The Relationship Between Heart Rate Variability, Lay Theories of Self-Regulation, And

The Relationship between Heart Rate Variability, Lay Theories of Self-Regulation, and

Ego-Depletion: Evidence of Psychophysiological Pathways of Self-Regulation

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the Graduate School of The Ohio State University

DeWayne P. Williams

Graduate Program in Psychology

The Ohio State University

2014

Master's Examination Committee:

Dr. Julian F. Thayer, Advisor

Dr. Baldwin M. Way

Dr. Kentaro Fujita

Dr. Michael Vasey

Copyrighted by

DeWayne P. Williams

2014

Abstract

Self-regulation (SR) is defined as the process by which people adopt and manage various goals and standards for their thoughts, feelings, and behavior, and then ensure that these goals and standards are met. Strong evidence shows that SR is a limited resource that when depleted, the individual experiences ego-depletion, a state where SR is operating at less than full capacity. However, to date, research has not examined the association between ego-depletion and physiological indices of SR capacity. The characteristic variability in the time series of heartbeats, or heart rate variability (HRV), has been considered a biomarker of SR capacity. One study found that individuals with high HRV did not experience task fatigue – a concept related to ego-depletion. Recent investigations also show that an individuals’ lay theories of SR predict ego-depletion, such that those who think that SR is a limited resource experience ego-depletion, while those who think

SR in an unlimited resource do not differ in performance. Drawing on these studies, the present investigation attempts to replicate and extend previous findings. It was hypothesized that only those with low resting HRV will experience depletion. Moreover, the present investigation was designed to examine the direct relationship between lay theories of SR and HRV. Using an electrocardiogram (EKG), baseline-resting period

HRV data were collected in 61 (42 White, 42 Women) participants that later completed a set of questionnaires, a depletion manipulation task, the Stroop task, and a second set of

ii questionnaires. Lay theories of SR were assessed using the Implicit Theories of

Willpower Scale (ITWS). Participants were randomly assigned to a depletion or non- depletion group: in the depletion manipulation, participants completed a task that required SR, presumably, depleting resources for later use. In contrast, those in the non- depletion manipulation completed a task that did not require SR. All participants then completed the Stroop task. Accuracy on the Stroop task was used to assess the degree of depletion between conditions and at varying levels of ITWS scores and baseline HRV. In line with previous studies, individuals in the depletion condition performed worse on the task than those in the non-depletion condition. Interestingly, this effect was independently moderated by both resting HRV and lay theories of SR. Individuals with low resting HRV or who think SR is limited experienced ego-depletion, where their respective counterparts did not show similar patterns. Additionally, analyses revealed a strong relationship between resting HRV and ITWS scores such that those with higher resting HRV were more likely to have unlimited theories of SR. These observations have important implications – throughout daily life, individuals are often faced with situations that require repeated SR behaviors. The present study suggests that having low HRV and/or holding limited-theories of SR may undermine successful prolonged SR.

Limitations, implications, and future directions are discussed.

iii

This thesis is dedicated to Linderek Dorman. Rest in peace my friend.

Acknowledgments

I would like to thank my advisors Dr. Julian Thayer and Dr. Baldwin Way for their continued and unwavering support, guidance, and wisdom provided throughout this process. I also thank Dr. Kentaro Fujita for his unwavering diligence, support, and wisdom he provided on this project. Additionally, I would like to thank Dr. Julian Koenig for his remarkable support and commitment. I also would like to thank Cameron Rankin for his dedication and time devoted to this project. I would like to thank Dr. Mike Vasey for agreeing to serve on my committee and providing feedback on this project. Finally, I thank Lassiter Speller, Dr. LaBarron Hill, and Dr. John Sollers III for their continued support and mentorship.

I would also like to thank my cohort for their invaluable time and effort in reading and listening to many revisions of this project. To my fellow colleagues in the Emotions and Quantitative Psychophysiology lab and Social Psychology Department, thank you for your helpful and thoughtful feedback on this project.

Last but certainly not least, I would like to thank my love (Lauren Miller), mother

(Patricia Williams), father (Terrence Williams), sister (Jazmin Williams), and daughter

(Mariyah Williams) for their continued support, understanding, and unconditional love throughout my endeavors.

Vita

2006...... Twinsburg High School

2011...... B.A. Psychology, The Ohio State University

2012 to present ...... Graduate Research Assistant, Department of

Psychology, The Ohio State University

Publications

Williams, D.P., Jarczok, M.N, Ellis R.,Thayer, J.F., Hillecke, T.K., Koenig. J., (in press). A Half-Year Follow-Up on the Test Retest Reliability of the Cold Pressor Task as a Measure of Pain Tolerance and Threshold. Pain Practice.

