Metamotivational Knowledge of Emotions As Motivational Input

Metamotivational Knowledge of Emotions as Motivational Input

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the

Graduate School of The Ohio State University

Seel Bee Lee

Graduate Program in Psychology

The Ohio State University

2020

Thesis Committee

Kentaro Fujita, Advisor

Lisa K. Libby, Advisor

Russ H. Fazio

Copyrighted by

Seel Bee Lee

2020

Abstract

Metamotivation research demonstrates that people may understand how to create task motivation fit by regulating their motivations to fit the demands of a given task (Scholer

& Miele, 2016; Nguyen, Carnevale, Scholer, Miele, & Fujita, 2019). To effectively regulate one’s motivation, one must understand (a) which task would benefit from what motivational states (i.e., task knowledge) and (b) how to instantiate the desired states

(i.e., strategy knowledge). The present research explored an alternative way to create task-motivation fit by strategically selecting tasks based on current motivational states.

To do so, one must be able to recognize what motivational state one is currently in (i.e., self-knowledge) in addition to task knowledge. Although there is some preliminary evidence for task knowledge, research to-date has not examined whether people can determine their motivational states, and by what mechanisms. We propose people’s feelings or emotional state may signal one’s current motivational states and guide their goal-directed behaviors. Given the association between emotions and the level of construal (e.g., Moran, Bornstein, & Eyal, 2019), the present research first examined to what extent people use emotions as cues to determine their motivational states within the context of construal level theory. Based on our prepositions, we hypothesized that people may attend to their emotional states to infer their current level of construal and therefore, strategically choose different tasks based on their current emotions. Studies 1a and 1b

ii demonstrate that people not only distinguish high-level and low-level emotions but they may also understand the differential benefits of high-level versus low-level emotions in task performance. Studies 2 and 3 extend these findings and provide empirical evidence that people strategically prefer either high-level or low-level task depending on the level of emotions. The results from Studies 1-3 suggest that people may understand the functional role of emotions in signaling one’s motivational states and they may be able to capitalize on their emotions to create task-motivation fit. However, this is not entirely conclusive given the results of Study 4, which did not replicate Studies 2 and 3.

Alternative explanations, future directions, and implications of these findings are discussed.

iii

Acknowledgments

I would like to express my deepest gratitude to my advisor, Kentaro Fujita, for his great mentorship, continuous support, and intellectual guidance throughout the research process. Whenever I ran into a brick wall during the research and writing process, he provided me insightful advice and constructive feedback and steered me in the right direction. I am also indebted to my collaborator and my friend, Tina Nguyen. Without her encouragement and dedicated involvement in every step throughout the process, this work would not have been possible. I am grateful to my advisor, Lisa Libby, and my committee member, Russ Fazio, for their invaluable suggestions and thoughtful comments on this project. I would also like to thank the members of the Fujita Lab and the Social Cognition Research Group for giving me helpful comments and warm support during the course of this research. Lastly, I thank my family, my parents and my little brother, Mati, for their unconditional love and support throughout my life. I would not be the person I am today without them. I am also grateful to my partner who accompanied me on this venture.

Vita

February 2014 ...... B.B.A., Sogang University

2014 ...... Graduate Teaching Associate, Yonsei University

2014-2016 ...... Brain Korea 21 PLUS Fellow, Department of

Psychology, Yonsei University

February 2017 ...... M.A., Psychology, Yonsei University

2018-2019 ...... University Fellow, The Ohio State University

2019 to present ...... Graduate Teaching/ Research Associate,

Department of Psychology, OSU

Publication

Lee, S. B., Bae, E., Sohn, Y. W., & Lee, S. (2016). Grit as a buffer against negative feedback: The effect of grit on emotional responses to negative feedback. The Korean

Journal of Social and Personality Psychology, 30(3), 25-45.

Fields of Study

Major Field: Psychology

Table of Contents

Abstract ...... ii Acknowledgments...... iv Vita ...... v List of Tables ...... viii List of Figures ...... ix Chapter 1. Introduction ...... 1 Metamotivation ...... 2 Metamotivational knowledge of Construal Level as Motivational Orientations ...... 4 Emotions as Motivational Input: Emotion as a Cue for Motivational States ...... 8 High-Level and Low-Level Emotions ...... 9 The Present Research ...... 11 Chapter 2. Studies 1a & 1b ...... 13 Method ...... 13 Results ...... 17 Discussion ...... 18 Chapter 3. Study 2...... 19 Method ...... 20 Results ...... 25 Discussion ...... 30 Chapter 4. Study 3...... 32 Method ...... 33 Results ...... 35 Discussion ...... 38 Chapter 5. Integrative Data Analysis ...... 40 Discussion ...... 43 Chapter 6. Study 4...... 46 Method ...... 47 Results ...... 47 vi

Discussion ...... 50 Chapter 7. General Discussion ...... 54 Enduring Questions and Alternative Explanations ...... 54 Future Directions and Implications ...... 57 Conclusion ...... 60 References ...... 61 Appendix A. Task Scenarios and Instructions for Studies 1a and 1b ...... 68 Appendix B. Task Stimuli for Study 2...... 70 Appendix C. Task Stimuli for Studies 3 and 4 ...... 72 Appendix D. All Figures ...... 73 Appendix E. All Tables...... 74

vii

List of Tables

Table 1. Results of the linear mixed effects model for Studies 1a and 1b ...... 74 Table 2. Results of the logistic regression for Studies 2-4 ...... 75 Table 3. Results of the one-way ANCOVA and descriptive statistics for Studies 2-4 and Integrative Data Analysis (IDA) ...... 76

viii

List of Figures

Figure 1. Mediation analysis (Study 3) ...... 73 Figure 2. Moderation analysis (Integrative Data Analysis)...... 73

Chapter 1. Introduction

Everyday life is saturated with numerous tasks to complete. Overloaded with endless to-do lists, effective goal pursuit is one of the biggest challenges people face in modern life. People need to manage competing demands on their limited time and resources as they strive to achieve multiple goals – and this requires successful self- regulation (Neal, Ballard, Vancouver, 2017). Appreciating the importance of self- regulation in successful goal attainment, researchers have devoted considerable research effort to understanding how people control their thoughts, feelings, and behavior (e.g.,

Mischel, Shoda, and Rodriguez, 1989; Tamir, 2016; Carver and Scheier, 1998). For example, the action control perspective (Kuhl, 1985) suggests that blocking out thoughts of competing action alternatives, inhibiting the accessibility of alternative goals, or controlling of unhelpful emotions enables individuals to carry out their intentions to complete a focal goal (e.g., Webb, Schweiger-Gallo, Miles, Gollwitzer, & Sheeran, 2012;

Shah, Friedman, & Kruglanski, 2002). In situations requiring delay of gratification, controlling attention and emotional reactions toward immediate temptations has shown to improve one’s ability to wait for a delayed reward (Mischel et al., 1989; Metcalfe &

Mischel, 1999). Although research in this area has provided insights into the mechanisms by which controlling thoughts, feelings, and behavior can promote or hinder self- regulation, regulation of motivation has received less attention. Given that motivation is a

1 driving force that influences our goal-directed thoughts, feelings, and behavior (Locke,

1996; Locke & Latham, 1990, 2002), understanding whether and how people monitor and modulate their motivations is a critical yet underappreciated research question.

Metamotivation

Literature on motivation regulation has primarily focused on identifying factors that increase or decrease one’s motivation, and the strategies people use to do so (e.g.,

Keller, 1987; Schwinger & Otterpohl, 2017). However, motivation differs not only in level but also in type. Prominent theories of motivation and cognition (e.g., self- determination theory, regulatory-focus theory, construal level theory) have established conceptual foundations for distinguishing different types of motivational orientation (e.g., intrinsic vs. extrinsic motivation, Deci & Ryan, 1985; 2010; promotion-focused vs. prevention-focused motivation, Carver & White, 1994; Higgins 1997; high- vs. low-level construal, Trope & Liberman, 2010). Building on these theories, the metamotivational approach proposes that people may regulate not only the level of motivation (i.e., motivational quantity) but also the type of motivation (i.e., motivational quality) in goal pursuit (Scholer & Miele, 2016; Scholer, Miele, Murayama, & Fujita, 2018).

Regulating motivational quality requires that people understand that motivational states are not universally effective, but that the effectiveness of certain motivational states involves context-specific trade-offs. One type of motivation could be more or less beneficial depending on the particular demands of situation. For example, when a task requires divergent and creative thinking (e.g., brainstorming), adopting a motivational orientation that prioritizes eagerness over vigilance (i.e., promotion-focused) should

2 enhance task performance. By contrast, when a task requires convergent and attentive processing mode (e.g., proofreading), adopting a motivational orientation that prioritizes vigilance over eagerness (i.e., prevention-focused) should enhance task performance

(Higgins 1997).

When one’s current motivational orientation “fits” the motivational demands of a task at hand, one experiences regulatory fit and “feels right” (Cesario, Grant, & Higgins,

2004; Higgins, 2000, 2002). Thus, matching the right type of motivational state to the right type of task (i.e., creating task-motivation fit) may optimize task engagement, task experience, and potentially enhance task performance (e.g., Higgins, 2000; Freitas &

Higgins, 2002). Appreciating the value of regulatory fit, metamotivational research examines whether and how people can create task-motivation fit on their own through the process of monitoring and controlling motivational states (Fujita, Scholer, Miele, &

Nguyen, 2018).

People can create task-motivation fit either by modulating their motivational states to fit a given task or by strategically selecting a task based on their motivational states (Scholer, Miele, Murayama, & Fujita, 2018; Fujita et al., 2018). The former requires at least two types of metamotivational knowledge. First, one must understand which motivational state is most beneficial for a performance on a given task (i.e., task knowledge). Next, once identifying the optimal motivational state for the task, one should know how to induce the state in oneself, by what means (i.e., strategy knowledge).

Pioneering work on metamotivation provides some evidence that people may have metamotivational task and strategy knowledge. People appear to understand the

3 differential benefits of distinct motivational states (e.g., promotion vs. prevention; high- level vs. low-level construal) in task performance and use such understanding to modulate their motivational states in response to the motivational demands of a given task (Scholer & Miele, 2016; Nguyen, Carnevale, Scholer, Miele, & Fujita, 2019).

Although these findings suggest people may have requisite metamotivational knowledge for the former way, the latter way of creating task-motivation fit has not been explored fully.

In an initial attempt, Scholer and Miele (2016) explored whether people understand how to create task-motivation fit by strategically selecting a task based on their motivational states in the context of regulatory focus (Higgins, 1997, 1998b). When given a promotion (vs. prevention) inducing recall activity, participants preferred to complete eager (vs. vigilant) task. Although their findings provide preliminary evidence that people may understand the benefits of matching the right type of task with their current regulatory focus, it is unclear whether people are able to do so in practice. To put such knowledge into practice, one must be able to recognize what motivational state one is currently in (i.e., self-knowledge; Scholer et al, 2018; Miele, Scholer, & Fujita, 2020).

However, the question of whether and how people determine their current motivational states has not been examined. The present research, thus, explores this question in the context of construal level theory.

Metamotivational knowledge of Construal Level as Motivational Orientations

How people subjectively construe a situation or an event influences their judgments and decisions. Construal level theory (CLT) proposes that people construe

4 events at varying levels of abstraction as a function of psychological distance (Trope &

Liberman, 2003; 2010). The term construal refers to people’s interpretation or representation of events, and involves their cognitive, affective, motivational, and behavioral orientations to these events. According to CLT, people tend to represent psychologically distant events by engaging in high-level construal—an orientation toward the abstract and essential features of objects or events—since specific details about those events are less available or subject to change. In contrast, as events become more proximal and incidental information becomes available, people tend to engage in low-level construal—an orientation toward the concrete and idiosyncratic features of objects or events. Building on the idea that individuals naturally adopt different levels of construal as psychological distance increases or decreases, CLT further proposes that the different levels of construal serve to expand or contract one’s mental horizons and thereby, impacting people’s preference, judgment, and behavior. Critically, researchers have documented that manipulating level of construal, independent of psychological distance, could affect performance on goal-relevant tasks (e.g., Trope, Liberman,

Wakslak, 2007; Trope & Liberman, 2010).

One regulatory task in which performance benefits from high-level relative to low-level construal is self-control. Successful self-control involves the process of advancing distal yet more valued ends rather than proximal but less valued ends (Fujita,

2011). Self-control conflicts arise when motivational concerns for these two ends compete. Substantial evidence has demonstrated that high-level (vs. low-level) construal enables people to mentally transcend the here and now, thereby enhancing self-control

(e.g., Fujita, Trope, Liberman, & Levin-Sagi, 2006; Malkoc, Zauberman, & Bettman,

2010). For example, people concerned with weight loss were more likely to choose an apple versus a candy bar when engaging in high-level (vs. low-level) construal (Fujita &

Han, 2009). In another study, thinking about why (vs. how) they engaged in an action—a manipulation that has been shown to activate high-level (vs. low-level) construal (Freitas,

Gollwitzer, & Trope, 2004)—helped participants to hold a hand grip longer in order to receive self-relevant information (Fujita et al., 2006). These findings suggest performance on tasks that involve elements of self-control benefits from engaging in high-level construal (hereafter referred to as high-level tasks).

