©2011

Laura Nicole Martin

ALL RIGHTS RESERVED

Emotion Regulation: Effects on Decision-making and Affective Responses

By Laura Nicole Martin

A Dissertation submitted to the Graduate School-Newark

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Graduate Program in Psychology

written under the direction of

Professor Mauricio R. Delgado

and approved by:

Dr. Mei-Fang Cheng

________________________

Dr. Mauricio R. Delgado

________________________

Dr. Kent Harber

________________________

Dr. Daniela Schiller

________________________

Dr. Elizabeth Tricomi

________________________

Newark, New Jersey

October 2011

ii

ABSTRACT OF THE DISSERTATION

Emotion Regulation: Effects on Decision-making and Affective Responses

By: Laura Nicole Martin

Dissertation Director: Professor Mauricio R. Delgado

Emotions influence our behavior by initiating adaptive response tendencies that

affect our subsequent decision-making. Emotions are often elicited by cues in our

environment that signal potential rewards or punishments. At times the influence of

emotions on behavior can be maladaptive; for instance, the positive emotions elicited by

a reward-conditioned cue (e.g., oster approach behavior (e.g.,

stopping at the drive thru), potentially leading to negative long-term consequences (e.g.,

obesity). One way to promote more goal-directed behavior in the face of environmental

cues may be to engage in emotion regulation, strategies to change the emotions we

experience (Gross, 1998). Previous research has shown that emotion regulation can

increase or decrease the intensity of both positive and negative emotions (Ochsner &

Gross, 2008). Researchers have begun to apply emotion regulation techniques to

decision-making (Sokol-Hessner et al, 2009; Seo and Barrett, 2007), but little is known

about the neural circuitry underlying the modulatory influences cognitive strategies have

on decisions.

iii

The current experiments probed emotion regulation of cues associated with

monetary rewards or punishments and effects on subsequent decision-making and

affective response. Experiments 1 -3 examined the effect of emotion regulation strategies

on decision-making. Imagery-focused regulation decreased risk-taking and associated

striatum activity in Experiment 1, which tested simple financial decisions. In Experiment

2, the financial decisions were more complex, and the imagery strategy no longer

affected risk-taking, suggesting this strategy is best suited to simple contexts.

Experiment 3 showed that cognitive reappraisal regulation was effective with more

complex decisions; reappraisal increased and decreased risk-taking, depending on the

goal of the strategy. Experiment 4 examined the effect of regulation on emotions

associated with loss cues. Here, imagery-focused regulation recruited cortical brain

regions and ameliorated negative emotion experienced when faced with a cue signaling

an unavoidable monetary loss. Taken together, the potential significance of these studies

lies in understanding how individuals can employ regulation strategies to their benefit

either by changing their decision-making in the face of potential rewards or by altering

their subjective experience of emotion in negative situations in which they have no

control over the outcomes.

iv

Dedication

To my dad, who taught me the value of knowledge.

To my mom, who showed me the wonder of mystery.

v

Acknowledgements

I am grateful to everyone who has helped me on this journey. First, I thank my

mentor, Mauricio Delgado, for his guidance throughout my graduate training. Mauricio

was supportive, guiding me through every step of the research process, while still

providing me the space and independence to grow to as a scientist. I appreciate the faith

he showed in me and all of the discussions that we had. I am thankful to my committee,

Mei Cheng, Kent Harber, Daniela Schiller, and Elizabeth Tricomi, for their support and

for their suggestions that shaped and improved this work. I am grateful for my mentors

during my work as a research assistant, Amir Raz and Jin Fan. They helped me to realize

my potential and to learn that graduate school was the right path for me. I am thankful

for the assistance I received from my research assistants, Stefanie Herrera, Nikhilesh

Gorukanti, Christina Cheung, Vicki Lee, and Pollytia Panagioutou, whose efforts greatly

enhanced this research. All of the members of the Delgado lab were wonderfully

supportive. I thank Mike Nizkikiewicz, Meredith Johnson, and Vicki Lee for keeping the

lab functioning and made sure I had all the resources I needed to complete this work. I

am enormously grateful to the people I started this journey with, Katie Dickerson, Swati

Bhattacharya, Dominic Fareri, and Tony Porcelli for their boundless friendship and

support over the last four years. This research would not exist without the research

participants; I am grateful to them for their time and for sharing their thoughts and

experiences, which were invaluable and sometimes surprising. I thank all of the graduate

students of the psychology program for their humor, encouragement, and positive

attitudes. It has been an amazing experience to be surrounded by people who care so

much about their work and about helping others succeed. I feel lucky to be a part of this

vi

particular Psychology Department, and I thank all of our faculty, especially Harold Siegel

and Ken Kressel, for their willingness to listen and their efforts to ensure that this

department is somewhere people are happy to work and learn. I felt the support of my

family and friends throughout my graduate career, and it helped me succeed. I am

thankful to my parents for always giving me the space to pursue what I wanted and for

helping me along the way. Finally, I am so thankful for the love and support of my

fiancé and partner, Will. This journey would not have been the same without him, and I

thank him for everything he did, both the tangible and the immeasurable.

vii

Table of Contents Abstract of the Dissertation ii

Dedication iv

Acknowledgments v

Table of Contents vii

List of Figures x

List of Tables xi

List of Appendices xii

Chapter 1: Introduction 1

1.1 Emotion and Emotion Regulation 3

1.1.1 Cognitive reappraisal 5

1.1.2 Imagery-focused regulation 7

1.1.3 Measuring emotional responses and detecting shifts in emotion 8

1.2 Neural Processes Underlying Emotion Regulation 10

1.2.1 Regulation of negative emotions 12

1.2.2 Regulation of positive emotions 14

1.3 Regulation and Decision-making 17

1.4 Overview of Experiments 19

Chapter 2: Experiment 1: The Influence of Imagery Regulation on Decision-making 24

2.1 Introduction and Hypotheses 24

2.2 Methods 27

viii

2.3 Results 33

2.4 Discussion 43

Chapter 3: The Effect of Imagery and Cognitive Emotion Regulation Strategies on Decision-Making 52

3.1 General Introduction and Hypotheses 52

3.2 General Methods 57

3.3 Experiment 2: The effect of imagery-focused regulation on risk-taking across different financial decisions 63

3.3.1 Methods 63

3.3.2 Results 64

3.3.3 Discussion 66

3.4 Experiment 3: The effect of cognitive emotion regulation strategies on risk-taking across different financial decisions 68

3.4.1 Methods 68

3.4.2 Results 69

3.4.3 Discussion 73

3.5 General Discussion 74

Chapter 4: Experiment 4: Regulation of Negative Emotions Associated with Conditioned Cues 78

4.1 Introduction and Hypotheses 78

4.2 Methods 81

4.3 Results 91

4.4 Discussion 96

ix

Chapter 5: General Discussion 103

5.1 Summary and Significance 103

5.2 Potential Limitations of the Emotion Regulation Strategies 107

5.3 Potential Limitations of the Operational Definition of Decision-making 109

5.4 External Validity of the Financial Consequences 110

5.5 Intensity of Emotional Responses 112

5.6 Individual Differences 113

5.7 Overall Conclusions and Future Directions 114

References 116

Figures 127

Tables 141

Appendices 154

Vita 164

x

List of Figures

Figure 1.1 Overview of Experiments 127

Figure 2.1 Schematic of the Task Used in Experiment 1 128

Figure 2.2 Decision-making Results of Experiment 1 129

Figure 2.3 Neuroimaging Results of Experiment 1: Effects of Strategy and Choice in the Striatum 130

Figure 2.4 Neuroimaging Results of Experiment 1: Effects of Strategy and Choice in the Midbrain and Insula 131

Figure 3.1 Schematic of the Task Used in Experiment 2 132

Figure 3.2 Decision-making Results of Experiment 2 133

Figure 3.3 Schematic of the Task Used in Experiment 3 134

Figure 3.4 Decision-making Results of Experiment 135

Figure 4.1 Schematic of the Task Used in Experiment 4 136

Figure 4.2 Rating Results of Experiment 4 137

Figure 4.3 Activity in the Left Dorsolateral Prefrontal Cortex ROI 138

Figure 4.4 Activity in the Right Superior Frontal Gyrus (BA 6) 139

Figure 4.5 Right Inferior Frontal Gyrus Activity (Relax > Look) Correlation with Emotion Regulation Scores 140

xi

List of Tables Table 2.1 Financial Decisions in Experiment 1 141

Table 2.2 Regions that Correlated with Increasing Probability of Reward in the Regulators Group 142

Table 3.1 Financial Decisions in Experiments 2 and 3 143

Table 3.2 Comparison of Imagery-focused and Reappraisal Strategies 144

Table 4.1 Anticipation Phase ANOVA: Brain Regions Showing a Main Effect of Strategy 145

Table 4.2 Anticipation Phase ANOVA: Brain Regions Showing a Main Effect of Cue Type 146

Table 4.3 Anticipation Phase ANOVA: Brain Regions Showing a an Interaction of Strategy and Cue Type 147

Table 4.4 Cue Phase ANOVA: Brain Regions Showing a Main Effect of Cue Type 148

Table 4.5 Cue Phase ANOVA: Brain Regions Showing a Main Effect of Strategy (All Relax > Look) 149

Table 4.6 Cue Phase ANOVA: Brain Regions Showing an Interaction of Strategy and Cue Type 150

Table 4.7 Loss Cue: Brain Regions Active for the Anticipation Phase Contrast of Relax vs. Look 152

Table 4.8 Variable Cue: Brain Regions Active for the Anticipation Phase Contrast of Relax vs. Look 153

xii

List of Appendices Appendix 1. Post Experimental Survey Used in Experiment 1 154

Appendix 2. Post Experimental Survey Used in Experiment 2 159

Appendix 3. Post Experimental Survey Used in Experiment 3 161

1

Chapter 1: Introduction

It is hard to imagine a life without emotion. Emotions are a major part of our

subjective experiences of the world. Emotions can be described as adaptive

physiological and behavioral response tendencies that are activated by important stimuli

and situations (James, 1894). Emotions signal to us that something is important and

requires our attention. From an evolutionary perspective, emotions prompt us to initiate

approach or avoidance behaviors, guiding us to towards rewards and away from harm

(Panksepp, 1998). The key aspect of these definitions of emotions is the idea that

emotions influence behavior.

Typically, the influence of emotion on our behavior is advantageous; for example,

negative emotions such as fear and anxiety can be important indicators of the need for

attention and vigilance. Imagine driving at high speeds on an unfamiliar highway when

sudden feelings of anxiety accompanied by autonomic changes are experienced. This

emotion is deemed protective, signaling potential caution in behavior. Yet, emotions can

also influence decision-making in maladaptive ways. A surge of anxious symptoms

while driving, for instance, could lead to an overwhelming stressful response with

dangerous consequences on the road. Like negative emotions, positive emotions such as

excitement and reward anticipation can also influence our decision-making. Often

positive emotions are triggered by cues in the environment associated with rewards. For

s golden arches.

Suddenly, you are craving salty French fries and this desire leads you to take the next exit

and go to the drive-through. If you are trying to lose weight, eating fries may be counter-

2

productive. Thus, at times our emotional responses may influence our behavior in

maladaptive ways. Exerting control over our emotional responses to cues is necessary to

facilitate goal-directed behavior.

As humans we have the unique ability to change the emotions we experience and

express using a collection of techniques called emotion regulation strategies (Gross,

1998b). Psychologists have been interested in the process of emotion regulation for

several decades, but research on emotion regulation has grown immensely in the last 10

years. Current research on emotion regulation seeks to understand the subjective,

behavioral, physiological and neural bases of emotion regulation (for a review, see

Ochsner & Gross, 2005; Ochsner & Gross, 2008). This research has created a clear

picture of the psychological processes and neural systems involved in using regulation

techniques to change the subjective experience of emotion. Whether and how the effects

of regulation efforts extend beyond subjective experience to actual behavior, however, is

not well understood. Additionally, most investigations of emotion regulation have

focused on negative emotions, likely because they are subjectively unpleasant to

experience and are often core symptoms of psychiatric disorders such as depression.

Although positive emotions are pleasant to experience, they foster approach tendencies

central to many maladaptive behaviors including over-eating and drug-seeking;

additionally, feelings of anticipation or craving for a rewarding stimulus, a drug, have

been linked to relapse in drug addiction (Weiss, 2005). It is important to understand the

psychological and neural processes underlying the influence of emotion regulation

strategies on decision-making as these regulation processes are a promising method to

ameliorate maladaptive decision-making.

3

The current research examines the application of emotion regulation strategies to

positive and negative cues that predict monetary gains and losses, respectively. The

positive cues immediately precede financial decisions between risky and safe options,

and the effect of regulation on risk-taking is examined in three experiments. The

negative cues immediately precede monetary losses, and the effect of regulation on the

subjective experience of negative emotion during anticipation of these outcomes is

examined in one experiment. Two experiments employ functional neuroimaging (fMRI)

techniques to characterize the shifts in neural processing that underlie the regulation-

driven changes in decision-making and experience of emotion. Before discussing the

details of these four experiments, the psychological and neural literatures on emotion and

emotion regulation and their relationship to decision-making will be reviewed.

1.1 Emotion and Emotion Regulation

Emotions color and enrich our lives. They signal to us that something is

important and requires our attention. Emotions are elicited by salient stimuli in the

environment, for instance those that we have learned to associate with rewards or

punishments like appetitive and aversive cues. Salient stimuli can also be those that are

discrepant, stimuli that are unexpected or that interrupt an ongoing process (Mandler,

1984). Thus, a basic, early definition of emotions described them as adaptive response

tendencies initiated by salient stimuli (James, 1894). This idea became a central

component of the James-Lange theory of emotions, which conceptualizes emotions as the

mental experience of physiological responses to significant environmental cues.

4

Modern theories of emotion expand this early definition of emotions; they posit

that emotions are shaped by appraisals of the stimuli and situation (Lazarus, 1991;

Schachter & Singer, 1962). This description of emotion accounts for the diverse role of

physiological responses such as arousal in emotion. Heightened arousal accompanies

several basic emotions, for instance, surprise, fear, and anger. Despite this common

physiological component, these emotions are experienced as distinct feelings, suggesting

that emotions are not merely perceptions of physiological changes. Appraisal theories of

emotion argue that we experience a physiological sensation, such as arousal, and we

rapidly interpret and label that feeling based on the current situation and available

information (Schachter & Singer, 1962). Importantly, Lazarus and others have argued

for a broad definition of appraisals that includes both conscious and unconscious

evaluative processes (Lazarus, 1991; Scherer, 1999).

Conceptualizing emotions as malleable tendencies that can be influenced by

appraisals is the foundation of theories of emotion regulation; if emotions were not

susceptible to change, strategies for regulation of emotion could not exist. From a

psychological perspective, we know that there are several different approaches that

people can use to change the intensity of the emotion they experience and whether and

how they express that emotion. These different methods of emotion regulation are

directly related to the different stages of the emotion generation process in which they

take effect. Using a process model of emotion, there are two main time points at which

emotion regulation efforts can take place, when first faced with an affective stimulus and

just after the initiation of an emotional response to that stimulus (Gross, 1998b).

Strategies used early in the emotion generation process are called antecedent-focused

5

strategies, and although there are several different types of antecedent-focused strategies,

their central goal is to alter the effect the stimulus has by changing whether or how it is

perceived (Gross, 1998a). Response-focused strategies are applied late in the emotion

generation process, and their goal is to alter the expression of emotion (Gross, 1998a).

Research has demonstrated that antecedent-focused strategies are more flexible

and effective than response-focused strategies at altering emotional experience (Gross,

2002), and that response-focused strategies like suppression, which involves limiting any

outward expression of emotion, are associated with memory deficits for the period that

regulation was used (Richards & Gross, 2000). Additionally, using response-focused

strategies has been found to increase autonomic responding (Gross, 2002); thus, most

research on emotion regulation has focused on understanding antecedent-focused

strategies.

1.1.1 Cognitive reappraisal. There are several types of antecedent-focused

emotion regulation strategies. The most dynamic antecedent-focused strategies are

cognitive change techniques, which involve directing and monitoring conscious thoughts

in order to alter emotional experiences (Gross, 1998b). One type of cognitive change

strategy is reappraisal, in which individuals reevaluate the meaning of a situation or

stimulus, thereby altering their emotional response. Reappraisal strategies were

developed from early work by Lazarus and colleagues who demonstrated that narrating

stressful situations in a detached, neutral way reduced physiological and subjective stress

responses (Lazarus & Alfert, 1964). Recent investigations of reappraisal have involved

training participants to change their thoughts about the emotional stimulus. For example,

given a photograph of a man who appears bruised and injured, one could reduce the

6

negative emotions elicited by the image by thinking that the man is an actor in makeup

and that he is not really hurt at all. This example describes a situation-focused

reappraisal method; to decrease emotion in a situation-focused way the initial evaluation

of the stimulus must be altered such that it can be perceived as not as bad as it seemed at

first (Ochsner, Ray, Cooper, Robertson, Chopra, Gabrieli et al., 2004). Reappraisal can

also be achieved through more self-focused cognitions, for instance distancing oneself

from the stimulus by taking the perspective of a detached observer (Ochsner, Ray,

Cooper et al., 2004). Perhaps because of the thoughtful, cognitive nature of reappraisal it

is particularly effective with complex emotional stimuli such as films (for review, see

Gross, 1998a).

Regardless of the specific method, reappraisal is a verbal process. It involves

changing the appraisal or meaning of the stimulus or situation by generating a different

narrative, for instance, a more positive or neutral one. To illustrate the importance of

narratives in appraisal, one study examined the effect of manipulating narratives by

supplying subjects with verbal descriptions prior to the presentation of negative pictures

(Foti & Hajcak, 2008). While all the pictures were negative, the narrative descriptions

were either negative or neutral. Participants showed lower ratings of unpleasantness and

arousal, for the images that were preceded by neutral descriptions, compared to images

preceded by negative descriptions. This simple design echoed the earlier findings of

Lazarus and demonstrated that the description individuals apply to an emotional stimulus

greatly influences their emotional response to it. These findings support the appraisal

theories of emotion.

7

1.1.2 Imagery-focused regulation. In addition to reappraisal, antecedent

regulation of emotions can also be achieved with strategies focused on distancing or

imagery generation. These strategies involve focusing thoughts away from the emotional

stimulus. Specifically, imagery-focused regulation involves focusing thoughts internally,

instead of at the emotional stimulus, by imagining a visual scene (Delgado, Gillis, &

Phelps, 2008; Delgado, Nearing, LeDoux, & Phelps, 2008). In the previous studies of

imagery-focused regulation, participants visualized calming scenes, for example nature

scenes, the ocean or a beautiful day at the park. Unlike reappraisal, imagery-focused

regulation does not directly alter the meaning of the emotional stimulus. Instead, the goal

of imagery-focused regulation is to create a new emotion, such as a feeling of calm, to

replace the emotion associated with the stimulus. Thus, imagery regulation involves

elements of distraction and relaxation. Imagery-focused strategies are fundamentally

different from reappraisal, but they share features with traditional cognitive behavioral

therapy (CBT) techniques in which patients are trained to use specific strategies or

imagery to cope with certain situations or stimuli (Delgado, Nearing, LeDoux et al.,

2008).

As reappraisal is well-suited for complex emotional stimuli, imagery-focused

regulation is appropriate for simple, repeated stimuli such as conditioned cues. Imagery-

focused regulation has been shown to successfully reduce arousal responses (measured

via skin conductance) to appetitive and aversive cues (Delgado, Gillis, & Phelps, 2008;

Delgado, Nearing, LeDoux et al., 2008). These results suggest that one mechanism by

which imagery-focused regulation changes emotional responses is via modulation of

arousal.

8

1.1.3 Measuring emotional responses and detecting shifts in emotion. The

efficacy of cognitive regulation techniques is typically characterized by the observed

changes in emotional experience and responses. Most studies of emotion regulation rely

on self-report ratings of emotion (i.e., emotion intensity) to detect changes in experienced

emotion. Self-

response, as emotional content is inherently subjective (Barrett, Mesquita, Ochsner, &

Gross, 2007). However, social desirability may affect self-report data by leading

responses to conform to the expected results making self-reports potentially unreliable

(Crowne & Marlowe, 1960). The time of self-report administration may also affect its

validity as research has shown that current emotions are perceived as more intense than

previous emotions (Van Boven, White, & Huber, 2009).

Given the importance of self-report, ratings of emotion and success at using the

emotion regulation strategies were employed in the current experiments. Additionally,

the main dependent variable of three of the current experiments was risk-taking, a

behavioral, rather than a self-report, measure. Assessing changes in choices as a function

of regulation is an important extension of the extant emotion regulation literature. If

emotion regulation alters choices in the current studies, it would demonstrate that the

influence of these strategies goes beyond the subjective experience of emotion.

Other measures of affective responses have been used to supplement self-reports.

For instance, expressive behavior captured by video-taping participants during the

experiment and then coding their facial behavior has been used to assess fluctuations in

emotion (Giuliani, McRae, & Gross, 2008; Goldin, McRae, Ramel, & Gross, 2008).

Further, autonomic measures such as skin conductance responses (SCRs) can assess

9

arousal levels and provide a measure of one component of the emotional response. Such

measures depend on sympathetic nervous system activity increases in response to

motivationally significant stimuli (e.g., affective stimuli), which, in the case of SCRs,

results in greater levels of sweat excretion and increased conductivity of the skin

(Critchley, 2002). However, interpretation of SCRs are limited by the observation that

sympathetic arousal can be caused by not just emotional changes, but also fluctuations in

cognition or attention (Critchley, 2002). These physiological measures typically affirm

self-reports of emotional shifts with regulation. The current experiments employed skin

conductance measurements to assess changes in arousal due to use of emotion regulation

strategies.

Brain imaging techniques are a useful counterpart to the subjective, behavioral,

and physiological assessments described above, as neuroimaging can highlight the

potential neural circuitry involved in emotion perception, experience, expression, and

regulation. Functional magnetic resonance imaging (fMRI), for example, can be used to

detect and contrast brain activity during the natural experience of emotion (for a review

see Kober et al., 2008) and during the experience of emotion under regulation (for a

review, see Ochsner & Gross, 2008). The efficacy of emotion regulation techniques can

be inferred from shifts in activity with regulation, although inferences must be made with

caution as the level of brain activity is not a direct index of the intensity of experienced

emotion. Neuroimaging data was collected for two of the current experiments,

Experiment 1 and Experiment 4. The goal of including neuroimaging methods is to

complement and extend behavioral and physiological measures of emotion and to

identify the underlying neural circuits that mediate how regulatory processes exert their

10

influence on emotional responses. Specifically, Experiment 1 assessed how imagery

regulation altered the neural systems that process risk and reward and Experiment 4

assessed how this same regulation strategy altered activity in brain regions that process

losses.

1.2 Neural Processes Underlying Emotion Regulation

How do emotion regulation processes manifest changes in the neural circuitry

involved in emotion? The consensus in the literature on emotion regulation, specifically

antecedent-focused strategies, is that prefrontal regions involved in cognitive control

influence processing in emotion-related brain regions such as the amygdala (for reviews

see Green & Malhi, 2006; Ochsner & Gross, 2008). Manipulating and reevaluating

situations and stimuli involve prefrontal brain regions important in cognitive control,

response selection, working memory, and keeping task demands online (Miller & Cohen,

2001). More specifically, cognitive reappraisal is thought to involve working memory

and selective attention mediated by the dorsal prefrontal cortex (PFC), inhibition initiated

by the ventral PFC (including the ventral lateral PFC, vlPFC; Lieberman et al., 2007),

regulation of control processes by the dorsal anterior cingulate cortex (ACC), and

consideration of the emotional states of oneself or another mediated by the medial PFC

(Ochsner & Gross, 2008). The dorsolateral region of PFC (dlPFC) is believed to play an

important, but somewhat indirect role in emotion regulation due to the fact that it does

not have direct connections to brain regions such as the amygdala (Delgado, Nearing,

LeDoux et al., 2008; Ochsner & Gross, 2008; Quirk & Beer, 2006). Indeed, the dlPFC

has been shown to be involved in a broad spectrum of cognitive tasks (Miller & Cohen,

11

2001), supporting the notion of its role as a domain-general system. The function of the

dlPFC during emotion regulation is likely to keep regulation goals online and maintain an

active representation of task demands.

Successful emotion regulation does not result solely from recruitment of

prefrontal regions; modulation of activity in regions involved in emotional learning such

as the amygdala, striatum and insula has been linked to regulation success (Ochsner &

Gross, 2008). These regions have direct and indirect anatomical connections to various

prefrontal sites and have been previously implicated in affective processing and

motivation with both negative and positive stimuli (Amaral & Price, 1984; Cardinal,

Parkinson, Hall, & Everitt, 2002). Additionally, a recent functional connectivity analysis

determined that prefrontal cortex activity during emotion regulation covaried with

amygdala activity, and reductions in negative emotion varied with the strength of

functional connectivity between prefrontal regions (orbitofrontal cortex and dorsal medial

PFC) and the amygdala (Banks, Eddy, Angstadt, Nathan, & Phan, 2007). In a related

study, a formal mediation analysis revealed that vlPFC disrupted or partially inhibited

activity in the amygdala via the medial PFC (Lieberman et al., 2007), which is

anatomically connected to both the vlPFC and amygdala (Ghashghaei & Barbas, 2002).

These studies support the theory that regulation is achieved by the engagement of

prefrontal brain regions whose increased activity modulates activity in brain regions

involved in emotion. These cortical-subcortical relationships will be described further in

the following sections, in which we will discuss how neuroimaging studies have

advanced our knowledge of the psychological and neural bases of regulation of negative

and positive emotions.

12

1.2.1 Regulation of negative emotions. Given their prominence and potential

clinical significance, negative emotions have historically been the target of emotion

regulation research. The goal of this research has been to understand the behavioral and

neural mechanisms through which cognitive strategies effectively decrease the intensity

of anticipated or experienced negative affect. One of the first neuroimaging studies of

emotion regulation examined how cognitive reappraisal could reduce the intensity of

negative emotion experienced when viewing aversive pictures (Ochsner, Bunge, Gross,

& Gabrieli, 2002). In this experiment, participants were presented with negative

emotional pictures and cued to either respond naturally or to actively engage in

reappraisal to decrease the intensity of negative emotion they experienced. As assessed

by online self-reports, subjective ratings of negative affect were decreased when

regulation strategies were used. Further, reappraisal techniques compared to a natural

response condition, led to increased responses in cortical brain regions such as the dorsal

and ventral lateral prefrontal cortex (PFC) and medial PFC and decreased activity in the

amygdala and medial orbitofrontal cortex (OFC). Although other studies at this time

were reporting modulation of amygdala and prefrontal cortex by conscious cognitive

effort (Schaefer, Jackson, Davidson, Aguirre, Kimberg, & Thompson-Schill, 2002), this

was one of the first reports demonstrating that the explicit use of an emotion regulation

strategy attenuates negative emotion and modulates activity in cortical and amygdala

regions. Numerous studies investigating the neural correlates underlying successful

reappraisal of negative emotions evoked by pictures followed. Across research reports,

the common theme has been that successful application of emotion regulation strategies

leads to increased activity in the PFC and decreased activity in regions mediating an

13

emotional response such as the amygdala (e.g., Goldin, McRae, Ramel, & Gross, 2008;

Harenski & Hamann, 2006; Kim & Hamann, 2007; Ochsner, Bunge, Gross et al., 2002;

Ochsner, Ray, Cooper et al., 2004; Phan, Fitzgerald, Nathan, Moore, Uhde, & Tancer,

2005; Urry, van Reekum, Johnstone, Kalin, Thurow, Schaefer et al., 2006).