Bhatt, R.,Williams, D.P., Kessier, M., Hillecke, T.K., Thayer, J.F., Koenig, J., (in press).The Dark Side of the Moon: Music may Reduce Pain but White Noise may Increase it! Music and Medicine.

Hill, L. K., Hu., D. D., Williams, D. P., Sofletea, G. M., Cochran, J., Sollers 3rd, J. J. & Thayer, J. F. (2010). Effects of Autonomic Innervation of the Heart as a Function of Effector Tissue. Biomedical Sciences Instrumentation, 46, 202-207.

Fields of Study

Major Field: Psychology

Table of Contents

Abstract ...... ii

Dedication………………………………………………………………………………..iv

Acknowledgments...... v

Vita ...... vi

List of Tables ...... viii

List of Figures ...... ix

Chapter 1: Introduction ...... 1

Chapter 2: Materials and Methods ...... 15

Chapter 3: Results………………………………………………………………………..24

Chapter 4: Discussion…………………………………………………………………....27

References ...... ……………34

Appendix A: Tables ...... 40

Appendix B: Figures…………………………………………………………………...... 44

Appendix C: Implicit Theories of Willpower Scale……………………………………..59

vii

List of Tables

Table 1. Sample Demographics ...... 40

Table 2. Baseline Characteristics by Depletion Group Manipulation ...... 41

Table 3. High and Low HRV Group Comparisons on Baseline Variables .....………….42

Table 4 Correlation Matrix of Variables….……………………………………………...43

viii

List of Figures

Figure 1. Conceptual Diagram of Self-Regulation ...... 44

Figure 2. Conceptual Representation of Model 1 in PROCESS ...... 45

Figure 3. Statistical Representation of Model 1 in PROCESS ...... 46

Figure 4. Conceptual Representation of Model 2 in PROCESS ...... 47

Figure 5. Statistical Representation of Model 2 in PROCESS ...... 48

Figure 6. Scatter Plot of Resting HRV and Lay Theories of SR ...... 49

Figure 7. Conceptual Diagram of Moderation Test between Lay Theories of SR,

Depletion, and Performance...... 50

Figure 8. Statistical Diagram of Moderation Test between Lay Theories of SR, Depletion, and Performance ...... 51

Figure 9. Interaction between Lay Theories of SR and Depletion on Performance ...... 52

Figure 10. Conceptual Diagram of Moderation Test between Resting HRV, Depletion, and Performance ...... 53

Figure 11. Statistical Diagram of Moderation Test between Resting HRV, Depletion, and

Performance ...... 54

Figure 12. Interaction between Resting HRV, Depletion, and Performance ...... 55

Figure 13. Conceptual Diagram of Moderation Test between Lay Theories of SR, Resting

HRV, and Performance in the Depletion Group ...... 56

Figure 14. Statistical Diagram of Moderation Test between Lay Theories of SR, Resting

HRV, and Performance ...... 56

Figure 15. Interaction between Resting HRV and Depletion on Performance ...... 56

Chapter 1: Introduction

Self-regulation (SR) is defined as the process by which people adopt and manage various goals and standards for their thoughts, feelings, and behavior, and then ensure that these goals and standards are met (Fujita, 2011). Imagine an individual who wants to become sober. This person is then asked by a friend to attend a college party. The individual is faced with an option of staying home or attending the party, but chooses to stay home, as there will likely be alcohol at the party. Here, the individual is behaving in a manner consistent with their goals by staying home instead of placing himself/herself in an environment with a temptation (alcohol) that may undermine his/her values (being sober).

SR is related to several concepts that are familiar but need to be differentiated.

While the present work focuses on SR, the following will emphasize the association and differences of these concepts with respect to SR (see Figure 1 for diagram and description of concepts). Recent research suggests that individuals with good SR show better self- control, which is defined as pursuing a larger-later reward over a smaller and immediate option (Fujita, 2011). For example, a dieter shopping in the local market is faced with countless food options. The individual shows both good SR and self-control if they are able to shop with a tendency to purchase organic and healthy food options. However, if this tendency is not automatic, and the individual is tempted by the unhealthy option, the

1 individual may have to inhibit the decision to purchase the unhealthy (e.g. cake) instead of the healthy (e.g. carrots). Inhibitory control, or the action of inhibiting a prepotent response to environmental stimuli and responding with a more desirable response, is an important aspect of successful SR and self-control. For example, an individual who has dieting goals and is faced with the options of eating an unhealthy (e.g. cake) or healthy

(e.g. carrots) snack may experience a conflict between the two options; particularly if he/she enjoys cake, but should eat the carrots in service of the long term dieting goals. If he has good inhibitory control, he/she may be able to inhibit the action (e.g. eating the cake) and instead behave in a self-controlled way (e.g. eat the carrots). This shows good inhibitory control when faced with a self-control conflict, which is an important part of good SR, that is, to continue to regulate behavior in service of his long-term dieting goals.