Other research suggests that low-level relative to high-level construal enhances performance on different types of regulatory tasks that require precision in behavioral execution and contextual sensitivity (e.g., Gollwitzer & Sheeran 2006; Schmeichel, Vohs,

& Duke 2011; Zimmerman & Kitsantas, 1996). For example, participants excelled in a dart-throwing task when they were instructed to focus on the concrete means rather than abstract ends of the task (Zimmerman & Kitsantas, 1996; 1997). Similarly, Schmeichel and his colleagues (2011) found that low-level (vs. high-level) construal led to superior performance on a stop-signal task—a task that requires vigilant attention to contextual cues and control on specific motor responses. These findings suggest performance on tasks that involve behavioral precision benefits from engaging in low-level construal

(hereafter referred to as low-level tasks). Collectively, both high-level and low-level construal can enhance performance depending on the nature of the task.

Initial investigations demonstrate that, although there are individual differences, people in general appear to recognize the differential regulatory benefits of high-level versus low-level construal and understand ways to prepare themselves to be engaged in high-level (vs. low-level) construal in anticipation of high-level (vs. low-level) task

(MacGregor, Carnevale, Dusthimer, & Fujita, 2017; Nguyen et al., 2019). For example, participants prepared themselves for tasks that require self-control by choosing preparatory exercise that instantiate high-level construal, such as thinking about why (vs. how) one engages in an action (MacGregor et al., 2017; Nguyen et al., 2019). These findings suggest that people may be able to create task-motivation fit by implementing strategies to engage in a desired level of construal. Yet the question of whether people can create task-motivation fit by strategically selecting a task based on their motivational states has not been explored in the CLT context.

As noted earlier, although people understand which task would benefit from what level of construal (i.e., task knowledge), they may not be able to strategically select a task unless they identify their current level of construal. This suggest the capacity for the application of metamotivational knowledge depends on whether and/or how well one is aware of their current motivational states (i.e., self-knowledge). One may conceptually understand the qualitative differences between different types of motivation. However, in order to identify what motivational states they are in, one should experientially realize what it feels like to be in particular motivational states. By doing so, people may use experiential cues to determine what motivational states they are in.

Emotions as Motivational Input: Emotion as a Cue for Motivational States

Our feelings, including moods, emotions and even metacognitive feelings (e.g., ease of recall or perceptual fluency), can function as a source of information in judgments and decision making (e.g., Clore, 1992; Clore, Gasper, & Garvin, 2001; Schwarz &

Clore, 1983; 1996; Schwarz, 2001; 2012). According to the affect-as-information hypothesis, people often refer to their own feelings when making evaluative judgments

(Schwarz, 2001; Clore et al., 2001). Importantly, our feelings can affect judgment not only when experienced as reactions toward the object of judgment but when experienced incidentally. For example, past research has shown people’s life satisfaction judgements were influenced by their current mood, which was affected by the weather of the day

(Schwarz & Clore, 1983). Similarly, when given a choice of tasks to perform, people may ask themselves implicitly, “How do I feel right now? Do I feel like I want to do this task?” and decide which task to perform based on their current feelings. In this way, we propose that our feelings or emotional states can convey information about our motivational states (e.g., metamotivational feelings; Miele & Scholer, 2018; Miele et al.,

2020) and be used as a basis for our judgments and decision-making.

The word “emotion” shares its root with “motivation”: both come from the Latin movere, meaning ‘to move.’ Our emotions, as like our motives, direct our thoughts and behavior and drive us toward our goals (e.g., Tying, Amin, Saad, & Malik, 2017).

Importantly, specific emotions are associated with different motivational orientations

(e.g., Higgins, 2011; Schwarz, 2001). For example, emotions that vary along a cheerful- dejected dimension are related to promotion focus whereas emotions along a quiescent-

8 agitated dimension are related to prevention focus. In one study in which participants’ regulatory focus was manipulated by the framing of performance feedback (gain vs. loss), even when the performance outcome was the same, the type of experienced emotion differed by one’s regulatory focus. Promotion-focused participants felt happy or discouraged while prevention-focused participants felt relaxed or tensed depending on performance outcome (Higgins, Shah, & Friedman, 1997). These findings suggest that people could infer their motivational orientation from their emotional experience.

We propose that people’s feelings or emotional states may serve as a cue that signal one’s current motivational states. Since the implementation of metamotivational knowledge requires accurate monitoring of motivational states, we further propose that people may attend to their emotional states in order to infer their motivational states and follow the metamotivational signal from emotions in making strategic task choice.

High-Level and Low-Level Emotions

Traditionally, emotions are categorized by their valence and arousal: positive to negative, and low to high activation (Russell, 1980; Watson and Tellegen, 1985). In addition to valence and arousal, emotions can be characterized by the level of appraisals underlying the experience of emotions: low-level to high-level emotions. We tend to experience concrete, low-level emotions such as happiness, fear, or anger in response to specific situation, event, or object. For instance, indulging in delicious chocolate cake might induce momentary feelings of happiness, a venomous snake might cause fear, or being provoked by someone’s rudeness might elicit anger. By contrast, we experience more abstract, higher-level emotions such as pride, shame, or moral outrage in response

9 to the broader meaning of actions or events. For example, we feel proud when we achieve something which has important meaning in our life. We feel shame when we fail at something and compare the incident of failure to global standards for ourselves (Lewis,

1971). Likewise, moral outrage arises when a particular behavior is evaluated in light of general moral principles (e.g., Batson, Chao, & Givens, 2007).

Recent work has provided some empirical evidence supporting the distinction between high-level and low-level emotions. For example, changing the level at which the disgusting stimulus is construed affects the intensity of core disgust vs. moral disgust, such that high-level construal elicited moral disgust whereas low-level construal elicited core disgust (Moran, Bornstein, & Eyal, 2019). The opposite direction of the relationship between the level of construal and the experience of emotions has been explored as well.

Han, Duhachek, and Agrawal (2014) examined how discrete emotions influence consumers’ decisions by altering their construal levels. They found feelings of guilt increased reliance on feasibility attributes and secondary features of the product in making decisions by inducing lower level of construal. Shame, by contrast, increased reliance on desirability attributes and primary features by inducing high-level construal.

Other researchers have found that pride increases perseverance and promotes delay of gratification, which requires highlighting the value of long-term goals over immediate desires, suggesting high-level construal was induced when experiencing pride (Williams

& DeSteno, 2008; Shimoni et al., 2019).

Together, these findings suggest the bidirectional link between specific emotions and level of construal. High-level emotions orient people to construe events at higher-

10 levels, in terms of their broader, abstract meaning and implications. By contrast, low- level emotions induce people to engage in lower-level construal, and orient them to focus on the unique and idiosyncratic demands of present circumstances. Given their link to construal level, people may interpret experiencing high-level vs. low-level emotions as indicating that they are engaged in high-level vs. low-level construal. In this way, we propose that emotions could signal what level of construal one is in and therefore, people may attend to their emotional states to infer their current level of construal.

The Present Research

Based on our propositions, we first examine whether people understand the motivational benefits of high-level versus low-level emotions in task performance. We also explore whether people capitalize on their emotions in creating task-motivation fit by choosing the right type of task based on the level of emotion. We conducted five studies to examine these questions. Studies 1a and 1b assessed people’s metamotivational knowledge of high-level versus low-level emotions. Specifically, we investigated the extent to which people recognize the possibility that high-level versus low-level emotions may enhance performance on tasks that demand high-level versus low-level construal, respectively. Studies 2-4 extend the findings from Studies 1a and 1b and examined whether people are able to create task-motivation fit by strategically selecting a task based on their current emotional states. To test this question, we manipulated high-level versus low-level emotion and examined whether manipulated emotions would predict people’s task choice in a context in which behavioral choice is consequential. We

11 hypothesize people who experience high-level (vs. low-level) emotion are more likely to pick high-level task over low-level task in order to create task-motivation.

Chapter 2. Studies 1a & 1b

The purpose of Studies 1a and 1b was to assess people’s general understanding of the motivational benefits of high-level versus low-level emotions in task performance.

We adopted a within-subjects repeated-measure design. Participants were asked to imagine choosing between performing high-level versus low-level tasks after recalling events that evoke high-level or low-level emotions. If they understand the motivational benefits of high-level versus low-level emotions, participants’ preferences for high-level versus low-level task should differ depending on the type of evoked emotion.

Specifically, we expected participants would prefer high-level (vs. low-level) task when high-level (vs. low-level) emotion is evoked.

Method

Participants

Given the lack of comparable published data with which to estimate effect sizes, we used general rules of thumb to determine sample sizes a priori for Studies 1a and 1b.

In Studies 1a and 1b, we set a target N = 100. A sensitivity power analysis in G*Power revealed that this N would provide 80% power to detect an effect of f = .08 for a mixed- design analysis of variance (ANOVA). Note that we fitted a linear mixed effects model for the primary statistical test of our hypotheses. However, since power analysis for linear mixed effects models is not yet available in G*Power, we conducted an alternative power

13 analysis for a mixed-design ANOVA, which is the most similar analysis method available in the software.

A total of 101 Amazon Mechanical Turk (MTurk) workers in the United States participated in Study 1a in exchange for $0.80 (Mage = 35.29, SDage = 10.51, 37 women,

63 men, 1 nonbinary). For Study 1b, a total of 104 MTurk workers participated in exchange for $0.80 (Mage = 34.62, SDage = 10.46, 37 women, 67 men).

Given concerns about the quality of data collected from MTurk (Chmielewski & Kucker,

2019; TurkPrime, 2018), we implemented data screening procedures recommended by

Bai (2018). We limited analyses to responses with GPS coordinate data that were located in the United States and were unique (i.e., nonrepeating) within the dataset. Also, we excluded participants who provided non-sensical or not suitable responses for open-ended questions. Participants who failed our attention checks (i.e., reported being “very” or

“extremely” distracted, taking the study “not at all” or “a little” seriously) were excluded as well, resulting in final N = 82 for Study 1a and N = 89 for Study 1b.

Materials and Procedure

The study materials and procedure for Studies 1a and 1b were identical except the subset of task stimuli presented for task selection.

Emotion cue generation for high-level and low-level emotions. First, every participant was asked to recall two past events that would make them feel proud or happy and generate an emotion cue for each event (i.e., a word or phrase that would help them recall the event). For instance, one participant provided “graduation” for a proud cue and

“wedding day” for a happy cue. The order in which participants generated which emotion

14 cue first was counterbalanced (i.e., emotion cue order variable in main analyses). They were then told that they would be asked to consider the proud and happy memories in various scenarios. Across scenarios, we showed each emotion cue to evoke either feelings of pride or happiness.

Preferences for high-level and low-level tasks. After generating emotion cues, participants were presented with a series of scenarios. Each described a choice of two performance tasks that would benefit from high-level vs. low-level construal and asked participants which task they would like to perform given that they were feeling proud vs. happy after recalling those respective memories (see Appendix A for complete instructions). Previous research suggests that high-level construal promotes self-control

(e.g., Fujita et al. 2006), whereas low-level construal promotes performance for the tasks requiring behavioral precision (e.g., Zimmerman & Kitsantas, 1997). Based on this work, high-level tasks involved elements of self-control (e.g., making a choice between a smaller amount of money today vs. a larger amount of money later), whereas low-level tasks involved contextual sensitivity and precision (e.g., dart-throwing). We created 12 pairs of tasks from a list of six high-level and six low-level tasks, with each task used twice (see Appendix A for all task scenarios). Across the twelve scenarios, pride was cued by recall of the proud memory in the half of the scenarios, and happiness was cued by recall of the happy memory for the other half (i.e., emotion condition). Whether proud

(vs. happy) memory was cued in the first half vs. last half of the scenarios was counterbalanced between participants. For each scenario, participants’ task preference,

15 our key dependent variable, was measured on a six-point fully-labeled scale (1 = strongly prefer {the name of low-level task}, 6 = strongly prefer {the name of high-level task}).

After analyzing the data from Study 1a, we were concerned about the possibility that some of the high-level tasks – such as receiving negative feedback – may require emotion regulation and that this may have interfered with the effect of evoked emotion in guiding participants’ task preference. Therefore, we replaced a subset of the high-level tasks with those that do not require emotion regulation for Study 1b (see Appendix A).

Anticipated task-level difficulty and enjoyment. People may prefer one task over another for various reasons that extend beyond motivations for creating task- motivation fit. For example, they may prefer a task simply because it seems easier to complete or more enjoyable. However, we were interested in whether emotion predict task preference above and beyond the perceived features of the task. To account for potential confounding effects that these features might have on task preference ratings, we measured expected task difficulty and task enjoyment for each task. Participants answered how difficult and how enjoyable they thought each of the task would be on a seven-point fully-labeled scale (1 = extremely easy/unenjoyable, 6 = extremely difficult/enjoyable).

Demographics and other measures. Lastly, participants reported their demographics and answered to our attention check questions. Participants were then debriefed and compensated for their participation.