Using a strategy with the goal to distance and detach oneself from the events

depicted in sad film clips, participants decreased their subjective feelings of sadness, and

self-report sadness ratings correlated with increases in BOLD signals in two regions of

frontal cortex, right OFC and dlPFC (Levesque, Fanny, Joanette, Paquette, Mensour,

Beaudoin et al., 2003). Detachment strategies have been shown to successfully decrease

physiological arousal, skin conductance responses (SCRs), elicited by the anticipation of

painful electric pulses while engaging anterolateral PFC (Kalisch, Wiech, Critchley,

Seymour, O'Doherty, Oakley et al., 2005). Interestingly, attempts to distract oneself

during the anticipation of pain did not successfully reduce subjective feelings of anxiety

(Kalisch, Wiech, Herrmann, & Dolan, 2006) suggesting that some strategies may be more

effective than others.

Imagery-focused techniques have been used to regulate physiological and neural

responses to conditioned fear (Delgado, Nearing, LeDoux et al., 2008). In this study, a

previously neutral stimulus, a blue square, elicited a conditioned response, increased

arousal as indexed by SCRs, due to repeated associations with an aversive outcome, an

electric shock (the unconditioned stimulus). The acquisition and expression of this

conditioned fear response has been shown to involve the amygdala (for review, see

Delgado, Olsson, & Phelps, 2006; Phelps & LeDoux, 2005). The use of imagery

techniques that promoted a feeling of calm

14

upon presentation of the conditioned stimulus (CS, the blue square)

decreased conditioned physiological responses, increased the BOLD response in the left

dorsal lateral prefrontal cortex (dlPFC), and attenuated the BOLD response in the

amygdala (Delgado, Nearing, LeDoux et al., 2008) compared to natural responding

during the conditioned stimulus. Notably, a connectivity analysis suggested that the

influence of dlPFC on the amygdala response could be indirectly mediated by the

ventromedial prefrontal cortex (vmPFC), a region previously linked with the extinction of

fears in both rodents (Milad & Quirk, 2002) and humans (Phelps, Delgado, Nearing, &

LeDoux, 2004). This study showed that imagery-focused regulation reduced fear

responses to the shock cues. The goal of experiment four is to examine whether this

same strategy is also effective with cues signaling aversive experiences more common in

daily life, the loss of money.

1.2.2 Regulation of positive emotions. While research over the last few years

has successfully highlighted the potential neural correlates underlying successful

regulation of negative emotions, only more recently have investigations begun to probe if

similar strategies (e.g., reappraisal, Giuliani, McRae, & Gross, 2008) and neural

underpinnings also mediate the control of positive emotions. The arousing feelings

associated with craving and excitement are some examples of positive emotions that can

trigger approach behaviors which become detrimental to well-being if not controlled, for

instance, higher than normal consumption of appetitive stimuli like food or drugs. An

understanding of the behavioral and neural processes underlying successful cognitive

regulation of positive emotions not only bolsters our understanding of general cognitive

control processes; it also has beneficial implications.

15

There is evidence suggesting overlap in the cortical loci implicated in the

successful regulation of positive and negative emotions. Similar to previous studies of

negative emotions, (e.g., Levesque, Fanny, Joanette et al., 2003) increases in BOLD

responses in PFC regions, such as dlPFC and ACC, have been reported to be associated

with diminished feelings of positive arousal elicited by affective films when using

regulation strategies (Beauregard, Levesque, & Bourgouin, 2001). Additionally, a direct

within-subjects comparison of use of reappraisal with positive and negative affective

pictures revealed that both positive and negative regulation involve cortical regions such

as the right lateral PFC, dorsal medial PFC, medial PFC, and bilateral lateral OFC (Kim

& Hamann, 2007). Notably, greater activation in PFC regions was observed when

participants attempted to regulate negative emotions, suggesting that while regulation of

positive and negative emotions may recruit similar cortical structures, the extent and level

of cortical activity may differ between the emotion types, as will the specific subcortical

targets of regulation.

The expectation of a potential reward can bring about positive emotions and

promote approach behaviors, with the caveat that such behaviors can be risky and

detrimental to o -being in extreme cases (Potenza & Winters, 2003). The

striatum, a subcortical region involved in reward-related processing and affective

learning, is a potential target for cortical modulation via cognitive strategies, given its key

role in reward prediction and expectations elicited by conditioned reinforcers (for review,

see Delgado, 2007; Knutson & Cooper, 2005; O'Doherty, 2004) and correlations with

drug-specific cravings (Breiter, Gollub, Weisskoff, Kennedy, Makris, Berke et al., 1997;

Sinha, Lacadie, Skudlarski, Fulbright, Rounsaville, Kosten et al., 2005). Regulation of

16

positive emotion associated with the expectation of reward has been recently investigated

using a conditioning design that varied the type of conditioned stimulus (paired with a

monetary reinforcer, CS+, or paired with no reinforcer, CS-) and type of instruction

(attend to natural emotions or regulate via imagery techniques) (Delgado, Nearing,

LeDoux et al., 2008). Physiological responses, measured by SCRs, demonstrated the

effectiveness of the imagery-focused regulation strategy as the heightened response to the

CS+ observed during natural responding was diminished during regulation. A similar

decrease was observed in BOLD signals in the striatum; that is, striatum signals were

reduced when a regulation strategy was used with a stimulus that predicted a potential

reward. In contrast, increases during emotion regulation trials were observed in dlPFC as

participants used the imagery technique. Echoing these results, another study observed

that regulation decreased striatum activity during reward-processing during a task in

which cues predicted monetary rewards of high or low magnitudes (Staudinger, Erk,

Abler, & Walter, 2009). Specifically, regulation focused on distancing oneself from the

potential rewards eliminated the differential BOLD response to cues associated with high

and low magnitude rewards typically exhibited in the striatum, suggesting that regulation

disrupted normal reward processing. The combination of increased prefrontal and

decreased striatum activity may be the positive analogue of increased PFC and decreased

amygdala activity systematically observed in studies of negative emotion regulation. As

understanding the regulation of conditioned fear has important implications for anxiety

disorders, understanding the regulation of conditioned reward may inform research on

addiction.

17

1.3 Regulation and Decision-making

While historically research in psychology may have segregated emotion and

cognition, it is now accepted that these domains largely overlap (Blair, Smith, Mitchell,

Morton, Vythilingam, Pessoa et al., 2007; Gray, 2004), and the emotions we experience

can have considerable influence on our decisions (Bechara, Damasio, & Damasio, 2000).

Emotions induced via subliminal methods have been shown to influence valuation, the

process by which value is assigned to something, and decision-making; for example,

thirsty participants were willing to pay greater amounts, wanted, poured and drank more

of a sugary beverage after being unconsciously exposed to happy faces (Winkielman,

Berridge, & Wilbarger, 2005). The opposite pattern occurred after unconscious exposure

to angry faces, demonstrating differential effects of positive and negative emotions. Of

course, explicit emotion manipulations can also affect decision-making, as evidenced by

the observation that watching emotionally-charged video clips disrupted typical

economic decision-making patterns such as the endowment effect, in which sellers assign

higher prices to owned items (Lerner, Small, & Loewenstein, 2004). Specifically, disgust

video inductions were associated with an absence of the endowment effect and sadness

video inductions with a reversal of it. In addition to emotion inductions, the existence of

a negative emotional state, for example acute stress, can influence cognition at both the

behavioral (Patil, Apfelbaum, & Zacny, 1995) and neural levels (Arnsten & Goldman-

Rakic, 1998). Further, acute stress modulates financial decision-making (Porcelli &

Delgado, 2009) and may put those who cannot cope with stress at risk of poor decision-

making, as illustrated by addiction relapse (Sinha, 2007). It is plausible that employing

18

cognitive emotion regulation strategies to control these emotions may foster better, more

goal-directed decision-making.

Some studies on self-control help provide insight into the potential mechanisms

involved in cognitive control of decision-making. Having depleted self-regulatory

resources, for example, has been shown to lead to greater impulse buying behaviors

(Vohs & Faber, 2007), suggesting that self-control mechanisms are important for making

advantageous decisions. Moreover, the successful use of self-control by dieters when

making choices about food consumption recruits cortical mechanisms (Hare, Camerer, &

Rangel, 2009). Activity in vmPFC in successful dieters reflected both taste and health

information of displayed food items, while activity in this region in non-self-controllers

reflected only taste information, suggesting different appraisal mechanisms for food

items in self-controllers compared to non-self-controllers. Taken together, these studies

provide evidence that greater self-control promotes decision-making in line with long-

term goals such as saving money and losing weight.

A recent study directly tested the effect of emotion regulation on financial

decision-making in which participants were faced with choices between a gamble and a

guaranteed amount (Sokol-Hessner, Hsu, Curley, Delgado, Camerer, & Phelps, 2009).

For the emotion regulation strategy, participants were instructed to reappraise,

specifically, to think about each decision as if they were a trader assembling a portfolio,

thus diminishing the importance of each individual decision. As a control condition,

participants thought of each decision in isolation. The authors identified individual

differences such that only half of the 30 subjects showed significantly reduced loss

aversion for reappraisal trials compared to control trials

19

reduced skin conductance responses during regulation trials compared to control trials,

-regulators

regulation and control trials.

Neuroimaging studies that specifically examine the effect of emotion regulation

on choices have not yet surfaced, but research close to this question has emerged. A

recent behavioral study examined regulation of craving in cigarette smokers (Kober,

Kross, Mischel, Hart, & Ochsner, 2009). Smokers were presented with cigarette-related

images and were cued either to regulate their feelings of craving by focusing on the long-

term consequences of smoking or to focus on the immediate sensory experience of

smoking (no regulation). Regulation reduced self-reported feelings of craving, and a

follow-up study showed that regulation was associated with increased activity in

prefrontal regions and decreased activity in craving-related brain regions such as ventral

striatum and ACC (Kober, Mende-Siedlecki, Kross, Weber, Mischel, Hart et al., 2010).

While early work with emotion regulation targeted negative emotions, recent studies

indicate that the focus of research on emotion regulation is shifting to the regulation of

positive emotions in the context of decision-making. This shift is likely driven in large

part due to the crucial role of positive emotions in approach behaviors that can become

maladaptive, such as the compulsive drug-seeking exhibited in addiction.

1.4 Overview of Experiments

Emotions, whether elicited by internal thoughts or cues in our environment, affect

our behavior and decision-making. Emotion regulation strategies can be used to control

or change these emotional responses, and strategy use is associated with physiological

20

and neural shifts. Emotion regulation is effective with naturalistic affective stimuli

(e.g., aversive photographs) and conditioned cues (e.g. shapes paired with unconditioned

stimuli like money or shock). Recent research suggests that using emotion regulation

strategies can alter decision-making, but these effects and their neural correlates have not

been fully characterized. The goal of this research is to examine the relationship between

emotion regulation and changes in decision-making and affective responses to

conditioned cues.

The dissertation experiments and predictions will be briefly introduced; see

Figure 1.1 for an overview of the experiments. In daily life, cues often signal

opportunities to take actions to gain rewards or avoid punishments, for example,

e salty, delicious French

fries. To capture the relationship between cues and subsequent rewards or punishments

in the laboratory, we employed simple visual stimuli (slot machine pictures and colored

squares) that were associated with monetary gains or losses. Participants completed

computer tasks involving these cues.

In Experiments 1 through 3, a picture of a slot machine signaled a financial

decision between a risky and a safe option. Because of the association between the slot

machine cues and opportunities to win money, the cues would evoke feelings of reward

anticipation and excitement. We predicted that these natural affective responses would

promote risk-taking to gain larger monetary rewards. To explore this relationship

between affective responses and risk-taking, we manipulated what participants thought

about during cue presentation. On some trials participants responded naturally to the cue,

and on other trials participants engaged in emotion regulation. We predicted that using

21

emotion regulation would alter affective responses to the cues and thereby alter risk-

taking during the financial decisions.

In Experiment 1, participants engaged in two types of imagery-focused regulation,

relaxing imagery and exciting imagery. Imagery-focused regulation has not been applied

to cues in a decision-making context, thus it is not known whether the previously

observed decreased affective responses to cues (e.g., Delgado, Gillis, & Phelps, 2008)

will also exert effects on actual choices. Because imagery regulation involves shifting

focus away from the opportunity for rewards, we expected regulation to alter decision-

making. Specifically, we hypothesized that relaxing imagery would decrease risk-taking

and that exciting imagery would increase risk-taking. During the experiment, fMRI data

were collected, and we predicted that engaging in relaxing imagery would decrease

activity in the ventral striatum, while engaging in exciting imagery would increase

activity during the decision phase. Decreased striatum activity would suggest dampened

responses to potential rewards, and increased striatum activity heightened responses.

The goal of Experiment 2 was to more fully characterize how imagery regulation

affects decision-making by testing the influence of imagery on risk-taking in a more

complex set of decisions. We included a mixed set of three different kinds of financial

decisions which varied with regard to which option had the greatest value, risky or safe.

We wanted to determine if the effects of imagery regulation would persist in a more

complex decision context. This study only employed the relax imagery strategy, and we

predicted that imagery would decrease risk-taking for all decision types. Skin

conductance measures were collected during the cue phase to assess whether imagery-

focused regulation changed arousal. We predicted that imagery regulation would lower

22

skin conductance responses, suggesting less arousal or excitement associated with the

upcoming decision.

The first two studies investigated an imagery-focused regulation strategy, but

there are a variety of strategies for emotion regulation. The third study aimed to

determine whether a more cognitive strategy, reappraisal, could be applied in a decision-

making context. The goal of Experiment 3 was to investigate the effectiveness of

reappraisal at altering risk-taking in a complex decision set. This experiment used the

same set of financial decisions as Experiment 2 in order to have a basis for comparison.

The reappraisal strategy involved replacing natural thoughts and reactions to the slot

machine cue with structured thoughts that would promote a new perspective on the

upcoming decision and opportunity for rewards. Two opposite reappraisal strategies

directed at

increasing or emphasizing the importance of the upcoming decision. For the

at decreasing or

deemphasizing the importance of the upcoming decision. We predicted that emphasize

reappraisals would decrease risk-taking and deemphasize reappraisals would increase it

for all decision types. Skin conductance measures were collected to assess whether

reappraisal changed arousal. We predicted emphasize reappraisals would increase skin

conductance and deemphasize reappraisals would decrease skin conductance. These

results would support the idea that emphasizing increases arousal, perhaps by increasing

anxiety, while deemphasizing decreases arousal, perhaps because participants were more

carefree about the decision.

23

Experiments 1 through 3 probed the regulation of cues associated with monetary

gains and effects on subsequent decision-making. Our environment is composed of cues

predicting both rewards and punishments; therefore, it is important to examine regulation

of negative cues. The previous three experiments examined the effect of regulation on

risk-taking, therefore, they did not directly assess changes in emotion experience.

Experiment 4 interrogated the influence of imagery-focused regulation on feeling of

negative emotion associated with monetary loss cues. This experiment also examined the

neural systems supporting imagery regulation of losses. We predicted that the imagery

regulation strategy would be effective at reducing reports of negative emotion, and that

regulation would recruit regions of the prefrontal cortex.

The overall goal of these experiments is to understand the relationship between

cues, affective responses, and decision-making. We chose to investigate monetary

rewards and punishments, because they are meaningful and common experiences in

everyday life that could be employed in a laboratory setting. These results of these

experiments will be described in detail in the following chapters.

24

Chapter 2

Experiment 1: The Influence of Imagery Regulation on Decision-making

2.1 Introduction and Hypotheses

Many cues in our environment signal potential rewards and elicit positive

emotional responses like excitement and anticipation, which initiate approach behaviors.

The ability to control emotional responses to such cues is essential for adaptive function.

For instance, an individual unable to cope with sudden urges elicited by a conditioned

stimulus (e.g., casino environment) may engage in maladaptive risk-seeking behavior

(e.g., gambling) that can potentially turn into a compulsive disorder (Kushner, Abrams,

Donahue, Thuras, Frost, & Kim, 2007). One promising intervention is the application of

cognitive strategies during the emotion generation process, a practice known as emotion

regulation, which results in an alteration in the affective experience of emotional stimuli

(Ochsner & Gross, 2005). The use of such cognitive strategies has been shown to

decrease physiological and subjective responses associated with the expectation of

prospective monetary rewards, which in turn modulate blood oxygenated level dependent

(BOLD) responses in the striatum (Delgado, Gillis, & Phelps, 2008; Staudinger, Erk,

Abler et al., 2009), a region previously associated with reward-related processing

(Delgado, 2007; Haber & Knutson, 2010; O'Doherty, 2004; Rangel, Camerer, &

Montague, 2008).

It is unclear, however, if the effects of emotion regulation can extend beyond

changes in emotional experience to changes in goal-directed behavior. Affective

responses elicited by salient cues are known to influence behavior, for instance cue-

induced drug craving is associated with increased drug-seeking (Weiss, 2005). Recently,

25

application of regulation strategies to drug cues has been found to reduce subjective

feelings of craving in cigarette smokers (Kober, Mende-Siedlecki, Kross et al., 2010) and

in cocaine abusers (Volkow, Fowler, Wang, Telang, Logan, Jayne et al., 2010), and lead

to decreased activation in regions such as the ventral striatum. While these studies did

not probe shifts in behavior associated with regulation of craving, it is possible that

regulation of such conditioned cues can extend to risk-taking behaviors such as drug-

seeking. The goal of Experiment 1 was to examine the effect of imagery-focused

regulation of a reward conditioned cue on subsequent behavior. Specifically, this study

probed if the successful use of cognitive strategies during presentation of a reward

conditioned stimulus (a slot machine) would influence decision-making (risk-taking in

financial decisions) and associated neural circuits such as the striatum.

We hypothesized that engaging in imagery regulation during the reward cue

would lead to decreased risky choices and attenuated associated BOLD signals in the

striatum. The imagery regulation technique involved focusing thoughts internally and

visualizing a calming scene such as a beach sunset, rather than focusing thoughts on the

reward cue and opportunity to gain money. Feelings of excitement and the anticipation

of receiving money rewards may promote risk-taking, thus, shifting mental focus away

from the potential rewards using the imagery strategy should decrease risk-taking. The

prediction of decreased activity in the striatum is motivated by previous observations that

imagery-focused regulation modulated the expectation of reward and blood-oxygen-

level-dependent (BOLD) responses in reward-related areas (Delgado, Gillis, & Phelps,

2008). The multifaceted human striatum is a region often identified during investigations

of risky decision-making (Christopoulos, Tobler, Bossaerts, Dolan, & Schultz, 2009;

26

Kuhnen & Knutson, 2005; Matthews, Simmons, Lane, & Paulus, 2004), whose signals

correlate with drug specific cravings (Sinha, Lacadie, Skudlarski et al., 2005) and

impulsive, risky decisions in substance users (Leland, Arce, Feinstein, & Paulus, 2006).

As previously mentioned, neural signals in the striatum have also been reported to be

modulated by emotion regulation strategies during expectation of monetary (Delgado,

Gillis, & Phelps, 2008; Staudinger, Erk, Abler et al., 2009) and drug (Kober, Mende-

Siedlecki, Kross et al., 2010; Volkow, Fowler, Wang et al., 2010) rewards. Thus, the

striatum provides an ideal target for potential regulatory influences that may occur during

decision-making under risk.

We investigated the effect of cognitive regulation on risk-taking and its neural

correlates using a functional magnetic resonance imaging (fMRI) experimental paradigm

that included both a cue and a decision phase. The cue phase consisted of the

presentation of a conditioned stimulus (CS+ or CS-

either a selection between risky and safe money options (CS+ trials) or a non-monetary

control decision between two different stamps (CS- trials; Figure 2.1).

decisions were quantified as the proportion of trials in which the risky option was chosen

for each type of cognitive instruction. Finally, we acquired post-experimental self-

assessm

probe how the successful application of emotion regulation modulates decision-making

under risk and its associated neural correlates.

27

2.2 Methods

Participants. Thirty-five right-handed volunteers participated in this study (17

female, 18 male). Three participants were excluded due to failure to comply with task

requirements (assessed by post-experimental questionnaires), which included not

following instructions and using an incorrect strategy. One additional participant was

excluded due to indifference during task performance as assessed by behavior (i.e.,

participant consistently chose one response) and self-report. Finally, data from one MRI

session was excluded due to equipment malfunction. Thus, final analysis was conducted

on thirty participants (15 female, 15 male; mean age: M = 20.87, SD = 2.99). Participants

responded to posted advertisements and gave informed consent according to the Rutgers

University Institutional Review Board for the Protection of Human Subjects in Research

and the Newark Campus Institutional Review Board of the University of Medicine and

Dentistry of New Jersey.

Procedure overview. After informed consent, the experimenter explained the

experimental task to the participants, and participants were trained on the imagery

emotion regulation techniques. Then, participants completed the experimental task while

inside of the MRI scanner. At the conclusion of the scanning session, participants

completed several questionnaires including a post-experimental questionnaire that

assessed compliance with the emotion regulation demands and measured perceived

successful use of cognitive strategies. Additional questionnaires that considered potential

individual differences included a measure of risk preferences (Holt & Laury, 2002), use

of emotion regulation strategies (Emotion Regulation Questionnaire; Gross & John,

2003) and behavioral inhibition and activation (BIS/BAS; Carver & White, 1994). Next,

28

participants were paid based on what they won in the game and debriefed. Finally, at

least a day after the scanning session, participants were asked to complete a paper

questionnaire with the five financial decisions from the scanner task along with two

variations where the amounts were either increased or decreased by $0.50. This

the absence of any regulation instruction.

The Slot Machine Game. The experimental task, the Slot Machine Game

(SMG) consisted of 90 trials, divided into 6 blocks of 15 trials. Each trial started with the

cue phase, involving the presentation of a conditioned stimulus (CS; a slot machine, CS+

or stamp machine, CS-) and a strategy ( Look , Relax or Excite ) for a variable

duration of 4, 6 or 8 s (Figure 2.1). The conditioned stimulus indicated if the trial

presented an opportunity to earn money (CS+) or not (CS-). The strategy was presented

above the CS and directed participants to either a) respond naturally to the slot machine,

engage in imagery-

experiments that used an imagery-focused regulation strategy (Delgado, Gillis, & Phelps,

2008; Delgado, Nearing, LeDoux et al., 2008). The cue phase was followed by the

decision phase, where participants were presented with two options for a fixed duration

of 4 s. For CS+ trials, participants chose between two monetary options: a gamble (risky

option) and a guaranteed amount (safe option) that varied with respect to probability and

amount, but were equated in their expected value. Expected value is the product of the

probability of winning and the amount that could be won. For CS- trials, the decision

29

carried no affective significance as participants chose between two different

representations of postage stamps with no monetary value. The CS- trials were a control

condition. They represented a decision with no affective or monetary significance. A

jittered 10 to 14 s inter-trial interval followed the decision phase.

Monetary incentives and payment. Participants received no immediate

feedback about the outcomes of their decisions. To ensure the perception that each

decision was independent and significant, six decisions were realized during the

experimental session. These outcome sessions occurred during three specific periods

during the experiment. The first outcome session occurred after the initial two task

blocks and as a result, reflected the resolution of those two task blocks. That is, two

decisions were resolved, with one decision being chosen from each of the two blocks just

completed. The second outcome session occurred after task blocks 3 and 4, while the

third and final outcome session occurred at the end of the experiment (after task blocks 5

and 6). During each of these three outcome sessions, participants first saw the computer

select two decisions from the five possible decision types by spinning a wheel. Next,

they watched the experimenter open their data file to identify their choices (risky or safe

option) for those decisions. Finally, participants were informed by the experimenter over

the intercom of the outcomes of the decisions and how much money they had won.

Participants were compensated a base rate of $20, plus whatever money they earned from

the six selected decisions. The decisions selected were the same for all participants

leading to an average earning of $53.33 (SD = $4.08).

Emotion regulation strategies. Prior to scanning, participants were extensively

trained on the task instructions, especially the application of the emotion regulation

30

techniques. They were informed that pictures of a slot machine and a stamp machine

would serve as cues to signal upcoming decisions involving either money or stamps,

respectively. They were also informed that a word presented above the picture would

serve as the strategy for that trial. There were three such strategies

participants were asked to look at the picture while

it was presented on the screen and react naturally while contemplating its significance for

Look strategy was paired with the slot

machine, they were asked to think that they would have to make a financial decision and

based on their choice, they could potentially win money. In contrast, when the Look

strategy was paired with the stamp machine, they would think about a potential decision

between two stamp

were prompted to imagine a calming scene such as a sunny day in a park. During the

training period, each participant generated his/her own image with guidance from the

experimenter with the requirements that such imagery would be relaxing and easy to

conjure up to facilitate regulation. Participants were instructed to think of the same image

each time the word Relax was presented, irrespective of type of trial (CS+, CS-). Finally,

participants were also presented with a third strategy, e emotion

regulation strategy, participants were to imagine an exciting scene, such as a roller

coaster ride, in order to increase their arousal.

Financial and control decisions. There were five different financial decisions in

the task (Table 2.1). Each lottery included a risky option with one of five different levels

of probability (0.20, 0.35, 0.50, 0.65, 0.80) and a safe option with an amount equivalent

to the expected value of the gamble (e.g. risky: 20% chance of winning $10.35 or safe:

31

100% chance of winning $2.07). The location (right or left side of screen) of the risky

and safe options was counterbalanced. For CS- trials, participants made decisions

between two stamps with different patterns, with four types of stamps included overall

and presentation location being counterbalanced. The stamp decisions were included as a

control condition for neuroimaging analyses. Participants used a MRI compatible

response unit and used either the index or middle finger of the right hand to make a

decision during both CS+ and CS- trials. Thus, the experiment included six different

types of trials that varied in respect to cognitive strategy (Look, Relax and Excite) and

affective significance of decision (CS+ and CS-). There were 60 CS+ trials with 20 of

each instruction and 30 CS- trials with 15 of each instruction. The five different financial

decisions were repeated 12 times (four times with each instruction).

Behavioral data analysis. quantified as the

proportion of trials in which the risky option was chosen for each instruction type (Look,

Relax, Excite). To examine the effect of regulation (Relax, Excite) on risk-taking, a

repeated-measures ANOVA with type of instruction as a within-subjects factor was

estimated using SPSS.

FMRI acquisition & analysis. Imaging data were acquired using a 3T Siemens

Allegra head-

Center for Advanced Imaging. Structural images were acquired using a T1-weighted

sequence (256 x 256 matrix, 176 1 mm sagittal slice). Functional images were acquired

using a single-shot gradient echo EPI sequence (TR = 2000 ms, TE = 25 ms, FOV = 192

cm, flip angle = 80°, bandwidth = 2604 Hz/px, echo spacing = 0.29 ms). Thirty-five

contiguous (3 x 3 x 3 mm voxels) oblique-axial images were acquired parallel to the AC-

32

PC line. Imaging data analysis was performed with Brain Voyager software (version 1.9:

Brain Innovation, Maastricht, The Netherlands). Data were corrected for excessive

motion (using a cutoff of 2 mm within a run) and slice scan time adjustments were made

using sinc interpolation. Spatial filtering was performed using a three-dimensional

Gaussian filter (4 mm FWHM) while temporal filtering was used with voxel-wise linear

detrending and high-pass filtering of frequencies (three cycles per time-course). Finally,

structural and functional data for each participant were transformed into standard

Talairach stereotaxic space (Talairach & Tournoux, 1988).