Good SR also predicts better emotional control when faced with specific stressors that can create emotional distress. Research finds that those with greater inhibitory capacities1 are more successful at regulating of emotions (Thayer & Lane, 2000). For example, a worker who is criticized by his/her boss may inhibit the impulse of responding with anger and instead, responds in a calm and understanding manner, behavior that is consistent with the goal of maintaining a source of income (good at SR).

Moreover, SR is predictive of cognitive control, or an individual’s ability to successfully complete tasks that may involve attention and/or working memory (Bishop, 2009; Gillie,

1 Here, “inhibitory capabilities” refers to neural inhibition, and not the aforementioned inhibitory control. Although these concepts are related and psychophysiological evidence will be provided drawing these parallels, it is important that these concepts stay separate here. 2

Vasey, & Thayer, 2014; Liu, Banich, Jacobson, & Tanabe, 2004; Thayer, Hansen, Saus-

Rose, & Johnsen, 2009). For instance, an individual with good SR ability is better able to stay focused when completing tasks that require attention (Hansen, Johnsen, Sollers,

Stenvik, & Thayer, 2004; Park, Van Bavel, Vasey, & Thayer, 2013; Thayer, Hansen, et al., 2009). Overall, SR is predictive of behavior that is consistent with an individual’s goals (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Fujita, 2011; Muraven &

Baumeister, 2000; Thayer & Lane, 2000) .

Researchers have begun to explore conditions where SR is less or more likely to be successful. Converging evidence shows that when an individual self-regulates, performance on a succeeding SR task worsen. This pattern prompted research to examine the effects of current SR effort on future SR success. In other words, individuals who perform consecutive SR tasks show decreased performance on a subsequent task .This phenomenon is known as ego-depletion and has become a focus in SR research (for review, see Hagger, Wood, & Stiff, 2010).

Ego-Depletion

Ego-depletion is defined as the condition that arises when the self’s resources have been expended and the self is temporarily operating at less than full power

(Muraven & Baumeister, 2000). In classic ego-depletion studies, participants are instructed to engage in a task that either does (depletion) or does not (non-depletion) require SR effort. Following this first task, all participants (irrespective of group), complete a second task that does require SR effort. In the non-depletion group (first task did not require SR) participants usually do not show impaired performance on the

3 subsequent task in comparison to the depletion group (first task did involve SR) who typically show impaired performance on the subsequent task. A widely used task in this kind of research is the Stroop task (Baumeister et al., 1998; Gailliot et al., 2007; Job,

Dweck, & Walton, 2010; Molden et al., 2012; Muraven & Baumeister, 2000), a task requiring participants to respond to the color of a word. However, the words themselves are written in various colors (e.g. the word “red” can be written in any color). Thus, when the semantics of the color-word and the actual color are inconsistent (e.g. the word “red” written in the color blue), participants must inhibit the semantics (red) and respond appropriately to the color of the word (blue). In a typical non-depletion condition, participants are not presented with any inconsistent pairs (all words appear in the same color as the word semantics) and therefore use no self-regulatory effort. In the depletion group, participants would receive both consistent and inconsistent word pairs, requiring the participants to inhibit what the word reads, and respond appropriately to the color of the word presented. All participants would then be responsible for squeezing a handgrip for as long as they could. This task requires SR, specifically inhibition, to continue to grip the device despite any pain or discomfort. Ego-depletion research predicts that individuals in the depletion group in comparison to the non-depletion condition will show impaired performance on the handgrip task. This phenomenon has received much attention (Hagger, Wood, & Stiff, 2010), and research over the past decade provides theoretical models that help to explain this effect, that will be summarized in the following section.

Strength Model of Self-Regulation

One model of SR that assists in understanding how individuals may become depleted is the Strength Model of SR (SM-SR) (Muraven & Baumeister, 2000). This model proposes that SR draws from a depletable resource, similar to a muscle. As a muscle is used, it contains less strength for subsequent tasks. Muraven and Baumeister

(2000) draw similar conclusions about SR – that it resembles a muscle, draws from a limited resource, and can become depleted with repeated use (Hagger et al., 2010;

Muraven & Baumeister, 2000). This model has been extensively tested, using similar paradigms and methods previously described in studies on ego-depletion.

