scores (R2 = 0.07, F(1, 25) = 1.73, p = 0.20) or mean B-CEA scores (R2 =

0.08, F(1, 25) = 2.29, p = 0.14). Moreover, AOEL scores (95% CI: -0.02, 0.02) scores did not have significant indirect effects on AUDIT scores while B-CEA scores had marginally significant indirect effects (95% CI: -0.003, 0.07). Excitement discrepancy scores did not have significant direct effects on AUDIT scores (95% CI: -0.08, 0.03). In a model with excitement discrepancy scores as the independent variable, CUDIT as the outcome variable, and COEL and

MEEQ scores as mediators, excitement discrepancy scores did not significantly predict mean

COEL scores (R2 = 0.004, F(1, 22) = 0.08, p = 0.78) or mean MEEQ scores (R2 = 0.01, F(1, 22)

= 0.23, p = 0.64). Moreover, both COEL scores (95% CI: -0.01, 0.02) and MEEQ scores (95%

CI: -0.03, 0.02) did not have significant indirect effects on CUDIT and excitement discrepancy scores did not have significant direct effects on CUDIT scores (95% CI: -0.05, 0.08). 50

3.7.5.2. WATs. Mediation models were used to determine the direct and indirect effects of excitement forecast and experience discrepancy scores on substance use problems. In a model with excitement discrepancy scores as the independent variable, AUDIT scores as the outcome, and alcohol WATs as the mediator, excitement discrepancy scores did not significantly predict mean alcohol WAT scores (R2 = 0.01, F(1, 25) = 0.27, p = 0.61). In mediation models, excitement discrepancy scores did not have significant indirect effects on AUDIT scores (95%

CI: -0.02, 0.03) or significant direct effects on AUDIT scores (95% CI: -0.07, 0.04). In a model with excitement discrepancy scores as the independent variable, CUDIT as the outcome, and cannabis WATs as the mediator, excitement discrepancy scores did not significantly predict mean cannabis WAT scores (R2 = 0.08, F(1, 30) = 0.18, p = 0.67). In mediation models, excitement discrepancy scores did not have significant indirect effects on CUDIT (95% CI: -

0.06, 0.04) or direct effects on CUDIT scores (95% CI: -0.09, 0.04).

Chapter 4: Discussion

The goal of this study was to determine whether the affective forecasting errors observed in past literature translated to the substance use context. In sum, this study determined that while forecasted, experienced, and remembered emotion intensities do differ, these differences do not follow the typical pattern of affective forecasting errors for most emotions. Specifically, we found that while participants did tend to overestimate how relaxed they would feel and how much fun they would have in consuming substances, they did not make the same errors for happiness, sexiness, and excitement. Moreover, participants tended to accurately remember how much enjoyment they had experienced from consuming substances and were quite accurate in determining for how long they would experience each emotion. Importantly, these patterns did not vary significantly between substances. As well as this, the study aimed to determine whether the presence of affective forecasting errors predicted problematic substance use. In general, affective forecasting errors were poor predictors of substance use problems, except in the cases of sexiness and relaxation. Finally, the study asked whether affective forecasting errors influenced substance use problems indirectly through System 1 or 2 cognitive mediators. In fact, this was not the case. Affective forecasting errors inconsistently predicted levels of any cognitive mediators and rarely influenced problematic substance use directly or indirectly, though several indirect effects trended towards significance. The scarcity of consistently significant effects in the majority of the analyses leads us to believe that affective forecasting errors may be related to problematic substance use as well as to substance use cognitions, but that this contention should be carefully examined in future studies with larger sample sizes.

The differences between the findings of the current study and the past literature can be interpreted in four ways. The first is that substance use fundamentally differs in some ways from 52

the previous contexts in which affective forecasting errors have been observed, and that this resulted in the lack of affective forecasting errors or rosy hindsight bias for three out of the five emotions we measured. Second, it is possible that the emotions of relaxation and fun differ in a fundamental way from sexiness, excitement, or happiness, and that this resulted in the lack of affective forecasting errors for the latter. Third, the scales in the current study were constructed specifically to avoid potential methodological artifacts, potentially influencing the results.

Finally, it is possible that there are limitations to the current study that prevented the discovery of affective forecasting errors that truly do exist; in essence, that the current study suffered from

Type 2 errors. The four possibilities will be considered in turn in the sections below.