A random-effects analysis was performed on the functional data using a general

linear model (GLM) that estimated beta weights for two boxcar predictors (cue phase and

decision phase) and one parametric predictor time-locked to the onset of the decision

phase that varied in accordance to the five levels of probability (0.20, 0.35, 0.50, 0.65,

0.80). This analysis allowed for the non-biased identification of functionally defined

regions of interest (ROIs) that correlated with risk level. Previous studies have examined

neural coding of expected value of rewards (e.g., Knutson & Cooper, 2005) and how this

process is modulated by emotion regulation (Delgado, Gillis, & Phelps, 2008; Staudinger,

Erk, Abler et al., 2009); thus a goal of the current study was to extend that research by

probing neural coding of risk (i.e., probability) information during decision-making and

examining modulation by emotion regulation. Statistical maps were created using the

False Discovery Rate (FDR) method with a threshold of q < 0.01 (Genovese, Lazar, &

Nichols, 2002), and functional ROIs were extracted based on a peak voxel center and a

cluster extent of 10 voxels in all directions. To test for modulation by emotion regulation,

mean parameter estimates (i.e., beta weights) were extracted from the functional ROIs

33

defined by the parametric probability predictor using a second GLM that included 18

different predictors that indicated the instruction (Look, Relax, Excite) and subsequent

choice (risky, safe, stamp) at the time of the cue phase and the instruction (Look, Relax,

Excite) and choice (risky, safe, stamp) at the time of the decision phase. Additionally,

missed trials and six motion parameters were included as predictors of no interest.

Analysis of variance (ANOVA) tests were then performed on the extracted beta weights

to probe the effects of emotion regulation on decision-making under risk during the

decision phase.

2.3 Results

Behavioral results.

Subjective ratings. Subjective ratings of excitement experienced during

presentation of the CS+ (the slot machine) and the CS- (the stamp machine) cues were

acquired throughout the experiment to verify the affective value attributed to CS+ trials.

Specifically, these ratings were collected six times during the experimental task, once

after each of the six experimental blocks of trials, and were independent of the emotion

regulation manipulation (i.e., did not include the instruction words Look, Relax, Excite).

Participants rated how excited they felt when they saw the slot machine and the stamp

machine using a Likert scale (1 = not at all excited; 7 = extremely excited). Using ratings

from all participants a comparison of the averaged ratings was made with a repeated-

measures ANOVA with CS type (slot machine, stamp machine) as a within subjects

factor. Participants felt significantly more excited about the slot machine (M = 5.32, SD

= 0.83) than the stamp machine (M = 2.97, SD = 1.19) during the task, F(1, 29) = 107.07,

34

p = 0.000, suggesting that participants associated the slot machine cue with an

opportunity for reward.

After the scanning session, all participants completed a post-experiment

questionnaire, which addressed whether they had effectively used the two imagery-

focused regulation strategies. Specifically, participants rated how successful they were at

visualizing relaxing imagery using a Likert scale in which 1 = not at all successful and 7

= very successful. Participants also completed this rating for the excite visualization.

These subjective ratings provide an index of regulation success, and they suggest that on

average participants felt fairly successful at the Relax (M = 5.07, SD = 1.76) and Excite

(M = 5.43, SD = 1.48) techniques.

Decision-making. To examine the effect of regulation on risk-taking, a repeated-

measures ANOVA with type of strategy (Look, Excite, Relax) as a within-subjects factor

and success ratings for relax and excite regulation as between-subjects factors was

estimated. Success ratings were included to account for the observed individual

differences in application of the emotion regulation strategies. The ANOVA revealed a

significant main effect of instruction, F(2, 32) = 5.47, p = 0.009, suggesting that

cognitive strategies can influence decision-making under risk. Moreover, a trend that

approached significance for an interaction of instruction and relax success ratings was

observed, F(8, 32) = 2.03, p = 0.07. Specifically, participants who experienced perceived

success in applying the relax strategy chose the risky option less often during Relax

compared to Look trials. The interaction of instruction and excite success ratings was not

significant, however, F(8, 32) = 1.08, p = 0.40. These results suggest that when

presented with a conditioned cue that represents reward, engaging in relax-focused

35

emotion regulation, but not excite-focused emotion regulation, alters subsequent

decision-making.

Given the effectiveness of the relax-focused regulation and the lack of excite-

focused regulation effects, all further analyses excluded the excite condition. To further

probe the observed effect of the relax emotion regulation strategy on risk-taking, we

divided participants into two groups based on their relax visualization success rating.

Participants who rated themselves as successful (ratings of 5 to 7) were considered to be

effective regulators (n = 20), while those that rated their performance as neutral or

unsuccessful (ratings of 1 to 4) were considered to be non-regulators (n = 10). Notably,

participants in the regulators group rated the relax strategy as significantly easier to

implement than participants in the non-regulators group [regulators: M = 6.3, SD = 0.73,

non-regulators: M = 4.5, SD = 1.84; t(28) = 3.85, p = 0.0006].

Using these two groups, the effect of Relax emotion regulation on decision-

making was probed with a repeated-measures ANOVA using type of instruction (Look,

Relax) as a within-subjects factor and group (regulator, non-regulator) as a between-

subjects factor. A significant interaction of type of instruction and group was found, F(1,

28) = 4.20, p = 0.05, suggesting that regulator status influenced the effect of the relax

emotion regulation strategy on decision-making. We then compared the proportion the

risky option was chosen across each instruction type (Look, Relax) for both the regulator

and non-regulator groups separately (Figure 2.2). In the regulators, the proportion that

the risky option was chosen was lower during Relax compared to Look trials, t(19) =

2.19, p = 0.04, suggesting that the successful use of emotion regulation strategies

36

modulated decision-making under risk. This difference in risk-taking between Relax and

Look was not observed in the non-regulators, t(9) = 1.11, p = 0.30.

To ensure the observed change in risk-taking in the regulator group was due to

decreases in risk-taking associated with successful use of the relax emotion regulation

strategy and not increases in risk-taking associated with the Look condition we assessed

decision-making in the absence of any instruction cues. Specifically, participants were

asked to complete a questionnaire with 15 financial decisions, which consisted of the five

financial decisions from the scanner task and two variations (the amounts plus and minus

$0.50). This questionnaire was completed at least one day after the scanning session and

in this follow-up decision-making questionnaire did not differ from those observed in the

Look condition for either group of participants, supporting the main result of decreases in

risky behavior after successful use of the relax emotion regulation strategy.

Decision-making: Reaction time. An ANOVA was performed to probe

differences in reaction time using instruction (Look, Relax) and choice (risky, safe) as

within-subjects variables and group (regulator, non-regulator) as a between-subjects

variable. No significant effects were observed for any of the contrasts, suggesting that

reaction time did not differ as a function of instruction or choice, or across regulators and

non-regulators.

Comparison of regulators and non-regulators on individual differences

measures. All participants completed a series of questionnaires to probe potential

individual differences. As previously described, the post-experimental questionnaire

divided participants into regulators and non-regulators based on their perceived success

37

in using the imagery-focused regulation strategy. While these groups differed with

respect to how emotion regulation influenced their decision-making, we did not find

differences between the groups on any of the individual difference measures we obtained.

Regulators and non-regulators did not show different levels of risk aversion as assessed

by the Holt & Laury (2002) questionnaire, suggesting that the different patterns of

decision-making observed in these groups were not due to different risk preferences.

These groups also did not differ on the subscale scores of the Emotion Regulation

Questionnaire, which assesses use of emotion regulation in daily life, (Gross & John,

2003). Finally, the groups showed no differences in approach- and avoidance-focused

motivation as measured by the Behavioral Inhibition and Activation Scales (Carver &

White, 1994). Although it is possible that these groups may differ in ways not probed by

these selected questionnaires, the results highlight the major difference between the two

groups as their success at visualizing the relaxing imagery.

Neuroimaging results.

All subjects. Neuroimaging analysis focused on the decision phase and sought to

indentify brain regions recruited during decision-making that were modulated by emotion

regulation. Regions of the brain involved in processing risk and reward were identified

using a GLM in which the probability of winning each risky lottery (0.20, 0.35, 0.50,

0.65, 0.80) was included as a parametric regressor time-locked to the onset of the

decision phase. This GLM revealed brain regions whose BOLD signals correlated with

increasing probability of reward, including various structures that have been previously

associated with risky decision-making in humans (see Table 2.2): the striatum with a loci

of activation that extended ventrally (Christopoulos, Tobler, Bossaerts et al., 2009; Hsu,

38

Krajbich, Zhao, & Camerer, 2009; Kuhnen & Knutson, 2005; Matthews, Simmons, Lane

et al., 2004; Tom, Fox, Trepel, & Poldrack, 2007), the midbrain (Tom, Fox, Trepel et al.,

2007), the insula (Kuhnen & Knutson, 2005), and the medial frontal cortex

(Christopoulos, Tobler, Bossaerts et al., 2009; Engelmann & Tamir, 2009).

Modulation of ventral striatum activity by emotion regulation was an a priori

prediction. To test for effects of emotion regulation, a second GLM was applied to the

left ventral striatum ROI to extract mean beta weights. This GLM included cue phase

and decision phase predictors that each specified the type of strategy (Look, Relax,

Excite) and option chosen (risky, safe). The cue and decision phase predictors were

matched in starting time and duration to their task events. The decision phase beta

weights were input into a repeated-measures ANOVA with instruction and choice as

within-subjects factors and success ratings for relax and excite regulation as between-

subjects factors. The ANOVA demonstrated a significant interaction of strategy and

choice, F(2, 30) = 4.70, p = 0.02, and a trend for an interaction of strategy, choice and

relax success rating, F(8, 30) = 1.94, p = 0.09, in the left ventral striatum. Echoing the

behavioral analysis, there were no interactions involving excite success ratings, F(8, 30)

= 1.25, p = 0.30. Post-hoc paired t tests showed that in trials without emotion regulation

(Look), the BOLD response was significantly greater when participants made risky

choices compared to safe ones, t(29) = 2.49, p = 0.02. This heightened natural response

to risky choices was diminished in the relax regulation trials such that there was no

difference in the level of activity for risky and safe choices, t(29) = 0.81, p = 0.42. These

results demonstrate that regulation dampened activity associated with risky choices.

39

Regulators: Shifts in striatum activity with regulation. Given the influence of

the relax emotion regulation strategy on risk-taking observed in the behavioral results,

along with the lack of behavioral or neural effects with the excite regulation strategy,

additional neuroimaging analyses were conducted focusing specifically on the regulators

group defined by relax success ratings. Using the 20 regulator participants, regions

whose BOLD signals correlated with increasing probability of reward were identified

with the parametric GLM described above. Of particular interest are results highlighting

the modulation of both left and right ventral striatum BOLD signals during risky

decision-making by the relax emotion regulation strategy (Figure 2.3A). In the left

ventral striatum (Figure 2.3B), a main effect of strategy, F(1, 19) = 6.85, p = 0.02, a main

effect of choice that approached significance, F(1, 19) = 4.25, p = 0.05, and an interaction

of strategy and choice, F(1, 19) = 6.76, p = 0.02, were observed. Specifically, greater

BOLD signals in the left ventral striatum were observed when participants chose the

risky option compared to when they chose the safe option during trials where they were

responding naturally [Look condition; t(19) = 3.51, p = 0.002], but not after they used

emotion regulation strategies [Relax condition; t(19) = 0.63, p = 0.54] as assessed by

post-hoc paired t tests. BOLD signals were lower during Relax than Look when the

choice was the risky option, t(19) = 2.90, p = 0.009, while no significant effects of

instruction were found when the decision was to take the safe option, t(19) = 0.08, p =

0.94. Finally, beta weights associated with control decisions (CS-), a choice between two

postage stamps, were also obtained. While both Look and Relax instructions were used

in the control trials, no modulation was expected in the ventral striatum as the control

decisions did not involve risky propositions or rewards. As expected, no significant

40

differences between Look and Relax beta weights for the control condition were seen,

suggesting that emotion regulation effects were particular to trials where a risky decision

was presented.

Similar patterns emerged in the right ventral striatum (Figure 2.3C), depicted by a

trend approaching significance for a main effect of strategy, F(1, 19) = 3.70, p = 0.07, a

significant main effect of choice, F(1, 19) = 4.88, p = 0.04, and an interaction of

instruction and choice, F(1, 19) = 7.06, p = 0.02. Greater activity in the right ventral

striatum was observed when participants chose the risky option, compared to when they

chose the safe option during trials in which they acted naturally [Look condition; t(19) =

4.00, p = 0.0008], but not after using emotion regulation [Relax condition; t(19) = 0.31, p

= 0.76]. Additionally, BOLD signals were influenced by strategy; specifically, activity

was lower during Relax than Look when the choice was risky, t(19) = 2.53, p = 0.02, as

observed in the left striatum ROI. Interestingly, when the choice was the safe option, in

the right ventral striatum ROI only, BOLD signals were greater during Relax than Look,

t(19) = 2.18, p = 0.04. There were no emotion regulation effects on the BOLD response

for control decisions (CS- trials). Taken together, these results suggest that the relax

emotion regulation strategy modulated brain activity in the striatum associated with

decision-making under risk, particularly decreasing BOLD responses for risky choices.

An additional analysis was performed to test if a specific level of probability (e.g.,

0.50) was driving the observed pattern of BOLD signals in the striatum. Mean beta

weights were extracted from the left ventral striatum region previously defined by the

parametric analysis of probability of reward using a model that included predictors for

instruction (Look, Relax) and level of probability (0.20, 0.35, 0.50, 0.65, 0.80). These

41

beta weights were entered into a repeated measures ANOVA which revealed a main

effect of strategy, F(1, 19) = 5.46, p = 0.03, and a trend for a main effect of probability,

F(4, 76) = 2.07, p = 0.09. Importantly, this region did not show a significant interaction

of strategy and probability, F(4, 76) = 0.27, p = 0.89. The lack of interaction between

strategy and level of probability coupled with the significant main effect of instruction

suggests that the decreased activity associated with risky choices observed in the Relax

condition is not primarily driven by one particular level of probability in this paradigm.

To probe potential interactions between the ventral striatum and other regions, an

exploratory correlation analysis was performed using the left ventral striatum.

Specifically, a whole brain correlation was conducted using the left ventral striatum ROI

as the seed region, which served to identify regions that may be functionally connected

with the striatum. The resulting statistical parametric map was thresholded at p < 0.01

using conservative Bonferroni corrections for multiple comparisons. A cluster in the

dorsal medial prefrontal cortex, located in the dorsal cingulate cortex (x, y, z, = 2, 7, 42),

was observed to correlate with BOLD signals in the left ventral striatum. A post-hoc test

of this region during the use of emotion regulation strategies was further conducted by

extracting beta weights using a simplified GLM with instruction predictions (e.g., Look,

Relax) during the cue phase. This post-hoc paired t test revealed that beta weights for

Relax (regulation condition) trials tended to be greater than those for Look (no regulation

condition), t(19) = 1.81, p = 0.09. While these results are deemed exploratory, they

suggest that the dorsal cingulate cortex was engaged in emotion regulation and may have

mediated control over the striatum during decision-making.

42

Regulators: Additional regions showing modulation by regulation. Within other

regions that correlated with increasing probability of reward during decision-making,

only regions in the midbrain, insula, and superior frontal gyrus (BA 6; encompassing

premotor cortex and supplementary motor area) were found to be modulated by strategy

and/ or choice in the regulators group. An ANOVA performed with beta weights

extracted from the left midbrain, for instance, showed a significant interaction of strategy

and choice, F(1, 19) = 4.60, p = 0.05, with a pattern of results resembling that of the

striatum (Figure 2.4A). Specifically, when participants chose the risky option, a paired t

test revealed that activity in the regulation condition was significantly lower than that in

the Look condition, t(19) = 2.32, p = 0.03, while no differences for safe choices were

seen, t(19) = 0.38, p = 0.71. In the right midbrain region, a main effect of strategy was

observed, F(1, 19) = 5.16, p = 0.04. During the CS- decisions, BOLD signals in the left

and right midbrain region did not vary as a function of instruction.

A trend for an interaction of strategy and choice and was also observed in the left

anterior insula, F(1, 19) = 3.92, p = 0.06, although a main effect of choice was the

primary effect in this ROI, F(1, 19) = 5.01, p = 0.04 (Figure 2.4B). Interestingly, a

different pattern was apparent in a smaller, more dorsal anterior insula ROI in the left

hemisphere, where a main effect of strategy was observed, F(1, 19) = 5.76, p = 0.03,

characterized by decreased activation during regulation. In both insula ROIs, activity

during CS- decisions was not affected by strategy. Finally, activity in the left superior

frontal gyrus (BA 6) during financial (CS+), but not control (CS-), decisions

demonstrated a main effect of strategy, F(1, 19) = 12.23, p = 0.002, such that activity was

decreased after regulation compared to after natural responding.

43

Non-regulators. Two exploratory analyses were conducted to test for effects of

strategy and choice in the non-regulator sample (n=10). First, using the left ventral

striatum ROI defined by the regulator group parametric risk analysis, we extracted mean

beta weights for the non-regulators with the model that included strategy and choice

predictors. An ANOVA found no significant effects of strategy or choice. Similar

results were found with the right ventral striatum ROI defined by the regulator group risk

analysis. Second, a parametric risk analysis was conducted in the non-regulator group

only, leading to the identification of a left ventral striatum ROI defined by this set of

participants. A follow-up ANOVA on beta weights extracted for this ROI did not show

any significant effects of strategy or choice. Interestingly, the non-regulator group did

not show the same heightened response to risky choices in the striatum that was observed

in the regulator group during the natural response condition (Look).

2.4 Discussion

Previous studies have highlighted how an array of emotion regulation strategies

can be used to alter the intensity of emotional experience (for review, see Green & Malhi,

2006; Ochsner & Gross, 2008). The present study suggests that imagery-focused emotion

regulation strategies influence subsequent decision-making. Specifically, participants

who were successful in their application of the imagery-focused relax regulation strategy

(i.e., regulator group) showed a decrease in risky behaviors; in particular selecting a safe,

compared to a risky monetary lottery more often after regulation. This shift in behavior

during decision-making under risk was accompanied by attenuation in BOLD signals in

the striatum, a structure previously linked with reward-related processing (Delgado,

2007; Haber & Knutson, 2010; O'Doherty, 2004; Rangel, Camerer, & Montague, 2008).

44

In contrast, participants who did not effectively use emotion regulation strategies (i.e.,

non-regulator group) failed to show behavioral or neural differences during decision-

making. While further research is necessary to fully understand the conditions in which

regulation can exert its effect (e.g., individual differences), these findings represent a

potential approach to control decision-making under risk that may become compulsive.

Relationship to previous work. The observed relax regulation results support the

idea that successful use of imagery strategies can foster more goal-directed behavior and

promote safer, compared to riskier decision-making. This is in slight contrast with recent

studies that suggest successful use of emotion regulation can lead one to reduce loss

aversion (Sokol-Hessner, Hsu, Curley et al., 2009) and maximize rewards (Seo & Barrett,

2007). One potential difference between these studies is the type of strategy employed.

The strategy used by Sokol-Hessner and colleagues (2009) was focused on the particular

task at hand, asking participants to place less weight on the outcome of a single decision,

experiment, we used a more general imagery-based strategy previously shown to be

successful in attenuating the physiological and neural correlates of conditioned fear

(Delgado, Nearing, LeDoux et al., 2008) and the expectation of reward (Delgado, Gillis,

& Phelps, 2008). While both strategies can be considered a form of cognitive control,

they might exert different influences in the underlying neural circuitry, as observed in

studies comparing reappraisal and distraction strategies during negative emotions

(Kalisch, Wiech, Herrmann et al., 2006; K. McRae, Hughes, Chopra, Gabrieli, Gross, &

Ochsner, 2010), that could cause different effects in behavior. This is an interesting

question that will be addressed in Experiments 2 and 3.

45

Regulator and non-regulator groups. Individual differences with respect to the

effective use of the imagery-based strategy were observed in the current experiment as

measured by post-experimental ratings. A regulator group was defined by perceived

success in applying the Relax strategy, while a non-regulator group comprised

participants who felt they were unable to successfully implement the Relax strategy.

Differences between these two groups were apparent in subjective ratings (how easy was

it to implement strategy), behavioral responses (picking between safe and risky options)

and neural signals (striatum responses during decision-making under risk). Of particular

interest, participants in the regulator group made fewer risky choices than their

counterparts. This behavior was not due to an inherent risk aversion, as both groups risk

preferences did not differ according to a paper test assessment (Holt & Laury, 2002).

Instead, this shift in behavior could be attributed to the successful use of cognitive

strategies.

This decrease in risk-taking with regulation was not observed in the group of self-

assessed non-regulators. Neither was the modulation of striatum activation by the

imagery strategy, consistent with previous studies suggesting that striatum signals during

decision-making can correlate with success in task performance (Schonberg, Daw, Joel,

& O'Doherty, 2007). The non-regulators did not show increased BOLD signal for risky

compared to safe choices during natural responding. This lack of difference could

indicate that the risky choices represented lower reward values for the non-regulators. It

is possible that no regulation-associated changes were observed in the striatum of non-

regulators, because there was no risky-safe difference during Look. While these null

findings should be interpreted with caution given the nature of null findings in fMRI

46

analysis and the small sample size of the non-regulator group (n = 10), the observations

are in line with the non- -reports and behavioral results.

Perhaps a different kind of regulation strategy such as reappraisal would have

been more affective at altering decision-making in the non-regulators. The non-

regulators rated the imagery strategy as significantly more difficult than the regulators,

suggesting employing imagery regulation may have been challenging. It is unlikely that

the non-regulators are simply unable to regulate reward cues, but rather the imagery

strategy may not have been the best fit or they may have needed additional training.

Finally, it is possible that fatigue contributed to the non-

using the imagery regulation, as the task duration was about 40 minutes.

While the regulators and non-regulators did not differ in any individual measures

used in our study, only a few individual differences measures were assessed. Additional

research may probe potential differences that allow some to exert better control over their

decisions. For instance, are there specific traits, or perhaps more likely, do certain

situational factors (e.g. type of strategy attempted, amount of effort applied) determine

whether a person will be able to successfully employ regulation?

Neural correlates of risk and regulation. The current study found that activity

in ventral striatum of regulators was influenced by the use of cognitive regulation, in

accordance with previous research (Delgado, Gillis, & Phelps, 2008; Staudinger, Erk,

Abler et al., 2009). Yet, such studies focused mostly on reward expectations and

learning, while the current paradigm focuses on the role of emotion regulation on

decision-making under risk. The human striatum is often identified during investigations

of reward and risky decision-making (Christopoulos, Tobler, Bossaerts et al., 2009;

47

Kuhnen & Knutson, 2005; Matthews, Simmons, Lane et al., 2004), showing greater

responses as expected reward values increase (Knutson & Cooper, 2005; Tom, Fox,

Trepel et al., 2007; Yacubian, Glascher, Schroeder, Sommer, Braus, & Buchel, 2006) and

patterns of activity that suggest processing of reward probabilities (Abler, Walter, Erk,

Kammerer, & Spitzer, 2006; Hsu, Krajbich, Zhao et al., 2009; Yacubian, Sommer,

Schroeder, Glascher, Braus, & Buchel, 2007). Building on previous research that

suggests expected value-related reward activity in the striatum is modulated by emotion

regulation (Delgado, Gillis, & Phelps, 2008; Staudinger, Erk, Abler et al., 2009), we

chose to model reward probability in our analyses to probe the role of the striatum in

probability (risk) coding during decision-making and the effects of emotion regulation on

this process. In our experiment, during the decision phase for regulation trials, regulators

showed decreased activity in the ventral striatum overall, and especially during trials

where a risky choice was made, suggesting that effective regulation can dampen the

natural heightened response to decisions involving risk.

The BOLD signal observed in the striatum during the decision-phase may reflect

deliberation with respect to the two options, the choice itself and a reaction to the choice

made. While our model accounted for increasing probability of reward, the magnitude of

the options might have influenced neural activity. The expected value of the risky and

safe options was equated, but the magnitude of the risky option was always higher. Thus,

the increased activity observed in ventral striatum for risky relative to safe choices during

Look trials could perhaps be explained by the greater magnitude of the risky option, in

turn suggesting that the lack of differentiation between risky and safe choices by the

ventral striatum during regulation may indicate regulation-induced disruption of the

48

ability to code reward magnitude. This interpretation is in line with a recent paper that

found that distance-focused regulation disrupted expected-value coding in the ventral

striatum such that during regulation trials ventral striatum activity failed to differentiate

high and low magnitude cues (Staudinger, Erk, Abler et al., 2009). Nevertheless,

participants who successfully employed cognitive strategies prior to making financial

decisions made fewer risky choices and showed attenuated BOLD signals in the striatum.

Whether emotion regulation specifically affects the coding of the magnitude of potential

rewards or the perception of probability (risk) inherent in the decision process is a topic

for further exploration that will continue to advance our understanding of the ability to

control emotional responses for adaptive function.

The ventral striatum is an integral component of a corticostriatal circuit involved

in motivated behaviors (Alexander & Crutcher, 1990; Balleine, Delgado, & Hikosaka,

2007; Haber & Knutson, 2010; Middleton & Strick, 2002), with important connections

with cortical regions, such as orbitofrontal cortex and the anterior cingulate cortex (Haber

& Knutson, 2010). Given the connectivity of the ventral striatum, it is plausible that the

observed decrease in ventral striatum activity in the regulator group during decision-

making after using emotion regulation may have been driven in part by cortical signals.

An exploratory analysis revealed that BOLD signals in dorsal cingulate cortex (BA 24)

correlated with those from the left ventral striatum, suggesting a potential functional

connectivity that may underlie the control of striatum responses during decision-making.

Further analysis of the BOLD response within this cingulate region revealed a trend for

greater recruitment during the use of regulation strategies than natural responding, which

is consistent with findings from previous emotion regulation studies (Eippert, Veit,

49

Weiskopf, Erb, Birbaumer, & Anders, 2007; Kim & Hamann, 2007; Ochsner, Bunge,

Gross et al., 2002; Phan, Fitzgerald, Nathan et al., 2005; Staudinger, Erk, Abler et al.,

2009). Although this analysis is exploratory and thus results should be interpreted with

caution, the findings point to the recruitment of cortical regions such as the dorsal

cingulate cortex during regulation as potential modulators of striatal responses during

decision-making.

In addition to the striatum, the effective use of regulation led to attenuation of

BOLD signals in the midbrain and insula. The midbrain results are particularly

interesting given that it includes dopaminergic centers that project to the striatum (Haber

& Knutson, 2010), and much like the striatum, BOLD signals in the midbrain increase in

conjunction with increasing reward values during decision-making under risk (Tom et al.,

2007), suggesting that cognitive strategies can have a global impact on the neurocircuitry

involved in reward and decision-making. Regulation strategies also had an effect in the

insula, a region implicated in risky decision-making (Clark, Bechara, Damasio, Aitken,

Sahakian, & Robbins, 2008; Kuhnen & Knutson, 2005) perhaps coding different levels of

risk (Preuschoff, Quartz, & Bossaerts, 2008). Specifically, a marginal interaction of

instruction and choice was seen in the anterior insula, while an instruction effect was

observed in a more dorsal anterior insula ROI. Future research is needed to fully

characterize the anatomical and functional dissociations within the insula as a function of

emotion regulation during decision-making.