While the SM-SR model posits that SR is a draws from a limited physical resource and thus, can be depleted, it does not specify the limited physical resource that

SR uses. Later research supported this notion, suggesting that glucose may be the physical resource that SR consumes (Gailliot et al., 2007). Several studies show that providing glucose to individuals protects against ego-depletion (Gailliot & Baumeister,

2007; Gailliot et al., 2007). For example, one study found those in the depletion group who were given a drink with glucose would not show impaired performance in comparison to those who had a drink with artificial sweetener. Additionally, lower blood glucose prior to the second task served as an index of depletion (i.e., lower blood glucose predicted lower performance on the second task). These sets of studies provide further evidence for the SM-SR model, suggesting that SR is a depletable resource, particularly because it draws energy from glucose.

However, recent evidence conflicts with the SM-SR postulates, while providing an alternate account for ego-depletion effects. Specifically, researchers have presented evidence that criticizes the SM-SR model from both a conceptual and empirical perspective (Beedie & Lane, 2012; Fujita, 2011; Gibson, 2007; Job et al., 2010; Kurzban,

2010; Molden et al., 2012).

From a conceptual standpoint, some have criticized the definition often used for self-control when referring to the SM-SR model (Fujita, 2011). Specifically, the definition of self-control has been defined as the effortful inhibition of impulses. In contrast, Fujita (2011) highlights the problem with this definition, suggesting that constricting self-control to this definition does not allow predictions to be made when self-control can happen automatically and/or without inhibitory control. Additionally, researchers often treat SR, inhibition, and self-control as synonymous concepts, also restricting research on the SM-SR model (see Fujita, 2011 for review and Figure 1).

From an empirical standpoint the SM-SR model, has been extensively tested. One main controversy of the glucose studies is that the design did not allow enough time for glucose to cross the blood-brain-barrier, and thus, observed results were not directly related to glucose use (Gibson, 2007). Additional research suggested that depletion effects could be ameliorated simply by believing that SR is unlimited (Job et al., 2010).

Therefore, researchers suggest that many of the observed ego-depletion effects, in addition to the predictions made by the SM-SR model, can be explained by motivational factors (Job et al., 2010; Molden et al., 2012).

Motivational Model of Self-Regulation

Molden et al. (2012) proposed a Motivation Model of Self-Regulation (MM-SR) to assist in explaining ego-depletion effects and glucose findings. In this report, the researchers highlight several studies that suggest ego-depletion effects are of a motivational nature. Within social psychological research, motivation is thought of in terms of value and expectancy (Molden et al., 2012). Specifically, those who have better expectations or place high value on a desired outcome are more motivated to pursue this goal. In contrast, those with low expectations and values placed on a goal are less motivated to pursue the outcome. Research shows that this motivation framework can also be used to explain the ego-depletion phenomenon.

One investigation found that individuals’ lay theories of SR could determine if the individual becomes depleted (Job et al., 2010). In this series of studies, participants were assigned to either a depletion or non-depletion group and completed two consecutive tasks, where performance on the second task was an indicator of depletion (similar to previous ego-depletion studies). However, prior to the tasks, participants completed a set of self-report trait questionnaires that included a questionnaire that assessed their beliefs about their own SR – that is, if they thought their own SR was limited (able to be depleted) or unlimited (unable to be depleted). These lay theories of SR can be thought of as assessing the motivation to engage in SR behaviors; that is, individuals’ expectations of their own SR abilities and success, particularly when faced with repeated SR. This study found that those in the depleted group who held limited theories about their SR experienced ego-depletion. In contrast, those in the depleted group who held unlimited

7 lay theories of SR did not experience ego-depletion; these participants performed as well as the non-depletion group. There were no differences between the unlimited and limited theorists in the non-depletion group. This work suggests that the motivation to engage in

SR, or individuals lay theories of SR, can affect how people perform when depleted (Job et al., 2010).

The notion that glucose is the physical resource that SR draws on has also been tested. Another investigation found that ingesting glucose is not necessary to eliminate the ego-depletion effects. Simply swishing glucose around in the mouth produced identical results as when the participant swallowed the glucose in earlier investigations

(Molden et al., 2012). The researchers propose that instead of glucose working directly in the brain (as this notion had been previously criticized), glucose works via dopaminergic receptors found in the mouth. Glucose binds to these receptors and consequently, sends reward signals to the brain. It was hypothesized that this signal increased motivation (in this case, increasing value via “reward” signals), giving participants the “energy” to persist on consecutive SR tasks. Although this converging evidence suggests that motivation may be a prominent factor in ego-depletion, research has not examined the direct relationship between a true physical indicator of SR capacity, as set forth by the

SM-SR model, and SR motivation, as set forth by the MM-SR model.