4.1. Substance Use as a Unique Context

To begin with, we must consider the ways in which the substance use context differs from the contexts of previous studies. One consideration is that substance use is generally a fleeting event that makes no significant impact on individuals’ overall lives. Past studies in the affective forecasting literature have mainly either examined monumental events (i.e. receiving tenure) or at the very least, events that could affect the participants’ lives for at least a somewhat significant period of time (i.e. dormitory selection). Many recent studies have found that both alcohol and cannabis use are highly prevalent among university students (Substance Abuse and

Mental Health Services Administration, 2017; American College Health Association, 2013;

American College Health Association, 2016). It is possible that the frequency of substance use in students’ daily lives affects how likely they are to make affective forecasting errors. For example, students may hear their peers’ substance use experiences often enough to have a realistic view of what their own experiences will look like, at least in terms of how happy, sexy, and excited they will feel in consuming substances. While individuals are often inaccurate at 53

making forecasts, few studies to date have examined the influence of others’ input on the accuracy of these forecasts.

Another way in which the substance use context differs from previous contexts examined is that substance use affects the body chemically, in a way that tenure or dormitory selection does not. Specifically, alcohol has been found to have effects on the amount of social ease individuals will feel and cannabis has been shown to act on CB1 receptor antagonists, thus inducing relaxation (Valenzuela, 1997; Tambaro & Bartolato, 2012). These effects may explain why the participants in the current study did experience the levels of happiness that they had expected to experience. Given that alcohol and cannabis may have caused the participants to experience chemical changes that led to a perception of happiness, it makes sense that they expected to feel happy when using alcohol and did in fact feel happy. While this theory may explain the lack of affective forecasting errors for happiness, the chemical effects of cannabis and alcohol should have also caused participants to experience high levels of relaxation, thus preventing them from making affective forecasting errors for relaxation. In fact, participants did overestimate how relaxed they would feel in consuming these substances.

4.2. Relaxation and Fun as Unique Emotions

It is possible that the emotions of relaxation and fun differ fundamentally from sexiness, happiness, and excitement in such a way that participants only made affective forecasting errors from the former. For example, ‘fun’ differs semantically from the rest of the emotions listed in that it is an adjective describing a situation, while ‘happiness’ is a state (Dictionary, n.d.).

Participants may have interpreted ‘fun’ as referring to the situation in which they would be consuming substances (i.e. a party) when making their forecast. When prompted to reflect on the amount of fun they were experiencing when consuming, it is possible that alcohol or cannabis 54

was more salient in the participants’ mind. Notably, when asked to report on their experiences, the questionnaire asked: “when you think about using cannabis/alcohol tonight, are you having fun?”, thus making substance use particularly prevalent in the participants’ mind.

Furthermore, another possibility why these errors may have occurred is that the emotions of relaxation and fun are strongly related to cannabis and alcohol use. Students may hold implicit theories about the relaxing effects of cannabis and alcohol. If these implicit theories are particularly well-ingrained, they may have prompted students to predict that they would experience these emotions more intensely than they actually did. It is possible that feelings such as ‘sexiness’ are related to these substances but in a different way. For example, if participants associate drinking with hangovers, they likely expect to feel lower levels of sexiness and not experience these feelings while drinking, thus preventing affective forecasting errors. Further efforts must be made to understand whether individuals may be more accurate at forecasting some emotions than others for such reasons.

4.3. Scale Construction as a Unique Component of the Study

Despite the bevy of articles that describe the presence of affective forecasting errors in numerous contexts, Levine and colleagues (2012) posited that some of these findings may have occurred due to methodological artifact. Specifically, the authors posited that individuals may be able to accurately forecast their emotions for a particular event. However, when reporting on their emotions as they are experiencing the event, participants may misinterpret the question as asking them about their general mood, not their emotions regarding the event. Therefore, the discrepancy between forecasts and experienced emotions may be due to the fact that participants are seemingly answering two different questions (i.e. “How will you feel about X candidate being elected?” versus “how do you feel in general now?”). 55

The scales in the current study were constructed specifically to limit such methodological artifact. The scales that asked participants to report on their current emotions as well as to report on retrospective emotions were phrased to make substance use salient in their mind. Levine and colleagues found that when rephrasing their questions to make a specific event salient for participants, affective forecasting errors were less likely to occur. Therefore, the inconsistent presence of forecasting errors in the current study may have occurred due to how the scales were constructed. Notably, this would not indicate that the study’s scales are resulting in Type II errors, but that the lack of noise in the current data due to the scales used has allowed for the finding that affective forecasting errors are less likely to occur than previously thought.

4.4. Study Limitations and Possibilities of Type 2 Errors

Finally, the current study includes limitations that may have precluded the observation of any affective forecasting errors. One limitation of the current study is that participants’ reports of their emotions while consuming where retained in the analyses as long as they occurred the night that they reported consuming. Therefore, some participants chose to complete all four questionnaires at the same time at the end of the night, thus biasing the results. As well as this, the current study only followed participants over the course of one week. Thus, we were unable to determine the accuracy of the participants who predicted that they would feel emotions such as happiness longer than one week. Such responses were re-coded to ‘one week’ in analyses, again potentially biasing the results.