Excite condition. This study employed two opposite imagery-focused regulation

strategies, Relax and Excite. No significant shifts in risk-taking or neural activity were

associated with the Excite strategy. There are several possible explanations for why we

50

did not observe an effect of excite regulation on decision-making, given that relax

regulation did influence decision-making. Although the majority of participants rated

themselves as successful at visualizing the exciting imagery, they also reported the need

to periodically update the exciting images that they thought about as over time these

images lost their potency. Additionally, there were some conflicts between what

participants wanted to imagine (e.g., Las Vegas casinos) and the instruction to think of

something non-task specific (e.g., a roller coaster). Finally, it is possible that the level of

excitement achieved with the Excite strategy may have been comparable to that which

participants experienced when naturally responding to the slot machine cue. If similar

affect was associated with the Excite and Look condition, that could underlie the lack of

observed differences in risk-taking between these conditions. Future work could address

this question by including affect ratings during the task.

Conclusions. Emotion regulation strategies have been traditionally used to

control emotional responses induced by stimuli such as pictures, movies or narratives that

evoke negative affect (for review, see Ochsner & Gross, 2008). More recently, such

strategies have also been applied to positive emotions evoked by pictures, food stimuli or

cues that predict reward (Delgado, Gillis, & Phelps, 2008; Hare, Camerer, & Rangel,

2009; Kim & Hamann, 2007; Staudinger, Erk, Abler et al., 2009; Wang, Volkow, Telang,

Jayne, Ma, Pradhan et al., 2009). Here, we extend these findings by focusing on the

influence of emotion regulation strategies on decision-making processes and associated

neural circuits. This research has applications ranging from simple decisions such as

dieting (e.g., Hare, Camerer, & Rangel, 2009) to more complex decisions where goal-

directed and habit learning systems may be at conflict, such as substance abuse (e.g.,

51

Balleine & O'Doherty, 2010; Everitt, Belin, Economidou, Pelloux, Dalley, & Robbins,

2008; Nelson & Killcross, 2006).

52

Chapter 3

The Effect of Imagery and Cognitive Emotion Regulation Strategies on Decision-

Making

3.1 General Introduction and Hypotheses

In daily life, appetitive cues signal the opportunity to gain rewards and promote

approach behaviors, actions aimed at increasing our chances of obtaining a reward.

Often larger rewards may be gained by taking a risk. For example, a billboard

advertising the current lottery jackpot may prompt us to consider buying a lottery ticket.

We compute the value of the available options, giving up a dollar for a small chance to

win millions or keeping the dollar, and determine whether the risk is worth taking. Thus,

our behavior, specifically our decision-making, is influenced by the value of available

rewards (Rangel, Camerer, & Montague, 2008)

decisions in a neutral emotional state; our affective and cognitive responses to the

rewards also influence our decision-making. The excitement evoked by seeing value of

the jackpot might cause us to overlook our low odds of winning the lottery. Fortunately,

we have the unique ability to alter our responses to salient cues using a variety of emotion

regulation strategies (e.g., Ochsner & Gross, 2008). For instance, when faced with an

emotional stimulus we can alter our affective response to it by using an imagery-focused

strategy, imagining a calming scene instead of focusing on the stimulus itself (Delgado,

Gillis, & Phelps, 2008; Delgado, Nearing, LeDoux et al., 2008), or by employing a

cognitive reappraisal strategy, changing our description of the stimulus to decrease or

increase its impact.

53

Experiment 1 and other recent research has demonstrated that these emotion

regulation strategies can be applied in decision-making contexts (Heilman, Crisan,

Houser, Miclea, & Miu, 2010; Martin & Delgado, 2011; Sokol-Hessner, Hsu, Curley et

al., 2009). However, it remains unclear whether imagery and reappraisal regulation

strategies produce the same effects on financial decision-making, specifically risk-taking.

Additionally, it is not known whether the effects of emotion regulation on risk-taking are

independent of or interact with the value of the decision options. The present

experiments address these two questions by examining the effects of two regulation

strategies, imagery-focused (Experiment 2) and reappraisal (Experiment 3), on risk-

taking in financial decisions in which the value of the two options is systematically

varied.

Two specific types of antecedent-focused (Gross, 2002) emotion regulation

strategies, imagery-focused (Delgado, Gillis, & Phelps, 2008; Delgado, Nearing, LeDoux

et al., 2008) and cognitive (e.g., Ochsner, Bunge, Gross et al., 2002; Ochsner & Gross,

2008) have been used with decision-making paradigms. Experiment 1 of this dissertation

probed shifts in financial decision-making associated with imagery-focused regulation

(Martin & Delgado, 2011). Participants visualized a relaxing image such as a beach

sunset when faced with cues signaling upcoming financial decisions. Participants who

felt successful at visualizing the imagery reduced their risk-taking (Martin & Delgado,

2011). Using a different financial task, Sokol-Hessner and colleagues found that

successful use of a cognitive technique, attempting to think like a stock trader assembling

a portfolio, during decision-making reduced risk-aversion and increased risk-taking

(Sokol-Hessner, Hsu, Curley et al., 2009). These studies examined regulation of

54

emotions and thoughts that were putatively elicited by the opportunity for money rewards

and the decision-making task.

Incidental emotions, those unrelated to the decision-making task, also can

influence behavior (Lerner, Small, & Loewenstein, 2004; Winkielman, Berridge, &

Wilbarger, 2005). One study that investigated the regulation of incidental emotions

showed that participants who used reappraisal while viewing disgust and fear movie clips

showed increased risk-taking in a subsequent financial risk-taking task, the Balloon

Analogue Risk Task (BART), compared to participants who engaged in suppression or

did not use emotion regulation during the movies (Heilman, Crisan, Houser et al., 2010).

As the financial risk-taking tasks and regulation strategies differed across these studies, it

remains unclear whether different types of regulation strategies affect decision-making in

a similar manner and which strategy is most effective for a given context. Additionally,

it is unknown if the effects of regulation on risk-taking would persist in more complex

financial decision contexts, for instance those in which a risk is worth taking.

Evidence from previous research suggests that reappraisal and imagery-focused

strategies may have different effects on behavior. Although reappraisal and imagery-

focused regulation have yielded similar decreases in affective responses in non-decision-

making studies, they were tested on different types of affective stimuli, pictures and

conditioned cues, and with different dependent measures, emotion experience self-reports

and skin conductance physiological measures. Moreover, imagery-focused regulation

(Delgado, Gillis, & Phelps, 2008; Delgado, Nearing, LeDoux et al., 2008; Martin &

Delgado, 2011) and reappraisal (Gross, 1998a; Ochsner & Gross, 2008) are

fundamentally different in two important ways. First, reappraisal involves focusing

55

attention and thoughts on the affective stimulus, whereas imagery-focused regulation

involves directing attention internally instead of at the affective stimulus. Second,

reappraisal involves decreasing (or increasing) the emotional response elicited by the

stimulus, whereas imagery-focused regulation involves creating a new emotion, typically

a feeling of calm, instead of directly altering the emotion elicited by the stimulus.

In the current experiments, these two strategies were applied to cues that preceded

financial decisions. Given the different mechanisms of imagery and reappraisal

regulation, it is unclear whether these strategies will elicit the same shifts in risk-taking.

Additionally, it is not known if emotion regulation will lead to global shifts in risk-taking

regardless of the value of the decision options, or if regulation effects will interact with

the value information. One way to assign value to each option is to calculate the

expected value of each decision option, the product of the probability of winning and the

amount to be won, for instance, a 50% chance of winning $10 has an expected value of

$5. Experiment 1 found that the imagery strategy decreased risk-taking (Martin &

Delgado, 2011); however, the design did not manipulate the relative expected values of

the decision options, leaving the relationship between value information and emotion

regulation untested.

In the current experiments, the financial decisions consisted of binary choices

between risky and safe options. Additionally, three different types of decisions were

created by manipulating which option, risky or safe, had the greatest EV: risky > safe,

safe > risky, and risky = safe. The type of regulation strategy was manipulated between

experiments. In Experiment 2 participants used imagery-focused strategies, and in

Experiment 3 a separate group of participants employed cognitive reappraisal strategies.

56

As in Experiment 1, the imagery participants engaged in regulation by visualizing a

relaxing scene in their mind (Relax), rather than focusing on the upcoming decision itself.

The reappraisal participants engaged in regulation during cue presentation by increasing

(Emphasize) or decreasing (Deemphasize) the subjective importance of the upcoming

decision by changing their thoughts. As in Experiment 1, both experiments included a

control condition (Look) in which participants responded naturally to the cue.

Additionally, the same set of decisions was used in both experiments, so that we could

make exploratory comparisons between the two strategy categories. The dependent

variable of interest was a measure of risk-taking, the proportion of time that the risky

option was chosen, as in Experiment 1. Physiological measures of general arousal, skin

conductance responses (SCRs), were collected during cue presentation to assess if

emotion regulation altered physiological responding.

We hypothesized that decision type and emotion regulation would exert

independent effects on risk-taking. We expected that participants' decision-making

would be affected by the different decision types such that risk-taking would be highest

when the EV of the risky option was greater than that of the safe option and lowest when

the EV of the risky option was less than that of the safe option. Although the reappraisal

and imagery strategies involve different psychological mechanisms, we predicted both

would alter risk-taking relative to the no regulation condition. With regard to the

imagery regulation, we hypothesized that relax regulation would bias choices in favor of

the safe options regardless of their EV. If this result was observed, it would suggest that

the imagery regulation simply decreases risk-taking. However, if the imagery regulation

biases choices in favor of the option with the greater value, regardless of whether that

57

option is risky or safe, this result would suggest that this strategy fosters more optimal

decision-making. Understanding the fundamental nature of imagery regulation will allow

for better real world application of this strategy. For Experiment 2, we hypothesized that

the reappraisal strategies would alter decision-making in opposite ways. Specifically,

regardless of the decision type, Emphasize reappraisals would decrease risk-taking and

Deemphasize reappraisals would increase risk-taking relative to no regulation. With

regard to physiological responses, we predicted that imagery regulation would decrease

SCRs relative to natural responding in Experiment 2. In Experiment 3, we hypothesized

that Emphasize regulation would increase SCRs and Deemphasize regulation would

decrease SCRs relative to natural responding.

3.2 General Methods

Procedure overview. Participants completed the experimental task on a PC

computer in a testing room at Rutgers University. Prior to starting the task, participants

received instructions for the game and were trained on in applying the emotion regulation

technique. During the experimental task skin conductance measurements were collected

to assess general arousal responses to the reward cues. The experimental task, the Slot

Machine Game (SMG), was adapted from that used in Experiment 1 and was

programmed using E-Prime (Psychology Software Tools). Briefly, the SMG consisted of

the presentation of a strategy word that prompted natural responding or regulation when

faced with the cue, a slot machine image signaling the opportunity to win money, and

finally, a financial decision between two options. There were two versions of the SMG,

which differed in the regulation techniques they involved. The imagery SMG

58

(Experiment 2) employed the imagery-focused regulation strategy used in Experiment 1

and previous work (Delgado, Gillis, & Phelps, 2008; Delgado, Nearing, LeDoux et al.,

2008). The reappraisal SMG (Experiment 3) employed two reappraisal strategies, which

were designed for this study and adapted from previous work (e.g., Ochsner et al., 2004,

Sokol-Hessner et al., 2009). After the task was complete, participants completed several

questionnaires, were debriefed, awarded research credits and paid.

The Slot Machine Game (SMG). In the SMG, each trial started with the

presentation of a strategy word for a fixed duration of 2 s followed by the cue phase,

which was the presentation of a reward cue (slot machine) for a variable duration of 4 or

6 s. The cue phase was followed by the decision phase, in which participants had a

maximum of 4 seconds to indicate their choice by pressing a button. The decisions were

always between two monetary options: a gamble (risky option, e.g., 50% chance of

winning $8.41) and a guaranteed amount (safe option, e.g., 100% chance of winning

$4.20) that varied with respect to probability and magnitude both within and across

decisions. A 4 or 6 s inter-trial interval (display of a fixation cross in the center of the

screen) followed the decision phase.

To create different decision types we manipulated the expected value (EV)

difference between the two options. Decisions consisted of options of equal EV (neutral

condition), greater EV for the risky option (good condition), or greater EV for the safe

option (bad condition); See Table 3.1 for a list of the financial decisions used.

Participants were not explicitly made aware of these contingencies. There were nine

unique financial decisions in the task, one of each combination of decision type (neutral,

good, bad) and level of risk (0.35, 0.50, 0.65). The location, right or left side of screen,

59

of the risky and safe options was counterbalanced. Participants responded using the

keyboard; the 1 key indicated a choice of the left option, the 2 key the right option.

The strategy word presented at the start of a trial directed participants either to

respond naturally to the slot machine, that is, to think about the decision coming up and

regulation, either imagery-focused (Experiment 2) or reappraisal (Experiment 3).

Prior to beginning the experimental task, all participants were extensively trained

on the task instructions, especially the application of the emotion regulation techniques.

They were informed that a slot machine picture would serve as the cue signaling an

upcoming decision and opportunity to win money. They were instructed that the word

presented just before the slot machine would serve as the strategy for that trial.

Importantly, in both Experiment 2 and Experiment 3 efforts were taken to reduce or

eliminate any experimental demand. Participants were directed to engage in the strategy

for only the duration of the slot machine image (reward cue) presentation; once the

decision options appeared, participants were to focus on the decision and make a choice.

Participants were explicitly told that no matter what word they saw at the beginning of

the trial, the decisions were always up to them, and they were free to choose however

they liked.

Monetary incentives and payment. Participants did not receive immediate,

within-task feedback about the outcomes of their decisions. To ensure the perception that

each decision was independent and significant, two randomly chosen decisions were

realized at the end of the experimental session, and participants were informed during

training that they would be paid based on their choices in two randomly selected

60

decisions. Participants were compensated whatever amount they won, with a minimum

possible compensation of $0 and a maximum possible compensation of $20. At the end

of the experiment, participants randomly selected two decisions, and they were paid

based on the option they chose in each decision. Participants in Experiment 2 received an

average of $7.75 (SD = $3.51), and participants in Experiment 3 received an average of

$8.11 (SD = $3.11).

Questionnaires. In addition to the SMG, all participants completed several

questionnaires including a post-experimental questionnaire that assessed compliance with

the emotion regulation demands and measured perceived successful use of cognitive

strategies. After the SMG, all participants completed questionnaires that assessed

individual differences that may be related to risk-taking or successful emotion regulation:

measures of risk preferences (Holt & Laury, 2002), use of emotion regulation strategies

(Emotion Regulation Questionnaire; Gross & John, 2003), behavioral inhibition and

activation (BIS/BAS; Carver & White, 1994), hope (the Hope Scale; Snyder, Harris,

Anderson, Holleran, Irving, Sigmon et al., 1991) and emotional intelligence (Trait Meta-

Mood Scale; Salovey, Mayer, Goldman, Turvey, & Palfai, 1995). Additional individual

differences measures were completed as part of an online survey prior to the

experimental session: differentiation of emotions (Kang & Shaver, 2004), self-esteem

(Rosenberg, 1989), social support (Social Provisions Scale; Cutrona & Russell, 1987) and

big five personality traits (Ten-Item Personality Inventory; Gosling, Rentfrow, & Swann,

2003). From these questionnaires, these 11 scores were used for the individual

differences analyses described below: risk preferences, ERQ reappraisal, ERQ

61

suppression, BAS reward, Hope agency, Hope pathways, TMMS repair, DOE range,

DOE openness, self-esteem, and TIPI emotional stability.

Skin conductance data collection and analysis. Skin conductance responses

(SCRs) were collected using the BIOPAC systems skin conductance module (BIOPAC

second and third fingers in their non-dominant hand. AcqKnowledge software was used

to analyze SCR waveforms (BIOPAC Systems, Goleta, CA). SCR waveforms were low-

pass filtered using a Blackman window (cutoff frequency = 31 Hz) and smoothed over

three consecutive data points prior to scoring. The level of SCR was assessed as the base

to peak difference for an increase in the 0.5 to 4.5 s window following the onset of the

reward cue (LaBar, LeDoux, Spencer, & Phelps, 1995). A minimum response criterion

of 0.02 microSiemens was used, and all responses below this criterion were scored as 0.

Responses were square-root transformed prior to statistical analysis to reduce skewness

(LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998). Participants that showed a low level

of responses (less than one standard deviation from the average number of responses)

were excluded from further analysis. Acquired SCRs for each participant were averaged

per type of trial (regulation or no regulation condition). Repeated measures ANOVA was

used to test for effects of regulation on physiological responding.

Data analysis. The SMG task included two independent variables: strategy type

(natural responding or regulation) and decision type (neutral, good, bad). There were

quantified as the average proportion that the risky option was chosen. Choice data were

analyzed to test for effects of strategy type and decision type on risk-taking using a

62

repeated-measures analysis of variance (ANOVA) in SPSS. Given that Experiment 1

found that self-reported success with employing the regulation strategy influenced the

risk-taking results, an additional ANOVA was conducted using the regulation success

ratings from the post-experiment questionnaire as a covariate.

Previous research found that women show greater emotional responses than men

(Kring & Gordon, 1998), and because of these differences in emotional expression many

studies of emotion regulation have used only women (Goldin, McRae, Ramel et al., 2008;

Kim & Hamann, 2007; K. McRae, Hughes, Chopra et al., 2010; Ochsner, Bunge, Gross

et al., 2002; Ray, Ochsner, Cooper, Robertson, Gabrieli, & Gross, 2005). There was one

study that included both genders and directly tested for gender differences, but only

neural differences were observed; regulation decreased negative emotions to a similar

degree in men and women (Kateri McRae, Ochsner, Mauss, Gabrieli, & Gross, 2008).

Given the limited data comparing emotion regulation effects in men and women and a

sample of men and women large enough to compare the genders, we performed

exploratory ANOVAs with strategy and decision type as within-subjects variables and

gender as a between-subjects variable.

A secondary goal of the study was to assess relationships between scores on the

11 individual difference measures and shifts in risk-taking associated with regulation. To

determine these relationships, regulation scores (RegScore) were calculated for each

participant by subtracting the proportion the risky option was chosen in the regulation

condition from Look for each decision type (good, bad, neutral). These RegScores were

63

3.3 Experiment 2: The effect of imagery-focused regulation on risk-taking across

different financial decisions

3.3.1 Methods.

Participants. Thirty-seven undergraduate volunteers participated in this study (19

females, 18 males). One participant was excluded due to failure to understand the

contingencies of the financial decisions (assessed by post-experimental questionnaire).

Final analysis was conducted on 36 participants (18 females; age: M = 20, SD = 3.6).

Participants completed this experiment at Rutgers University for Psychology course

credit and gave informed consent according to the Rutgers University Institutional

Review Board for the Protection of Human Subjects in Research.

Imagery SMG. Participants completed the imagery SMG (Figure 3.1), which

consisted of 72 total trials divided into 4 blocks of 18 trials each. In the imagery SMG,

-focused regulation, which

involved imagining a calming scene instead of focusing thoughts on the reward cue.

Training in the imagery SMG involved showing participants example pictures of calming

scenes (a sunny day in the park, the beach), asking them to generate their own images,

verifying these images were appropriate (i.e., not exciting or related to the task), and

instructing them not to close their eyes during the imagery. Each trial in the imagery

SMG consisted of presentation of a strategy word, Look or Relax, a financial decision,

and an inter-trial interval. The timing and other details of the imagery SMG were as

described in the General Methods section. After the SMG, participants completed several

questionnaires, as described in the General Methods.

64

3.3.2 Results.

Subjective ratings: Regulation success. After completing the experimental task,

participants completed a post-experiment questionnaire which addressed whether they

had effectively used the imagery strategy. Participants rated how successful (1 = not at

all successful and 7 = very successful) they were at using the relax imagery. Participants

who rated their performance 5-7 were considered to be effective regulators (n = 29) while

those that rated their performance 1-4 were considered to be non-regulators (n = 7).

While there were individual differences in perceived self-reported success, on average

participants (M = 5.39, SD = 1.20) felt somewhat successful.

Decision-making.

the risky option was chosen. To test the effects of strategy and decision type on risk-

taking, a repeated measures ANOVA was performed with strategy (look, relax) and

decision type (neutral, good, bad) as within-subjects variables (Figure 3.2). The ANOVA

revealed only a main effect of decision type, F(1.50, 52.83) = 72.91, p = 0.001

Greenhouse-Geisser corrected. Paired t tests demonstrated that participants were

sensitive to the EV of the options as risk-taking was greatest for good decisions and

lowest for bad decision types (good: M = 0.68, SD = 0.28; neutral: M = 0.38, SD = 0.26;

bad: M = 0.20, SD = 0.17). There was no main effect of strategy, F(1, 35) = 0.03, p =

0.87, or interaction of strategy and decision type, F(2, 70) = 0.52, p = 0.60.

Experiment 1, which used the same imagery strategy in a decision-making context

found that perceived success at visualizing the relaxing imagery was necessary to observe

shifts in risk-taking (Martin & Delgado, 2011). Given the observed individual

differences in imagery success ratings in the current study, an additional ANOVA was

65

performed that included the relax success ratings as a covariate. This ANOVA identified

only a significant main effect of decision type, as in the primary ANOVA, F(1.51, 51.32)

= 3.54, p = 0.048 Greenhouse-Geisser corrected. There was no significant interaction of

strategy and success rating, F(5, 34) = 1.15, p = 0.29, or of strategy, decision type and

success rating, F(2, 68) = 0.10, p = 0.90.

Gender effects. To probe for gender effects, an ANOVA with strategy (Look,

Relax) and decision type (neutral, good, bad) as within-subjects variables and gender as a

between-subjects variable was conducted. The ANOVA revealed a main effect of

decision type, F(1.50, 50.88) = 74.68, p = 0.001, and an interaction of strategy, decision

type and gender, F(2, 68) = 3.47, p = 0.04. To explore this three-way interaction, t tests

were conducted. The Look and Relax conditions were compared for each decision type

in men and women separately. There were no significant effects of regulation in men for

bad, t(17) = 0.39, p = 0.70, good, t(17) = 0.78, p = 0.44, or neutral decisions, t(17) = 0.17,

p = 0.86 decisions. There were no significant effects of regulation in women for bad,

t(17) = 1.53, p = 0.15, good, t(17) = 0.91, p = 0.37, or neutral decisions t(17) = 1.59, p =

0.13. Comparisons of risk-taking in men and women for each condition did reveal

differences. Specifically, men took significantly more risks than women in two

conditions, Relax Bad, t(17) = 2.25, p = 0.04, and Relax Good, t(17) = 2.55, p = 0.02.

While males took more risks in certain task conditions, there was no significant main

effect of gender on risk-taking, F(1, 34) = 2.39, p = 0.13.

Individual differences. Since no significant effects of emotion regulation on risk-

taking were observed, it was not appropriate to test for relationships between shifts in

risk-taking and individual differences.

66

Reaction time. A repeated measures ANOVA was performed to test for effects of

strategy (Look, Relax) and decision type (good, bad, neutral) on reaction times for the

decision phase. The ANOVA revealed only a main effect of decision type that

approached significance, F(1.71, 60.10) = 2.73, p = 0.081 (Greenhouse-Geisser

corrected). The main effect of strategy, F(2, 35) = 0.20, p = 0.66, and the interaction of

strategy and decision type, F(2, 70) = 0.74, p = 0.48, were not significant. Reaction times

were fastest for bad decisions and slowest for good decisions (bad: M = 1902.64, SD =

324.70, neutral: M = 1962.34, SD = 373.17, good: M = 1999.42, SD = 315.07).

Skin Conductance. To probe for effects of regulation on physiological

responding, SCRs were input into a one-way repeated measures ANOVA with strategy

(Look, Relax) as the factor. This ANOVA did not find a significant effect of strategy,

F(1, 31) = 1.52, p = 0.23. However, participants may have habituated to the cue, as the

same slot machine image was repeated throughout the task. A second ANOVA was

performed using SCRs to the cues presented in the first half of the task (Blocks 1 and 2)

only. This ANOVA also did not find any effect of strategy, F(1, 31) = 1.48, p = 0.23.

3.3.3 Discussion. This experiment examined the influence of imagery regulation

on risk-taking in a financial decision-making task. Additionally, this study tested the

effect of different decision types, manipulated by varying which option, risky or safe, had

the greatest expected value, on risk-taking. No effect of regulation was observed, but

there was a strong effect of decision type on risk-taking. Experiment 1 used the same

imagery strategy, and found that it resulted in reduced risk-taking for those participants

who felt successful. There are several important differences between the two experiment

67

paradigms, which may account for the current lack of a regulation effect. The previous

study employed a different financial decision set, one that contained only choices

between options with equal expected value (like the neutral decisions in the current

studies). The presence of decisions in which it was more advantageous to take a risk

changed the overall decision context in the current study. Given the more complex level

of information to be considered at the decision phase in the current study compared to the

previous one, it is likely that greater attentional resources were needed to make a

decision. The imagery strategy theoretically affects decision-making by focusing

thoughts away from the decision and creating a feeling of calm during the cue phase just

before the decision phase. In the previous study, this effect of regulation carried over to

the decision phase; however, in the current study, the greater need to focus on the details

of the decision options may have overridden the effects of regulation. Another possibility

is that it may have been more difficult to engage in the relaxing imagery during the

reward cue in the current study, because the specific options that would follow the cue

were uncertain, and participants knew that they were going to face real consequences for

two of their decisions. It seems that the imagery strategy may be best suited for simple

decision contexts.

Unlike the imagery strategy, the cognitive strategies tested in the next Experiment

involved focusing on the upcoming decision. The cognitive strategies were applied to the

same reward cue and set of decisions that were used in Experiment 2. This consistency

allowed for general comparison of the efficacies of the two strategies. Given the greater

68

task-focus of the cognitive reappraisal strategies, will they produce significant shifts in

risk-taking?

3.4 Experiment 3: The effect of cognitive emotion regulation strategies on risk-

taking across different financial decisions

3.4.1 Methods.

Participants. Thirty-six undergraduate volunteers participated in this study (18

females, 18 males; age: M = 22, SD = 6.1). Participants completed this experiment at

Rutgers University for Psychology course credit and gave informed consent according to

the Rutgers University Institutional Review Board for the Protection of Human Subjects

in Research.

Reappraisal SMG. The reappraisal SMG consisted of 108 total trials divided into

4 blocks of 27 trials each (Figure 3.3). The reappraisal SMG involved two different

reappraisal strategies, which prompted participants to change their thoughts about the

reward cue and t

prompted participants to think that the upcoming decision was very important, and they

participants to think of the upcoming decision as one of many opportunities to win

money, and that it did not matter that much what happened on this particular one.

Training in the reappraisal SMG involved giving participants example statements

they could think for each reappraisal strategy. For Emphasize they were given examples

69

ing up is not a big deal; I will have other chances; even if I

presentation of a strategy word, Look, Emphasize, or Deemphasize, a financial decision,

and an inter-trial interval. The timing and other details of the reappraisal SMG were as

described in the General Methods section. After the SMG, participants completed several

questionnaires, as described in the General Methods.

3.4.2 Results.

Subjective ratings: Regulation success. After completing the experimental task,

all participants completed a post-experiment questionnaire which addressed whether they

had effectively used the regulation strategies. Participants rated how successful (1 = not

at all successful and 7 = very successful) they were at using the two reappraisal strategies.