As previously mentioned, SR is not limited to isolated concepts such as cognition, behavior, and emotion, but has been shown to be closely related to underlying physiological processes and subsequently, to health (Brosschot, Gerin, & Thayer, 2006;

Brosschot, Verkuil, & Thayer, 2010; Thayer, Ahs, Fredrikson, Sollers, & Wager, 2012;

Thayer, Yamamoto, & Brosschot, 2010; Thayer & Lane, 2009). Specifically, brain areas involved in regulating the self in a direction consistent with goals and values, are also responsible for the regulation of the cardiovascular, inflammatory, and endocrine systems, all essential for a healthy individual (Thayer & Lane, 2000; Thayer, Loerbroks,

& Sternberg, 2011).

Neurovisceral Integration Model

The Model of Neurovisceral Integration (NIM) provides a theoretical and conceptual framework integrating psychological concepts associated with SR (i.e. behavioral, emotional, cognitive), structures of the central nervous system that a crucial for SR (e.g. medial prefrontal cortex and amygdala), and physiological indices of autonomic regulation ( Thayer et al., 2012; Thayer & Lane, 2000). The NIM highlights that cortical brain regions are responsible for regulating autonomic, behavioral, cognitive, and emotional adaption (see Thayer et al., 2012 for review); in other words, responsible for SR. This set of brain structures are a part of the central autonomic network (CAN) – structurally, the CAN includes brain areas such as the anterior cingulate cortex, insular, ventromedial prefrontal cortices, and the central nucleus of the amygdala (Napadow et al., 2008; Thayer et al., 2012; Thayer & Lane, 2000, 2009).

Within the CAN, subcortical brain areas such as the amygdala, which has been largely associated with threat, vigilance, emotions, and lower-order processing in comparison to cortical areas such as prefrontal cortices and the anterior cingulate cortex, which have been associated with error detection, working-memory, attention, and the processing of a wide range of stimuli (Bush, Luu, & Posner, 2000; Matthews, Paulus,

Simmons, Nelesen, & Dimsdale, 2004; Thayer & Lane, 2000; Wittfoth, Küstermann,

Fahle, & Herrmann, 2008; Zubieta et al., 2003). Higher-order processing is important in the regulation and control of prepotent cognitive, behavioral, and emotional responses. In the previous example of the employee who has both good SR and self-control, the NIM posits that the employee may exhibit better top-down inhibitory control of subcortical brain areas (e.g. amygdala) and therefore, controlling the original negative impulsive and prepotent response of anger.

The primary output of the CAN is at preganglionic sympathetic and parasympathetic neurons, which regulate the autonomic nervous system (ANS). The ANS contains two branches; (1) the sympathetic nervous system (SNS), and (2) the parasympathetic nervous system (PNS). In a healthy organism, PNS activity is dominant at rest, showing higher-order processing through cortical brain areas such as prefrontal cortices. Thus, research has shown that that the PNS is largely controlled by cortical brain regions responsible for cognitive, emotional, and homeostatic SR.(Thayer et al., 2012;

Thayer & Lane, 2009). Therefore a hyperactive PNS is reflective of greater top-down inhibitory control via higher-order cortical function (Thayer & Lane, 2000).

Both the PNS and SNS dually innervate organs such as the heart, lungs, pancreas, and small intestines (Bear, Connors & Paradiso, 2001). The NIM posits that because the output of the CAN is at the ANS, one can proxy CAN activity using measures from the periphery, particularly the heart. One measure of the heart that is influenced by the heart’s own pacemaker, the SNS, and the PNS, is heart rate variability (HRV). HRV is defined as the beat-to-beat fluctuations between heartbeats in milliseconds (ms). While

10 both branches of the ANS innervate the heart and contribute to both mean heart rate (HR) and HRV, PNS influence over the heart is of particular interest, given its unique link to cortical brain areas, SR, and health. Moreover, the heart is under tonic inhibition by the vagus (main nerve in the PNS) and therefore, can influence heart period changes within milliseconds, whereas SNS influences occur much slower (seconds to minutes). Thus, in a resting state, low vagally-mediated HRV has been linked with a dysregulation of emotions, cognitions, and peripheral organs (Thayer et al., 2012, 2011; Thayer & Lane,

2000).

As the vagus innervates the periphery, the vagus is not only responsible for regulating the heart, but also organs implicated in the endocrine and immune systems.