An obvious limitation to the study is its sample size. While 46 participants made affective forecasts, few participants completed the study at all four time points (n = 17), resulting in a large amount of missing data. While efforts were made to choose analyses that were unlikely to be affected by missing data (i.e. growth curve analysis), the study may have still been 56

underpowered to detect duration biases in chi-square analyses. According to power analyses, the full sample size would only provide a maximum power of 0.33 to detect effect small effect sizes

(< 0.30). Furthermore, the chi-square analyses in the current study were split between cannabis and alcohol users, thus further decreasing sample size and power.

Finally, the study asked participants to report on the substance that they were consuming, allowing for either alcohol or cannabis use. Such options did not allow for the possibility of poly-substance use, which is common in the general adult population (Subbaraman & Kerr,

2015). Engaging in poly-substance use may have affected the accuracy of participants’ judgements of their emotions. For example, Lukas and Orozco (2001) reported that individuals who consume alcohol and cannabis use simultaneously reported more episodes of euphoria.

Given that participants had forecasted feeling quite happy when using cannabis (77 out of 100 on average), poly-substance use may have helped increase their experiences of happiness, thus precluding the detection of any affective forecasting errors for this emotion. Relatedly, poly- substance use may have prevented their classification into the correct primary substance use group, again affecting statistical analyses and comparisons between cannabis and alcohol users.

4.5. Dual-Processes Models of Affective Forecasting

One surprising finding in the current study is that affective forecasting errors, when they did occur, did not consistently affect participants’ substance use cognitions. Few past studies have directly discussed affective forecasting errors in a dual-processes context and even fewer studies have examined cognitive effects of affective forecasting errors. Existing studies that discussed dual-processes theory perspectives on affective forecasting have posited that System 1 cognitive biases may affect memories of past events and that such memories may result in inaccurate forecasts for future events. The current study throws a wrench into such theories, as it 57

finds that affective forecasting errors do not consistently affect System 1 or System 2, as measured by the study. Therefore, it is unclear why individuals go on to make more forecasting errors.

While it is possible that the measures used in this study were not able to detect the changes in cognitions caused by forecasting errors, this is unlikely. A total of four measures of cognition (WAT, MEEQ, B-CEOA, Outcome Expectancies), all targeting different aspects of cognition and both System 1 and 2, were rarely influenced by forecasting errors. Therefore, it seems more likely that the affective forecasting errors were simply not strong enough to be able to impact cognitions in any significant way. The forecasted and experienced emotion intensities for happiness and relaxation were, with one exception, less than 10 points apart. While such discrepancies did account for some variance in cognitions, there are likely simply stronger predictors of cognitions, such as social influences. Overall, the lack of strong effects and the inconsistency of forecasting errors found in this study demonstrate the need to examine the effects of errors on cognition, given their overall inconsistency.

4.6. Implications

Given that the current study is one of the first to examine whether affective forecasting errors exist in a substance use context, the above-stated results must be replicated and the implications below should be mainly considered if the results remain consistent after replication.

Nevertheless, the inconsistency and overall lack of either forecasting errors or rosy hindsight bias within substance use may lead to one explanation of the unhealthy decisions that some individuals may make regarding alcohol and cannabis use. Given the results of this study, both the presence and absence of forecasting errors should push towards increased interventions for alcohol and cannabis abuse on campus. 58

As described in Table 2, participants expected to feel mainly intensely positive emotions when consuming alcohol and cannabis. As demonstrated by the lack of affective forecasting errors, participants expected to feel very happy when consuming alcohol or cannabis, then went on to experience happiness when consuming, and later went on to remember feeling happy. Such patterns may explain why students continue to make unhealthy substance use decisions. Despite the negative health effects of alcohol consumption as well as acute negative effects, such as hangovers, students still seem to enjoy consuming substances overall. It is unclear whether this enjoyment results from the physical effects of alcohol and cannabis, the contexts in which they are consumed, or a variety of other factors, but the overall intense levels of happiness experienced and remembered when consuming substances may increase students’ consumption and motivation to consume.