Participants who rated their performance 5-7 were considered to be effective regulators,

Emphasize n = 26, Deemphasize n = 21, both n = 17, while those that rated their

performance 1-4 were considered to be non-regulators: Emphasize n = 10, Deemphasize

n = 15, both n = 6. While there were individual differences in perceived self-reported

success, on average participants felt somewhat successful (Emphasize: M = 5.17, SD =

1.34, Deemphasize: M =4.86, SD = 1.84), and there was no significant difference

between reported success for Emphasize and Deemphasize, t(35) = 1.01, p = 0.32.

Decision-making.

the risky option was chosen. To test the effects of strategy and decision type on risk-

taking, repeated measures ANOVA with strategy (Look, Emphasize, Deemphasize) and

70

decision type (neutral, good, bad) as within-subjects variables (Figure 3.4). The ANOVA

revealed a main effect of decision type, F(1.56, 54.54) = 82.99, p = 0.001, and a main

effect of strategy, F(1.37, 47.96) = 7.42, p = 0.005, both Greenhouse-Geisser corrected

for non-sphericity. The interaction of strategy and decision type approached significance,

F(4, 140) = 1.97, p = 0.102. With regard to the main effect of decision type, risk-taking

was greatest for good decisions and lowest for bad decision types (good: M = 0.66, SD=

0.17; neutral: M = 0.41, SD = 0.21; bad: M = 0.26, SD = 0.19). With regard to the main

effect of strategy, risk-taking was greatest for Deemphasize trials and lowest for

Emphasize trials (Deemphasize: M = 0.52, SD = 0.22; Look: M = 0.44, SD = 0.17;

Emphasize: M = 0.37, SD = 0.22).

As in Experiment 1 and 2, we tested whether success ratings for the two emotion

regulation strategies exerted any influence on the risk-taking results. We addressed this

question by computing an ANOVA that included the Emphasize and Deemphasize

success ratings as covariates. This ANOVA revealed that the main effects of decision

type, F(1.56, 51.29) = 2.00, p = 0.16 (Greenhouse-Geisser corrected), and strategy,

F(1.38, 45.60) = 2.44, p = 0.12 (Greenhouse-Geisser corrected), were no longer

significant. The interaction of strategy and decision type approached significance, F(4,

132) = 2.23, p = 0.07. The interaction of strategy and Emphasize success, F(1.38, 45.60)

= 2.01, p = 0.16 (Greenhouse-Geisser corrected), and the interaction of strategy and

Deemphasize success, F(1.38, 45.60) = 0.49, p = 0.55 (Greenhouse-Geisser corrected),

were not significant. The three-way interaction of strategy, decision type, and Emphasize

success showed a trend that approached significance, F(4, 132) = 2.19, p = 0.074, and the

three-way interaction of strategy, decision type, and Deemphasize success was not

71

significant, F(4, 132) = 0.38, p = 0.83. The trend for a three-way interaction of strategy,

decision type, and Emphasize success was explored with t tests. For the purpose of these

comparisons, participants were divided into two groups, regulators and non-regulators,

based on their Emphasize success ratings. In the regulators group, significantly greater

risk-taking (p < 0.05) was observed during Deemphasize for the following comparisons:

Emphasize versus Deemphasize for good, neutral, and bad decisions and Deemphasize

versus Look for good and bad decisions. The comparisons of risk-taking in Emphasize

versus Look for good and neutral decisions approached significance (p = 0.07 and p =

0.09, respectively), with greater risk-taking occurring in the Look condition. In the non-

regulators group, the only significant effect was lower risk-taking in Emphasize than

Look for good decisions (p = 0.01).

Gender effects. To test for effects of gender, an exploratory ANOVA was

conducted with strategy (Look, Emphasize, Deemphasize) and decision type (neutral,

good, bad) as within-subjects variables and gender as a between-subjects variable. Like

the main analysis, this ANOVA revealed a main effect of decision type, F(1.56, 53.19) =

81.58, p = 0.001, a main effect of strategy, F(1.36, 46.25) = 7.31, p = 0.005 (both

Greenhouse-Geisser corrected), and an interaction of strategy and decision type that

approached significance, F(4, 136) = 1.99, p = 0.10. The interactions of strategy and

gender, F(1.36, 46.25) = 0.51, p = 0.53, and strategy, decision type and gender, F(4, 136)

= 1.23, p = 0.30, were not significant. There was no significant main effect of gender,

F(1, 34) = 1.06, p = 0.31.

Individual differences. To explore whether any individual difference measures

were related to observed shifts in decision-making (RegScore), correlation analyses were

72

performed. A significant negative correlation was observed between the Deemphasize

RegScore for bad decisions and self-esteem, R = -0.347, p = 0.048. The relationship

revealed that the more participants increased their risk-taking in Deemphasize relative to

Look, the lower their self-esteem scores.

Reaction time. A repeated measures ANOVA was performed to test for effects of

strategy (Look, Emphasize, Deemphasize) and decision type (good, bad, neutral) on

reaction times for the decision phase. The ANOVA revealed only a significant main

effect of decision type, F(1.29, 45.26) = 11.81, p = 0.001 (Greenhouse-Geisser

corrected). The main effect of strategy, F(2, 70) = 0.38, p = 0.68, and the interaction of

strategy and decision type, F(4, 140) = 0.24, p = 0.92, were not significant. Reaction

times were fastest for bad decisions and slowest for good decisions (bad: M = 1998.17,

SD = 411.87, neutral: M = 2077.02, SD = 441.28, good: M = 2173.88, SD = 464.88).

Skin conductance. To probe for effects of regulation on physiological

responding, SCRs were tested using one-way repeated measures ANOVA with strategy

(Look, Emphasize, Deemphasize) as the factor. This ANOVA did not find a significant

effect of strategy, F(1.58, 45.71) = 1.76, p = 0.19. However, participants may have

habituated to the cue, as the same slot machine image was repeated throughout the task.

A second ANOVA was performed using SCRs to the cues presented in the first half of

the task (Blocks 1 and 2) only. This ANOVA revealed a trend for a main effect of

strategy, F(2, 58) = 3.05, p = 0.055. Skin conductance responses were greatest in

Emphasize (M = 0.28, SD = 0.22), and there was no significant difference between SCRs

in Look (M = 0.25, SD = 0.19) and Deemphasize (M = 0.24, SD = 0.20).

73

3.4.3 Discussion. This experiment examined the influence of reappraisal

regulation on risk-taking in a financial decision-making task. Additionally, the effect of

different decision types, manipulated by varying which option, risky or safe, had the

greatest expected value, on risk-taking was probed. The two reappraisal strategies did

successfully alter decision-making; Emphasize reappraisals decreased risk-taking and

Deemphasize reappraisals increased it. Including the self-reported Emphasize and

Deemphasize success ratings in the analysis resulted in the previously significant main

effects of strategy and decision type no longer being significant. This result likely

indicates that participants were consciously aware of the effects of the reappraisal

strategies on their risk-taking. They may have used observations of their behavior to

inform their success ratings, which were made at the end of the experiment. Because the

reappraisal strategies involved maintaining focus on the upcoming decision, it is

reasonable that there was a relationship between success rating and risk-taking.

The reappraisal strategies may have been more effective than the imagery strategy

because they maintained focus on the decision and modulated emotions associated with

the decision. The effect of Deemphasize reappraisal was similar to that observed by

Sokol-

(2009). Emphasize reappraisal seemed to increase risk aversion as participants made

fewer risky choices in this condition. Skin conductance results suggest that the

Emphasize reappraisals may have increased arousal, perhaps indicating increased anxiety

about the decision.

74

3.5 General Discussion

The current studies examined shifts in risk-taking as a function of different types

of decisions and engaging in imagery-focused and cognitive emotion regulation strategies

when faced with cues signaling opportunities to win money. In accordance with our

hypotheses, both experiments revealed effects of decision type such that risk-taking was

elevated when the risky option had the greatest expected value and decreased when the

risky option had the lowest expected value. These results suggest that participants are

sensitive to the expected value of choices, and they alter their decision-making to favor

the option with the greatest expected value, regardless of whether it is risky or safe. The

imagery regulation strategy did not alter decision-making, but the cognitive regulation

strategies did. Cognitive reappraisals that heightened the perceived importance of the

next decision decreased risk-taking, while cognitive reappraisals that reduced the

perceived significance of the next decisions increased risk-taking.

To understand why the reappraisal strategies altered risk-taking, but the imagery-

focused strategy did not, it is helpful to consider the mechanistic differences between

these two strategies. The imagery and reappraisal strategies differ on two main

dimensions: attention and effects on emotion (Table 3.2). During imagery regulation

attention is focused away from the emotional stimulus, and instead is directed internally;

however, during reappraisal, attention remains directed at the emotional stimulus.

Imagery regulation generates a new emotion, one related to the scene that is imagined, for

instance, a feeling of calm would accompany a visualization of the ocean. In reappraisal,

the emotional response to the stimulus itself is modulated, either increased or decreased,

depending on the specific thoughts applied.

75

Because the decision set contained three different types of decisions, more

attention may have been required to make a decision in Experiments 2 and 3 relative to

Experiment 1. The reappraisal strategies maintained attention on the upcoming

decisions, while the imagery strategy did not. This difference could explain why the

imagery strategy did not alter risk-taking in Experiment 2.

Another explanation for the lack of imagery effects on risk-taking is suggested by

the skin conductance results. The imagery strategy did not decrease skin conductance,

indicating that this strategy did not significantly reduce arousal. Earlier studies found

that imagery regulation decreased skin conductance (Delgado, Gillis, & Phelps, 2008;

Delgado, Nearing, LeDoux et al., 2008), thus similar decreases were expected in the

current study. This lack of arousal modulation may have prevented behavioral shifts

from occurring. However, it is important to consider that in Experiment 3 the

deemphasize strategy also did not alter skin conductance, yet risk-taking was affected.

Although the imagery strategy did not significantly alter risk-taking, participants

felt somewhat successful at engaging in the relaxing imagery. Interestingly, exploratory

comparisons of the success ratings for the Relax strategy and the Emphasize and

Deemphasize strategies using independent samples t tests did not find any significant

differences in reported success. It possible that participants accurately assessed and

reported on their success at visualizing that imagery, but that the effects of the imagery

did not carry over to the decision phase or were overridden by the increased attention to

the decision options. These two strategies were tested in separate groups of participants.

It is possible that some individuals would feel more successful at one type of strategy if

these strategies were tested in a within-subjects design.

76

Prior work with the cognitive and imagery strategies in decision-making contexts

revealed individual differences such that both strategies were affective only with a subset

of the sample (Martin & Delgado, 2011; Sokol-Hessner, Hsu, Curley et al., 2009). Thus,

a secondary goal of the current study was to examine whether any individual differences

measures, for instance in emotion differentiation, habitual emotion regulation, emotional

intelligence, behavioral inhibition and activation, personality traits, self-esteem, or social

support, may explain for whom these techniques are effective. The current study found

that only one of these individual difference measures, self-esteem, was significantly

related to observed changes in risk-taking with regulation. Specifically, participants with

lower self-esteem scores tended to increase their risk-taking in Deemphasize relative to

Look for the bad decisions. This finding is interesting, as gambling more for bad

decisions was economically a poor decision; the safe option had a higher expected value

in the bad decisions. While overall Deemphasize reappraisals increased risk-taking, this

correlation suggests that people with lower self-esteem increased their risk-taking more

for bad decisions than participants with high self-esteem.

Of the 11 individual differences measures tested, only one was related to changes

in decision-making. One caveat is that this sample of Rutgers undergraduate students

may not capture the variability in the population. The current data do lend support to the

idea that overall, differences in the efficacy of emotion regulation techniques observed

between subjects are not due to different traits, but rather different states, situational

factors such as the amount of effort applied to engage in the technique. This result is

encouraging, as it suggests that most people will be able to benefit from using emotion

regulation techniques.

77

This research has potential clinical applications as it demonstrates that changing

how we experience an appetitive cue can alter our behavior. Appetitive cues signal

opportunities for rewards, but certain patterns of reward-seeking can become

maladaptive, for instance, drug-seeking in addiction. Understanding which strategies are

most helpful in altering behavior that is linked to reward cues is an important step in

developing potential treatments. Recent work with addicted populations suggests the

promise of emotion regulation techniques; studies have found that by employing

regulation strategies cigarette smokers lowered their feelings of craving (Kober, Kross,

Mischel et al., 2009; Kober, Mende-Siedlecki, Kross et al., 2010) and both cigarette

smokers (Kober, Mende-Siedlecki, Kross et al., 2010) and cocaine addicts (Volkow,

Fowler, Wang et al., 2010) reduced neural signatures of drug craving. Future research

needs to be conducted to determine if regulating affective responses to drug cues using

cognitive strategies can foster shifts in actual behavior (i.e., reduce drug-seeking after

coming in contact with drug cues).

78

Chapter 4

Experiment 4: Regulation of Negative Emotions Associated with Conditioned Cues

4.1. Introduction and Hypotheses

The previous three experiments examined the application of emotion regulation

strategies to cues associated with financial gains. Using regulation lead to shifts in the

level of risk-taking in financial decisions. In daily life, we encounter both appetitive

cues, those signaling potential gains, and aversive cues, those signaling potential losses.

The fourth study investigates the application of imagery-focused emotion regulation to

cues signaling monetary losses. Although much previous research has examined the

regulation of negative emotions, applying regulation to conditioned cues that signal

actual financial consequences has not been examined. Additionally, given the mixed

efficacy of the imagery-focused regulation strategy in the domain of monetary gains, the

current experiment provided an opportunity to probe this strategy in a different context,

that of monetary losses. The goals of this study were to determine whether imagery-

focused emotion regulation would decrease affective responses to cues associated with

losing money and to identify the brain regions that support the regulation process. This

study departs from the decision-making theme of the previous ones in order to directly

assess regulation-driven changes in the subjective and neural experience of losses.

Many previous emotion regulation studies employed visual stimuli such as

complex photographs (e.g., International Affective Picture Systems, IAPS) or film clips

and demonstrated that regulation decreased subjective reports of negative emotion

experienced (Goldin, McRae, Ramel et al., 2008; Levesque, Fanny, Joanette et al., 2003;

79

Ochsner, Bunge, Gross et al., 2002; Ochsner, Ray, Cooper et al., 2004; Phan, Fitzgerald,

Nathan et al., 2005; Urry, van Reekum, Johnstone et al., 2006). Regulation has also been

successfully applied to aversive conditioned cues, stimuli that are associated with the

physical experience of primary reinforcers such as pain (Kalisch, Wiech, Critchley et al.,

2005) or shock (Delgado, Nearing, LeDoux et al., 2008). While emotion regulation is an

important tool for coping with the anticipation of pain, it is unclear if regulation would be

effective with a secondary reinforcer commonly encountered in daily life, the loss of

money. Previous research has demonstrated that cues associated with the loss of money

elicit negative emotions and increase arousal responses (Delgado, Labouliere, & Phelps,

2006).

The current study employs the same imagery-focused strategy that was used in the

shock cue study (Delgado, Nearing, LeDoux et al., 2008). In that study, shifts in

affective responses were assessed with a physiological measure, skin conductance. The

current study included subjective emotion rating measures in addition to skin

conductance in order to fully capture the shifts in affective responding associated with

emotion regulation. Additionally, fMRI data were collected to probe shifts in neural

responses to loss cues with regulation. Are the neural circuits involved in the regulation

of cues associated with a secondary reinforcer, money, the same as those involved in the

regulation of a primary reinforcer, shock?

The goal of the current study was to examine whether imagery-focused regulation

would decrease the intensity of negative emotion experienced during the anticipation of

unavoidable monetary losses. Simple visual cues, three different colored squares, were

associated with three different monetary outcomes: always losing money (loss cue), never

80

losing money (safe cue), and sometimes losing money (variable cue). Participants

experienced three phases for each cue type. In the first phase, the cue phase, participants

saw the square appear on the screen. Next, a question mark appeared inside of the

square, the anticipation phase, and participants waited to see the outcome of the square.

Participants either responded naturally (look condition), or engaged in imagery-focused

regulation (relax condition) during the anticipation phase. When the question mark

disappeared, the anticipation phase ended, and participants saw the outcome of the square

(loss or safe).

Most neuroimaging studies of emotion regulation have varied the strategy, natural

responding or regulation, in a pseudo-random, event-related manner. This structure

requires participants to switch strategies often. We varied the strategies less frequently,

by presenting a new strategy word only every six trials. Additionally, participants were

only explicitly instructed to engage in the relaxing imagery during the anticipation phase

for each cue. They were free to respond naturally during the cue phase, which came just

before the anticipation phase. This design allowed us to explore regulation effects during

the cue period-

We hypothesized that engaging in regulation during anticipation would

successfully decrease subjective feelings of negative emotion, and these emotional shifts

would be accompanied by decreased physiological arousal responses and reduced neural

activity in brain regions involved in emotion processing. Previous studies of negative

emotion regulation have found that regulation reduces activity in brain regions involved

in emotion including the amygdala and increases activity in brain regions involved in

cognitive control including the dorsolateral, ventrolateral, ventromedial, and anterior

81

cingulate cortices (for reviews, see Green & Malhi, 2006; Ochsner & Gross, 2005;

Ochsner & Gross, 2008).

Understanding whether emotion regulation is beneficial when faced with cues

signaling actual money losses is important not only because negative emotions are

unpleasant to experience, but also because negative emotions may promote risk-taking in

decision making contexts. Decisions framed as losses may elicit negative affect (De

Martino, Kumaran, Seymour, & Dolan, 2006) and the presence of a loss frame increases

risky decision-making (Kahneman & Tversky, 1979). These studies suggest that cues

indicating potential losses may shift choices in favor of risky options, which may be less

optimal. It is possible that reducing the negative emotions elicited by loss cues or frames

may eliminate or reduce risk-taking in these situations. Before examining the effects of

regulation of negative emotions on decision-making, it is important to understand the

effects of emotion regulation on the expectation of monetary losses.

4.2 Methods

Participants. Twenty-nine volunteers participated in this study (15 females, 14

males). Two participants were excluded, because they did not follow the emotion

regulation instructions as determined by the post-experiment questionnaire. Two

participants were excluded due to excessive movement during the MRI scan, and one

participant was excluded due to an error in the functional image acquisition settings.

Final analysis was conducted on 24 participants (12 females, 12 males; mean age: M =

20.29, SD = 2.61). Participants completed this experiment for pay at the Advanced

Imaging Center, a joint venture of Rutgers University and the University of Medicine and

82

Dentistry of New Jersey. All participants gave informed consent according to the

Rutgers University Institutional Review Board for the Protection of Human Subjects in

Research and the Newark Campus Institutional Review Board of the University of

Medicine and Dentistry New Jersey.

Procedure overview. After signing the consent form, participants were trained

on the experimental task on a PC computer in a testing room at Rutgers University. Next,

participants were escorted by the experimenter to the Imaging Center, and they

completed the experimental task while inside the MRI scanner. After the scanning

session, participants completed several questionnaires: a brief post-experiment

questionnaire that assessed compliance with instructions, the Emotion Regulation

Questionnaire that measured use of emotion regulation strategies (Gross & John, 2003),

and the behavioral inhibition and activation scales that measured approach and avoidance

motivation (BIS/BAS; Carver & White, 1994). After the questionnaires, participants

received their payment in cash and were debriefed.

The experimental task was programmed in E-Prime version 2.0 (PST, Pittsburgh)

and presented to participants in the MRI scanner via a back projection system.

Participants made responses with their right hands using an MRI compatible response

box. The experimental task consisted of two different games, the Gambling Game and

the Colored Squares Game. The purpose of the Gambling Game was to provide

participants with a bank of earned experimental dollars. The Colored Squares Game was

the main experimental task, and its purpose was to expose participants to cues signaling

money losses. When faced with the cues, participants either responded naturally or

engaged in imagery-focused regulation. These money losses were deducted from the

83

Gambling Game earnings. Participants played each game twice. Each Gambling Game

lasted three minutes and the Colored Squares Game about 18 minutes (some responses

were self-paced, so the duration varied across participants).

Gambling Game.

whether the numerical value of a card was lower or higher than 5. Participants earned

money (2 experimental dollars) for correct guesses and lost money (1 experimental

dollar) for incorrect guesses. Participants had two seconds to guess the value of the card,

and they did so by pressing the 1 key to guess low or the 2 key to guess high. After the

time elapsed, the actual value of the card was revealed along with either a check mark

earnings across participants the Gambling Games were fixed; participants won 30

experimental dollars in the first game and 36 in the second one.

Colored Squares Game. Two independent variables were manipulated in the

Colored Squares Game: cue type (loss, safe, variable) and strategy type (natural

responding, emotion regulation). In the game, participants saw three different colored

squares (cues) that were associated with different monetary outcomes (Figure 4.1). The

loss square was associated with always losing money, the safe square with never losing

money, and the variable square with a probability of losing money (50% loss, 50% safe).

The associations between the square colors (yellow, blue, and purple) and these outcomes

were explicitly explained to participants during training; therefore, they did not have to

learn them. For the variable square, participants were not informed of the exact

consisted of three main phases. The cue phase consisted of the appearance of the square

84

in the center of the screen for 4 or 6 s. Next, in the anticipation phase, a question mark

appeared inside of the square for 4 or 6 s; during this phase participants waited to see the

outcome of the square. The outcome phase consisted of the presentation of the monetary

outcome, loss (negative dollar sign) or safe (pound sign), for 1 s. After the outcome, a

fixation cross was presented for 7 s, followed by a rating question, which remained on the

screen until participants responded. The trial ended with a 10 s inter-trial-interval

(fixation cross). The color of each cue type (loss, safe, variable) was counter-balanced

across participants.

The Colored Square Game trials were grouped into 10 mini-blocks of six square

trials each. The task was divided into two big blocks each consisting of 5 mini-blocks.

The order of the mini-blocks was counterbalanced across participants, by using two

different sequences. At the beginning of each mini-block, participants were presented

with a strategy word for 4 s; the strategy word indicated to them whether to respond

about the upcoming outcomes during the anticipation period (indicated by a question

mark appearing inside of the square), observe all the outcomes, and to respond naturally.

when the question mark appeared (anticipation phase), to imagine a relaxing scene in

told to keep their eyes open and to attend to all the outcomes.

Prior to the MRI scan, the experimenter explained the instructions for the two

games. Training in the use of the Relax imagery-focused strategy involved showing

85

participants example pictures of calming scenes (a beach sunset), asking them to generate

their own images, verifying these images were appropriate (i.e., not exciting or task-

related), and instructing them not to close their eyes during the imagery. Participants

then completed short practice versions of both the Gambling Game and the Colored

Squares Game.

Monetary losses. Given that our manipulation depended on negative emotions

being elicited by the presentation of the loss and variable cues, the monetary outcomes

and payment procedures were explicitly outlined and emphasized during training.

Participants were told that each loss outcome they experienced in the Colored Squares

Game was equivalent to two experimental dollars subtracted from their earnings in the

Gambling Game. Further, they were told that the experimental dollars would be

converted to real dollars at the end of the experiment by a spin of a computerized wheel

with 8 possible values ranging from $1 to $8. Participants saw this wheel and practiced

spinning it during training. Participants were informed that they would be only be

guaranteed $20 for their participation, and that they might receive bonus money if the

amount they earned in the Gambling Games was greater than the amount they lost in the

Colored Squares Games.

At the end of the experiment, while still in the scanner, participants spun the

wheel to determine the value of each experimental dollar. To equate earnings across

participants, the outcome of the spin was fixed. Participants saw how many experimental

dollars they won in each Gambling Game minus how many they lost in each Colored

Squares Game, and then they saw their total earnings in actual dollars.

86

Rating questions. There were 60 total Colored Squares Game trials, 30 in each

round of the game. There were 20 trials of each square type (loss, safe, variable); 10

trials were without regulation (Look), and 10 trials were with regulation (Relax). There

were three different Likert scale rating questions, which were distributed across the cues.

The emotion intensity question assessed the level of negative emotion experienced during

the anticipation phase (1 = very weak, 7 = very strong); this question was given in both

look and relax trials and was a central dependent variable for assessing the effect of

regulation. The focus question assessed how much the participant thought about the

outcome of the square during the anticipation phase (1 = not at all, 7 = very much); this

question only occurred in look trials. The relax success question assessed how successful

the participant felt at visualizing relaxing imagery during the anticipation phase (1 = not

at all, 7 = very successful); this question only occurred in relax trials.

Behavioral data analysis. We tested for effects of strategy (2: look, relax) and

cue type (3: loss, safe, variable) on the emotion intensity ratings using repeated measures

analysis of variance (ANOVA) in SPSS 19. Additionally, we probed for effects of cue

type on both the focus ratings and relax success ratings, separately, using repeated

measures ANOVA. The ANOVA results were further explored with paired t tests.

Skin conductance acquisition and analysis. Skin conductance responses

(SCRs) were collected using the BIOPAC systems skin conductance module (BIOPAC

Systems, Goleta, CA). MRI compatible radiotranslucent electrodes were used, along

with a transducer amplifier to correct for potential sources of noise and acquisition of

distal phalanges of the second and third fingers in their non-dominant hand.

87

AcqKnowledge software was used to analyze SCR waveforms (BIOPAC Systems,

Goleta, CA). SCR waveforms were filtered using a 1 Hz low-pass filter. The level of

SCR was assessed as the base to peak difference for an increase in the 0.5 to 4.5 s

window following the onset of the reward cue (LaBar, LeDoux, Spencer et al., 1995). A

minimum response criterion of 0.02 microSiemens was used, and all responses below this

criterion were

data was reviewed to determine if it could be scored. Most data were not scoreable. The

poor quality of the data may have been due to difficulty collecting SCRs in the MRI

environment, low arousal responses to the cues, or some combination of two factors.

Acquisition of SCR data is challenging in the MRI environment due to signal interference

from the scanner. Given that the data could not be scored, no SCR results are available to

be reported.

FMRI acquisition and analysis. Imaging data were acquired at the University

Heights Center for Advanced Imaging using a 3T Siemens Allegra head-only scanner

with a standard head coil. Structural images were acquired using a T1-weighted

sequence (256 x 256 matrix, 176 1 mm sagittal slice). Functional images were acquired

using a single-shot gradient echo EPI sequence (TR = 2000 ms, TE = 25 ms, FOV = 192

cm, flip angle = 80°, bandwidth = 2604 Hz/px, echo spacing = 0.29 ms). Each functional

volume consisted of thirty-five contiguous (3 x 3 x 3 mm voxels) oblique axial images

acquired parallel to the anterior commissure (AC), posterior commissure (PC) line.

Imaging data preprocessing and analysis were performed with Brain Voyager

software (version 1.10 and 2.2: Brain Innovation, Maastricht, The Netherlands). Data

preprocessing consisted of correction for motion (6 parameters; motion greater than 6

88

mm within run resulted in exclusion from analysis) slice scan time adjustment (sinc

interpolation), spatial filtering (three-dimensional Gaussian filter 8 mm FWHM), and

temporal filtering (voxel-wise linear detrending and high-pass filtering of frequencies

with three cycles per time-course). Motion correction parameters were included in the

statistical model. Mean intensity analyses were performed to assess deviations from the

first volume; to account for observed variability, deviation parameters were included in

the statistical model. Functional images were co-registered with the high-resolution

structural images, and structural and functional data were transformed into standard

Talairach stereotaxic space (Talairach & Tournoux, 1988).