Specifically, vagal activity has not only been linked to cardiovascular activity, but also linked to immune and inflammatory function, as well as glucose regulation, where a hypoactive vagus predicts not only cardiovascular disease, but also more generally, all- cause mortality (Jarczok, Koenig, Schuster, Thayer, & Fischer, 2013; Thayer & Fischer,

2009; Thayer & Lane, 2000; Thayer, Loerbroks, & Sternberg, 2011a; Thayer et al., 2010;

Williams & Thayer, 2009; Thayer, 2009). Overall, the NIM suggests that both psychological and physiological SR share common brain areas and this pathway can be measured using resting vagally-mediated HRV. Higher HRV is reflective of greater cortical activity , self-regulatory capacity, and better overall health. For example, evidence shows that SR predicts weight loss in patients with Type-II diabetes and mental disorders (e.g. major depressive disorder) (Gross & Munoz, 1995; Maes & Karoly, 2005).

Using the NIM framework, research has examined individual differences in HRV and

11 how they predict SR performance. For example, a classic study finds that individuals with higher resting vagally-mediated HRV show better performance on physical and cognitive tasks (Hansen et al., 2004).

HRV and Depletion

Converging evidence confirm that resting HRV can serve as a proxy of emotion, cognitive, and behavioral regulation capacity (see Thayer et al., 2009 for review).

Although ego-depletion has been extensively studied in both motivational and capacity domains, no studies have directly investigated ego-depletion and resting HRV. However, one study does examine HRV and SR persistence and fatigue, similar to classic ego- depletion paradigms (Segerstrom & Nes, 2007). In this study, 168 participants were faced with the option of either eating cookies or carrots. In the “fatigue” condition (similar to a depletion condition), participants were instructed to eat the carrots and not the cake. Here, it is suggested that participants needed to exert SR effort (inhibit wanted to eat the cookies) to comply with the experimenters demands. In contrast, those in non-fatigue condition, participants were told to eat the cookies, and not the carrots. Here, it is suggested that no SR strength was used (similar to a non-depletion condition). All participants then completed an unsolvable anagram task, where the primary dependent variable was persistence on the task (in minutes). Results show that in the fatigue- condition, those with higher resting HRV persisted longer on the task than those with low resting HRV. Although the authors do not mention ego-depletion, this study serves as preliminary evidence that resting levels of HRV may predict depletion such that

12 individuals with low HRV may experience depletion (in this case, fatigue) and individuals with high HRV may not experience ego-depletion.

Present Study

Both the MM- and SM - SR assist in explaining ego-depletion effects. Drawing on these models and the NIM, the present investigation attempts to investigate the association of HRV, ego-depletion, and motivation (lay theories SR). As HRV has been shown to represent a proxy of SR capacity, it is hypothesized that HRV moderates ego- depletion effects. Irrespective of condition (depletion and non-depletion), it is expected that HRV will predict performance, such that those with higher HRV will show better performance on the second task (main effect of HRV). It is also expected that those in the non-depletion group, irrespective of HRV, will perform better in comparison to those in the depletion group (main effect of group). Furthermore, it is hypothesized that HRV will moderate this classic depletion effect such that individuals with low HRV in the depletion condition would show impaired performance in comparison to individuals with low HRV in the non-depletion group and individuals with high HRV in both conditions (interaction effect).

To extend current concepts, the relationship between the motivation to engage in

SR behaviors and SR capacity, as indexed by HRV, will be examined for the first time.

Limited theorist and individuals with low HRV maybe similar, insomuch that task fatigue displayed by individuals with low HRV is related to depletion (Segerstrom & Nes, 2007).

This would then suggest that both individuals with low HRV and limited theorist are likely to experience ego-depletion in comparison to their counterparts (high HRV and

13 unlimited theorists, respectively). Thus, a trait measure created by Job et al. (2010) to assess individuals’ beliefs about their own SR was included in the study design. It is hypothesized that results will replicate the Job et al (2010) study, showing that lay theories of SR moderate the ego-depletion effect such that those who believe that their

SR is unlimited, will not experience ego-depletion in comparison to limited-resource theorists. Moreover, if HRV and lay theories of SR are to show similar patterns of results as hypothesized above, then it is assumed that these two constructs may be related – that is, limited theorist may be more likely to have low HRV, whereas unlimited theorist may be more likely to have high HRV. Overall, the following investigation examines the relationship between ego-depletion, lay theories of self-regulation, and resting HRV, a measure of SR capacity.