In the case of relaxation and fun, where affective forecasting errors did occur, students’ unhealthy substance use may also be impacted by such errors. While students over-estimated how much fun they would have and how relaxed they would feel, they were accurate in remembering how they felt during their substance use experience. Accurate memories of having underestimated the intensity of positive emotions could theoretically result in reduced substance use behavior, particularly if participants felt (and remembered feeling) disappointed in their substance use experience. However, a closer look at the data points to a reduction in substance use behavior being unlikely, despite such forecasting errors. While participants overestimated how relaxed they would feel and how much fun they would experience, their overall levels of fun and relaxation were still high (at the lowest end, 70.11 on a scale of 100 for relaxation when using alcohol). Students’ overall levels of enjoyment of substance use, despite its sometimes- underwhelming effects, may be a factor that results in substance use problems on campus. 59

Despite the evidence demonstrated in this study, it is important to point out that the current study is not experimental, and thus, causal effects of forecasting errors on substance use cannot be claimed. For example, while discrepancies between sexiness forecasts and experiences significantly predicted AUDIT scores in surface analyses, there is a clear lack of directionality in these findings. Therefore, it is possible that individuals’ forecasting errors lead to gradual increases in alcohol use disorder symptoms. However, it is also possible that individuals who are already demonstrating AUDIT symptoms go on to make more affective forecasting errors due to the characteristics of Alcohol Use Disorder. This limitation also applies to the analyses conducted on cognitions in this study. While it is possible that affective forecasting errors sometimes affect outcome expectancies and word associates, it is also possible that individuals with more substance-use related cognitions make more forecasting errors. Studies that attempt to experimentally manipulate forecasting errors are essential in order to learn more about causality in this context. For example, future studies will seek to modify participants’ cognitions to determine if such modifications result in more extreme forecasting errors.

4.7. Future Directions

The lack of consistent results in the current study points to a need to continue examining what factors do and do not result in affective forecasting errors in the context of substance use.

Future studies should examine potential moderators of forecasting errors, such as participants’ levels of physical arousal when making a forecast as well as their substance use quantity when reporting experienced emotions. Furthermore, the current study did not examine moderators such as participants’ prior substance use, gender, or age. Future studies should endeavor to examine these factors as well as others.

As well as this, future studies should attempt to remedy some of the limitations of the current study. For example, an emotion intensity scale that ranges from 1 to 10 as opposed to 0 to

100 may enable participants to make more accurate assessments of their emotions. A larger sample size may allow for more powerful analyses. Furthermore, the scales used to report experienced emotions should allow participants to report poly-substance use as well as to more accurately report the quantity of alcohol or cannabis consumed.

If replications find consistent affective forecasting errors in cannabis and alcohol use, future studies should attempt to expand on the current study. For example, forecasting errors, or lack thereof, can be examined in other substances, such as cocaine and hallucinogens.

Furthermore, a broader range of emotions can also be tested. Studies can attempt to do this by providing a different set of emotions for participants to predict and report on, or they may allow for open-ended questions such that participants can write in an emotion that they expect to experience, as well as its predicted intensity and duration.

4.8. Conclusion

The current study was an initial attempt to determine whether the affective forecasting errors found in extant literature also apply to substance use. In particular, the study attempted to answer whether individuals are able to accurately determine how much enjoyment they will feel when using alcohol and cannabis and whether they are able to accurately remember this enjoyment post-use. The results of the study were inconsistent; however, the majority of results point to a lack of forecasting errors within the context of substance use. Given the possible role of expected emotions in perpetuating increased substance use, it is imperative that studies on this topic continue, potentially leading to interventions to help students have realistic expectations of the emotional effects of substance use. 61

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Table 1.

Demographics for full sample (n = 46)

Sex Frequency Valid Percent % Male 8 17.4 Female 35 76.1

Age Frequency Valid Percent % 19 - 21 30 69.8% 22 – 24 10 23.3% 25+ 3 6.9%

School Year Frequency Valid Percent % 1 2 4.7 2 14 32.6 3 13 30.2 4 + 14 32.6

Family Income Frequency Valid Percent % Well Below Average 1 2.3 Below Average 16 37.2 Average 21 48.8 Above Average 4 9.3 Well Above Average 1 2.3

Table 2.

Sample sizes at each time point in analyses

Time Point Total N Alcohol Cannabis Unknown Users Users Substance

Forecast 39 20 11 8

Experiences 35 21 13 1

Retrospective – Yesterday 31 17 11 4

Retrospective – Last Week 29 17 9 3

Table 3.

Total sample sizes for each analysis completed in study

Analysis Min. N Max. N

Analyses of Variance 26 34

Growth Curve Models 172 187

Chi-Square Analyses 23 32

Polynomial Regression 24 31

Mediation Models 23 31

Note. Only ANOVAs, chi-square analyses split by substance. Growth curve model Ns include restructured data (long-format), resulting in a larger sample size. Minimum and maximum Ns provided as number of responses varied slightly for each emotion.

Table 4.