Data analysis focused on testing for effects of strategy (look, relax) and cue type

(loss, safe, variable) on blood oxygen level dependent (BOLD) signal change in the

anticipation and cue phases of the task. To estimate beta weights for these conditions, we

generated a general linear model (GLM) that included a separate predictor for each

unique task condition at each task phase: six cue predictors (strategy by cue type, e.g.,

Look Loss, Relax Loss), six anticipation predictors (strategy by cue type), and eight

outcome predictors (strategy by cue type by outcome). Additionally, the GLM included

predictors of no interest for strategy word presentation, rating question presentation,

missed trials, motion parameters, and mean intensity parameters. Each predictor was

modeled as a box car convolved with a canonical two gamma hemodynamic response

function. A whole brain random-effects analysis was performed on the functional data

using this GLM. Statistical parametric maps (SPMs) were created using a threshold of p

< 0.005, cluster threshold corrected.

89

Each map was corrected for multiple comparisons using the cluster level

statistical threshold estimator plugin tool from the Brain Voyager analysis package

(Forman, Cohen, Fitzgerald, Eddy, Mintun, & Noll, 1995; Goebel, Esposito, &

Formisano, 2006). To determine the likelihood of observing of clusters various sizes

clusters in the map, this tool uses Monte Carlo simulations. First, each map was

thresholded at p = 0.005. Then, at least 1000 (the recommended number) iterations were

run to estimate the rate of cluster level false positives. The analysis generates a range of

minimum cluster sizes, and each is associated with an alpha value determined by its

frequency. For all maps, the cluster size associated with a false positive rate of 0.005 or

less was chosen. Clusters smaller than then cluster size were excluded from the SPM

before additional analysis.

To test for effects of strategy and cue type, four different analyses were

performed. First, beta weights were estimated and extracted for the anticipation phase for

three a priori regions of interest (ROIs) using the above GLM. The ROIs were the

regions of dorsolateral prefrontal cortex (dlPFC), ventromedial prefrontal cortex

(vmPFC), and amygdala indentified by the previous study of imagery regulation and

shock cues (Delgado, Nearing, LeDoux et al., 2008). The beta weights for each ROI

were input into a repeated measures ANOVA with strategy (look, relax) and cue type

(loss, safe, variable) using SPSS 19. This analysis would determine whether the signal

change in these regions showed significant effects of strategy or cue type.

For the second analysis, a whole brain ANOVA was conducted in Brain Voyager,

and regions showing a main effect of strategy, a main effect of cue type, and an

interaction of strategy and cue type were identified for the anticipation and cue phases,

90

separately. Because regulation was explicitly directed during anticipation, the

anticipation ANOVA results most directly assessed effects of regulation. The cue phase

data were tested to explore for effects of being in a regulation mini-block, even though

regulation was not instructed during the cue phase. Beta weights were extracted from the

functional regions of interest (fROIs) defined by these six statistical parametric maps

(SPMs) using a peak voxel center and a cluster extent of 10 voxels in all directions.

Paired t tests were performed on the beta weights as necessary to characterize the

observed effects. Regions showing larger beta weights for Relax than Look were deemed

to be involved in regulation.

For the third analysis, a whole brain contrast plus correlation analysis was

performed in Brain Voyager to identify brain regions in which activity was greater during

regulation (compared to natural responding) and was correlated with shifts in negative

emotion intensity during regulation. Shifts in emotion intensity were quantified by the

difference of Look and Relax emotion intensity ratings (Look Relax, emotion

regulation score). This analysis should identify brain regions supporting regulation

processes.

The final analyses were done to identify brain regions recruited more strongly

during regulation than natural responding during the anticipation period (when active

regulation occurred). Specifically, whole brain contrasts of Relax greater than Look were

performed separately for the two negative cues, loss and variable.

91

4.3 Results

Behavioral results: Subjective ratings. To examine whether relax regulation

reduced the negative emotion experienced while anticipating the outcome of the squares,

a 2 strategy (look, relax) by 3 cue type (loss, safe, variable) repeated measures ANOVA

was performed on the emotion intensity ratings (Figure 4.2). The ANOVA revealed a

main effect of strategy, F(1, 23) = 7.90, p = 0.01, a main effect of cue type, F(1.22,

27.95) = 61.61, p = 0.001 (Greenhouse-Geisser corrected), and an interaction of strategy

and cue type, F(1.52, 35.03) = 4.95, p = 0.02. Paired t tests were conducted to

characterize the nature of the observed interaction. Relax regulation decreased the

intensity of negative emotion for the loss [look M = 4.50, relax M = 4.00; t(23) = 1.77, p

= 0.09] and variable [look M = 3.73, relax M = 2.96; t(23) = 3.85, p = 0.001] cues, but

there was no difference for the safe cue [look M = 1.61, relax M = 1.60; t(23) = 0.20, p =

0.85].

Additional analyses probed the influence of cue type on the two other ratings

collected during the Colored Squares Game, the focus and relax success ratings. To

determine if the level of focus on the cue outcomes during anticipation varied as a

function of cue type, we used a one-way repeated measures ANOVA with cue type (loss,

safe, variable) as the factor. There was a significant main effect of cue type, F(1.38,

31.66) = 37.18, p = 0.001. Subjects reported focusing the most on outcomes for the

variable cue (M = 5.6, SD = 1.47); ratings for the loss (M = 2.92, SD = 1.71) and safe (M

= 2.85, SD = 1.53) cues were significantly lower than those for the variable cue and did

not differ from each other. A final analysis assessed whether success at using the relax

imagery differed across the three cue types. A repeated-measures ANOVA with cue type

92

(loss, safe, variable) as the factor revealed that the main effect of cue type was not

significant, F(2, 46) = 1.30, p = 0.28, suggesting that participants felt equally successful

using the imagery regulation for all the cues.

Neuroimaging Results.

Overview. Four main analyses were performed on the neuroimaging data: an

independent ROI analysis, whole brain strategy by cue type ANOVA, a whole brain

contrast plus correlation analysis that used emotion rating regulation scores, and whole

brain Relax versus Look contrasts for the loss and variable cues. The ROI analyses were

performed on beta weights estimated during the anticipation phase. The whole brain

ANOVAs tested beta weights from both the anticipation and cue phases, separately.

Finally, the emotion regulation score correlation analysis and the contrasts were

performed with anticipation phase data only, because active regulation putatively

occurred during this phase.

A priori ROIs. We estimated beta weights for the anticipation phase using the

GLM for three ROIS identified by the previous study of imagery regulation of shock cues

(Delgado, Nearing, LeDoux et al., 2008). These beta weights were input into repeated

measures ANOVAs with strategy (2: look, relax) and cue type (3: loss, safe, variable) as

the factors. The dlPFC ROI (x, y, z = -43, 28, 30) showed a trend for a main effect of

strategy, F(1, 23) = 3.51, p = 0.07, characterized by greater BOLD signal during

regulation (Figure 4.3). There was no main effect of cue type, F(2, 46) = 1.10, p = 0.34,

nor an interaction, F(2, 46) = 0.24, p = 0.79. The vmPFC ROI (x, y, z = 0, 35, -8)

revealed only a significant interaction of strategy and cue type, F(2, 46) = 3.26, p =

0.047; the main effect of strategy, F(1, 23) = 0.62, p = 0.44, and main effect of cue type,

93

F(1.56, 35.91) = 0.81, p = 0.43 (Greenhouse-Geisser corrected), were not significant.

Paired t tests determined that the there was a trend for higher beta weights in Look

relative to Relax for the loss cue, t(23) = 1.75, p = 0.09, but no significant differences

were observed for the safe, t(23) = 1.56, p = 0.13, or variable, t(23) = 1.25, p = 0.23 cues.

Finally, the amygdala ROI (x, y, z = -20, 0, -20) did not show any significant effects:

main effect of strategy, F(1, 23) = 0.54, p = 0.47, cue type, F(2, 46) = 0.72, p = 0.49,

interaction, F(2, 46) = 0.17, p = 0.84.

Whole brain ANOVAs.

Anticipation phase. To determine the brain regions involved in emotion

regulation in this task, a whole brain strategy (2: look, relax) by cue type (3: loss, safe,

variable) ANOVA was performed on the anticipation phase BOLD data using Brain

Voyager. Regions identified by the main effect of strategy statistical parametric map

(SPM) that showed a greater response during Relax were the right medial frontal lobe

(BA 6, see Figure 4.4) and left precentral gyrus (BA 4). Regions that showed greater

response during Look were the right inferior parietal lobe, bilateral fusiform gyrus, right

middle temporal gyrus and the left cerebellum. These regions are involved in visual

attention, which is consistent with the greater focus on the cues during Look compared to

Relax. See Table 4.1 for a list of all regions identified by this analysis.

The main effect of cue type SPM identified a number of brain regions, which are

all reported in Table 4.2. We grouped these regions into four categories based on the

observed pattern of activity for the three different cues. Regions that were potentially

sensitive to negative valence would show this pattern of activity: loss > variable > safe,

and regions sensitive to positive valence would show the opposite pattern: safe > variable

94

> loss. Sensitivity to uncertainty would be reflected in this pattern: variable > loss = safe,

while sensitivity to certainty would be reflected in this pattern: loss = safe > variable.

There were no regions that fit the negative valence pattern. One region, left postcentral

gyrus, had activity consistent with the positive valence pattern. Two regions fit the

uncertainty sensitivity pattern, left cuneus and right cuneus. Only the right subcallosal

gyrus (BA 25) conformed to the certainty pattern. Other patterns of activity were

observed in the remaining regions identified by the main effect of cue SPM.

The final SPM for the anticipation phase revealed regions that showed an

interaction of instruction and cue type including the precentral gyrus, postcentral gyrus,

parahippocampal gyrus, inferior parietal lobe, and superior and middle temporal gyri (see

Table 4.3 for a complete list). Based on the behavioral results, which showed decreased

negative emotion ratings for the loss and variable cues, we were interested in brain

regions that showed a consistent pattern. Specifically, we looked for regions that showed

increased activity in Relax compared to Look for the loss and variable cues. No regions

showed this specific pattern. Regions showing greater BOLD response in Relax

compared to Look for the loss cue were the superior temporal gyrus and inferior parietal

lobe; the postcentral and middle temporal gyri showed trends for this effect. No regions

showed greater BOLD signal in Relax relative to Look for the variable cue. Regions

demonstrating increased BOLD signal in Relax compared to Look for the safe cue were

the precentral, postcentral, and parahippocampal gyri. Several regions evinced

increased responses during natural responding relative to regulation. Regions that

showed greater responses during Look for the loss cue were the middle temporal and

parahippocampal gyri. The postcentral gyrus, middle temporal gyrus, and inferior

95

parietal lobes all demonstrated greater BOLD in Look relative to Relax for the variable

cue. Finally, no regions showed increased responses in Look compared to Relax for the

safe cue.

Cue phase. To assess how BOLD signal varied with cue type, we performed a

second whole brain 2 X 3 ANOVA on the cue phase data. Of primary interest, were

regions identified in the main effect of cue type SPM. As an exploratory analysis, we

examined the SPMs for the main effect of strategy and interaction of strategy and cue

type. These SPMS identified regions whose BOLD signal change pattern suggests

modulation due to the state of being in a relax mini-block, when regulation was not

explicitly instructed. We grouped regions identified by the main effect of cue type SPM

into the four categories discussed above. No regions were identified that conformed to

the negative valence pattern. A region of the right caudate nucleus fit with the positive

valence pattern. No regions were consistent with the uncertainty pattern. Three regions,

the inferior parietal lobe, the right precuneus, and the right lingual gyrus conformed to the

certainty pattern. Several other regions were identified by the main effect of cue type

SPM that did not conform to any of these four patterns; see Table 4.4 for a complete list

of all active regions. The main effect of strategy SPM revealed a number of brain

regions, all of which showed greater activity during Look (see Table 4.5). For the

interaction of strategy and cue type SPM, a large number of regions were detected. A full

list of the regions identified by this SPM is reported in Table 4.6.

Emotion regulation score correlation. The correlation analysis revealed brain

regions in which activity was greater during regulation compared to natural responding

and was correlated with the shift in negative emotion ratings from Look to Relax. For the

96

loss cue, this analysis did not reveal any regions. For the variable cue, only a region of

the right inferior frontal gyrus (BA 45) was negatively correlated with emotion regulation

scores. Greater activity in this region in Relax (relative to Look) was associated with

higher negative emotion ratings in Relax (relative to Look), and lower activity in this

region in Relax (relative to look) was associated with lower negative emotion ratings in

Relax (relative to Look).

Relax versus Look contrasts. The final analysis was just a simple contrast of

Relax versus Look for the anticipation phase. For the loss cue, a region of the left middle

frontal gyrus (BA 9/44), the middle and superior temporal gyri (BA 21, 22), and two

regions of the inferior parietal lobe (BA 40) showed greater activity during Relax relative

to Look (Table 4.7). These regions were potentially involved in the process of

regulation. Several regions showed the opposite pattern, greater activity during Look.

They are the right medial frontal lobe (BA 8), right parahippocampal gyrus, right

occipital lobe, and left cerebellum.

The Relax versus Look contrast for the variable cue (Table 4.8) did not reveal any

regions whose activity was greater in Relax. The regions that showed greater activity

during Look included the right postcentral gyrus (BA 43), left precentral gyrus (BA) 6),

right fusiform gyrus, and left occipital lobe (BA 19).

4.4 Discussion

This experiment demonstrated that imagery-focused emotion regulation

effectively decreases negative emotion elicited by loss conditioned cues. Although the

observed decreases in negative emotion were quite clear, the neural circuits supporting

97

regulation were less well elucidated. The brain regions implicated in emotion regulation

in this task, those showing greater BOLD signal during Relax compared to Look trials,

were somewhat different from those identified in previous studies. The current results

and their implications will be reviewed.

First, the ratings results will be discussed. The emotion intensity ratings

demonstrated significant decreases in negative emotion during regulation trials for the

loss and variable cues. There was no difference for the safe cue, but because the Likert

rating scale used only assessed negative emotion ranging from weak to strong, this result

is not surprising. The safe cue was a positive cue in the context of the experiment. It is

important to consider that although efforts were taken to make the financial consequences

of the cues salient, the negative emotion ratings for the loss and variable cues were not

very high. During Look, the average rating for the loss cue was 4.50, and the average

rating for the variable cue was 3.73; on a 7 point scale ranging from 1 = weak to 7 =

strong, these rating suggest moderate levels of negative emotion.

The ratings of how much participants thought about the cue outcomes for Look

trials, the focus ratings, were also telling. Participants reported thinking about the

outcome of the variable cue significantly more than that of the loss or safe cue. This

result is expected, as the outcome of each trial of the variable cue was not known. It is

focus ratings for the loss cue were so low. In this task,

participants had no control over the outcome of any of the cues; moreover, the outcomes

of the loss and safe cues were 100% deterministic. The low focus ratings for the loss cue

could indicate tha

outcome. This response is quite adaptive and suggests some amount of spontaneous

98

regulation may have occurred during Look trials for the loss cue. If something bad is

going to ha

negative outcome. If participants were spontaneously regulating during Look with the

loss cue, that may complicate or weaken the neural comparisons of Relax versus Look.

Turning to the neuroimaging results, the patterns of BOLD signal for the a priori

ROIs suggested some overlap in the regulation of primary and secondary reinforcers.

The trend for greater activity observed during regulation in the dlPFC ROI is consistent

with the previous study by Delgado and colleagues (2008). Additionally, numerous other

regulation studies have implicated the dlPFC in emotion regulation (Ochsner & Gross,

2008). The observed result of increased activity in vmPFC ROI for Look trial compared

to Relax trials is opposite of that found in the previous shock study, which showed a

relative increase in vmPFC activity during regulation relative to natural responding.

Additionally, the lack of modulation of the BOLD signal in the amygdala ROI in the

current study is not consistent with the previous study, which found that emotion

regulation decreased activity in the amygdala; however, previous research did not find a

role for the amygdala in responding to cues associated with money losses (Delgado, Jou,

& Phelps, 2011). The vmPFC is interconnected with the amygdala and plays a central

role in modulating the amygdala during the processes of extinction (Delgado, Olsson, &

Phelps, 2006; Phelps, Delgado, Nearing et al., 2004) and emotion regulation (Delgado,

Nearing, LeDoux et al., 2008). It is possible that the vmPFC may not have been recruited

during regulation in the current task, because the loss and variable cues did not elicit

significant amygdala activation during natural responding.

99

Previous research has found that emotion regulation strategies engage regions of

the prefrontal cortex, including the dorsal and ventral lateral regions, and the anterior

cingulate cortex (Delgado, Gillis, & Phelps, 2008; Delgado, Nearing, LeDoux et al.,

2008; Green & Malhi, 2006; Ochsner & Gross, 2008). The main effect of strategy SPM

determined by the whole brain strategy by cue type ANOVA for the anticipation phase

identified only two regions that showed greater activity during regulation compared to

natural responding, a region of the right superior frontal gyrus (BA 6) and a region of the

left precentral gyrus (BA 4). However, these regions do not exactly correspond to those

prefrontal areas typically implicated in emotion regulation. Previous studies of regulation

have identified more anterior regions of the superior frontal gyrus including Brodmann

areas 8 and 9. The regions identified in this task were more posterior and included

regions of the primary motor cortex. These regions are different from what was predicted

based on the previous work, and therefore it is difficult to make conclusions. Because

these two regions were the only regions that showed a main effect of increased activity

during regulation, it is likely that they were recruited to support the process of regulation.

The regions identified by the main effect of strategy SPM for the cue phase

included those involved in attention and visual processing as well as areas of the

prefrontal cortex. All of these regions showed greater activity during natural responding

compared to regulation, revealing that neural activity during the cue phase was very

different for Look and Relax trials. Although regulation was not explicitly directed

during the cue phase, these results demonstrate that neural activity during regulation was

reduced in brain regions involved in attention and visual processing, suggesting that less

attention was paid to the cues during Relax mini-blocks.

100

Regions identified as having a main effect of cue type for the anticipation and cue

phases were categorized based on their specific pattern of activity for the three cue types.

For the anticipation phase, the postcentral gyrus demonstrated a pattern of activity that

suggested it was tracking positive valence. The postcentral gyrus includes the sensory

cortex. Future research is needed to understand its role in differentiating the valence of

the cues. The cuneus showed sensitivity to uncertainty, perhaps indicating greater visual

processing of the variable cue. Finally, activity in the right subcallosal gyrus (BA 25),

also known as the subgenual cingulate, was greatest for the certain cues, loss and safe.

The subgenual cingulate is involved in emotion (Kober, Barrett, Joseph, Bliss-Moreau,

Lindquist, & Wager, 2008), and it is part of the affective processing subsection of the

anterior cingulate cortex (Bush, Luu, & Posner, 2000).

Regions identified by the main effect of cue type in the cue phase were fairly

different than those identified for the anticipation phase. These differences could be due

to regulation, phase, or a combination of both. For the cue phase, a region of the right

caudate nucleus seemed to be tracking positive valence, which is consistent with previous

research (Balleine, Delgado, & Hikosaka, 2007; Delgado, Locke, Stenger, & Fiez, 2003;

Delgado, Stenger, & Fiez, 2004). The three regions that showed greater BOLD signal for

the certain cues were brain regions involved in visual attention, suggesting that the cues

signaling known outcomes may have captured attention at a higher level during initial

presentation (cue phase).

Finally, brain regions identified as showing an interaction of strategy and cue type

for the anticipation phase consisted mostly of regions in the inferior parietal and temporal

lobes. Regions showing this interaction for the cue phase were primarily identified in the

101

inferior parietal, temporal and occipital cortices. These results suggest that regions

involved in attention and visual processing showed differential effects of strategy and cue

type.

The correlation analysis was conducted to identify brain regions whose activity

was related to observed shifts in emotion intensity from Look to Relax. For the variable

cue, this analysis determined that activity in a region of the right inferior frontal gyrus

(BA 45) was negatively correlated with emotion intensity shifts (emotion regulation

score: Look minus Relax intensity ratings). The nature of the observed relationship was

such that increased activity in this region in Relax relative to Look was associated with

higher negative emotion ratings in Relax relative to Look. This pattern suggests that this

region was involved in emotional responses to the cues, as greater activity in this region

was related to greater reports of negative emotion.

Finally, the Relax versus Look contrasts for the loss and variable cues showed

which regions were recruited during regulation. For the loss cue, one prefrontal region

showed greater activity in Relax compared to Look, the left middle frontal gyrus (BA

9/44). This region overlaps with those prefrontal regions identified in other regulation

work. For the variable cue, no regions showed greater recruitment during regulation

compared to natural responding. It is unclear why several brain regions including a

region of the prefrontal cortex were involved in regulation with the loss cue, but similar

regions were not involved for the variable cue. Reactions to the variable cue may have

differed more across participants than reactions to the loss cue, leading to less consistent

neural activity for the variable cue.

102

Overall, this study demonstrated that the imagery regulation strategy ameliorated

negative emotions elicited by loss cues. The neural mechanisms of this strategy share

some overlap with those of previous, namely increased activity in prefrontal brain regions

during regulation. The observed shifts in neural activity from Look to Relax were

widespread in the brain, suggesting that regulation affected processing in regions

involved in attention and vision. These results hint that the primary responses evoked by

the loss and variable cues were more perceptual and attentional rather than visceral. The

cues may have been processed more on a cognitive level than a basic emotional one.

103

Chapter 5: General Discussion

5.1 Summary and Significance

Overall summary. What we think about when faced with cues signaling potential

monetary gains or losses affects how we feel and what we chose. Four empirical

experiments demonstrated these effects. The first three experiments tested the effect of

emotion regulation on risk-taking in financial decisions. The fourth experiment

examined the influence of emotion regulation on feelings of negative emotion associated

with monetary loss cues. Within-subjects design was used in all experiments and all

experiments included a natural responding (Look) condition in addition to the regulation

condition(s).

In the first experiment, participants engaged in imagery-focused regulation when

presented with cues signaling financial decisions in which money could be won.

Generating relaxing imagery during the cue decreased risk-taking in the subsequent

financial decisions for participants who felt successful at visualizing the imagery.

Neuroimaging data collected during the decision-making task revealed that responses to

risky choices in the ventral striatum were decreased after using regulation.

Experiment 2 sought to further explore this effect of imagery regulation by testing

it with a more complex set of financial decisions. Specifically, three types of financial

decisions that varied with regard to which option had the greater value (risky, safe or

equal) were used. When tested in this diverse decision environment, the relax imagery

strategy did not have any effect on risk-taking. Experiment 3 employed the same

financial decisions as Experiment 2, but it tested cognitive reappraisal regulation

104

strategies instead of imagery regulation. Cognitive reappraisal involved participants

changing their thoughts about the upcoming decision during the cue presentation before

the decision appeared. Two cognitive reappraisals were used, one in which participants

thought about the decision as being very important (Emphasize) and one in which they

thought about the decision as being insignificant (Deemphasize). These reappraisals

significantly altered risk-taking such that participants took the most risks on

Deemphasize trials and the fewest risks on Emphasize trials.

To elucidate a complete characterization of the Relax imagery strategy, the last

experiment tested whether imagery regulation would successfully decrease negative

emotions in response to cues signaling monetary losses. Imagery regulation decreased

negative emotion and altered neural responding. The regions affected by regulation were

diverse and not localized to the prefrontal cortex. Brain regions involved in attention and

visual processing showed decreased activity during regulation compared to natural

responding.

Neural circuitry involved in regulation. The current experiments enhance and

affirm current knowledge about the neural systems involved in emotion regulation.

Typically, emotion regulation studies have focused on changes in neural activity during

regulation of emotional stimuli. Experiment 1 added to this literature by demonstrating

that applying emotion regulation strategies to stimuli affects subsequent neural

processing during decision-making. Experiment 1 examined regulation-induced shifts in

neural signals in subcortical brain regions involved in processing risk and reward.

Imagery-focused regulation reduced activity associated with risky choices in the ventral

striatum, midbrain, and insula. These results are consistent with previous research

105

showing that activity in subcortical brain regions involved in affective learning and

emotion is modulated by emotion regulation.

Cortical regions were implicated in emotion regulation in both Experiments 1 and

4. In Experiment 1 an exploratory analysis determined that activity in a region of dorsal

cingulate cortex demonstrated both greater signal during regulation (compared to natural

responding) and correlation with activity in the ventral striatum. These results suggest

that the dorsal cingulate cortex was involved in imagery regulation and modulated

activity in the striatum. Modulation of the striatum by cortical regions is supported by

the existence of cortico-striatal loops and anatomical connections between regions of the

cingulate cortex and the striatum (Haber & Knutson, 2010).

While the primary neuroimaging goal of Experiment 1 was to identify regulation-

induced changes in neural activity during the decision phase, the aim of Experiment 4

was to probe the brain regions recruited during regulation of affective stimuli (monetary

cues). In Experiment 4 cortical regions showed greater signal during regulation trials

compared to natural responding. Cortical regions that showed greater signal during

regulation compared to natural responding regardless of the type of cue were the right

superior frontal gyrus (BA 6) and the left precentral gyrus (BA 4). These regions are

more posterior than those typically implicated in emotion regulation, but most previous

research employed reappraisal rather than imagery and tested regulation of more complex

stimuli (i.e., photographs rather than simple, repeated shapes). Notably, the intensity of

negative emotions evoked by the cues in the current study was moderate. A simple

contrast of regulation versus natural responding for the loss cue, which was rated the

106

most negative, revealed that the left middle frontal gyrus (BA 9/44) showed a pattern of

greater signal during regulation. This region is quite consistent with previous work.

Taken together, the current studies illustrate the circuitry involved in regulation.

Regions of frontal and cingulate cortex are recruited during regulation. These regions are

involved in cognitive control and keeping task goals online (Bush, Luu, & Posner, 2000;

Miller & Cohen, 2001). Regulation decreases activity in brain regions involved in

emotion and affective learning such as the striatum, insula, and amygdala. More

conceptually, emotions can be thought of as fairly automatic, rapid responses to

important cues in the environment. Structuring or interrupting these responses using

emotion regulation dampens their intensity and their associated neural activity via the

engagement of brain regions involved in control.

Significance. These studies involved novel paradigms and contribute new

knowledge to the emotion regulation literature. Most research on emotion regulation has

assessed changes in subjective emotion experience, typically via Likert rating self-

reports. The current research extends our knowledge about the effects of emotion

regulation by applying emotion regulation in decision-making contexts. The first three

studies demonstrate that there is a relationship between cognitions and affective

responses and subsequent choices, specifically, that choices can be altered by using

emotion regulation strategies. The last study showed that regulation can dampen the

emotional response to loss cues, which had not been examined previously. This research

is significant as appetitive and aversive cues can influence behavior by prompting

approach and avoidance tendencies. Approaching appetitive stimuli such as drug cues,

can lead to maladaptive patterns of behaviors such as drug-seeking. Curbing affective

107

reactions to these cues using emotion regulation techniques may help prevent the

development of addictions in at risk populations. Before making broad generalizations

about the application of this work, there are a few potential limitations to be considered.

These limitations will be discussed below.

5.2 Potential Limitations of the Emotion Regulation Strategies

There are several limitations of the emotion regulation strategies employed these

experiments. First, regulation was instructed and specifically directed in all tasks.

Second, there may be differences in the effectiveness of the imagery regulation strategy

at altering emotions versus decision-making. Third, it is useful to consider the difference

between regulation of emotions and regulation of behavior. These three topics are

explored in greater detail below.