Chapter 2: Materials and Methods

Participants

Seventy-one undergraduate students (49 White, 26 male) were recruited to participate in the study using the Research Experience Program (REP) at The Ohio State

University. Participants were given partial course credit in exchange for their participation.

Measures

Heart Rate Variability

Continuous heart rate (HR) data were collected throughout the study using a 7- lead electrocardiogram (EKG) at a sampling rate of 1000 Hz using a Mindware™ 2000D

(MW2000D) Impedance Cardiograph package. HR was measured in beats per minute

(bpm). To assess HRV, the variability between successive R-spikes (or variability within inter-beat-intervals, IBIs) was calculated. An IBI is defined as the time marked at the first

R-spike, minus the time of the subsequent R-spike. Participants successive IBIs, in milliseconds (ms), were extracted using HRV 3.0.11 Analysis software and within each period, IBIs were written in a single text file and analyzed using Kubios HRV analysis package 2.0 (Tarvainen, Niskanen, Lipponen, Ranta-aho, & Karialainen 2014; http://kubios.uku.fi/), calculating both time- and frequency-domain indices of HRV. All analyses were conducted in accordance with the Task Force (1996) guidelines. The

15 primary time-domain measure of HRV was the root mean square of the successive differences (RMSSD). RMSSD primarily reflects PNS influence over the heart (Task

Force of the European Society of Cardiology and The North American Society of Pacing and Electrophysiology, 1996; Thayer, Hansen, & Johnsen, 2010). RMSSD values were log transformed (ln) to better approximate a normal distribution in order to meet the assumptions of linear analyses (Thayer, Hansen, & Johnsen, 2010). Lastly, high frequency peak (HFpeak) values were obtained from the spectral analysis as a measure of respiratory frequency to control for the potential influence of respiration on HRV (for review see Thayer, Loerbroks, & Sternberg, 2011; Thayer, Sollers, Ruiz-padial, & Vila,

2002).

Lay Theories of Self-Regulation

The 12-item Implicit Theories about Willpower Scale (ITWS) introduced by Job et al. (2010) is designed to assess individuals’ lay theories of strenuous mental activities

(sample item: “After a strenuous mental activity, you feel energized for further challenging activities”) and resisting temptations (sample item: “If you have just resisted a strong temptation, you feel strengthened and you can withstand any new temptations”).

Participants answered on a 1 “strongly agree” to 6 “strongly disagree” scale, where higher scores are in greater agreement with limited-resource theories. Scores were averaged across all 12-items and showed good internal consistency (α= .75). See

Appendix C for scale items and reverse coding.

Need For Cognition and Affect

The 18-item Need for Cognition Scale (NCS) and 26-item Need for Affect Scale

(NAS) were primarily used to ensure that participants did not suspect the true purposes of the ITWS and as such, was embedded within these two scales. The NCS assesses individuals tendency to engage in and enjoy effortful thoughts and cognitions (e.g. problem solving) (Cacioppo & Petty, 1982). Participants answered on a 1 (strongly disagree) to 7 (strongly agree) scale. The NAS assessed individuals’ tendency to engage in and enjoy the experience of emotions participants answered on a 1 (extremely unlike me) to 5 (extremely like me) scale (Maio & Esses, 2001).

Self-Control Schedule

The 36-item Self-Control Schedule (SCS) measures a person’s general ability and tendency to apply self-control methods to the solution of behavioral problems

(Rosenbaum, 1980). Participants answered on a 1 (very uncharacteristic, extremely undescriptive) to 6 (very characteristic, extremely descriptive) scale, with higher scores reflecting a greater ability to apply self-control in the face of behavioral problems. This scale showed extremely high internal consistency (α=.99).

Consumer Impulsiveness Scale

The 12-item Consumer Impulsiveness Scale (CIS) uses adjectives to assess individuals’ tendency for impulsive behavior (Puri, 1996). Participants answer on a 1

(usually would describe me) to 7 (seldom would describe me) scale, with lower values representing greater values. However, this scale showed poor internal consistency

(α=.331) and thus, results using this scale should be interpreted with caution.

Difficulties in Emotion Regulation Scale

The 36-item Difficulties in Emotion Regulation Scale (DERS) assess individuals’ perceived difficulties in emotion regulation (Gratz & Roemer, 2004). Participants answered on a 1 (almost never) to 5 (almost always) scale, with higher values reflecting a greater difficulty with controlling emotions. The DERS contains 6 subscales, including the NONACCEPT (non-acceptance of emotional responses), GOALS (difficulty engaging in goal directed behavior), IMPULSE (impulse control difficulties),

AWARENESS (lack of emotional awareness), STRATEGIES (limited access to emotion regulation strategies), and CLARITY (lack of emotional clarity). This scales has high internal reliability (α=.815).