Results

Preferences for high-level versus low-level tasks. Since each participant provided task preferences repeatedly across 12 scenarios of different task pairs, we analyzed task preferences using linear mixed modeling to control for the independent effect of participant and scenario (Judd, Westfall, & Kenny, 2012). We used the lme4 package (Bates, Maechler & Bolker, 2012) in R to perform a linear mixed effects analysis. Emotion condition (effect-coded: pride = 1, happiness = -1), task difficulty, task enjoyment, and emotion cue order were entered into the model as fixed effects, and participant and scenario were entered as random intercepts. To control for the potential influence of anticipated task difficulty and enjoyment on task preference, the difference scores between task difficulty and enjoyment ratings for high-level vs. low-level tasks were calculated for each task choice set and these difference scores were used as covariates1.

In what follows, we will present only those results that are theoretically relevant to our research hypotheses. The complete output of the analyses is presented in Table 1.

The models revealed significant effects of emotion condition on task preference in both studies [Study 1a: β = 0.25, t(889) = 2.68, p < .01; Study 1b: β = 0.18, t(966) = 1.98, p <

.05]2. As expected, participants preferred high-level tasks given proud (Study 1a: M =

1 In Studies 1a and 1b, because emotion condition was manipulated within subjects and task perception variables were only measured once, we were unable to examine the impact of emotion condition on task perception. 2 We also performed a linear mixed effects analyses to examine the effect of emotion condition on task preference when task perception variables (i.e., task difficulty and task enjoyment) were excluded from the analysis. The pattern of findings did not change substantively [Study 1a: β = 0.25, t(890) = 2.40, p = .017; Study 1b: β = 0.18, t(967) = 1.79, p = .075]. 17

3.53, SD = 1.98; Study 1b: M = 3.81, SD = 1.91) compared to given happy (Study 1a: M

= 3.28 SD = 1.95; Study 1b: M = 3.62, SD = 1.93) memory recall. The results suggest that people may recognize the differential motivational benefits of high-level versus low-level emotions for performance on high-level versus low-level task.

Discussion

The results from Studies 1a and 1b provide preliminary evidence that people may choose different tasks based on their emotional state to create task-motivation fit.

However, there are some limitations to Studies 1a and 1b. First, participants were asked to imagine feeling proud or happy and indicated task preferences for numerous combinations of described tasks. As the tasks were hypothetical, participants’ task preference ratings may not reflect what they would actually chose if their decisions were consequential. Second, although participants were asked to recall their own proud or happy memories in the scenarios, it was not clear whether participants actually experienced the emotional state when reporting their task preferences. Third, given the nature of the within-subjects design in which participants were exposed to both pride and happiness, it is possible that the study design itself induced participants to consider relative differences between pride and happiness. Thus, it is not clear if the effect of emotion condition on task preference observed in Studies 1a and 1b would persist if people’s emotional states were manipulated and people were making real consequential choices. Studies 2-4 were designed to address these concerns.

Chapter 3. Study 2

The purpose of Study 2 was to extend the findings from Studies 1a and 1b by examining the effect of high-level versus low-level emotions on task selection in a context in which behavioral choice is consequential. To directly examine the effect of emotions on behavioral decisions, we manipulated high-level versus low-level emotions between participants. To make the decisions more consequential, participants learned about one set of high-level and low-level tasks, and chose the task that they would like to complete first with the expectation that they might perform the tasks in the order they selected. In these ways, we created a context in which participants’ choices supposedly had consequences and investigated whether participants were able to recognize the motivational benefits of high-level and low-level emotions even when the relative difference between high-level versus low-level emotions was not implied by study design itself. We predicted that participants would choose different tasks based on their emotional state to create task-motivation fit. Specifically, participants experiencing high- level emotions would be more likely to select high-level task whereas participants experiencing low-level emotions would be more likely to select low-level task.

Method

Participants

A total of 200 MTurk workers in the United States participated in exchange for

$1.00 (Mage = 36.88, SDage = 11.32, 89 women, 110 men, 1 nonbinary). Given the lack of comparable published data with which to estimate effect sizes, we used general rules of thumb to determine sample sizes a priori. As Study 2 adopted between-subjects design and involved a statistically less powerful binary outcome measure, we doubled the sample size relative to Studies 1a and 1b and set a target N = 200. A sensitivity power analysis in G*Power revealed that this N would provide 80% power to detect an effect of

2 2 ηp = .20 and 90% power to detect an effect of ηp = .23 for one of the primary statistical tests of our hypotheses—a one-way analysis of covariance (ANCOVA). Using the exclusion criteria described in Studies 1a and 1b, we had a final N = 161 for Study 2.

Materials and Procedure

The study employed a between-subjects design to test the effect of manipulated emotion (pride vs. happiness) on task choice (a binary outcome) and task preference (a continuous outcome). Participants were randomly assigned to emotion condition. We note in advance that we were interested in examining whether manipulated emotions would predict participants’ task choice as well as the strength of their preferences.

Although one might argue that task choice is more direct measure of people’s behavioral decisions, we were concerned that as a binary outcome, it may be statistically less powerful. We thus took a convergent approach, keeping in mind that while one of the measures may show the predicted results, the other may not.

Preview of high-level and low-level performance tasks. Participants first previewed one high-level and one low-level tasks, adapted from previous research

(Nguyen et al., 2019). The purpose of task previews was to familiarize participants with the tasks’ demands so they could make informed choices later in the study. The order of task previews (i.e., task order variable in the main analyses) was counterbalanced between participants.

The high-level task (i.e., “See the Big Picture” task) was an adaptation of the

Gestalt Completion Test (Ekstrom, French, Harman, & Dermen, 1976) used in previous construal level experiments (Wakslak, Trope, Liberman, & Alony, 2006). Participants were first instructed that this task would assess their ability to see a whole picture from the incompletely drawn image. Participants were then presented with a fragmented picture of school bus for the practice trial and told that their task was to identify what the object in the image is (see Appendix B for task stimuli). This task requires participants to generate organized wholes from perceptual parts through global processing of visual information – performance should thus benefit from high-level construal.

The low-level task (i.e., “Spot the Missing Detail” task) was an adaption of the picture completion task of the Wechsler Intelligence Scale for Children (Wechsler, 1991) used in previous construal level experiments (e.g., Wakslak et al., 2006). Participants were instructed that this task would assess their ability to detect missing parts of various images and that their task was to identify missing components within the images.

Participants were then presented with an image of a dresser drawer missing one of the drawer knobs for the practice trial and asked to identify a missing part (i.e., a drawer

21 knob; see Appendix B for task stimuli). This task asks participants to visually scan the image systematically for local and detailed features – performance should thus benefit from low-level construal.

Emotion priming. After practicing each task, participants were asked to describe a future event that would make them feel proud vs. happy, depending on emotion condition. Instructions used in each writing prompt were adapted from previous research by Katzir et al. (2010). Specifically, participants in the pride condition were asked to describe a future event that would make them to experience strong feelings of pride and self-worth, whereas participants in the happiness condition were asked to describe a future event that would make them to experience strong feelings of fun and enjoyment.

To confirm the reliability of the emotion priming manipulation, we conducted a pilot study with an independent sample of 99 MTurk workers (i.e., the same population as the main experiment). In the pilot study, we measured participants’ affective states using PANAS (Watson, Clark, & Tellegen, 1988) after asking participants to describe a future proud versus happy event. The PANAS consists of a 10-item subscale measuring positive affect (e.g., proud, interested) and the other 10-item subscale measuring negative affect (e.g., upset, distressed). In addition to the original list of 20 emotion adjectives in the PANAS, we included one additional emotion adjective, joyful, to capture relevant affective state for happiness condition. Participants indicated on 5-point fully-labeled scale (1 = very slightly or not at all, 5 = extremely) how much they experienced each affective state.

Given that pride and happiness are both positive emotions and that it is very likely people experience both pride and happiness when thinking about the proud event, we focused on whether participants in the pride condition experienced pride relatively more than participants in the happiness condition. First, we computed a pride specificity score by subtracting the overall mean ratings of the other 10 positive adjectives from the mean proud rating. An independent t-test on pride specificity revealed that participants in the pride (M = 0.04, SD = 0.89) as compared to the happiness condition (M = - 0.62, SD =

1.09) experienced more pride relative to other positive affective states, t(82) = 3.00, p <

.01.

Second, we also calculated difference scores by subtracting mean ratings of joyful from mean ratings of proud to compare the relative activation of the target emotions between conditions more directly. An independent t-test on difference scores reaffirmed that participants in the pride (M = 0.15, SD = 1.11) as compared to the happiness condition (M = - 0.45, SD = 1.10) experienced pride relatively more than joy, t(97) =

2.71, p < .01.

Task selection and preferences for high-level versus low-level task. Next, participants were presented with the high-level and low-level tasks again. Depending on what was ostensibly random assignment, they were told that they might be asked to complete both tasks in the order that they choose. Participants were then asked to choose the task they would like to complete first. Following their initial binary choice between high-level versus low-level task, they also indicated to what extent they preferred to complete high-level versus low-level task on a six-point fully-labeled scale ranging from

Strongly prefer task #1 to Strongly prefer task #2. The numbering order of two tasks for the binary choice and continuous task preference rating was counterbalanced between participants.

Anticipated task performance, task difficulty, task enjoyment, and task duration. Participants were then asked to rate expected task performance, task difficulty, task enjoyment, and task duration for each task [How well do you think you would perform on the task (1 = extremely poorly, 6 = extremely well)? How difficult do you think the task will be (1 = extremely easy, 6 = extremely difficult)? How enjoyable do you think task will be (1 = extremely unenjoyable, 6 = extremely enjoyable)? How long does the task feel like it would take to complete (slider bar from 1 = very short to 100 = very long)?]. Since the potential benefit of creating task-motivation fit is enhanced task performance, we measured expected task performance as a potential mediator to explore underlying mechanism by which manipulated emotions would guide task choice and task preference. In addition, as discussed in Study 1a and 1b, we measured anticipated task difficulty, task enjoyment, and task duration and these task perception variables were entered as control variables in the main analyses to account for potential confounding effects.

After completing ratings of task perception variables, we informed participants that they were not selected to complete the performance tasks.

Mood awareness. It is possible that, although individuals may understand the motivational benefits of high-level versus low-level emotion, they may not necessarily be able to capitalize on their emotional state if they do not recognize what emotional state

24 they are in at the moment. That is, the effect of manipulated emotions in guiding task selection might be only observed for those who are more sensitive to their feelings. Given this possibility, we measured participants’ sensitivity to their current moods using Mood

Awareness Scale (MAS; Swinkels & Giuliano, 1995) as a potential moderator. The MAS consists of 10 items, a self-report measure with two factors: Mood Labeling and Mood

Monitoring. The first factor, Mood Labeling, consists of 5 items, such as “Right now I know what kind of mood I’m in”. The second factor, Mood Monitoring, consists of 5 items, such as “I am sensitive to changes in my mood.” Participants rated the extent to which each statement describes themselves on 5-point full-labeled scale (1=Does not describe me, 5=Describes me extremely well). The internal reliability coefficients of the

MAS scale were α = .84 for the overall scale, α = .83 for Mood Labeling, and α = .85 for

Mood Monitoring.

Demographics and other measures. Finally, participants reported their demographics and answered to our attention check questions. Participants were then debriefed and compensated for their participation.

Results

Construal level manipulation check. Language is a useful window into cognitive abstraction level in that an individual’s current mental states affect their language choice (e.g., Schwanenflugel, Harnishfeger, & Stowe, 1988). Therefore, we measured linguistic abstraction by analyzing text data from participants’ written responses for the event descriptions to assess their level of construal. Linguistic

25 abstraction scores3 were calculated through syntax-based computerized coding methods provided by Johnson-Grey and colleagues (2019). Specifically, we computed overall concreteness scores ranging from 1 (abstract) to 5 (concrete), based on Brysbaert’s coding scheme (see Brysbaert, Warriner, and Kruperman, 2014 and Johnson et al., 2019, for the details) and reverse-coded the computed scores so that higher scores indicate greater linguistic abstraction (i.e., induced level of construal; 1 = concrete to 5 = abstract). As expected, the linguistic abstraction scores were significantly higher in the pride condition (M = 3.70, SD = 0.15) than in the happiness condition (M = 3.54, SD =

0.18, t(159) = 6.16, p < .001)4. The results suggest that participants in the pride condition engaged in higher level of construal when describing proud event compared to participants in the happiness condition.

Order effects. We conducted chi-square tests and independent t-tests to examine whether there was the order effect of learning high-level and low-level tasks on task

3 Johnson et al. (2019) provides syntax for the computerized text analysis to calculate three different linguistic abstraction scores (i.e., the Syntax-LCM, the LISC LCM, and the Brysbaert scores). Research suggests although these scores are intended to measure similar constructs (i.e., linguistic abstraction), the correlation between them are usually weak (e.g., Johnson et al., 2009; Puddle-Ducks, 2019). This is because each score is computed by different coding schemes based on different word dictionaries. Indeed, correlations between these scores range from r = -0.02 to r = 0.50 across Studies 2-4. We thus take a convergent approach, keeping in mind that while one of the scores may show the predicted results, the others may not. We calculated all three scores and analyzed on them respectively. Since Brysbaert scores showed most consistent pattern across Studies 2-4, we treat this as an index of induced level of construal and only reported analyses with this index. 4 In analyzing text data, we were concerned about the possibility that simply repeating the instructions for pride versus happiness condition or the frequency of target emotion related words (e.g., proud, pride, joyful, enjoy, happy, fun) used in event descriptions may have resulted in differences in linguistic abstraction scores between condition. Therefore, we analyzed whether the emotion words for the pride vs. happiness condition differed in their mean abstraction scores. The linguistic abstraction scores did not differ between word groups, t(24) = 0.85, p = .41. 26 choice and task preference, respectively. Analyses revealed no significant differences in task choice, χ2 (1, N = 161) = 3.80, p = .051, and task preference ratings by task order, t(159) = 1.35, p = .18. However, given that the order effect could be interpreted as marginally significant, task order variable was included as covariate in the main analyses.