All four studies used instructed regulation strategies. This means that participants

were trained on what to think about for regulation trials when faced with the gain or loss

cues. Within the tasks, participants were directed to engage in the regulation techniques

via the presentation of strategy words. Thus, participants did not have to generate

regulation strategies on their own, nor did they have to decide when to engage in

regulation. Additionally, in all of the studies participants were not made explicitly aware

that the goal of the strategies was to change their emotional responses. To avoid

experimenter demand, we did not tell participants that they were to change their

emotions. Instead, we explained to them what to think about for each regulation strategy.

In daily life, we are not guided through our emotional experiences in this way; we have to

choose when to use regulation and determine which strategy is most appropriate. Despite

108

these differences between real world and laboratory experiences of regulation, these

studies demonstrate the promise and potential of emotion regulation strategies. If

participants can effectively use these strategies in the lab setting, it suggests they could be

applied in daily life. Future research is needed to test whether long-term training in the

use of regulation would lead to greater shifts in behavior, for instance risk-taking.

With regard to the imagery strategy, it is interesting to compare its effects on

decision-making versus negative emotions. Experiment 1 found that the strategy

effectively decreased risk-taking, but only for participants who felt they had successfully

visualized the relaxing imagery. In Experiment 2, the imagery strategy had no effects on

risk-taking, even when success was taken into account. Finally, in Experiment 4, the

imagery strategy decreased negative emotion associated with money losses. Previous

research has demonstrated that the imagery strategy decreases responses to cues signaling

money gains and cues signaling shock (Delgado, Gillis, & Phelps, 2008; Delgado,

Nearing, LeDoux et al., 2008). Taking all of these findings together, the underlying

message is that the imagery strategy is most effective in simple contexts.

It is important to distinguish regulation of emotions from regulation of behavior in

this work. All of the experiments employed emotion regulation strategies, however, the

first three experiments focused on changes in behavior, risk-taking, instead of changes in

emotion. The broad theory supporting the current work is that emotions influence

behavior by promoting approach or avoidance tendencies. Emotion regulation strategies

effectively change emotional responses to affective stimuli such as conditioned cues. In

the decision-making studies, conditioned cues predicted financial decisions, opportunities

to engage in risk-taking or to choose risk-free options. We theorized that natural

109

emotional responses to these cues, excitement and reward anticipation, would promote

risk-taking to gain larger rewards. Further, we hypothesized that engaging in emotion

regulation when faced with the conditioned cues would alter (decrease in Experiment 1

and 2 and Decrease or Increase in Experiment 3) these natural responses and therefore

alter risk-taking. It was not possible to assess both emotion ratings and decision-making

on the same trials in these experiments, so we relied on skin conductance measures to

ascertain whether shifts in actual emotion responses had occurred with regulation. Skin

conductance measures from Experiment 3 provided evidence that the reappraisal

regulation strategies did change actual affective responses during exposure to the cues.

Skin conductance measures from Experiment 2 did not demonstrate that any shifts in

arousal occurred as a function of imagery regulation, but this null finding was consistent

with the absence of an effect of imagery regulation on risk-taking.

5.3 Potential Limitations of the Operational Definition of Decision-making

A goal of this research was to understand how emotion regulation can alter

decision-making. The modern environment is filled with cues that signal opportunities

for rewards and potential punishments. It is important to understand how changing

thoughts and emotional reactions to theses cues can change decision-making. We chose

to study financial decision-making, and to examine risk-taking as the dependent variable

and the operational definition of decision-making. The first three experiments measure

changes in risk-taking as a function of regulation. The choices that participants made

were either risky or safe, and these choices were the outputs of the decision process.

Focusing on the specific choices was a very specific measure of decision-making. Of

110

course, the term, decision-making, encompasses a wide variety of situations. Decisions

can range from very complex and temporally extended, for instance, deciding which

house to buy, to very simple and short in duration, Coke or Sprite. All of the decisions

studies in these experiments were simple; they involved only two options, and the options

contained the same kinds of information, probability and amount. All of the decisions in

these experiments were also of short durations; participants had four seconds to indicate

their choice. The nature of the decisions used potentially limits the generalizability of the

results. It is possible that emotion regulation strategies would not be as effective when

used with complex or temporally extended decisions; however, before testing that

relationship it would be important to understand the influence of emotions on complex

and/ or temporally extended decisions. The current research does represent one kind of

experience that occurs in everyday life, cues signaling opportunities for gains or losses;

therefore, this research is informative about how changing natural reactions to those cues

impacts simple choices.

5.4 External Validity of the Financial Consequences

The external validity of the financial consequences of all four experiments was

somewhat limited. All four studies involved actual financial outcomes that were

experienced by the participants, but these outcomes never involved the loss of money

from the partici s. The worst possible outcome in the decision-making

studies was to receive no money and the worst possible outcome of the loss cues study

was to receive $20, the minimum amount that was guaranteed. All three decision-making

studies involved risky options in which the probability of winning the full amount of

111

money or receiving nothing was known. If a risky option was chosen and the participant

t win any. However,

since participants always had a choice between a guaranteed amount of money (safe

option) and risky option, it can be argued that losing on a risky choice is equivalent to

losing the guaranteed amount they could have had, had they chosen the safe option. Still,

we cannot be certain that participants conceptualized the risky options in this manner.

The experimental decisions present a different kind of risk than that encountered in the

real world. When a bet is made gambling at a casino, for example, a sum of money is put

on the line and there is a chance (typically an unknown probability) that that sum will be

lost. Given that the consequences of losing a gamble in the decision-making experiments

were less severe, one might have expected that people would often or even almost always

take a risk. The data from the three experiments show that this is not the case. People

are quite risk averse in these experiments, choosing the risky option less than half of the

time. If the contingencies of the decisions had involved actual losses, it is likely that

participants would have exhibited even greater levels of risk aversion in their choices.

In the fourth experiment, participants earned money during the Gambling Game,

and the money losses they experienced for the loss and variable cues were taken from

these earnings. Although efforts were taken to make participants feel endowed with these

Gambling Game earnings, it is possible that the losses were not salient. Further, losses

experienced in daily life typically have greater consequences and involve reductions to

the total sum of money owned.

The three decision-making studies demonstrated that people were fairly risk

averse. During the natural responding conditions (Look), participants chose the risky

112

option only about 35-45% of the time. Additionally, reaction time data revealed that

when participants chose the safe option decision times were shorter than when they chose

the risky option, suggesting that the safe option was the pre-potent response. If

pick the safe option, then it may be easier to enhance that

tendency than decrease it with regulation. The failure of the Excite imagery regulation

strategy in Experiment 1 to increase risk-taking above the levels observed in Look might

be in part be due to participants risk aversion and tendency to chose the safe response.

However, the cognitive Deemphasize strategy in Experiment 3 did increase risk-taking

above levels observed in Look, suggesting that cognitive regulation can effectively

modulate risk-taking in the opposite direction of the pre-potent response.

5.5 Intensity of Emotional Responses

The intensity of the emotions generated by the cues used in all four studies was on

average moderate based on review of the subjective ratings. It is difficult to evoke strong

emotional responses in a laboratory setting. In the first three studies, we attempted to

elicit feelings like excitement and pleasure with the presentation of the slot machine

reward cues, which always signaled the opportunity to gain money. Although

participants were paid based on their choices in selected decisions from the tasks, there

was no feedback about the outcomes of decisions within the task. Trial-by-trial feedback

likely would have increased the emotional intensity of the cues. We did not include trial-

by-trial feedback in the tasks, as it is known that feedback influences subsequent

decision-making, and we wanted to examine the effect of emotion regulation without

confounding it with feedback. The fourth study did contain trial-by-trial outcome

113

information yet, emotion ratings were still low in this study. All of the tasks used simple,

repeated stimuli; this design could have fostered habituation and lowered the emotion

intensity of the cues.

5.6 Individual Differences

Emotion regulation techniques were not affective for all participants. In

Experiment 1 two-thirds of the sample (regulators) felt successful at visualizing the

relaxing imagery. The other one-third (non-regulators) did not feel successful, and

decreases in risk-taking and striatum activity were not observed. It is possible that the

imagery strategy may have been difficult for these participants; perhaps they would have

been more successful with a different type of strategy, for instance cognitive reappraisal.

Given that there are different kinds of emotion regulation strategies, it is highly unlikely

that the ability to visualize and engage in mental imagery is equivalent to regulation

ability.

A large number of individual difference measures were included in Experiments 2

and 3. Only one of these measures, self-esteem, was significantly related to observed

shifts in risk-taking with regulation, and then only for one specific condition. The

participants in these experiments were undergraduate students; therefore, it is possible

that this sample was somewhat homogenous, and the variability observed in the

individual differences measures may not be representative of the general population.

Based on this sample, the most parsimonious conclusion is that differences observed

across participants in the overall effect of regulation on risk-taking are not due to any

particular traits, but rather situational factors. Importantly, this sample represents healthy

114

young adults. Deficits in emotion regulation have been observed in many clinical

disorders including depression and schizophrenia.

The current studies did not reveal any gender differences in the effects of emotion

regulation on risk-taking. It is possible that gender differences would emerge with larger

sample sizes, suggesting that if there are real gender differences, they may be small

effects.

5.7 Overall Conclusions and Future Directions

Broadly, this collection of experiments illustrates the power of our thoughts.

Changing what we think about when faced with appetitive and aversive cues affects how

we feel and how we act. Emotion regulation strategies typically used to alter subjective

affective responses to negative stimuli and situations can be applied in the context of

positive stimuli and decision-making. This research demonstrates that imagery-focused

strategies are most effective in simple contexts. Reappraisal strategies altered risk-taking

in a complex decision context; future research is needed to characterize the effects of

reappraisal strategies on risk-taking in simple decision contexts. Given the effectiveness

of reappraisal strategies with the set of mixed decision types, future investigations of

emotion regulation applications to subsequent behavior should utilize cognitive strategies

like reappraisal.

Based on the current findings, it would be beneficial to explore the effects of

more extensive training in use of cognitive emotion regulation strategies. Perhaps with

long-term practice, people could develop habits of regulation, which would help them to

make their responses to appetitive and aversive cues more in line with their long term

115

goals. The current research demonstrates the potential of emotion regulation strategies to

influence behavior; future research is needed to determine the limits of and extents of

these influences. Additionally, future research should test these strategies in clinical

populations, for instance drug addicted individuals, to determine if emotion regulation

will alter behavior in those individuals.

116

References

Abler, B., Walter, H., Erk, S., Kammerer, H., & Spitzer, M. (2006). Prediction error as a linear function of reward probability is coded in human nucleus accumbens. Neuroimage, 31(2), 790-795.

Alexander, G. E., & Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends in Neuroscience, 13(7), 266-271.

Arnsten, A. F., & Goldman-Rakic, P. S. (1998). Noise stress impairs prefrontal cortical cognitive function in monkeys: evidence for a hyperdopaminergic mechanism. Archives of General Psychiatry, 55(4), 362-368.

Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience, 27(31), 8161-8165.

Balleine, B. W., & O'Doherty, J. P. (2010). Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35(1), 48-69.

Beauregard, M., Levesque, J., & Bourgouin, P. (2001). Neural correlates of conscious self-regulation of emotion. Journal of Neuroscience, 21(18), RC165.

Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex, 10(3), 295-307.

Blair, K. S., Smith, B. W., Mitchell, D. G., Morton, J., Vythilingam, M., Pessoa, L., Fridberg, D., Zametkin, A., Sturman, D., Nelson, E. E., Drevets, W. C., Pine, D. S., Martin, A., & Blair, R. J. (2007). Modulation of emotion by cognition and cognition by emotion. Neuroimage, 35(1), 430-440.

Breiter, H. C., Gollub, R. L., Weisskoff, R. M., Kennedy, D. N., Makris, N., Berke, J. D., Goodman, J. M., Kantor, H. L., Gastfriend, D. R., Riorden, J. P., Mathew, R. T., Rosen, B. R., & Hyman, S. E. (1997). Acute effects of cocaine on human brain activity and emotion. Neuron, 19(3), 591-611.

Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4(6), 215-222.

117

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.

Christopoulos, G. I., Tobler, P. N., Bossaerts, P., Dolan, R. J., & Schultz, W. (2009). Neural correlates of value, risk, and risk aversion contributing to decision making under risk. Journal of Neuroscience, 29(40), 12574-12583.

Clark, L., Bechara, A., Damasio, H., Aitken, M. R., Sahakian, B. J., & Robbins, T. W. (2008). Differential effects of insular and ventromedial prefrontal cortex lesions on risky decision-making. Brain, 131(Pt 5), 1311-1322.

Cutrona, C. E., & Russell, D. W. (1987). The provisions of social relationships and adaptation to stress. Advances in Personal Relationships, 1, 37-67.

De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684-687.

Delgado, M. R. (2007). Reward-related responses in the human striatum. Annals of the New York Academy of Sciences, 1104, 70-88.

Delgado, M. R., Gillis, M. M., & Phelps, E. A. (2008). Regulating the expectation of reward via cognitive strategies. Nature Neuroscience, 11(8), 880-881.

Delgado, M. R., Jou, R. L., & Phelps, E. A. (2011). Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers. Frontiers in Neuroscience, 5, 71.

Delgado, M. R., Labouliere, C. D., & Phelps, E. A. (2006). Fear of losing money? Aversive conditioning with secondary reinforcers. Social Cognitive Affective Neuroscience, 1(3), 250-259.

Delgado, M. R., Locke, H. M., Stenger, V. A., & Fiez, J. A. (2003). Dorsal striatum responses to reward and punishment: effects of valence and magnitude manipulations. Cognitive, Affective, and Behavioral Neuroscience, 3(1), 27-38.

Delgado, M. R., Nearing, K. I., LeDoux, J. E., & Phelps, E. A. (2008). Neural circuitry underlying the regulation of conditioned fear and its relation to extinction. Neuron, 59(5), 829-838.

118

Delgado, M. R., Olsson, A., & Phelps, E. A. (2006). Extending animal models of fear conditioning to humans. Biological Psychology, 73(1), 39-48.

Delgado, M. R., Stenger, V. A., & Fiez, J. A. (2004). Motivation-dependent responses in the human caudate nucleus. Cerebral Cortex, 14(9), 1022-1030.

Eippert, F., Veit, R., Weiskopf, N., Erb, M., Birbaumer, N., & Anders, S. (2007). Regulation of emotional responses elicited by threat-related stimuli. Human Brain Mapping, 28(5), 409-423.

Engelmann, J. B., & Tamir, D. (2009). Individual differences in risk preference predict neural responses during financial decision-making. Brain Research, 1290, 28-51.

Everitt, B. J., Belin, D., Economidou, D., Pelloux, Y., Dalley, J. W., & Robbins, T. W. (2008). Review. Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Philosophical Transactions of the Royal Society of London: B Biological Sciences, 363(1507), 3125-3135.

Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magnetic Resonance Medicine, 33(5), 636-647.

Foti, D., & Hajcak, G. (2008). Deconstructing reappraisal: descriptions preceding arousing pictures modulate the subsequent neural response. Journal of Cognitive Neuroscience, 20(6), 977-988.

Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15(4), 870-878.

Giuliani, N. R., McRae, K., & Gross, J. J. (2008). The up- and down-regulation of amusement: Experiential, behavioral, and autonomic consequences. Emotion, 8(5), 714-719.

Goebel, R., Esposito, F., & Formisano, E. (2006). Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Human Brain Mapping, 27(5), 392-401.

119

Goldin, P. R., McRae, K., Ramel, W., & Gross, J. J. (2008). The neural bases of emotion regulation: Reappraisal and suppression of negative emotion. Biological Psychiatry, 63(6), 577-586.

Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, .37(6), pp.

Gray, J. R. (2004). Integration of emotion and cognitive control. Current Directions in Psychological Science, 13(2), 46-48.

Green, M. J., & Malhi, G. S. (2006). Neural mechanisms of the cognitive control of emotion. Acta Neuropsychiatrica, 18(3-4), 144-153.

Gross, J. J. (1998a). Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74(1), 224-237.

Gross, J. J. (1998b). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2(3), 271-299.

Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology, 39(3), 281-291.

Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348-362.

Haber, S. N., & Knutson, B. (2010). The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology, 35(1), 4-26.

Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324(5927), 646-648.

Harenski, C. L., & Hamann, S. (2006). Neural correlates of regulating negative emotions related to moral violations. Neuroimage, 30(1), 313-324.

120

Heilman, R. M., Crisan, L. G., Houser, D., Miclea, M., & Miu, A. C. (2010). Emotion regulation and decision making under risk and uncertainty. Emotion, 10(2), 257-265.

Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, Vol. 92, No. 5, 1644-1655.

Hsu, M., Krajbich, I., Zhao, C., & Camerer, C. F. (2009). Neural response to reward anticipation under risk is nonlinear in probabilities. Journal of Neuroscience, 29(7), 2231-2237.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.

Kalisch, R., Wiech, K., Critchley, H. D., Seymour, B., O'Doherty, J. P., Oakley, D. A., Allen, P., & Dolan, R. J. (2005). Anxiety Reduction through Detachment: Subjective, Physiological, and Neural Effects. Journal of Cognitive Neuroscience, 17(6), 874-883.

Kalisch, R., Wiech, K., Herrmann, K., & Dolan, R. J. (2006). Neural correlates of self-distraction from anxiety and a process model of cognitive emotion regulation. Journal of Cognitive Neuroscience, 18(8), 1266-1276.

Kang, S.-M., & Shaver, P. R. (2004). Individual Differences in Emotional Complexity: Their Psychological Implications. Journal of Personality, 72(4), 687-726.

Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. Journal of Cognitive Neuroscience, 19(5), 776-798.

Knutson, B., & Cooper, J. C. (2005). Functional magnetic resonance imaging of reward prediction. Current Opinion in Neurology, 18(4), 411-417.

Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., & Wager, T. D. (2008). Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. Neuroimage, 42(2), 998-1031.

Kober, H., Kross, E. F., Mischel, W., Hart, C. L., & Ochsner, K. N. (2009). Regulation of craving by cognitive strategies in cigarette smokers. Drug and Alcohol Dependence, 106(1), 52-55.

121

Kober, H., Mende-Siedlecki, P., Kross, E. F., Weber, J., Mischel, W., Hart, C. L., & Ochsner, K. N. (2010). Prefrontal-striatal pathway underlies cognitive regulation of craving. PNAS Proceedings of the National Academy of Sciences of the United States of America, 107(33), 14811-14816.

Kring, A. M., & Gordon, A. H. (1998). Sex differences in emotion: Expression, experience, and physiology. Journal of Personality and Social Psychology, 74(3), 686-703.

Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47(5), 763-770.

Kushner, M. G., Abrams, K., Donahue, C., Thuras, P., Frost, R., & Kim, S. W. (2007). Urge to gamble in problem gamblers exposed to a casino environment. Journal of Gambling Studies, 23(2), 121-132.

LaBar, K. S., Gatenby, J. C., Gore, J. C., LeDoux, J. E., & Phelps, E. A. (1998). Human amygdala activation during conditioned fear acquisition and extinction: a mixed-trial fMRI study. Neuron, 20(5), 937-945.

LaBar, K. S., LeDoux, J. E., Spencer, D. D., & Phelps, E. A. (1995). Impaired fear conditioning following unilateral temporal lobectomy in humans. Journal of Neuroscience, 15(10), 6846-6855.

Lazarus, R., & Alfert, E. (1964). Short-Circuiting of Threat by Experimentally Altering Cognitive Appraisal. Journal of Abnormal Psychology, 69, 195-205.

Lazarus, R. S. (1991). Progress on a cognitive-motivational-relational theory of emotion. American Psychologist, .46(8), 819-834.

Leland, D. S., Arce, E., Feinstein, J. S., & Paulus, M. P. (2006). Young adult stimulant users' increased striatal activation during uncertainty is related to impulsivity. Neuroimage, 33(2), 725-731.

Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychological Science, 15(5), 337-341.

122

Levesque, J., Fanny, E., Joanette, Y., Paquette, V., Mensour, B., Beaudoin, G., Leroux, J.-M., Bourgouin, P., & Beauregard, M. (2003). Neural circuitry underlying voluntary suppression of sadness. Biological Psychiatry, 53(6), 502-510.

Mandler, G. (1984). Mind and Body: Psychology of Emotion and Stress. New York: W. W. Norton & Company.

Martin, L. N., & Delgado, M. R. (2011). The Influence of Emotion Regulation on Decision-making under Risk. Journal of Cognitive Neuroscience, 23(9), 25692581.

Matthews, S. C., Simmons, A. N., Lane, S. D., & Paulus, M. P. (2004). Selective activation of the nucleus accumbens during risk-taking decision making. Neuroreport, 15(13), 2123-2127.

McRae, K., Hughes, B., Chopra, S., Gabrieli, J. D., Gross, J. J., & Ochsner, K. N. (2010). The neural bases of distraction and reappraisal. Journal of Cognitive Neuroscience, 22(2), 248-262.

McRae, K., Ochsner, K. N., Mauss, I. B., Gabrieli, J. J. D., & Gross, J. J. (2008). Gender differences in emotion regulation: An fMRI study of cognitive reappraisal. Group Processes & Intergroup Relations, 11(2), 143-162.

Middleton, F. A., & Strick, P. L. (2002). Basal-ganglia 'projections' to the prefrontal cortex of the primate. Cerebral Cortex, 12(9), 926-935.

Milad, M. R., & Quirk, G. J. (2002). Neurons in medial prefrontal cortex signal memory for fear extinction. Nature, 420(6911), 70-74.

Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience. Vol 24 2001, 167-202.

Nelson, A., & Killcross, S. (2006). Amphetamine exposure enhances habit formation. Journal of Neuroscience, 26(14), 3805-3812.

O'Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14(6), 769-776.

123

Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14(8), 1215-1229.

Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9(5), 242-249.

Ochsner, K. N., & Gross, J. J. (2008). Cognitive emotion regulation: Insights from social cognitive and affective neuroscience. Current Directions in Psychological Science, 17(2), 153-158.

Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., & Gross, J. J. (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. Neuroimage, 23(2), 483-499.

Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotions. Series in affective science. Affective neuroscience: The foundations of human and animal emotions xii, 466 pp New York, NY, US: Oxford University Press.

Patil, P. G., Apfelbaum, J. L., & Zacny, J. P. (1995). Effects of a cold-water stressor on psychomotor and cognitive functioning in humans. Physiology & Behavior, 58(6), 1281-1286.

Phan, K. L., Fitzgerald, D. A., Nathan, P. J., Moore, G. J., Uhde, T. W., & Tancer, M. E. (2005). Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biological Psychiatry, 57(3), 210-219.

Phelps, E. A., Delgado, M. R., Nearing, K. I., & LeDoux, J. E. (2004). Extinction learning in humans: role of the amygdala and vmPFC. Neuron, 43(6), 897-905.

Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron, 48(2), 175-187.

Porcelli, A. J., & Delgado, M. R. (2009). Acute stress modulates risk taking in financial decision making. Psychological Science, 20(3), 278-283.

124

Potenza, M. N., & Winters, K. C. (2003). The neurobiology of pathological gambling: Translating research findings into clinical advances. Journal of Gambling Studies, 19(1), 7-10.

Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008). Human insula activation reflects risk prediction errors as well as risk. Journal of Neuroscience, 28(11), 2745-2752.

Quirk, G. J., & Beer, J. S. (2006). Prefrontal involvement in the regulation of emotion: convergence of rat and human studies. Current Opinion in Neurobiology, 16(6), 723-727.

Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9(7), 545-556.

Ray, R. D., Ochsner, K. N., Cooper, J. C., Robertson, E. R., Gabrieli, J. D. E., & Gross, J. J. (2005). Individual differences in trait rumination and the neural systems supporting cognitive reappraisal. Cognitive, Affective, & Behavioral Neuroscience, 5(2), 156-168.

Richards, J. M., & Gross, J. J. (2000). Emotion regulation and memory: The cognitive costs of keeping one's cool. Year of Publication 2000. Journal of Personality and Social Psychology, 79(3), 410-424.

Rosenberg, M. (1989). Society and the adolescent self-image (rev. ed.). 1989. Society and the adolescent self-image (rev. ed.). xxxii, 347 pp. Middletown, CT, England: Wesleyan University Press; England.

Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P. (1995). Emotional attention, clarity, and repair: Exploring emotional intelligence using the Trait Meta-Mood Scale. In Emotion, disclosure, & health (pp. 125-154). Washington, DC: American Psychological Association; US.

Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69(5), 379-399.

Schaefer, S. M., Jackson, D. C., Davidson, R. J., Aguirre, G. K., Kimberg, D. Y., & Thompson-Schill, S. L. (2002). Modulation of amygdalar activity by the conscious regulation of negative emotion. Journal of Cognitive Neuroscience, 14(6), 913-921.

125

Scherer, K. R. (Ed.). (1999). Appraisal Theory. West Sussex: John Wiley & Sons Ltd.

Schonberg, T., Daw, N. D., Joel, D., & O'Doherty, J. P. (2007). Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making. Journal of Neuroscience, 27(47), 12860-12867.

Seo, M. G., & Barrett, L. F. (2007). Being Emotional during Decision Making-Good or Bad? An Empirical Investigation. Academy of Management Journal, 50(4), 923-940.

Sinha, R. (2007). The role of stress in addiction relapse. Current Psychiatry Reports, 9(5), 388-395.

Sinha, R., Lacadie, C., Skudlarski, P., Fulbright, R. K., Rounsaville, B. J., Kosten, T. R., & Wexler, B. E. (2005). Neural activity associated with stress-induced cocaine craving: a functional magnetic resonance imaging study. Psychopharmacology (Berl), 183(2), 171-180.

Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., Yoshinobu, L., Gibb, J., Langelle, C., & Harney, P. (1991). The will and the ways: development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60(4), 570-585.

Sokol-Hessner, P., Hsu, M., Curley, N. G., Delgado, M. R., Camerer, C. F., & Phelps, E. A. (2009). Thinking like a trader selectively reduces individuals' loss aversion. Proceedings of the National Academy of Sciences, U.S.A., 106(13), 5035-5040.

Staudinger, M. R., Erk, S., Abler, B., & Walter, H. (2009). Cognitive reappraisal modulates expected value and prediction error encoding in the ventral striatum. Neuroimage, 47(2), 713-721.

Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging. New York: Thieme Medical Publishers, Inc.

Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-518.

126

Urry, H. L., van Reekum, C. M., Johnstone, T., Kalin, N. H., Thurow, M. E., Schaefer, H. S., Jackson, C. A., Frye, C. J., Greischar, L. L., Alexander, A. L., & Davidson, R. J. (2006). Amygdala and ventromedial prefrontal cortex are inversely coupled during regulation of negative affect and predict the diurnal pattern of cortisol secretion among older adults. Journal of Neuroscience, 26(16), 4415-4425.

Vohs, K. D., & Faber, R. J. (2007). Spent Resources: Self-Regulatory Resource Availability Affects Impulse Buying. Journal of Consumer Research, 33(4), 537-547.

Volkow, N. D., Fowler, J. S., Wang, G. J., Telang, F., Logan, J., Jayne, M., Ma, Y., Pradhan, K., Wong, C., & Swanson, J. M. (2010). Cognitive control of drug craving inhibits brain reward regions in cocaine abusers. Neuroimage, 49(3), 2536-2543.