Means and (SDs) for forecasted, experienced, and remembered emotions

Substance Emotion Forecasts Average Memory Memory (n = 39) Experience – 1 day – 1 week (1 – 4 time (n = 32) (n = 29) points) (n = 35)

Alcohol Happiness 85.25 (8.35) 78.48 (16.33) 85.53 (12.35) 79.12 (13.61)

Relaxation 74.74 (12.52) 70.11 (20.36) 66.06 (28.83) 75.47 (19.17)

Fun 85.25 (21.30) 78.69 (18.36) 85.29 (8.38) 78.71 (13.17)

Sexiness 55.28 (34.32) 44.34 (29.61) 40.00 (27.16) 38.24 (31.07)

Excitement 72.41 (26.10) 64.50 (25.39) 72.41 (26.10) 66.76 (22.98)

Cannabis Happiness 77.73 (25.92) 78.58 (8.01) 84.50 (11.41) 71.11 (28.04)

Relaxation 90.45 (15.57) 75.06 (16.92) 81.81 (20.41) 78.89 (25.83)

Fun 77.27 (20.42) 69.09 (16.45) 73.33 (22.91) 66.11 (24.21)

Sexiness 34.09 (35.69) 48.89 (20.51) 47.56 (29.18) 43.89 (27.81)

Excitement 48.64 (30.91) 62.33 (17.64) 56.11 (32.19) 61.67 (29.58)

Table 5

Happiness forecast-experience discrepancy as a predictor of AUDIT score (n = 30)

AUDIT Score

Variable b (se)

Constant 9.80 (3.28)***

Happiness Forecast 0.10 (0.12)

Happiness Experience -0.13 (0.14)

Happiness Forecast Squared -0.002 (0.002)

Happiness Forecast x Happiness Experience 0.003 (0.004)

Happiness Experience Squared -0.001 (0.002)

R2 0.19

Surface tests a1 -0.02 (0.23) a2 0.00 (0.004) a3 0.23 (0.13) a4 -0.01 (0.01)

Table 6

Happiness forecast-experience discrepancy as a predictor of CUDIT score (n = 27)

CUDIT Score

Variable b (se)

Constant 7.87 (3.51)*

Happiness Forecast -0.10 (0.13)

Happiness Experience -0.30 (0.15)

Happiness Forecast Squared 0.002 (0.002)

Happiness Forecast x Happiness Experience 0.004 (0.004)

Happiness Experience Squared 0.003 (0.002)

R2 0.29

Surface tests a1 -0.40 (0.25) a2 0.01 (0.01) a3 0.20 (0.13) a4 0.001 (0.01)

Table 7

Relaxation forecast-experience discrepancy as a predictor of AUDIT score (n =31) AUDIT

Variable b (se)

Constant 7.66 (4.31)*

Relaxation Forecast -0.10 (0.27)

Relaxation Experience 0.03 (0.07)

Relaxation Forecast Squared 0.003 (0.004)

Relaxation Forecast x Relaxation Experience -0.003 (0.003)

Relaxation Experience Squared 0.002 (0.002)

R2 0.10

Surface tests a1 -0.06 (0.27) a2 0.002 (0.003) a3 -0.12 (0.29) a4 0.01 (0.01)

Table 8

Relaxation forecast-experience discrepancy as a predictor of CUDIT score (n = 27)

CUDIT

Variable b (se)

Constant -1.77 (3.75)

Relaxation Forecast 0.18 (0.24)

Relaxation Experience 0.05 (0.06)

Relaxation Forecast Squared 0.003 (0.004)

Relaxation Forecast x Relaxation Experience -0.01 (0.003)

Relaxation Experience Squared 0.003 (0.002)

R2 0.52*

Surface tests a1 0.24 (0.24) a2 0.00 (0.004) a3 0.12 (0.25) a4 0.01 (0.005)*

Table 9

Fun forecast-experience discrepancy as a predictor of AUDIT score (n = 28)

AUDIT

Variable b (se)

Constant 5.35 (4.21)

Fun Forecast 0.06 (0.13)

Fun Experience 0.09 (0.20)

Fun Forecast Squared 0.002 (0.001)

Fun Forecast x Fun Experience -0.003 (0.01)

Fun Experience Squared -0.001 (0.002)

R2 0.15

Surface tests a1 0.14 (0.32) a2 -0.002 (0.01) a3 -0.03 (0.11) a4 0.004 (0.01)

Table 10

Fun forecast-experience discrepancy as a predictor of CUDIT score (n = 24) CUDIT

Variable b (se)

Constant 3.63 (4.47)

Fun Forecast -0.03 (0.27)

Fun Experience 0.20 (0.23)

Fun Forecast Squared 0.01 (0.01)

Fun Forecast x Fun Experience -0.01 (0.01)