Task choice. We simultaneously regressed task choice (0 = low-level task, 1 = high-level task) on emotion condition (effect-coded: -1 = happiness, 1 = pride), task difficulty, task enjoyment, task duration, and task order using logistic regression. As in

Studies 1a and 1b, we calculated difference scores between task difficulty, enjoyment, and duration ratings for high-level vs. low-level tasks. All differences scores were standardized and entered as covariates5, in addition to task order (effect-coded: -1 = low- level task first, 1 = high-level task first). Contrary to predictions, results revealed no significant differences in task choice by emotion condition, β = -.36, SE = .46, Wald χ2

(1, N = 161) = .62, p = .436. Instead, participants were more likely to choose low-level task (i.e., “Spot the Missing Detail” task; 78%) over high-level task (i.e., “See the Big

Picture” task) regardless of emotion conditions, β = -1.55, SE = .35, Wald χ2 (1, N = 161)

= 20.22, p < .001 (see Table 2 for the complete output).

5 It is possible that the high-level or low-level tasks were perceived as more enjoyable or easier depending on emotion condition, and that such differences guided task preference. To examine this possibility, we tested whether task perception ratings differed by emotion condition. Results of independent t-tests showed there was no significant effect of condition on the task perception ratings: low-level task: task difficulty: t(159) = .85, p = .40; task enjoyment: t(159) = .79, p = .43; task duration: t(159) = -.72, p = .47; high-level task: task difficulty: t(159) = .83, p = .41; task enjoyment: t(159) = .24, p = .81; task duration: t(159) = -.04, p = .97. This suggests that it is appropriate to use task perception variables as covariates. 6 When task perception variables (i.e., task difficulty, task enjoyment, and task duration) were excluded from the analysis, the pattern of findings did not change substantively, β = -0.37, SE = 0.39, Wald χ2 (1, N = 161) = 0.90, p = .34. 27

Task preference. To examine the effect of emotion on task preference, we conducted a one-way ANCOVA. Again, the difference scores of task difficulty, enjoyment, duration ratings between high-level and low-level tasks and task order were entered as covariates to control for the potential effects of perceived task features on task preference. Analyses revealed that although the main effect of emotion on task preference

2 7 was not statistically significant, F (1, 155) = 2.77, p = .10, ηp = .018 , the direction of the effect was consistent with our predictions such that participants in the pride condition preferred the high-level task (M = 2.78, SD = 1.55) to a greater extent as compared to those in the happiness condition (M = 2.52, SD = 1.45) (see Table 3 for adjusted means).

Expected task performance as a mediator to predict behavioral choices. To explore whether the effect of emotion is mediated by expected task performance, we conducted mediational analyses using bootstrapping with 10,000 samples (Preacher &

Hayes, 2004), implemented with the PROCESS macro Model 4 (model specifications: X

= emotion condition, M = expected task performance, Y = task choice/task preference;

Hayes, 2017). Contrary to our predictions, the indirect effect of emotion on task choice and task preference was not significant (task choice as Y: β = .01, SE = .11, 95% CI

[-.20, .23]; task preference as Y: β = .01, SE = .06, 95% CI [-.12, .12]).

Induced level of construal as a mediator to predict behavioral choices.

Previous analyses on linguistic abstraction scores confirmed that pride induced more abstract, higher-level of construal whereas happiness induced more concrete, lower-level

7 Without task perception variables as covariates, this finding was weaker: F(1,155) = 1.51, p = 2 .22, ηp = .009. 28 of construal. It is possible that emotion condition would influence task choice or task preference via induced level of construal. To examine this possibility, we conducted mediational analyses with the PROCESS macro Model 4 (model specifications: X = emotion condition, M = linguistic abstraction scores, Y = task choice/ task preference;

Hayes, 2017). Contrary to predictions, the indirect effect of emotion on task choice and task preference was not significant (task choice as Y: β = -.01, SE = .09, 95% CI

[-.19, .19]; task preference as Y: β = -.02, SE = .06, 95% CI [-.13, .10]).

Moderation effect of mood awareness. To assess the potential moderating effect of mood awareness, we conducted moderation analyses. First, we conducted logistic regression predicting task choice from emotion condition, mood awareness, and their interaction. Mood awareness variable was standardized before computing interaction term. Contrary to expectations, there was no significant interaction between emotion condition and mood awareness, β = -.11, SE = .19, Wald χ2 (1, N = 161) = .31, p = .58, indicating that the effect of emotion on task choice was not moderated by mood awareness. Given that the subscales of MAS - Mood Monitoring and Mood Labeling - are conceptually separable constructs, we also explored if the effect would be moderated by any of these indices. Again, there were no significant interactions [mood monitoring as a moderator: β = -.04, SE = .19, Wald χ2 (1, N = 161) = .04, p = .84; mood labeling as a moderator: β = -.17, SE = .20, Wald χ2 (1, N = 161) = .74, p = .34].

For the task preference data, we conducted moderation analyses using Process

Model 1 (model specifications: X = emotion condition, W = mood awareness/ mood monitoring/ mood labeling, Y = task preference; Hayes, 2017). There were no significant

29 interactions between emotion condition and any of the mood awareness indices [mood awareness as W: β = -.11, t(157) = -.90, p = .37; mood monitoring as W: β = -.01, t(157)

= -.10, p = .92; mood labeling as W: B = -.20, t(157) = -1.70, p = .09].

Discussion

Study 2 found the expected pattern of the effect of high-level versus low-level emotion on task preferences, although the effect was not statistically significant.

Specifically, participants in the pride condition (i.e., high-level emotion) appeared to prefer the high-level relative to the low-level task as compared to participants in the happiness condition (i.e., low-level emotion). We believe that the results provide preliminary evidence that people may recognize the motivational benefits of high-level and low-level emotions, and may be able to capitalize on their emotional state in order to create task-motivation fit.

However, we did not observe a significant difference in task choice by emotion condition. Unfortunately, there was an unexpected asymmetry in task choice. Participants overwhelmingly chose the low-level task over high-level task, regardless of emotion condition. It is possible that participants formed an initial preference during task previews before entering emotion manipulation stage, and this might have constrained the ability of the emotion manipulations to have an effect. Indeed, the impact of feelings as a source of information for judgments decreases the more other relevant inputs are accessible

(Schwarz, 2001). People may be less likely to rely on their feelings when they have high expertise or more information available in the domain of judgment. Therefore, it is possible that such a strong skewed preference for one task over another overshadowed

30 the effect of manipulated emotions. For these reasons, in Study 3, we implemented materials to reduce this bias in task selection.

Chapter 4. Study 3

Study 3 was designed as a conceptual replication of Study 2 with a few important changes to address potential issues with Study 2. First, we changed task stimuli so that tasks seemed to be more plausible and relevant for participants (i.e., MTurk workers), and so that performance of the tasks did not rely solely on visual perception. We selected tasks based on pretest results which suggested that high-level and low-level tasks did not differ in terms of their perceived task difficulty and enjoyment. Second, we modified our paradigm to prevent participants from making decisions prior to the emotion manipulation. Participants in Study 3 learned about tasks after rather than before the emotion manipulation and they were given task descriptions without any previews. Third, unlike Study 2 in which participants were asked to choose a task that they would like to complete first (implying that they would do both tasks but in different order), participants in Study 3 chose a task with the expectation that they might perform only the selected one later in the study. We also informed participants that they might receive a bonus based on their task performance. These changes were designed to create a context in which participants’ task choices were more consequential and meaningful than in Study 2.

Fourth, although the results of pilot study discussed in Study 2 confirmed the reliability of the emotion priming manipulation, we added a measure of participants’ affective states in the present study as for secondary manipulation check. However, as participants

32 completed the affective states measure after responding to task related measures, which was several minutes later since emotion priming, note that this measure may have not captured participants’ affective state right after emotion priming.

Method

Participants

A total of 200 MTurk workers participated in exchange for $0.80 (Mage = 39.40,

SDage = 12.55, 98 women, 102 men). Using the exclusion criteria described in Study 2, we had a final N = 145 for Study 3.

Materials and Procedure

The study materials and procedure were similar to that of Study 2 but the following changes were made. Procedurally, participants were first assigned to emotion priming condition and then learned about two tasks without task previews. We also included a measure of affective states.

Emotion priming. As in Study 2, participants were randomly assigned to one of two emotion conditions and asked to write about the future event that would make them feel proud versus happy.

High-level and low-level tasks. After writing about a proud or happy event, participants were presented with descriptions of two HITs (Human Intelligence Task8).

Provided in the format of HIT description, we expected the tasks seemed to be more

8 A Human Intelligence Task, or HIT, represents a single, virtual task that MTurk workers can work on, submit an answer, and collect a reward for completing. HITs are created by Requester customers such as researchers in order to be completed by MTurk workers (Amazon Mechanical Turk, n.d.). 33 relevant and plausible for participants. In addition, given MTurk workers attempt to complete as many HITs as possible per day (Hara et al., 2018), we assumed participants would be more motivated to make a good choice, thus their task choices were more consequential.

The high-level task (i.e., HIT titled “Categorize workers’ feedback”) was described as requiring broadening one’s perspective and seeing the big picture.

Specifically, participants were instructed that this HIT requires identifying similar ideas and capturing the essence of workers’ feedback. The low-level task (i.e., HIT titled

“Proofread fundraising letters for donors”) was described as requiring narrowing one’s focus of attention to details. Participants were instructed that this HIT requires careful line-by-line review of text for typos, grammar, and style (see Appendix C for task stimuli). The presentation order of two tasks (i.e., task order) was counterbalanced. To help insure that decisions were based on maximizing task performance, participants were told that they might be invited to participate for the chosen HIT and get a bonus based on performance.

Task selection, task preference, and task perception ratings. Participants were asked to choose one task that they would like to perform. Participants were then asked to rate task preference ("Right now, to what extent would you prefer to do HIT #1 or HIT

#2?") on a six-point fully-labeled scale ranging from Strongly prefer HIT #1 to Strongly prefer HIT #2. Whether a task was labeled HIT #1 or HIT#2 was counterbalanced for both choice and preference measures. Next, as in Study 2, participants were asked to rate expected task performance, difficulty, enjoyment, and duration for each task.

Measures for affective states. We measured participants’ current affective state using the modified PANAS (Watson et al., 1988) developed for the pilot for Study 2.

Mood awareness. As in Study 2, we measured mood awareness as a potential moderator using the same scale (MAS; Swinkels & Giuliano, 1995). The internal reliability coefficients of the MAS scale were α = .78 for the overall scale, α = .83 for

Mood Labeling, and α = .79 for Mood Monitoring.

Results

Target emotion manipulation check. We calculated an index of pride specificity as well as target emotion difference scores and conducted the same analyses as in Study 2 for the purpose of secondary manipulation check. Analyses did not reveal significant differences in pride specificity and difference scores between emotion condition (pride specificity: t(143) = 0.34, p = .73; difference scores: t(143) = 0.88, p = .38). As noted earlier, the lack of significant differences in emotion may reflect the fact that the measure was placed at the end of the survey rather than immediately after the manipulation.

Construal level manipulation check. We analyzed participants’ written responses for the event descriptions and computed linguistic abstraction scores as an index of the level of construal as in Study 2. Again, in support of high-level and low-level emotion manipulation, the linguistic abstraction scores were significantly higher in the pride condition (M = 3.68, SD = 0.14) than in the happiness condition (M = 3.55, SD =

0.18, t(143) = 4.91, p < .001). The results suggest that participants in the pride condition engaged in higher level of construal as compared to participants in the happiness condition.

Order effects. As in Study 2, we conducted a chi-square test and an independent t-test to examine whether there was the order effect of learning high-level and low-level tasks on task choice and task preference, respectively. Analyses revealed that there were no significant differences in task choices, χ2 (1, N = 145) = .46, p = .50, and task preference ratings, t(143) = -1.02, p = .31, by task order. We thus omit task order from subsequent analyses.

Task choice. As in Study 2, we regressed task choices (0 = low-level task, 1 = high-level task) on emotion condition (effect-coded: -1 = happiness, 1 = pride), task difficulty, task enjoyment, and task duration9 using logistic regression. Contrary to predictions, there was no significant difference in task choice by emotion condition, β =

-.47, SE = .62, Wald χ2 (1, N = 145) = 0.57, p = .4510. Although there was less bias in the choice data relative to Study 2, participants were more likely to choose high-level task

(i.e., “Categorize workers’ feedback” task; 60%) over low-level task (i.e., “Proofread fundraising letters for donors” task) regardless of emotion condition, β = 1.74, SE = .53,

Wald χ2 (1, N = 145) =11.02, p < .001.