Wang, G. J., Volkow, N. D., Telang, F., Jayne, M., Ma, Y., Pradhan, K., Zhu, W., Wong, C. T., Thanos, P. K., Geliebter, A., Biegon, A., & Fowler, J. S. (2009). Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation. Proceedings of the National Academy of Sciences, U.S.A., 106(4), 1249-1254.

Weiss, F. (2005). Neurobiology of craving, conditioned reward and relapse. Current Opinion in Pharmacology, 5(1), 9-19.

Winkielman, P., Berridge, K. C., & Wilbarger, J. L. (2005). Unconscious Affective Reactions to Masked Happy Versus Angry Faces Influence Consumption Behavior and Judgments of Value. Personality and Social Psychology Bulletin, 31(1), 121-135.

Yacubian, J., Glascher, J., Schroeder, K., Sommer, T., Braus, D. F., & Buchel, C. (2006). Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain. Journal of Neuroscience, 26(37), 9530-9537.

Yacubian, J., Sommer, T., Schroeder, K., Glascher, J., Braus, D. F., & Buchel, C. (2007). Subregions of the ventral striatum show preferential coding of reward magnitude and probability. Neuroimage, 38(3), 557-563.

127

Figures Figure 1.1 Overview of Experiments

Figure 1.1. Overview of experiments. Experiment 1: imagery-focused monetary gain cues risk-taking in financial decisions & neural activity. Experiment 2: imagery-focused monetary gain cues risk-taking in financial decisions & arousal (skin conductance). Experiment 3: reappraisal monetary gain cues risk-taking in financial decisions & arousal (skin conductance). Experiment 4: imagery-focused monetary loss cues emotion ratings & neural activity.

128

Figure 2.1 Schematic of the Task Used in Experiment 1

Figure 2.1. Schematic of the Slot Machine Game, the experimental task used in Experiment 1. The number line on the left shows the task phases and their durations. A picture of a slot machine indicated a money decision and a picture of a stamp machine indicated a stamp decision. Money trials involved decisions between risky and safe options, and stamp trials involved control decisions between two representations of

indicated natur -focused regulation.

129

Figure 2.2 Decision-making Results of Experiment 1 Figure 2.2. Risk-taking in Experiment 1 was affected by strategy (Look = natural responding, Relax = imagery regulation) and self-reported success at using the imagery regulation. Regulators (n = 20) and Non-regulators (n = 10) groups were defined based on success ratings. Regulators showed decreased risk-taking during Relax trials, while Non-average proportion the risky option was chosen for decisions made on a paper questionnaire that completed days after the experimental session.

130

Figure 2.3 Neuroimaging Results of Experiment 1: Effects of Strategy and Choice in the Striatum Figure 2.3. A) Bilateral striatum correlated with increasing probability of reward during decision-making under risk. B) Mean parameter estimates for left ventral striatum reveal an interaction between instruction (Look, Relax) and choice (risky, safe). C) A similar result is observed in the right ventral striatum (Error bars indicate ±s.e.m.).

131

Figure 2.4 Neuroimaging Results of Experiment 1: Effects of Strategy and Choice in the Midbrain and Insula

Figure 2.4. The effect of strategy and choice in (A) the midbrain and (B) the insula. The left midbrain BOLD responses demonstrated an interaction of strategy and choice such that activity to risky choices was significantly reduced after regulation (Relax) compared to after responding naturally (Look). A main effect of choice was observed in the left anterior insula, with greater responses to risky compared to safe choices (Error bars indicate ±s.e.m.).

132

Figure 3.1 Schematic of the Task Used in Experiment 2

Figure 3.1. Schematic of the Imagery Slot Machine Game, the experimental task used in Experiment 2. The number line on the left indicates the phases of the task and their durations.

133

Figure 3.2 Decision-making Results of Experiment 2

Figure 3.2.Risk-taking was not affected by emotion regulation (Look = no regulation condition, Relax = regulation condition), but it was affected by decision type (good, bad, neutral).

134

Figure 3.3 Schematic of the Task Used in Experiment 3

Figure 3.3. Schematic of the Reappraisal Slot Machine Game, the experimental task used in Experiment 3. The number line on the left indicates the phases of the task and their durations.

135

Figure 3.4 Decision-making Results of Experiment 3

Figure 3.4. Risk-taking was significantly affected by emotion regulation (Emphasize, Deemphasize) and by decision type (good, bad, neutral).

136

Figure 4.1 Schematic of the Task Used in Experiment 4

Figure 4.1. Experimental task used in Experiment 4, the Colored Squares Game. The phases and durations of the task are indicated along the number line on the left side. Trials for all three cue types (loss, safe, and variable) are illustrated.

137

Figure 4.2 Rating Results of Experiment 4

Figure 4.2. Negative emotion intensity ratings for the Loss and Variable cues were significantly lower during regulation (Relax) compared to natural responding (Look). Emotion intensity ratings were made using a Likert scale in with 1 = weak and 7 = strong negative emotions.

138

Figure 4.3 Activity in the Left Dorsolateral Prefrontal Cortex ROI

Figure 4.3. Results from Experiment 4. The left dorsolateral prefrontal cortex a priori ROI showed a main effect of strategy, with greater BOLD signal in Relax relative to Look.

139

Figure 4.4 Activity in the Right Superior Frontal Gyrus (BA 6)

Figure 4.4. Whole brain ANOVA Results from Experiment 4. The right superior frontal gyrus (BA 6) was identified by the main effect of strategy whole brain ANOVA. This region shows greater activity during regulation (Relax) than natural responding (Look).

140

Figure 4.5 Right Inferior Frontal Gyrus Activity (Relax > Look) Correlation with Emotion Regulation Scores

Figure 4.5. Correlation Results of Experiment 4: Contrast plus correlation analysis for the variable cue. A) BOLD signal in the right inferior frontal gyrus (BA 45) was greater during Relax and negatively correlated with emotion regulation scores, Look minus Relax emotion ratings. B) Scatter plot showing that that greater activity in this region in Relax (relative to Look) was associated with higher negative emotion ratings in Relax (relative to Look).

141

Tables Table 2.1 Financial Decisions in Experiment 1 Risky Option Safe Option Decision Probability Amount Probability Amount

1 0.20 $10.35 1 $2.07 2 0.35 $11.66 1 $4.08 3 0.50 $12.18 1 $6.09 4 0.65 $6.28 1 $4.08 5 0.80 $2.59 1 $2.07

142

Table 2.2 Regions that Correlated with Increasing Probability of Reward in the Regulators Group Talairach Coordinates Region Laterality BA Voxels x y z t-stat Superior frontal gyrus L 6 161 -3 -16 55 6.39 Medial frontal gyrus R 32/6 347 6 2 49 6 Medial frontal gyrus L 32/6 365 -3 0 49 5.35 Inferior frontal gyrus L 44 119 -39 8 31 6.69 Insula L 132 -36 5 13 5.73 Ventral striatum L 340 -15 5 4 7.38 Thalamus L 342 -6 -10 4 6.86 Ventral striatum R 284 12 2 1 6.61 Thalamus R 283 6 -16 1 7.12 Insula L 300 -36 11 -2 8.79 Hippocampus R 194 21 -28 -2 7.48 Midbrain R 889 6 -22 -8 11.4 Midbrain L 747 -6 -16 -8 9.54 Midbrain L 850 -6 -25 -8 8.72 Lingual gyrus R 17 804 18 -91 -8 8.81 Lingual gyrus L 17 344 -18 -94 -8 7.46 Occipital lobe R 18 544 24 -85 -14 8.08 Cerebellum L 200 -1 -49 -28 5.76 Note. BA = Brodmann area; L = left; R = right; Map was FDR corrected, q(FDR) < 0.01

143

Table 3.1 Financial Decisions in Experiments 2 and 3

Risky Option Safe Option Decision Type Probability Amount EV Probability Amount EV

Good 0.35 $11.74 $4.11 1 $2.88 $2.88 Good 0.5 $8.30 $4.15 1 $2.91 $2.91 Good 0.65 $6.45 $4.19 1 $2.93 $2.93

Neutral 0.35 $11.85 $4.15 1 $4.15 $4.15 Neutral 0.5 $8.41 $4.21 1 $4.20 $4.20 Neutral 0.65 $6.56 $4.26 1 $4.26 $4.26

Bad 0.35 $11.59 $4.06 1 $5.27 $5.27 Bad 0.5 $8.15 $4.08 1 $5.30 $5.30 Bad 0.65 $6.30 $4.10 1 $5.32 $5.32

Note. EV = expected value

144

Table 3.2 Comparison of Imagery-focused and Reappraisal Strategies Strategy Dimension Imagery Regulation Reappraisal Regulation Attention Directed internally Directed at stimulus Effects on Emotion Create a new emotion Alter emotion elicited by the stimulus

145

Table 4.1 Anticipation Phase ANOVA: Brain Regions Showing a Main Effect of Strategy

Talairach

Coordinates Region Laterality BA Voxels x y z F p Relax > Look Superior frontal gyrus R 6 198 5 4 60 12.8589 0.00156 Precentral gyrus L 4 254 -34 -11 54 14.8755 0.0008 Look > Relax Inferior parietal lobe R 40 512 41 -65 42 15.6741 0.00062 Middle temporal gyrus R 21 317 69 -38 -13 16.7907 0.00044 Fusiform gyrus R 18 963 20 -95 -21 35.4603 5E-06 Fusiform gyrus L 18 714 -28 -95 -21 31.661 0.00001 Cerebellum L 282 -22 -77 -43 17.9991 0.00031

146

Table 4.2 Anticipation Phase ANOVA: Brain Regions Showing a Main Effect of Cue Type Talairach Coordinates Region Laterality BA Voxels x y z F p Postcentral gyrus L 1/2 514 -22 -32 60 12.41 5E-05 Superior frontal gyrus/ Middle frontal gyrus L 6 988 -4 4 54 18.10 2E-06 Precentral gyrus R 4 256 23 -23 51 8.54 0.0007 Superior frontal gyrus R 6/8 330 20 18 45 9.83 0.0003 Cingulate R 31 567 14 -32 33 12.30 5E-05 Cingulate L 24 495 -7 8 27 10.15 0.0002 Insula L 169 -31 19 21 7.61 0.0014 Corpus callosum (white matter) L 652 -4 -26 15 10.00 0.0002 Cuneus L 18 277 -25 -98 9 8.18 0.0009 Cuneus/ Occipital lobe R 18 665 29 -92 3 8.95 0.0005 Superior temporal gyrus L 22 253 -61 -26 3 10.68 0.0002 Subcallosal gyrus R 25 289 14 19 -12 8.76 0.0006 Cerebellum L 695 -10 -53 -15 10.41 0.0002 Fusiform gyrus R 37 307 44 -44 -18 8.50 0.0007 Note. BA =Brodmann area; L = left; R = right

147

Table 4.3 Anticipation Phase ANOVA: Brain Regions Showing a an Interaction of Strategy and Cue Type

Talairach

Coordinates Region Laterality BA Voxels x y z F p Precentral gyrus R 4/6 300 17 -14 55 11.56 9E-05 Postcentral gyrus R 4 319 23 -29 49 9.01 0.0005 Postcentral gyrus R 1/2 450 47 -23 45 9.09 0.0005 Superior temporal gyrus R 22 816 47 -35 15 11.20 0.0001 Middle temporal gyrus R 21 337 47 -20 -3 8.56 0.0007 Middle temporal gyrus R 21 564 41 -35 -3 13.28 3E-05 Parahippocampal gyrus R 35 505 11 -35 -9 10.22 0.0002 Parahippocampal gyrus L 19 656 -10 -47 -6 10.18 0.0002 Inferior parietal lobe L 40 289 -37 -38 30 10.21 0.0002 Inferior parietal lobe L 40 188 -55 -23 31 8.83 0.0006 Note. BA =Brodmann area; L = left; R = right

148

Table 4.4 Cue Phase ANOVA: Brain Regions Showing a Main Effect of Cue Type

Talairach

Coordinates Region Laterality BA Voxels x y z F p Postcentral gyrus R 1/2 223 50 -32 58 7.77 0.0012 Inferior parietal lobe R 40 198 32 -38 39 7.42 0.0016 Inferior parietal lobe R 40 298 41 -56 36 7.45 0.0016 Precuneus R 7 209 5 -59 30 8.90 0.0005 Lingual gyrus R 18/19 183 26 -56 3 7.94 0.0011 Caudate R 513 5 16 0 9.65 0.0003 Middle temporal gyrus R 21 613 51 -14 -3 12.60 4E-05 Fusiform gyrus L 21 359 -64 5 -24 16.31 4E-06 Note. BA =Brodmann area; L = left; R = right

149

Table 4.5 Cue Phase ANOVA: Brain Regions Showing a Main Effect of Strategy (All Relax > Look)

Talairach

Coordinates Region Laterality BA Voxels x y z F p Superior parietal lobe R 7 798 38 -56 57 29.16 2E-05 Inferior parietal lobe R 40 1000 44 -50 45 22.35 9E-05 Middle frontal gyrus L 8 331 -43 16 42 13.81 0.0011 Precentral gyrus L 6/4 265 -46 -8 42 17.00 0.0004 Inferior parietal lobe L 40 418 -58 -53 36 14.65 0.0009 Middle frontal gyrus R 9/8 944 44 19 36 27.41 3E-05 Precuneus R 19/7 571 5 -74 33 13.68 0.0012 Middle frontal gyrus R 9/46 409 41 43 30 20.02 0.0002 Lingual gyrus/ Cuneus L 17 892 -4 -95 0 16.17 0.0005 Middle temporal gyrus R 21 510 69 -35 -3 29.24 2E-05 Lingual gyrus L 18 607 -16 -98 -3 17.28 0.0004 Middle temporal gyrus L 21 631 -58 -35 -9 17.21 0.0004 Cerebellum R 390 26 -74 -33 16.13 0.0005 Cerebellum L 430 -13 -44 -36 22.65 9E-05 Cerebellum L 349 -37 -68 -36 17.06 0.0004 Note. BA =Brodmann area; L = left; R = right

150

Table 4.6 Cue Phase ANOVA: Brain Regions Showing an Interaction of Strategy and Cue Type

Talairach

Coordinates Region Laterality BA Voxels x y z F p Paracentral lobule/ Precuneus L 194 -13 -41 61 8.82 0.0006 Inferior parietal lobe R 40 375 34 -41 51 11.10 0.0001 Precuneus R 7 747 15 -56 48 18.33 1E-06 Precuneus L 7 692 -19 -58 48 12.93 4E-05 Inferior parietal lobe L 40 539 -37 -38 48 9.07 0.0005 Precuneus R 7 668 20 -56 46 13.81 2E-05 Precuneus L 685 -22 -50 39 11.50 9E-05 Inferior parietal lobe R 106 35 -46 39 7.96 0.0011 Inferior frontal gyrus L 44 235 -52 10 30 9.00 0.0005 Interior parietal lobe L 40 153 -43 -32 30 7.43 0.0016 White matter R 471 35 -35 27 7.98 0.0011 Cuneus R 18 217 17 -77 18 8.00 0.001 Insula L 183 -31 -26 18 7.66 0.0013 White matter L 375 -19 -47 18 7.32 0.0017 Cuneus L 18 472 -22 -69 15 12.92 4E-05 Cuneus L 18 472 -22 -69 15 12.92 4E-05 Inferior frontal gyrus L 46 331 -49 37 9 8.51 0.0007 Cuneus/ Occipital lobe R 31 733 23 -62 9 11.13 0.0001 Occipital lobe R 19 109 37 -80 9 6.76 0.0027 Occipital lobe L 19 526 -30 -70 6 8.61 0.0007 Superior temporal gyrus L 42/ 22 145 -45 -26 6 8.77 0.0006 Superior temporal gyrus L 22 359 -40 -32 5 12.38 5E-05 Superior temporal gyrus R 22 283 47 -20 4 8.92 0.0005 Superior temporal gyrus R 22 132 59 -17 3 7.73 0.0013 Inferior temporal gyrus R 37 657 50 -68 0 9.75 0.0003 Middle temporal gyrus L 21/37 209 -49 -53 -3 8.27 0.0009 Fusiform gyrus/ Lingual gyrus L 19 235 -25 -53 -6 7.95 0.0011 Fusiform gyrus/ Occipital lobe R 19 476 35 -68 -9 9.48 0.0004 Occipital lobe L 19 307 -46 -69 -9 8.28 0.0008 Middle temporal gyrus L 21 725 -58 -26 -12 12.23 6E-05 Fusiform gyrus R 20 248 38 -35 -17 8.22 0.0009

151

Table 4.6 (continued) Talairach Coordinates Region Laterality BA Voxels x y z F p Cerebellum L 258 -28 -32 -30 9.15 0.0005 Cerebellum R 238 17 -29 -36 9.29 0.0004 Note. BA =Brodmann area; L = left; R = right

152

Table 4.7 Loss Cue: Brain Regions Active for the Anticipation Phase Contrast of Relax vs. Look

Talairach

Coordinates Region Laterality BA Voxels x y z F p Relax > Look Middle frontal gyrus L 9/44 662 -37 16 27 4.95 5.3E-05 Inferior parietal lobe R 40 4431 44 -38 27 4.54 0.00015 Superior temporal gyrus L 22 1090 -46 -41 18 3.97 0.00061 Middle temporal gyrus R 21 4431 44 -44 6 4.54 0.00015 Look > Relax Superior frontal gyrus R 8 930 5 46 45 -4.94 5.4E-05 Occipital lobe R 19 583 35 -74 36 -3.74 0.00106 Parahippocampal gyrus R 30 205 12 -38 0 -3.50 0.00192 Cerebellum L 695 -10 -47 -6 -4.05 0.0005 Parahippocampal gyrus R 35 753 17 -32 -6 -4.92 5.7E-05 Cerebellum L 540 -10 -35 -12 -4.35 0.00024 R Fusiform Gyrus R 18 2815 17 -102 -18 -4.07 0.00048 L Fusiform Gyrus L 18 6165 -28 -95 -21 -5.81 6E-06 Cerebellum L 632 -49 -62 -36 -4.23 0.00031 Note. BA =Brodmann area; L = left; R = right

153

Table 4.8 Variable Cue: Brain Regions Active for the Anticipation Phase Contrast of Relax vs. Look

Talairach

Coordinates Region Laterality BA Voxels x y z F p Relax > Look No regions Look > Relax Precentral gyrus L 6 699 -61 -5 33 -3.74 0.00107 Postcentral gyrus R 43 794 50 -11 21 -4.05 0.0005 Middle temporal gyrus R 21 2450 47 -26 0 -5.61 0.00001 Fusiform gyrus R 37 598 38 -35 -9 -4.25 0.0003 Occipital lobe L 19 5554 -43 -86 -12 -4.66 0.00011 Fusiform gyrus R 18 1968 23 -95 -18 -4.52 0.00015 Note. BA =Brodmann area; L = left; R = right

154

Appendices

Appendix 1. Post Experimental Survey Used in Experiment 1 Post Experimental Survey

ID ______________ Date ____________

1. a) Overall, how positive or negative did the EXCITE imagery make you feel? 1 2 3 4 5 6 7 Very Negative Very Positive b) Overall, how excited did the EXCITE imagery make you feel? 1 2 3 4 5 6 7 Not Excited Very at all Excited 2. a) Overall, how positive or negative did the RELAX imagery make you feel? 1 2 3 4 5 6 7 Very Negative Very Positive b) Overall, how excited did the RELAX imagery make you feel? 1 2 3 4 5 6 7 Not Excited Very at all Excited 3. a) Overall, how positive or negative did what you thought about for LOOK

make you feel? 1 2 3 4 5 6 7 Very Negative Very Positive

b) Overall, how excited did what you thought about for LOOK imagery make you feel?

1 2 3 4 5 6 7 Not Excited Very at all Excited

155

4. How excited were you about the money? (1 =not at all excited, 7 =very excited)

Look 1 2 3 4 5 6 7 Excite 1 2 3 4 5 6 7 Relax 1 2 3 4 5 6 7 5. How excited were you about the stamps? (1 =not at all excited, 7 =very

excited) Look 1 2 3 4 5 6 7 Excite 1 2 3 4 5 6 7 Relax 1 2 3 4 5 6 7 6. How did you feel when you saw the slot machine? (1 =not at all excited,

7 =very excited) Look 1 2 3 4 5 6 7 Excite 1 2 3 4 5 6 7 Relax 1 2 3 4 5 6 7 7. How did you feel when you saw the stamp machine? (1 =not at all excited,

7 =very excited) Look 1 2 3 4 5 6 7 Excite 1 2 3 4 5 6 7 Relax 1 2 3 4 5 6 7 8. a) What did you think about when you saw LOOK and the slot machine?

156

b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult? 1 2 3 4 5 6 7 very easy very difficult 9. a) What did you think about when you saw LOOK and the stamp machine? b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult? 1 2 3 4 5 6 7 very easy very difficult 10. a) What did you think about when you saw EXCITE and the slot machine? How vivid was the image you thought about? b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult? 1 2 3 4 5 6 7 very easy very difficult

c) How successful were you at EXCITE visualization with the slot machine, on a scale of 1-7 where 1 =very successful and 7 =very unsuccessful?

1 2 3 4 5 6 7 very successful very unsuccessful 11. a) What did you think about when you saw EXCITE and the stamp

machine? How vivid was the image you thought about? b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult? 1 2 3 4 5 6 7

very easy very difficult

157

c) How successful were you at EXCITE visualization with the stamp machine, on a scale of 1-7 where 1 =very successful and 7 =very unsuccessful?

1 2 3 4 5 6 7 very successful very unsuccessful 12. a) What did you think about when you saw RELAX and the slot machine? How vivid was the image you thought about? b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult?

1 2 3 4 5 6 7

very easy very difficult c) How successful were you at RELAX visualization with the slot machine,

on a scale of 1-7 where 1 =very successful and 7 =very unsuccessful? 1 2 3 4 5 6 7 very successful very unsuccessful 13. a) What did you think about when you saw RELAX and the stamp machine? How vivid was the image you thought about? b) How easy was this task for you, on a scale of 1-7 where 1 =very easy and 7 =very difficult? 1 2 3 4 5 6 7

very easy very difficult

c) How successful were you at RELAX visualization with the stamp machine, on a scale of 1-7 where 1 =very successful and 7 =very unsuccessful?

1 2 3 4 5 6 7 very successful very unsuccessful

158

14. What do you think was the difference between the instructions (Look, Excite, Relax)?

15. For the money choices only, in general, how did you decide which option

to pick? 16. Do you think the instructions affected how you made your choices?

159

Appendix 2. Post Experimental Survey Used in Experiment 2 1. What did you think about when you saw LOOK and then the slot machine? 2. What did you think about when you saw RELAX and then the slot machine?

How easy or difficult was it to think about what you imagined for the RELAX trials?

1 2 3 4 5 6 7

very easy very difficult

How successful or unsuccessful were you at the RELAX visualization? 1 2 3 4 5 6 7 very unsuccessful very successful 3. Overall, how positive or negative did what you thought about for LOOK make you feel? 1 2 3 4 5 6 7 very negative very positive 4. Overall, how excited did what you thought about for LOOK make you feel? 1 2 3 4 5 6 7 not excited very at all excited 5. Overall, how positive or negative did the RELAX imagery make you feel? 1 2 3 4 5 6 7 very negative very positive

160

6. Overall, how excited did the RELAX imagery make you feel? 1 2 3 4 5 6 7 not excited very at all excited 7. How excited were you about the money? 1 2 3 4 5 6 7 not excited very at all excited 8. Do you believe that the money is real and will actually be paid to you if you win? 1 2 3 4 5 6 7 do not totally believe believe 9. What do you think was the difference between the two instruction words LOOK and RELAX? 10. In general, how did you decide which option to pick? 11. Do you think the instruction words, LOOK and RELAX, affected how you made your choices?

161

Appendix 3. Post Experimental Survey Used in Experiment 3 1. What did you think about when you saw LOOK and then the slot machine? 2. What did you think about when you were EMPHASIZING the slot machine?

How easy or difficult was it to think about the slot machine and the decision in a new way for the EMPHASIZE trials?

1 2 3 4 5 6 7

very easy very difficult

How successful or unsuccessful were you at EMPHASIZING? 1 2 3 4 5 6 7

very unsuccessful very successful 3. What did you think about when you were DEEMPHASIZING the slot machine?

How easy or difficult was it to think about the slot machine and the decision in a new way for the DEEMPHASIZE trials?

1 2 3 4 5 6 7

very easy very difficult

How successful or unsuccessful were you at DEEMPHASIZING? 1 2 3 4 5 6 7

very unsuccessful very successful 4. Overall, how positive or negative did what you thought about for LOOK make you feel? 1 2 3 4 5 6 7 very negative very positive

162

5. Overall, how excited did what you thought about for LOOK make you feel? 1 2 3 4 5 6 7 not excited very at all excited 6. Overall, how positive or negative did EMPHASIZING make you feel? 1 2 3 4 5 6 7 very negative very positive 7. Overall, how excited did EMPHASIZING make you feel? 1 2 3 4 5 6 7 not excited very at all excited 8. Overall, how positive or negative did DEEMPHASIZING make you feel? 1 2 3 4 5 6 7 very negative very positive 9. Overall, how excited did DEEMPHASIZING make you feel? 1 2 3 4 5 6 7 not excited very at all excited 10. How excited were you about the money? 1 2 3 4 5 6 7 not excited very at all excited 11. Do you believe that the money is real and will actually be paid to you if you win? 1 2 3 4 5 6 7 do not totally believe believe

12. How similar was EMPHASIZING to LOOK?

1 2 3 4 5 6 7 not at all exactly the same

163

13. How similar was DEEMPHASIZING to LOOK?

1 2 3 4 5 6 7 not at all exactly the same 14. In general, how did you decide which option to pick? 15. Do you think the instruction words, EMPHASIZE and DEEMPHASIZE, affected how you made your choices?

164

Vita

Laura N. Martin

1980 Born January 21 in New Orleans, Louisiana. 1998 Graduated from Benjamin Franklin High School, New Orleans, Louisiana. 1998-2002 Attended Emory University, Atlanta, Georgia. 2002 B.S. in Neuroscience and Behavioral Biology, Emory University, Atlanta,

Georgia.

2001-2002 Research Assistant, Yerkes Regional Primate Research Center, Emory University, Atlanta, Georgia

2002-2005 Research Assistant, New York State Psychiatric Institute & Columbia

University, New York, New York. 2005-2006 Research Coordinator, Mount Sinai School of Medicine, New York, New

York. 2006-2011 Graduate work in Psychology, Rutgers University, Newark, New Jersey. 2008-2011 National Research Service Award (NRSA) Predoctoral Fellowship,

Decision-

2006 Age, rapid-cycling, and pharmacotherapy effects on ventral

prefrontal cortex in bipolar disorder: a cross-Psychiatry, vol. 59(7), p. 611-8.

2006 Hippocampus and amygdala morphology in attention-

Archives of General Psychiatry, vol. 63(7), p.795-807.

2007 Suggestion overrides the Stroop effect in highly hypnotizable

331 338. 2007 A Anterior cingulate activity in bulimia nervosa: A fMRI case

stu e78-e82. 2008 -related processing in the human brain: Developmental

l .20(4), p. 11911211.

165

2011 Neural correlates of positive and negative emotion regulation: Implications for decision- Decision-making, Affect and Learning: Attention and Performance XXIII, p. 311-327.

2011 The influence of emotion regulation on decision-making under

risk Journal of Cognitive Neuroscience.