Fun Experience Squared 0.001 (0.002)

R2 0.15

Surface tests a1 0.17 (0.34) a2 -0.003 (0.01) a3 -0.23 (0.37) a4 0.02 (0.01)

Table 11

Sexiness forecast-experience discrepancy as a predictor of AUDIT score (n = 27)

AUDIT

Variable b (se)

Constant 7.58 (1.61)***

Sexiness Forecast 0.02 (0.03)

Sexiness Experience 0.05 (0.05)

Sexiness Forecast Squared 0.001 (0.001)

Sexiness Forecast x Sexiness Experience -0.001 (0.001)

Sexiness Experience Squared 0.001 (0.001)

R2 0.18

Surface tests a1 0.08 (0.04) a2 0.001 (0.001) a3 -0.03 (0.08) a4 0.003 (0.001)*

Table 12

Sexiness forecast-experience discrepancy as a predictor of CUDIT score (n = 24)

CUDIT

Variable b (se)

Constant 7.53 (1.70)***

Sexiness Forecast 0.01 (0.04)

Sexiness Experience 0.06 (0.06)

Sexiness Forecast Squared 0.00 (0.001)

Sexiness Forecast x Sexiness Experience -0.01 (0.002)

Sexiness Experience Squared -0.001 (0.002)

R2 0.34

Surface tests a1 0.07 (0.05) a2 -0.01 (0.003)* a3 -0.05 (0.08) a4 0.004 (0.003)

Table 13

Excitement forecast-experience discrepancy as a predictor of AUDIT score (n = 27)

AUDIT Score

Variable b (se)

Constant 7.53 (2.00) **

Excitement Forecast 0.03 (0.04)

Excitement Experience 0.06 (0.05)

Excitement Forecast Squared 0.001 (0.001)

Excitement Forecast x Excitement Experience -0.002 (0.002)

Excitement Experience Squared 1.98E-05 (0.001)

R2 0.09

Surface tests a1 0.08 (0.08) a2 -0.001 (0.002) a3 -0.03 (0.05) a4 0.003 (0.003)

Table 14

Excitement forecast-experience discrepancy as a predictor of CUDIT score (n = 24) CUDIT Score

Variable b (se)

Constant 6.12 (2.30) *

Excitement Forecast 0.02 (0.04)

Excitement Experience 0.04 (0.06)

Excitement Forecast Squared 0.002 (0.002)

Excitement Forecast x Excitement Experience -0.004 (0.002)

Excitement Experience Squared -0.001 (0.001)

R2 0.22

Surface tests a1 0.06 (0.08) a2 -0.003 (0.002) a3 -0.03 (0.08) a4 0.01 (0.004)

Intensity of Happiness 90.0

85.0

80.0

75.0 Alcohol Intensity Cannabis 70.0

65.0

60.0 1 2 3 4 5 6 7 Time

Figure 1.1. Growth curve of happiness intensity during forecasts, four experience time points, and memories

Intensity of Relaxation 95.0

90.0

85.0

80.0

75.0 Alcohol Intensity Cannabis 70.0

65.0

60.0 1 2 3 4 5 6 7 Time

Figure 1.2. Growth curve of relaxation intensity during forecasts, four experience time points, and memories

Intensity of Fun

90.0

85.0

80.0

75.0 Alcohol Intensity 70.0 Cannabis

65.0

60.0 1 2 3 4 5 6 7 Time

Figure 1.3. Growth curve of fun intensity during forecasts, four experience time points, and memories

Intensity of Sexiness

70.0

65.0

60.0

55.0

50.0

Alcohol Intensity 45.0 Cannabis

40.0

35.0

30.0 1 2 3 4 5 6 7

Time

Figure 1.4. Growth curve of sexiness intensity during forecasts, four experience time points, and memories

Intensity of Excitement

80.0

75.0

70.0

65.0

60.0 Alcohol Intensity 55.0 Cannabis 50.0

45.0

40.0 1 2 3 4 5 6 7 Time

Figure 1.5. Growth curve of excitement intensity during forecasts, four experience time points, and memories

Figure 2.1. Surface analysis of the effect of happiness forecast and experience discrepancy on drinking problems as measured by the AUDIT

Figure 2.2. Surface analysis of the effect of happiness forecast and experience discrepancy on cannabis use problems as measured by the CUDIT

Figure 2.3. Surface analysis of the effect of relaxation forecast and experience discrepancy on drinking problems as measured by the AUDIT

Figure 2.4. Surface analysis of the effect of relaxation forecast and experience discrepancy on cannabis use problems as measured by the CUDIT

Figure 2.5. Surface analysis of the effect of fun forecast and experience discrepancy on drinking problems as measured by the AUDIT