Task preference. To examine the effect of emotion on task preference, we conducted a one-way ANCOVA with task difficulty, task enjoyment, and task duration as

9 As in Study 2, we examined whether task perception ratings differed by emotion condition. Results showed there were no differences in task perception ratings as a function of the condition: low-level task: task difficulty: t(143) = .64, p = .52; task enjoyment: t(143) = .41, p = .69; task duration: t(143) = -.04, p = .97; high-level task: task difficulty: t(143) = -.06, p = .96; task enjoyment: t(143) = -.61, p = .54; task duration: t(143) = .10, p = .93. 10 As in Study 2, we also examined the effect of emotion condition on task choice or task preference when task perception variables (i.e., task difficulty, task enjoyment, and task duration) were excluded from the analyses. The pattern of findings did not change substantively: task choice: χ2 (1, N = 145) = .02, p = .89; task preference: t(143) = .51, p = .61. 36 covariates. Again, analyses revealed that although the main effect of emotion on task

2 preference was not statistically significant, F (1, 140) = 2.48, p = .12, ηp = .017, the direction of the effect was consistent with our hypothesis such that participants in the pride condition preferred high-level task (M = 3.87, SD = 1.92) compared to participants in the happiness condition (M = 3.72, SD = 1.83).

Expected task performance as a mediator to predict behavioral choices. To examine whether the effect is mediated by expected task performance, we conducted mediational analyses as described in Study 2. The indirect effect of emotion on task choice and task preference was not significant (task choice as Y: β = -.13, SE = 1.08, 95%

CI [-3.00, 1.83]; task preference as Y: β = -.03, SE = .12, 95% CI [-.26, .20]).

Induced level of construal as a mediator to predict behavioral choices. To examine the indirect effect of emotion on task choice and task preference through level of construal, we conducted mediational analyses as in Study 2. The analyses revealed that the indirect effect of emotion on task choice was significant, B = .17, SE = .08, 95% CI

[.03, .35], indicating that the effect was mediated by level of construal (see Figure 1). As expected, emotion condition was positively associated with level of construal, β = .07, SE

= .01, p < .001, and level of construal predicted task choice, β = 2.59, SE = 1.13, p < .05, indicating that pride (vs. happiness) guided the choice of high-level (vs. low-level) task through high-level construal. Meanwhile, the indirect effect of emotion on task preference was not significant, β = .09, SE = .06, 95% CI [-.03, .21].

Moderation effect of mood awareness. To assess the potential moderating effect of mood awareness, we conducted moderation analyses as in Study 2. Contrary to

37 expectations, the effect of emotion on task choice was not moderated by any indices of mood awareness [mood awareness as a moderator: β = -.10, SE = .18, Wald χ2 (1, N =

145) = .29, p = .59; mood monitoring as a moderator: β = .05, SE = .17, Wald χ2 (1, N =

145) = .07, p = .79; mood labeling as a moderator: β = -.22, SE = .18, Wald χ2 (1, N =

1145) = 1.65, p = .20]. The effect of emotion on task preference was not moderated by any indices of mood awareness as well [mood awareness as a moderator: β = -.06, t(141)

= -.39, p = .70; mood monitoring as a moderator: β = .02, t(141) = .14, p = .89; mood labeling as a moderator: β = -.15, t(141) = -.98, p = .33].

Discussion

Replicating Study 2, Study 3 found that participants in the pride relative to happiness condition exhibited stronger preferences for the high-level relative to low-level task, although this effect was not statistically significant. However, again, we did not observe a significant difference on task choice. We also once again observed an unexpected asymmetry in task choice. In contrast to Study 2, participants were more likely to choose high-level task (i.e., “Categorize workers’ feedback” task; 60%) over low-level task (i.e., “Proofread fundraising letters for donors” task) regardless of emotion condition. Although the bias in task selection was reduced, it is possible that it may have interfered with the effect of emotion on task choice.

Mediational analyses revealed that there was a significant indirect effect of emotion on task choice through induced level of construal. Although we did not find similar results from Study 2, this finding may suggest construal level as a potential underlying mechanism by which high-level versus low-level emotions guide task choice.

As hypothesized, the level of emotions influences the level of construal, which in turn guide people’s strategic task choice.

Based on these findings, we speculate that there may indeed exist an effect of high-level and low-level emotions but its effect size is very small or suppressed by extraneous factors (Shrout & Bolger, 2002), thus remain undetected within the individual, presumably underpowered, studies. Therefore, we decided to combine datasets of Study 2 and Study 3 and conduct integrative data analysis (IDA; Curran & Hussong, 2009) in order to increase statistical power to test the presumed effect of high-level versus low- level emotions in guiding participants’ task choice and task preference.

Chapter 5. Integrative Data Analysis

Given that we observed consistent expected pattern across Studies 2 and 3 and these studies essentially address the same question, we took integrative data analysis

(IDA) approach. However, when applying IDA, we kept in mind that the task stimuli offered for task choice and task preference ratings differed between Study 2 and Study 3 and this might be a potential source of between-study heterogeneity. Therefore, we treated individual study dataset as a fixed and known unique characteristic of each individual observation nested within study. To accomplish this, the effect-coded dataset variable was entered as a predictor in our analysis models (fixed-effects IDA; Curran &

Hussong, 2009).

Task choice. We regressed task choices (0 = low-level task, 1 = high-level task) on emotion condition (effect-coded: -1 = happiness, 1 = pride), dataset (effect-coded: -1 =

Study 2, 1 = Study 3), interaction between emotion condition and dataset, task difficulty, task enjoyment, task duration, and task order (effect-coded: -1 = low-level task first, 1 = high-level task first) using logistic regression. All task perception variables were standardized and entered as covariates as in previous analyses. Analysis revealed that there was no significant difference in task choice by emotion condition, β = -0.37, SE =

0.36, Wald χ2 (1, N = 306) = 1.05, p = .3111. There was significant difference in task choice between studies, β = 0.48, SE = 0.18, Wald χ2 (1, N = 306) = 6.97, p < .01, indicating low-level task was selected more in Study 2 whereas high-level task was selected more in Study 3. As we have already observed opposite asymmetries in task choices across Studies 2 and 3, it was not surprising to observe this difference. The interaction between emotion condition and dataset was not significant, β = -0.02, SE =

0.18, Wald χ2 (1, N = 306) = 0.01, p = .92.

Task preference. We conducted a 2 (emotion condition: pride vs. happiness) X 2

(dataset: Study 2, Study 3) ANCOVA with task difficulty, task enjoyment, task duration, and task order as covariates to analyze task preference data. Consistent with predictions, results revealed a significant main effect of emotion such that participants in the pride condition generally preferred the high-level task (M = 3.30, SD = 1.82) compared to participants in the happiness condition (M = 3.08, SD = 1.74, F (1, 299) = 4.86, p < .05,

2 12 ηp = .016) . Task preference ratings did not differ by study, F (1, 299) = 2.62, p = .11.

Also, the interaction between emotion condition and dataset was not significant, F (1,

299) = 0.12, p = .73. Taken together, these results indicate that task preference ratings were guided by emotion condition and the effect of emotion was consistent across

Studies 2 and 3.

11 Results did not change substantively in analyses without task perception variables as covariates, β = -0.18, SE = 0.26, Wald χ2 (1, N = 306) = .48, p = .49. 12 When task perception variables were excluded from the analysis, the effect of emotion on task 2 preference did not reach statistical significance, F(1,301) = 1.190, p = .276, ηp = .004. We note, however, that there were no significant differences in the task perception variables as a function of condition – providing some justification for using them as covariates to control for the influence of task-specific features on preferences. 41

Expected task performance as a mediator to predict behavioral choices. To explore whether the effect is mediated by expected task performance, we conducted mediational analyses using the PROCESS macro Model 4 (model specifications: X = emotion condition, M = expected task performance, Y = task choice/ task preference;

Hayes, 2017). The indirect effect of emotion on task choice and task preference was not significant (task choice as Y in PROCESS Model 4: β = -.01, SE = .16, 95% CI

[-.33, .30]; task preference as Y in PROCESS Model 4: β = -.01, SE = .07, 95% CI

[-.15, .13]).

Induced level of construal as a mediator to predict behavioral choices. We also conducted mediational analyses with induced level of construal as a mediator using

PROCESS macro Model 4 (model specifications: X = emotion condition, M = level of construal, Y = task choice/ task preference; Hayes, 2017). The indirect effect of emotion on task choice and task preference was not significant (task choice as Y: β = .07, SE

= .06, 95% CI [-.04, .19]; task preference as Y: β = .03, SE = .05, 95% CI [-.06, .12]).

Moderation effect of mood awareness. To assess the potential moderating effect of mood awareness, we conducted moderation analyses using the PROCESS macro

Model 1 (model specifications: X = emotion condition, W = mood awareness/ mood monitoring / mood labeling, Y = task choice/ task preference; Hayes, 2017). The effect of emotion on task choice was not moderated by any indices of mood awareness [mood awareness as W: χ2 (1, N = 306) = 1.17, p = .28; mood monitoring as W: χ2 (1, N = 306)

= 0.32, p = .57; mood labeling as W: χ2 (1, N = 306) = 2.11, p = .14]. The effect of emotion on task preference was moderated by mood awareness but in opposite direction,

B = -.16, t(300) = -2.37, p = .02. Contrary to our predictions, participants who were low in mood awareness preferred the high-level task when they experienced pride, β = .30, t(300) = 3.24, p = .001 (Figure 2). However, participants who were high in mood awareness did not show such strategic task selection, β = -.01, t(300) = -.13, p = .90.

Additional moderation analyses with subscales as a moderator revealed that the significant moderating effect of mood awareness was driven by mood labeling, β = -.18, t(300) = -2.74, p = .006, rather than mood monitoring, β = -.09, t(300) = -1.30, p = .20.

Discussion

Integrative data analyses revealed that participants indeed preferred different tasks based on their emotional states. Specifically, participants in the pride condition (i.e., high-level emotion) preferred high-level task whereas participants in the happiness condition (i.e., low-level emotion) preferred low-level task. We believe that these data extend findings from Studies 1a and 1b in two ways. First, whereas Studies 1a and 1b assessed whether participants could distinguish the motivational benefits of high-level versus low-level emotions in hypothetical scenarios that allowed them to directly compare pride and happiness, Studies 2-3 manipulated emotional states and had participants make consequential choices. These studies thus provide further evidence that people may recognize the motivational benefits of high-level and low-level emotions and are able to capitalize on their emotional state in order to create task-motivation fit.

In addition, integrative data analyses found that the level of mood awareness moderated the effect of emotion on task preference ratings. Specifically, participants who are low in mood awareness, especially low in mood labeling, preferred the high-level (vs.

43 low-level) task when experiencing a high-level (vs. low-level) emotion. This may suggest that people are more likely to follow motivational signal from their emotions when they do not label their emotions. Indeed, recent work demonstrates affect labeling—putting feelings into words—can attenuate our emotional experience (e.g., Torre & Lieberman,

2018; Lieberman, Inagaki, Tabibana, & Crockett, 2011). Given these findings, it is possible that people experience target emotion less once they label and are aware of their emotions and thus, the signal for the motivational states associated with emotions become weak accordingly. If this is true, the process of high-level versus low-level emotions in guiding strategic task choice may be more implicit than we originally thought.

However, we did not observe the effect of emotion on task choice and that effect was not moderated by mood awareness. As discussed in Studies 2 and 3, unexpected asymmetries in task choice may suggest the possibility that participants’ biased preferences for certain tasks constrained any effect of the emotion manipulation. Given that the effect of emotion on task preferences was small, the binary choice measure may have been too insensitive to capture the effect.

At the same time, it is possible that the emotion priming methods used in Studies

2 and 3 (i.e., writing about a future event that would make participants feel proud or happy) was not strong enough to manipulate the level of target emotion effectively for some participants. Given that participants were asked to imagine a future event that has not yet happened, it is possible that there were differences in the level of emotions experienced during manipulation depending on some features of imagined events (e.g., how easy or difficult imagining the event was, whether participants imagined the event

44 that they have experienced before or not). In addition, although the pilot study data confirmed the manipulation’s reliability, we were not able to ascertain from the results of

Study 3 if participants in the pride condition experienced emotion pride relatively more than participants in the happiness condition. Concerned about these potential issues, we attempted to replicate the findings in Study 4 while modifying the emotion manipulation method to increase the size of its effect.

Chapter 6. Study 4

The goal of Study 4 was to replicate findings from Studies 2 and 3 while attempting to increase the power within an individual study in two ways. First, we doubled the target sample size (N = 400) to address the concern that the effect size may have been too small to detect given the N in Studies 2 and 3. We conducted a priori sensitivity analysis using G*Power, which revealed that this N would provide 80% power

2 to detect an effect of ηp = .018 (the effect size revealed by our previous analyses) for one of the primary statistical tests of our hypotheses—a one-way analysis of covariance

(ANCOVA). Because we anticipated excluding some participants from analyses, we decided to collect data from N = 420.

Second, we modified the emotion manipulation methods in an attempt to make the manipulation as strong as possible. Specifically, rather than asking participants to imagine a future event that has not yet happened, participants in Study 4 were asked to recall a past event that they have already experienced. We expected that recalling their own memories would manipulate the level of target emotions more effectively than imagining the hypothetical future events, thereby having stronger effects on participants’ task choice and task preference ratings.

Method

Participants

A total of 420 MTurk workers participated in exchange for $0.80 (Mage = 40.90,

SDage = 13.41, 227 women, 193 men). Using the exclusion criteria consistent with those in previous studies, we had a final N = 351.

Materials and Procedure

The study materials and procedure of Study 4 were almost identical with that of

Study 3 except for the emotion manipulation.

Emotion manipulation. Participants were prompted to recall and describe a past event from their life that makes them feel proud (vs. happy) depending on emotion condition.

Mood awareness. As in Studies 2 and 3, we measured mood awareness as a potential moderator using the same scale (MAS; Swinkels & Giuliano, 1995). The internal reliability coefficients of the MAS scale were α = .81 for the overall scale, α =

.77 for Mood Labeling, and α = .76 for Mood Monitoring.

Results

Target emotion manipulation check. We computed an index of pride specificity and target emotion difference scores and conducted the same analyses as in pilot for

Study 2 and in Study 3. Analyses revealed that participants in the pride condition experienced pride relatively more (pride specificity: M = -0.31, SD = 0.79; difference scores: M = 0.20, SD = 0.92) than participants in the happiness condition (pride

47 specificity: M = -0.64, SD = 0.90, t(349) = 3.67, p < .001; difference scores: M = - 0.07,

SD = 1.06, t(349) = 2.59, p < .01).

Construal level manipulation check. As in Studies 2 and 3, we analyzed participants’ written responses for the event descriptions and computed linguistic abstraction scores to assess their level of construal. The linguistic abstraction scores were significantly higher in the pride condition (M = 3.59, SD = 0.15) than in the happiness condition (M = 3.53, SD = 0.14, t(344) = 4.06, p < .001). These results support that the pride condition induced higher level of construal compared to the happiness condition.

Order effects. As in Studies 2 and 3, we examined whether there was the order effect of learning about the high-level and low-level tasks on task choice and task preference. Analyses revealed that there were no significant differences in task choices and task preference ratings by task order (task choice: χ2 (1, N = 351) = .00, p = .97; task preference: t(349) = 0.47, p = .64;). As the order of task descriptions did not yield any effects, we omit this variable from subsequent analyses and discussion.

Task choice. We regressed task choices (0 = low-level task, 1 = high-level task) on emotion condition (effect-coded: -1 = happiness, 1 = pride), task difficulty, task enjoyment, and task duration using logistic regression. All differences scores were standardized and entered as covariates13. Task choice did not differ by emotion condition,

13 As in Studies 2 and 3, there were no differences in task perception ratings as a function of emotion condition: low-level task: task difficulty: t(349) = -.05, p = .96; task enjoyment: t(349) = .98, p = .33; task duration: t(349) = 1.12, p = .26; high-level task: task difficulty: t(349) = -.99, p = .32; task enjoyment: t(349) = -.49, p = .63; task duration: t(349) = .88, p = .38. 48

β = .36, SE = .36, Wald χ2 (1, N = 351) = 0.99, p = .3214. Although participants were slightly more likely to choose the low-level (i.e., “Proofread fundraising letters for donors” task; 54%) over the high-level task (i.e., “Categorize workers’ feedback” task), there was no significant bias in task selection, β = -.44, SE = .25, Wald χ2 (1, N = 351) =

2.94, p = .65.

Task preference. To analyze task preference data, we conducted a one-way

ANCOVA with task difficulty, enjoyment, and duration as covariates. Analyses revealed that the effect of emotion on task preference was not statistically significant, F (1, 343) =

0.010, p = .922. In contrast to Studies 2 and 3, we did not observe the expected pattern in task preference ratings: participants in the pride condition did not preferred the high-level task (M = 3.29, SD = 1.85) as compared to participants in the happiness condition (M =

3.36, SD = 1.86) (see Table 3 for adjusted means).

Expected task performance as a mediator to predict behavioral choices. To examine whether the effect is mediated by expected task performance, we conducted mediational analyses as described in Studies 2-3. The indirect effect of emotion on task choice and task preference was not significant (task choice as Y in PROCESS Model 4: β

= -.03, SE = .23, 95% CI [-.50, .42]; task preference as Y in PROCESS Model 4: β = -.01,

SE = .07 95% CI [-.15, .14]).

Induced level of construal as a mediator to predict behavioral choices. We also conducted mediational analyses with the level of construal as a mediator as

14 The pattern of findings did not change substantively when task perception variables were not included as covariates: task choice: χ2 (1, N = 351) = 1.57, p = .21; task preference: t(349) = -.36, p = .72. 49 described in Studies 2 and 3. The indirect effect of emotion on task choice and task preference was not significant (task choice: β = .03, SE = .03, 95% CI [-.02, .08]; task preference: β = .04, SE = .02, 95% CI [-.01, .09]).

Moderation effect of mood awareness. We examined the potential moderating effect of mood awareness as in Studies 2-3. The effect of emotion on task choice was not moderated by any indices of mood awareness [mood awareness as a moderator: β = -0.09,

SE = 0.11, Wald χ2 (1, N = 351) = 0.64, p = .42; mood monitoring as a moderator: β = -

0.13, SE = 0.11, Wald χ2 (1, N = 351) = 0.64, p = .42; mood labeling as a moderator: β =

-0.09, SE = 0.11, Wald χ2 (1, N = 351) = 0.64, p = .42]. The effect of emotion on task preference was not moderated by any indices of mood awareness as well [mood awareness as a moderator: β = -.08, t(347) = -.76, p = .45; mood monitoring as a moderator: β = -.12, t(347) = -1.21, p = .23; mood labeling as a moderator: β = .00, t(347)

= .03, p = .97].

Discussion

Although we doubled the sample size to increase statistical power and implemented new materials, neither task choice nor task preference was guided by emotion condition. There may be several reasons why we did not replicate the findings from Studies 2 and 3. Given that the most noticeable difference between Studies 2-3 and

Study 4 was the emotion manipulation methods, we suspect that this was the main cause of failed replication.

In Studies 2 and 3, participants were asked to imagine a future event that would elicit target emotion (Katzir et al., 2010). By contrast, in Study 4, participants were asked

50 to recall the past event that had elicited target emotion. Given differences between imagined versus recalled emotions, it is possible that the context (prospective vs. retrospective) of eliciting target emotion acted as an unintended trigger which influenced participants’ motivation to attend to their emotional states and follow the signals from emotions in making task choice. We suspect that participants in prospective context were more motivated to experience the target emotion and thereby, attended to their emotional states and became more sensitive to them as motivational cues. By contrast, participants in retrospective context were less motivated to attend to their emotional states simply because they recalled already experienced emotion.

One of the factors that might have influenced participants’ motivation is judged usability. The concept of “judged usability” was initially proposed by Higgins (1996, p.

136), which refers to the judged appropriateness or relevance of applying stored knowledge to a stimulus. According to Higgins (1996), activated information is applied to the judgment only if it is judged “usable.” Indeed, it has been found judged usability moderates priming effects such that low (vs. high) usability make priming effects less

(vs. more) likely (e.g., Croizet & Fiske, 1999). Similarly, it is possible that the prospective context motivated participants to attend to their emotional states and this made the cues from their emotional states more salient and applicable in guiding their behavioral decisions (i.e., high judged usability). In contrast, retrospective context might have made participants more aware of the source of their emotion and this made the cues from their emotional states to be judged as less usable (e.g., Lombardi, Bargh, & Higgins,

1987).

Alternatively, it is possible that the emotion priming methods used in Studies 2 and 3 manipulated factors other than the target emotion. Some researchers have suggested that imagining future emotional events may activate different goals (e.g.,

Katzir et al, 2010; Shimoni, Berger, Eyal, 2019). For instance, Katzir and her colleagues

(2010) found that imagined pride (vs. happiness) helps inhibitory control. As a mechanism for this finding, they suggested that imagining pride-eliciting event activated the pursuit of long-term goals since attaining long-term goals is linked to the experience of pride, whereas imagining happiness-eliciting event primed the pursuit of short-term goals. If this is the case, it is possible that participants’ task preference ratings in Studies

2-3 were guided by similarly activated goals. However, it is unclear how the tasks in

Study 2 (i.e., perceptual tasks which require global versus local processing of visual information) and Study 3 (i.e., categorizing workers’ feedback vs. proofreading) involve long-term vs. short-term trade-offs. Therefore, it seems unlikely that participants’ preferences were based on long-term versus short-term goals, even if those goals were activated by the manipulation.

Although we doubt that participants’ task preference ratings were driven by long- term versus short-term goals, it is possible that the imagined emotions indeed activated different emotion-experience goals. Shimoni and her colleagues (2019) studied how imagined pride (vs. joy) influences children’s delay of gratification. They suggest that rather than evoke an immediate emotional experience, imagined emotions encourage children to pursue goals whose fulfilment would produce the targeted emotion. Their arguments have two implications. First, rather than experience pride or happiness,

52 participants in Studies 2 and 3 may have only been cognitively primed with these concepts. However, pilot study data confirmed the reliability of target emotion manipulation. As such, we believe we can rule out the first possibility.

Second, participants who were primed with pride or joy might pursue the goal to experience the target emotion; that is, they may have been motivated to prolong their emotional states in their selection of performance tasks. Given that high-level (vs. low- level) tasks require people to engage in high-level (vs. low-level) construal – and the link between level of construal and the experience of high-level and low-level emotions (e.g.,

Moran et al., 2019), participants may have selected the task that induces the construal level that sustains their emotional states. If so, participants might have chosen a task to serve their emotion-experience goals—goals that involve specific emotional states as the direct desired endpoint (Mauss & Tamir, 2014)—rather than to create task-motivation fit based on their emotional states. Since participants were instructed to select a task that would maximize their performance, however, we believe participants should have prioritized performance goals over emotion goals. Nonetheless, future studies may attempt to differentiate emotion goals versus performance goals and examine which drives strategic task choice.

In sum, our data may indicate that there are boundary conditions or alternative explanations for the effects of high-level versus low-level emotion in guiding behavioral choices. Future studies will attempt to not only replicate the previous findings but also to specify boundary conditions for the effect and its underlying mechanism.

Chapter 7. General Discussion

The present research proposes the functional role of emotions as a cue for our motivational states and examined this idea within the context of CLT. By taking metamotivational approach, we examined whether people have metamotivational knowledge of motivational benefits of high-level and low-level emotions and whether they are able to translate such knowledge into behavior to create task-motivation fit.

Studies 1a and 1b demonstrated that people not only distinguish high-level and low-level emotions but they may also understand differential benefits of these emotions in task performance. Studies 2 and 3 extend these findings and provide empirical evidence that people may strategically choose different tasks depending on the level of emotions. The results from Studies 1-3 suggest that people may understand motivational benefits of high-level and low-level emotions in signaling their current motivational states and they may be able to capitalize on their emotions to create task-motivation fit when it needed to do so. However, this is not entirely conclusive given the results of Study 4, which did not replicate Studies 2-3. Our data may indicate there exist boundary conditions or alternative explanations for the effect of the level of emotions on strategic task selection.

Enduring Questions and Alternative Explanations

First, as discussed in Study 4, it is possible the prospective versus retrospective context of emotion manipulations may have influenced participants’ motivation to attend

54 to their emotional states. We speculate that a prospective context may motivate people to attend to their emotional states and opportunity for experiencing imagined emotions, thus increasing the judged usability of these cues. By contrast, a retrospective context may make the source of recalled emotions more salient, thereby decreasing participants’ motivation to follow the signals from their emotions (e.g., Lombardi et al., 1987). This may suggest that the effect of high-level versus low-level emotion in guiding task selection might be only observed for those who are motivated to attend to and capitalize on their emotional states. To test this, we could create a situation in which participants were made to believe that they possess some expertise in attending to their emotional states and it is applicable to use cues from their emotional states in judgments before manipulating the target emotion in order to increase judged usability (Croizet & Fiske,

1999).

Second, we also discussed another way in which how imagined versus recalled emotions could differ in Study 4. Whereas imagined emotions activate emotion goals

(Shimoni et al., 2019), recalled emotions may not and thus fail to guide people’s strategic task choice. To test this alternative explanation, we could prime emotion goals without manipulating emotional states (e.g., by activating the concepts of target emotion with external cues; Papies, 2016) and see if emotion goals would predict participants’ task selection.

Third, it is possible that both emotion goals and performance goals guided people’s task choice conjointly given that selecting a high-level (vs. low-level) task not only helps people to sustain their high-level (vs. low-level) emotional states but also

55 would benefit their performance by creating task-motivation fit. Therefore, in Studies 2 and 3, both goals may have activated simultaneously and functioned together in guiding participants’ strategic task choice. However, there could be situations when they conflict or are not necessarily fulfilled simultaneously by the same task. For example, imagine that one experiences a high-level emotion such as moral outrage and is given a choice between a high-level task that may elicit different types of high-level emotion such as pride versus a low-level task that may elicit low-level emotion such as happiness.

Motivationally, the high-level task fits with their current level of construal; however, emotionally, both tasks are not related to the experience of current emotion but may fulfill emotion goals (presumably, emotion regulation or improvement in this case).