Figure 2.6. Surface analysis of the effect of fun forecast and experience discrepancy on cannabis use problems as measured by the CUDIT

Figure 2.7. Surface analysis of the effect of sexiness forecast and experience discrepancy on drinking problems as measured by the AUDIT

Figure 2.8. Surface analysis of the effect of sexiness forecast and experience discrepancy on cannabis use problems as measured by the CUDIT

100

Figure 2.9. Surface analysis of the effect of excitement forecast and experience discrepancy on drinking problems as measured by the AUDIT

101

Figure 2.10. Surface analysis of the effect of excitement forecast and experience discrepancy on cannabis use problems as measured by the CUDIT

102

Appendices

Appendix A: Alcohol Use Disorders Identification Test

Scale: 5-pt (Never [0] – 4 or More Times a Week [4])

1. How often do you have a drink containing alcohol?

Scale: 5-pt (1-2 [0] – 10 or more [4])

2. How many drinks containing alcohol do you have on a typical day when you are drinking?

Scale: 5-pt (Never [0] – Daily/Almost Daily [4])

3. How often do you have six or more drinks on one occasion?

4. How often during the last year have you found that you were not able to stop drinking once you had started?

5. How often during the last year have you failed to do what was normally expected of you because of drinking?

6. How often during the last year have you needed a first drink in the morning to get yourself going after a heavy drinking session?

7. How often during the last year have you had a feeling of guilt or remorse after drinking?

8. How often during the last year have you been unable to remember what happened the night before because of your drinking?

Scale: 3-pt (No [0]/ Yes, but not in the past year [2]/Yes [4])

9. Has a relative, friend, doctor, or other health care worker been concerned about your drinking or suggested you cut down?

10. Have you or someone else been injured because of your drinking?

103

Appendix B: Cannabis Use Disorders Identification Test

Scale: 5-pt (Never [0] – 4 or more times a week [4])

1. How often do you use marijuana?

Scale: 5-pt (1-2 [0] – 10 or more [4])

2. How many hours were you stoned on a typical day when you had been using cannabis?

Scale: 2-pt (No [0] – Yes [4])

3. Have you or someone else been injured as a result of your use of cannabis in the past 3 months?

4. Has a friend, relative or doctor or other health worker been concerned about your use of cannabis over the past 3 months?

Scale: 5-pt (Never [0] – Daily or almost daily [4])

5. How often were you "stoned" for 6 or more hours?

6. How often during the past 3 months did you find that you were not able to stop using cannabis once you had started?

7. How often during the past 3 months did you fail to do what was normally expected from because of using cannabis?

8. How often during the past 3 months did you need to use cannabis in the morning to get yourself going after a heavy session of using cannabis?

9. How often during the past 3 months did you have a feeling of guilt or remorse after using cannabis?

10. How often during the past 3 months have you had a problem with your memory or concentration after using cannabis?

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Appendix C: Word Associates (WATs)

This section asks you about how you would respond in the future to a variety of situations. For the following phrases, type with the first behavior that comes to mind.

Example: If I feel hungry, then I will... Have a snack.

Please type your response in the text field. Remember to respond with the FIRST behavior that "pops to mind."

Work quickly!

Task: 1. If I am in a bad mood, then I will ______2. If I want to feel happy, then I will ______3. If I feel bored, then I will ______4. If I am going to a party, then I will ______5. If I want to feel more comfortable or relaxed in an unfamiliar situation, then I will ______6. If I am feeling lonely, then I will ______7. If I want to fit in or feel more included with my peers, then I will ______8. If I am stressed out, then I will ______9. If I want to have fun, then I will ______10. If I want to be more open to experiences, then I will ______11. If I want to relax, then I will ______12. If I want to have a really good time, then I will ______13. If I feel nervous or anxious, then I will ______14. If I feel upset or depressed, then I will ______15. If I want to get rid of physical pain, then I will_____ 16. If I feel like celebrating, then I will ______17. If I have trouble sleeping, then I will ______18. If I want to forget my worries or problems, then I will _____ 19. If I want to be more sociable, then I will ______20. If I want to feel more self-confident, then I will ______

Self-Coding Categories: Recreation, Violence, Family/Friends, Food, Alcohol, Marijuana, Other Drugs, Other

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Appendix D: Outcome Expectancy Liking Task (OEL)

This question asks you to tell us about what you think the effects of using alcohol/cannabis would be. We do not assume that you have used alcohol. Please answer the question even if you do not drink/use cannabis. We are interested in what you anticipate would happen.

Anticipate means to expect, or to think, something is going to happen. For example: They anticipated a storm was coming, so they prepared for it.

Directions: Please enter the four most important things that you would expect or anticipate to happen. Then indicate how much you would like or not like this outcome if it happened.

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Appendix E: Brief Comprehensive Effects of Alcohol Scale (B-CEOA)

Choose from DISAGREE TO AGREE depending on whether you expect the effect to happen to you IF YOU WERE UNDER THE INFLUENCE OF ALCOHOL. These effects will vary, depending on the amount of alcohol you typically consume. Check one answer for the four boxes after each statement. There are no right or wrong answers.

Scale: 4-pt Likert (Disagree – Agree)

1. After a few drinks of alcohol, I would be more likely to enjoy sex more.

2. After a few drinks of alcohol, I would be more likely to be courageous.

3. After a few drinks of alcohol, I would be more likely to feel calm.

4. After a few drinks of alcohol, I would be more likely to be a better lover.

5. After a few drinks of alcohol, I would be more likely to act sociable.

6. After a few drinks of alcohol, I would be more likely to talk to people more easily.

7. After a few drinks of alcohol, I would be more likely to feel peaceful.

8. After a few drinks of alcohol, I would be more likely to be brave and daring.

9. After a few drinks of alcohol, I would be more likely to take risks.

10. After a few drinks of alcohol, I would be more likely to feel dizzy (Reverse scored)

11. After a few drinks of alcohol, I would be more likely to feel moody (Reverse scored)

12. After a few drinks of alcohol, I would be more likely to be clumsy (Reverse scored)

13. After a few drinks of alcohol, I would be more likely to be loud, boisterous, or noisy. (Reverse scored)

14. After a few drinks of alcohol, I would be more likely to act aggressively. (Reverse scored)

15. After a few drinks of alcohol, I would be more likely to feel guilty. (Reverse scored)

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Appendix F: Marijuana Effects Expectancy Questionnaire – Brief (MEEQ)

The following questions ask about what you think the effects of marijuana and other THC products are. Please answer the following questions honestly; there are no right or wrong answers.

Scale: 5-pt Likert scale (Disagree Strongly – Agree Strongly)

1. Marijuana makes it harder to think and do things (harder to concentrate or understand; slows people down when they move). (Reverse scored)

2. Marijuana helps a person relax and feel less tense (helps a person unwind and feel calm).

3. Marijuana helps people get along better with others and it can help a person feel more sexual (talk more; feel more romantic).

4. Marijuana makes people feel more creative and perceive things differently (music sounds different; things seem more interesting).

5. Marijuana generally has bad effects on a person (people become angry or careless; after feeling high a person feels down). (Reverse scored)

6. Marijuana has effects on a person’s body and gives people cravings (get the munchies/hungry; have a dry mouth; hard to stop laughing). (Reverse scored)

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Appendix G: Affective Forecasting, Experiencing, Remembering Questionnaires

Affective Forecasts

If you are planning to drink alcohol this weekend, please think about your upcoming weekend plans and answer the following questions.

If you are not planning to drink alcohol this weekend, please imagine that you will be drinking alcohol this weekend and answer the following questions.

Scale: 0 – 100

On a scale of 0 to 100 (where 0 means not at all and 100 means extremely), how intensely do you anticipate you will experience this effect?

1. Feel happy 2. Feel relaxed 3. Have fun 4. Feel sexy 5. Feel excited

Scale: 5-pt (Only while drinking – I don’t expect to experience this)

How long do you expect to experience this effect?

1. Feel happy 2. Feel relaxed 3. Have fun 4. Feel sexy 5. Feel excited

Affective Experiences

1. Which of the following have you been doing this evening? (Drinking alcohol/Using cannabis/Neither)

2. Enter the number of drinks/amount of cannabis you have had below:

Scale: (Yes/No)

When you think about drinking alcohol tonight, are you:

1. Happy 2. Relaxed 3. Having fun 109

4. Feeling sexy 5. Feeling excited

Right now, as you are drinking alcohol/using cannabis, how ______are you on a scale of 0 to 100?

1. Happy 2. Relaxed 3. (...Much) Fun (...are you having) 4. Sexy (...are you feeling) 5. Excited

Affective Memories

3. Which of the following have did you do yesterday/last week? (Drinking alcohol/Using cannabis/Neither)

4. Enter the number of drinks/amount of cannabis you have had:

Scale: (Yes/No)

When you think about how you drank alcohol last night/last week, are you:

1. Happy 2. Relaxed 3. Having fun 4. Feeling sexy 5. Feeling excited

When you used alcohol/cannabis yesterday/last week, how ______were you on a scale of 0 to 100?

1. Happy 2. Relaxed 3. (...Much) Fun (...were you having) 4. Sexy (...were you feeling) 5. Excited

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