Alternatively, although it would be difficult, we could create an experimental setting in which we might disentangle subsequent choices based on emotion goals vs. performance goals. For example, we could manipulate low-level emotion such as happiness while priming the concept of high-level emotion such as pride (e.g., by completing scrambled words or word search activating cognitive representations related to pride) and compare which would predict task selection.

Fourth, integrative data analyses revealed that the effect of emotion was moderated by mood awareness – specifically mood labeling – such that only those who were low in mood labeling showed predicted strategic task choice depending on emotion condition. Given that affect labeling can attenuate our emotional experience (Torre &

Lieberman, 2018; Lieberman et al., 2011), it is possible those who are high in mood awareness label their affective states, which then attenuates the drive and/or motivational

56 states induced by the emotion manipulation. In contrast, those who are low in mood awareness may experience emotive states more strongly or longer. This finding may highlight the implicit nature of the process how emotions guide strategic task choice. As an initial attempt to further investigate the nature of the underlying process, we could manipulate target emotional states with less explicit methods (e.g., music, movie, photos, or scenarios that does not explicitly contain the concepts of target emotion) in order to reduce the possibility of people being aware of and labeling their emotions. In summary, we suspect our data indicate merely experiencing a target emotion is not sufficient.

Instead, perhaps people would be motivated to follow the motivational signal from their emotions if they were not aware of the source or the conceptual knowledge about targeted emotion. Further investigations might elucidate these propositions.

Future Directions and Implications

There are several ways to extend our findings. A potential moderator that is directly relevant to the current research is people’s metamotivational knowledge of high- level and low-level emotions. Although we assessed people’s general understanding of the motivational benefits of high-level and low-level emotions in task performance in

Studies 1a and 1b, we did not investigate its implications for people’s behavioral decisions directly. We expect individual differences in metamotivational knowledge should moderate the effect of manipulated emotions on task choice such that those with greater metamotivational knowledge would be more likely to create task-motivation fit by selecting the right type of task based on their emotions. We could measure the level of metamotivational knowledge as in Studies 1a and 1b, and examine whether it moderates

57 the effect of manipulated emotions on strategic task choice. To avoid overlap between the knowledge assessment and emotion manipulation, we might assess the former using one set of high-level vs. low-level emotions (moral vs. visceral disgust; Moran et al., 2019) while manipulating another (love vs. lust; Förster, Ö zelsel, & Epstude, 2010).

The current research highlighted the functional role of emotions in signaling one’s current motivational states and guiding strategic task choice. Research on instrumental emotion regulation (Tamir, 2009; Tamir, Mitchell, & Gross, 2008) suggest another aspect of how emotions might function as motivational input in creating task-motivation fit. In one study, for instance, participants preferred to neutralize their feelings rather than being happy or sad before interacting with a stranger (Erber, Wegner, and Therriault, 1996). In another study, participants chose to listen to anger-inducing music (vs. exciting or neutral music) when they expected to play confrontational game (Tamir et al., 2008). Given that emotions direct our choices and behavior (Gross, 2007; 2013), we assume the process of instrumental emotion regulation may involve shifting, intensifying, or maintaining certain motivational states. Thus, the strategic emotional preference shown in emotion regulation research may reflect people’s metamotivational knowledge of the functional role of emotions as a tool for inducing optimal motivational states according to the situational demands. Based on these propositions, future studies could examine whether people strategically regulate their emotions to create task-motivation fit. This line of future research together with the current research program would provide an integrative framework for the fuller understanding of functional role of emotions as motivational input.

Furthermore, the question of whether people understand the functional role of emotions as motivational input can be pursued extensively beyond the purview of CLT.

For example, emotions along a cheerful-dejected dimension such as happiness or sadness are associated promotion focus, whereas emotions along a quiescent-agitated dimension such as calmness or anxiety are associated prevention focus (Higgins et al., 1997). Given this link, we could test whether people understand the motivational benefits of promotion-focused versus prevention-focused emotions in regulatory focus domain.

Metamotivational self-knowledge is probably the most crucial and challenging type of metamotivational knowledge. Although people could learn and develop task and strategy knowledge, some may still fail to implement such knowledge effectively because they cannot determine what it is like to experience certain motivational state. Moreover, self-knowledge, compared to strategy or task knowledge, might be more difficult to be taught, given self-knowledge is acquired through introspection and inner observation

(Gertler, 2020). The present work suggests novel directions for helping those who have difficulties with inferring their motivational states by highlighting the role of emotions in metamotivational awareness. Although researchers have recently begun to document empirically the link between emotions and different types of motivational orientations

(e.g., Higgins et al., 1997; Moran et al., 2019; Han et al. 2014), our findings suggest that lay people may have some tacit knowledge of this relationship. Even if people cannot articulate this knowledge explicitly, they may experientially or implicitly realize that they perform certain tasks better when they are feeling in a particular way. This suggest people could benefit from connection between emotions and motivations by

59 spontaneously attending to their emotions to infer what motivational states they are in.

Researchers and practitioners may capitalize on the functional role of emotions and help people to have better metamotivational self-knowledge. Furthermore, understanding what factors influence one’s capacity for metamotivational self-knowledge would be an important next step for future research.

Conclusion

The current line of research sought to extend our understanding of informative and directive functions of affective states in monitoring and modulating our motivation by leveraging metamotivational approach. This work highlights how a metamotivational approach allows researchers to generate nuanced predictions regarding individual differences in motivation regulation, and more broadly self-regulation. In this way, we believe this approach could yield productive avenues for future research and encourage cross-disciplinary dialogue for the development of fine-tuned interventions to encourage people for strategic use of emotions and promote self-regulation in their daily lives.

References

Amazon Mechanical Turk. (n.d.). About Amazon Mechanical Turk. https://www.mturk.com/worker/help Bai, H. (2018). Evidence that a large amount of low quality responses on MTurk can be detected with repeated GPS coordinates. https://www.maxhuibai.com/blog/evidence-that-responses-from-repeating-gps- are-random Bandura, A., Caprara, G. V., Barbaranelli, C., Gerbino, M., & Pastorelli, C. (2003). Role of affective self‐regulatory efficacy in diverse spheres of psychosocial functioning. Child development, 74(3), 769-782. Bates, D., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0. Batson, C. D., Kennedy, C. L., Nord, L. A., Stocks, E. L., Fleming, D. Y. A., Marzette, C. M., ... & Zerger, T. (2007). Anger at unfairness: Is it moral outrage? European journal of social psychology, 37(6), 1272-1285. Batson, C. D., Chao, M. C., & Givens, J. M. (2009). Pursuing moral outrage: Anger at torture. Journal of Experimental Social Psychology, 45(1), 155-160. Bandura, A., Caprara, G. V., Barbaranelli, C., Gerbino, M., & Pastorelli, C. (2003). Role of affective self‐regulatory efficacy in diverse spheres of psychosocial functioning. Child development, 74(3), 769-782. Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior research methods, 46(3), 904-911. Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior. New York, NY: Cambridge University Press. Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of personality and social psychology, 67(2), 319-333. Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: Transfer from" feeling right.". Journal of personality and social psychology, 86(3), 388- 404. Chmielewski, M., & Kucker, S. C. (2020). An MTurk crisis? Shifts in data quality and the impact on study results. Social Psychological and Personality Science, 11(4), 464-473. Clore, G. L. (1992). Cognitive phenomenology: Feelings and the construction of judgment. The construction of social judgments, 10, 133-163.

Clore, G. L., Gasper, K., & Garvin, E. (2001). Affect as information. In J. P. Forgas, (Eds.). Handbook of Affect and Social Cognition (p. 121-144). Mahwah, NJ.: Lawrence Erlbaum Associates. Clore, G. L., & Huntsinger, J. R. (2007). How emotions inform judgment and regulate thought. Trends in cognitive sciences, 11(9), 393-399. Croizet, J. C., & Fiske, S. T. (2000). Moderation of priming by goals: Feeling entitled to judge increases judged usability of evaluative primes. Journal of Experimental Social Psychology, 36(2), 155-181. Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological methods, 14(2), 81-100. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer US. Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (p. 416–436). Sage Publications Ltd. Ekstrom, R. B., French, J. W., Harman, H. H., & Dermen, D. (1976). Manual for kit of factor-referenced cognitive tests. Princeton, NJ: Educational Testing Service. Erber, R., Wegner, D. M., & Therriault, N. (1996). On being cool and collected: mood regulation in anticipation of social interaction. Journal of personality and social psychology, 70(4), 757-766. Eyal, T., & Fishbach, A. (2010). Do global and local systems feel different? Psychological Inquiry, 21(3), 213-215. Förster, J., Özelsel, A., & Epstude, K. (2010). How love and lust change people’s perception of relationship partners. Journal of Experimental Social Psychology, 46(2), 237-246. Freitas, A. L., Gollwitzer, P., & Trope, Y. (2004). The influence of abstract and concrete mindsets on anticipating and guiding others' self-regulatory efforts. Journal of experimental social psychology, 40(6), 739-752. Freitas, A. L., & Higgins, E. T. (2002). Enjoying goal-directed action: The role of regulatory fit. Psychological science, 13(1), 1-6. Fujita, K. (2011). On conceptualizing self-control as more than the effortful inhibition of impulses. Personality and social psychology review, 15(4), 352-366. Fujita, K., Scholer, A. A., Miele, D. B., & Nguyen, T. (2019). On metamotivation: Consumers’ knowledge about the role of construal level in enhancing task performance. Journal of the Association for Consumer Research, 4(1), 57-64. Fujita, K., Trope, Y., Liberman, N., & Levin-Sagi, M. (2006). Construal levels and self- control. Journal of personality and social psychology, 90(3), 351-367. Fujita, K., & Han, H. A. (2009). Moving beyond deliberative control of impulses: The effect of construal levels on evaluative associations in self-control conflicts. Psychological Science, 20(7), 799-804. Gasper, K., & Clore, G. L. (2002). Attending to the big picture: Mood and global versus local processing of visual information. Psychological science, 13(1), 34-40.

Gertler, B. (2020). Self-Knowledge. In E. N. Zalta (Eds.), The Stanford Encyclopedia of Philosophy. Retrieved May 25, 2020, from https://plato.stanford.edu/archives/spr2020/entries/self-knowledge/ Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta‐analysis of effects and processes. Advances in experimental social psychology, 38, 69-119. Gross, J. J. (Eds.). (2007). Handbook of emotion regulation. The Guilford Press. Gross, J. J. (Eds.). (2013). Handbook of emotion regulation. The Guilford Press. Gross, J. J., & Thompson, R. A. (2007). Emotion Regulation: Conceptual Foundations. In J. J. Gross (Eds.), Handbook of emotion regulation (p. 3–24). The Guilford Press. Han, D., Duhachek, A., & Agrawal, N. (2014). Emotions shape decisions through construal level: The case of guilt and shame. Journal of Consumer Research, 41(4), 1047-1064. Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., & Bigham, J. P. (2018, April). A data-driven analysis of workers' earnings on Amazon Mechanical Turk. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-14). Harmon-Jones, E. (2003). Anger and the behavioral approach system. Personality and Individual Differences, 35, 995–1005. Harmon-Jones, E., Harmon-Jones, C., & Price, T. F. (2013). What is approach motivation? Emotion Review, 5, 291–295. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications. Higgins, E. T. (1996). Knowledge Activation: Accessibility, Applicability, and Salience. . In E. T. Higgins and A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (p. 133-168). The Guilford Press . Higgins, E. T. (1997). Beyond pleasure and pain. American psychologist, 52(12), 1280- 1300. Higgins, E. T. (1998b). Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 46, 1-46. Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55, 1217–1230. Higgins, E. T. (2002). How self-regulation creates distinct values: The case of promotion and prevention decision making. Journal of Consumer Psychology, 12, 177–191. Higgins, E. T. (2011). Beyond pleasure and pain: How motivation works. Oxford University Press. Higgins, E. T., Shah, J., & Friedman, R. (1997). Emotional responses to goal attainment: strength of regulatory focus as moderator. Journal of personality and social psychology, 72(3), 515-525. Johnson-Grey, K. M., Boghrati, R., Wakslak, C. J., & Dehghani, M. (2020). Measuring abstract mind-sets through syntax: Automating the linguistic category model. Social Psychological and Personality Science, 11(2), 217-225.

Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of personality and social psychology, 103(1), 54-69. Katzir, M., Eyal, T., Meiran, N., & Kessler, Y. (2010). Imagined positive emotions and inhibitory control: The differentiated effect of pride versus happiness. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(5), 1314-1320. Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of instructional development, 10(3), 2-10. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature reviews neuroscience, 2(9), 635-642. Kuhl, J. (1985). Volitional mediators of cognition-behavior consistency: Self-regulatory processes and action versus state orientation. In J Kuhl et al. (Eds.) Action control (pp. 101-128). Springer, Berlin, Heidelberg. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2016). Tests in linear mixed effects models. R package version, 2, 33. Lewis, H. B. (1971). Shame and guilt in neurosis. Psychoanalytic review, 58(3), 419-438. Lieberman, M. D., Inagaki, T. K., Tabibnia, G., & Crockett, M. J. (2011). Subjective responses to emotional stimuli during labeling, reappraisal, and distraction. Emotion, 11(3), 468-480. Locke, E. A. (1996). Motivation through conscious goal setting. Applied and preventive psychology, 5(2), 117-124. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Prentice-Hall, Inc. Loewenstein, G. (2005). Hot-cold empathy gaps and medical decision making. Health psychology, 24(4Suppl), S49-S56. Lombardi, W. J., Higgins, E. T., & Bargh, J. A. (1987). The role of consciousness in priming effects on categorization: Assimilation versus contrast as a function of awareness of the priming task. Personality and Social Psychology Bulletin, 13(3), 411-429. MacGregor, K. E., Carnevale, J. J., Dusthimer, N. E., & Fujita, K. (2017). Knowledge of the self-control benefits of high-level versus low-level construal. Journal of personality and social psychology, 112(4), 607-620. Malkoc, S. A., Zauberman, G., & Bettman, J. R. (2010). Unstuck from the concrete: Carryover effects of abstract mindsets in intertemporal preferences. Organizational Behavior and Human Decision Processes, 113(2), 112-126. Martin, L. L., Ward, D. W., Achee, J. W., & Wyer, R. S. (1993). Mood as input: People have to interpret the motivational implications of their moods. Journal of Personality and Social Psychology, 64(3), 317–326. Mathews, A., Ridgeway, V., & Holmes, E. A. (2013). Feels like the real thing: Imagery is both more realistic and emotional than verbal thought. Cognition & emotion, 27(2), 217-229.

Mauss, I. B., & Tamir, M. (2014). Emotion goals: How their content, structure, and operation shape emotion regulation. In J. J. Gross (Eds.), Handbook of emotion regulation (p. 361–375). The Guilford Press. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: dynamics of willpower. Psychological review, 106(1), 3-19. Miele, D. B., & Scholer, A. A. (2018). The role of metamotivational monitoring in motivation regulation. Educational Psychologist, 53(1), 1-21. Miele, D. B., Scholer, A. A., & Fujita, K. (2020). Metamotivation: Emerging research on the regulation of motivational states. In Advances in Motivation Science (Vol. 7, pp. 1-42). Elsevier. Mischel, W., Shoda, Y., & Rodriguez, M. I. (1989). Delay of gratification in children. Science, 244(4907), 933-938. Moran, T., Bornstein, O., & Eyal, T. (2019). The level of construal involved in the elicitation of core versus moral disgust. Emotion. Advance online publication. https://doi.org/10.1037/emo0000709 Neal, A., Ballard, T., & Vancouver, J. B. (2017). Dynamic self-regulation and multiple- goal pursuit. Annual Review of Organizational Psychology and Organizational Behavior, 4, 401-423. Nguyen, T., Carnevale, J. J., Scholer, A. A., Miele, D. B., & Fujita, K. (2019). Metamotivational knowledge of the role of high-level and low-level construal in goal-relevant task performance. Journal of personality and social psychology, 117(5), 876–899. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, instruments, & computers, 36(4), 717-731. Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161-1178. Satterthwaite, F. (1946). An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin, 2(6), 110-114. Scherer, K. R. (1999). Appraisal theory. In T. Dalgleish & M. J. Power (Eds.), Handbook of cognition and emotion (p. 637–663). John Wiley & Sons Ltd. Schmeichel, B. J., Vohs, K. D., & Duke, S. C. (2011). Self-control at high and low levels of mental construal. Social Psychological and Personality Science, 2(2), 182-189. Scholer, A. A., & Miele, D. B. (2016). The role of metamotivation in creating task- motivation fit. Motivation Science, 2(3), 171-197. Scholer, A. A., Miele, D. B., Murayama, K., & Fujita, K. (2018). New directions in self- regulation: the role of metamotivational beliefs. Current Directions in Psychological Science, 27(6), 437-442. Schwanenflugel, P. J., Harnishfeger, K. K., & Stowe, R. W. (1988). Context availability and lexical decisions for abstract and concrete words. Journal of Memory and Language, 27(5), 499-520.

Schwarz, N. (2001). Feelings as information: Implications for affective inﬂuences on information processing. In L. L. Martin & G. L. Clore (Eds.), Theories of mood and cognition: A user's guidebook (p. 159-176). Psychology Press. Schwarz, N. (2011). Feelings-as-information theory. In N. Schwarz, P. Van Lange, A. Kruglanski, & E. T. Higgins (Eds.) Handbook of theories of social psychology (p. 289-308). Thousand Oaks. Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: informative and directive functions of affective states. Journal of personality and social psychology, 45(3), 513-523. Schwartz, A., Eyal, T., & Tamir, M. (2018). Emotions and the big picture: The effects of construal level on emotional preferences. Journal of Experimental Social Psychology, 78, 55-65. Schwinger, M., & Otterpohl, N. (2017). Which one works best? Considering the relative importance of motivational regulation strategies. Learning and individual differences, 53, 122-132. Shimoni, E., Berger, A., & Eyal, T. (2019). Priming pride promotes delay of gratification. Motivation and Emotion, 43(5), 786-802. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. Swinkels, A., & Giuliano, T. A. (1995). The measurement and conceptualization of mood awareness: Monitoring and labeling one's mood states. Personality and social psychology bulletin, 21(9), 934-949. Tamir, M. (2009). What do people want to feel and why? Pleasure and utility in emotion regulation. Current Directions in Psychological Science, 18(2), 101-105. Tamir, M. (2016). Why do people regulate their emotions? A taxonomy of motives in emotion regulation. Personality and Social Psychology Review, 20(3), 199-222. Tamir, M., Mitchell, C., Gross, J.J. (2008). Hedonic and instrumental motives in anger regulation. Psychological Science, 19, 324–328. Torre, J. B., & Lieberman, M. D. (2018). Putting feelings into words: Affect labeling as implicit emotion regulation. Emotion Review, 10(2), 116-124. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological review, 110(3), 403-421. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological review, 117(2), 440-463. Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. Journal of consumer psychology, 17(2), 83-95. Moss, A. (2018). After the bot scare: Understanding what’s been happening with data collection on mturk and how to stop it. Retrieved June 23, 2020, from https://www.cloudresearch.com/resources/blog/after-the-bot-scare-understanding- whats-been-happening-with-data-collection-on-mturk-and-how-to-stop-it/

Tyng, C. M., Amin, H. U., Saad, M. N., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in psychology, 8, 1454. doi: 10.3389/fpsyg.2017.01454 Wakslak, C. J., Trope, Y., Liberman, N., & Alony, R. (2006). Seeing the forest when entry is unlikely: probability and the mental representation of events. Journal of Experimental Psychology: General, 135(4), 641-653. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology, 54(6), 1063-1070. Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological bulletin, 98(2), 219-235. Webb, T. L., Schweiger Gallo, I., Miles, E., Gollwitzer, P. M., & Sheeran, P. (2012). Effective regulation of affect: An action control perspective on emotion regulation. European review of social psychology, 23(1), 143-186. Wechsler, D. (1991). WISC-III: Wechsler intelligence scale for children: Manual. Psychological Corporation. Williams, L. A., & DeSteno, D. (2008). Pride and perseverance: the motivational role of pride. Journal of personality and social psychology, 94(6), 1007-1017. Zimmerman, B. J., & Kitsantas, A. (1996). Self-regulated learning of a motoric skill: The role of goal setting and self-monitoring. Journal of Applied Sport Psychology, 8(1), 60-75. Zimmerman, B. J., & Kitsantas, A. (1997). Developmental phases in self-regulation: Shifting from process goals to outcome goals. Journal of educational psychology, 89(1), 29-36.

Appendix A. Task Scenarios and Instructions for Studies 1a and 1b

Instructions

Imagine you just recalled an event that makes you feel PROUD/HAPPY (either a proud or happy cue shown here).

Imagine you have a choice between two tasks:  Putt Putt Task: A task that assesses your motor skills in putting golf balls with as few swings as possible in a set amount of rounds.  Time Management Task: A task that assesses your choice as a student between studying for a midterm vs. hanging out with friends.

Given that you would feel PROUD/HAPPY after recalling the event and you want to perform as well as you can on the task, which task would you prefer to complete?

Task Scenarios

Type 1a 1b Task Scenario Low- X X Proofreading Task: A task that assesses your ability to identify and correct level typos in a long written passage that contains various misspellings. Low- X X Cross Out Letters Task: A task that assesses your ability to pay careful level attention to details and cross out instances of the letters “Z” and “Q” in a long written passage. Low- X X Color Word Task: A task that assesses your ability to identify the font color level of a word instead of the meaning of the word. Low- X X Free Throw Task: A task that assesses your hand-eye coordination in level shooting basketball free throws in a set amount of time. Low- X X Putt Putt Task: A task that assesses your motor skills in putting golf balls level with as few swings as possible in a set amount of rounds. Low- X X Dart Throwing Task: A task that assesses your accuracy and precision in level throwing darts at a bullseye target 8 feet away. High- X Emotion Regulation Task: A task that assesses your choice between getting level frustrated vs. controlling your emotions during a disagreement with a close friend. High- X Evaluative Feedback Task: A task that assesses your choice between level reading positive feedback to self-affirm vs. reading negative feedback to self-improve. High- X X Open Mindedness Task: A task that assesses your choice between ignoring level vs. listening to a fellow organization member raise criticism of the organization to help it improve. High- X X Healthy Diet Task: A task that assesses your choice, as a dieter, between a level healthy snack vs. an unhealthy snack. High- X X Time Management Task: A task that assesses your choice as a student level between studying for a midterm vs. hanging out with friends. High- X X Recycling Task: A task that assesses your choice between recycling an level empty plastic bottle later despite the inconvenience vs. throwing the bottle in the trash. High- X Pick a Prize Task: A task that assesses your ability to make a series of level choices between a smaller amount of money today vs. a larger amount of money later. High- X Workplace Ethics Task: A task that assesses your ability to speak up and level prioritize honesty over cheating in a workplace competition for vacation days.

Appendix B. Task Stimuli for Study 2

Low-level task

High-level task

Appendix C. Task Stimuli for Studies 3 and 4

Low-level task

High-level task

Appendix D. All Figures

Figure 1. Mediation analysis for Study 3 for the effect of emotion condition on task choice mediated by the level of construal

Figure 2. Interaction between emotion condition and mood awareness predicting task preference (Integrative Data Analysis). High and low values for mood awareness represent + 1 and - 1 SD of the mean.

Appendix E. All Tables

Table 1. Results of the linear mixed effects model for Studies 1a and 1b

Study 1a (N = 82) Study 1b (N = 89) Coefficient β SE df t p β SE df t p (Intercept) 3.41 0.20 15.15 17.46 < .001 3.71 0.21 13.15 17.31 < .001 Emotion Condition 0.26 0.10 887.90 2.68 < .01 0.18 0.09 965.58 1.98 < .05 Task Difficulty -0.60 0.06 947.92 -9.96 < .001 -0.41 0.06 1003.19 -7.33 < .001 Task Enjoyment 0.51 0.06 888.08 8.09 < .001 0.59 0.06 1007.59 10.62 < .001 Emotion Cue Order -0.01 0.09 79.82 -0.09 0.930 0.00 0.08 86.50 0.04 0.968 Note. We used Satterthwaite’s (1946) approximate degrees of freedom for assessing significance of the test statistics as implemented in the lmerTest package (Kuznetsova, Brockhoff, & Christensen, 2016).

Table 2. Results of the logistic regression on task choice for Studies 2-4

Study 2 (N = 161) Study 3 (N = 145) Study 4 (N = 351) Coefficient β SE p Exp(β) β SE p Exp(β) β SE p Exp(β) Constant -1.55 0.35 < .001 0.21 1.74 0.53 < .001 5.71 -0.44 0.25 0.09 0.65 Emotion Condition -0.36 0.47 0.43 0.70 -0.47 0.62 0.45 0.63 0.36 0.36 0.32 1.44 Task Difficulty -0.52 0.35 0.14 0.60 -0.73 0.68 0.28 0.48 -1.32 0.32 < .001 0.27 Task Enjoyment 0.59 0.33 0.08 1.81 3.75 0.89 < .001 42.71 2.50 0.37 < .001 12.20 Task Duration -0.64 0.34 0.06 0.53 -1.30 0.62 0.04 0.27 -1.08 0.37 < .01 0.34 Task Order 0.23 0.24 0.35 1.26 ------Model Summary Hosmer and Lemeshow 27.87 21.82 4.01 χ2 df 8 8 8 p value < .001 .005 .856

Table 3. Results of the one-way ANCOVA and descriptive statistics for task preference by emotion condition for Studies 2-4 and Integrative Data Analysis (IDA)

Effect of emotion condition Observed Mean (SD) Adjusted Mean

2 F df p ηp Pride Happiness Pride Happiness

Study 2 2.77 (1, 155) .10 0.018 2.78 (1.55) 2.52 (1.45) 2.82 2.49 Study 3 2.48 (1, 140) .12 0.017 3.87 (1.92) 3.72 (1.83) 3.94 3.65 IDA 4.86 (1, 298) .03 0.016 3.30 (1.82) 3.08 (1.74) 3.35 3.05 Study 4 0.01 (1, 343) .92 0.000 3.29 (1.85) 3.36 (1.86) 3.33 3.32 Note. Different scores between high-level and low-level tasks for task difficulty, task enjoyment, and task duration ratings and task order were entered as covariates. Adjusted means are derived by holding constant the covariates at their mean values.