Spielberg Trait Anxiety Inventory

The 20-item Spielberg Trait Anxiety Inventory (STAI-T) assesses trait feelings of anxiety (Spielberger, Gorsuch, & Luchene, 1970). Participants answered on a 1 (almost never) to 4(almost always) where higher scores reflect greater trait anxiety. The STAI-T shows high internal consistency (α=.898).

Ruminative Response Scale

The Ruminative Response Scale (RRS) assesses trait level rumination, including three subscales that assess rumination style (Treynor, Gonzalez, & Nolen-Hoeksema

2003). Participants answered on a 1 (almost never) to 4 (almost always) scale. Subscales included RRSreflect (reflective rumination; analytical and problem solving rumination),

RRSbrood (Brooding rumination; wallowing and sulking rumination), and RRSdamp

(dampening rumination; sadness and despair rumination). The RRS showed excellent internal reliability (α=.912).

Behavioral Identification Form

The Behavioral Identification Form (BIF) is designed to assess individuals’ tendency to think of behavior in abstract (consequences, implications) or concrete

(details, mechanics) terms (Vallacher & Wegner, 1989). The BIF contains 25-items describing a variety of behaviors. Participants had the opportunity to choose an option describing the behavior as means to an abstract (1) accomplishment or a concrete (2) behavior. Scores were coded such that higher scores reflect thinking that is more abstract with lower scores representing thinking that is more concrete.

Procedure

Participants were brought into a room equipped with a camera and microphone for observation and communication purposes. Participants were then given informed consent and an explanation of the study that focused on the physiological equipment so that the aims of the study were not revealed. Following informed consent, participants’ demographic information, including height, weight, age, and ethnicity were collected and recorded. Additionally, participants were assigned to either the depletion or non-depletion group (similar to ego-depletion experiments described in Chapter 1) at this time; however, participants were unaware of this selection. Participants were then attached to an ECG, and given a keyboard so they could respond to the experimental stimuli.

First, participants completed a baseline-resting period where they sat in a chair and viewed a blank gray screen (were instructed to rest and try not to move) for 5 minutes.

Baseline-resting HRV was assessed during this time. Participants then completed a questionnaire period that included the NAS, NCS, and IT WS. Following these questionnaires, participants completed the Crossing out e’s task (Job et al., 2010; Mark

Muraven & Baumeister, 2000). During this task, all participants were given the same two randomly chosen pages of text from a statistics book and were instructed not to read the text. Instead, participants were told that on the first page, they would be responsible for crossing out any letter “e” that they saw on the page. Participants had 2.5 minutes to complete the first page of this task. Following this, participants were given instructions for the second page, which varied by condition. Participants in the non-depletion condition were told to continue to cross out any e that they saw on the second page. In the depletion condition, the second page involved a complex set of rules. The rules were as follows: (1) no e can be crossed out if it is directly adjacent to another vowel, including forward and backward (e.g. the “e” in the word read cannot be crossed out); and (2) no e can be crossed out that is one letter removed from another vowel, including forward and backward (e.g. the “e” in the word “vowel” cannot be crossed out). Participants also had

2.5 minutes to complete this phase of the experiment. After becoming accustomed to crossing out any e on the first page, it was expected that participants in the depletion condition will inhibit the action of crossing out any e, and correctly cross out an e that follows these complex rules. In contrast, the non-depletion group continued to cross out any e and thus, did not need to inhibit any stimuli (e). Overall, it is expected that those in the depletion group, but not the non-depletion group, will have to self-regulate to complete the second page correctly (Job et al., 2010).

Directly following this task, participants completed the Stroop task (Liu et al.,

2004; Pardo, Pardo, Janer, & Raichle, 1990; Stroop, 1935). The words Green, Blue,

Purple, and Red appeared on the screen in either their respective color (congruent trials, e.g. the word “red” appears in red), or a different color (incongruent trial, e.g. the word

“red” appears in blue). The ink color of the words was restricted to appear in the colors red, blue, green, and purple. All participants were given the same set of instructions – participants were to use the keypad on a standard QWERTY keyboard to respond to the appropriate color of the word. The numbers 1,3,7, and 9 on the keyboard were covered with cut-to-size and laminated colored paper that represented one of the four colors in the experiment. Participants were asked to respond to the color of the word presented as quickly and as accurately as they could using the colored buttons on the keypad.

Participants first completed 12-trials of practice and then completed 60 experimental trials; 30 trials were congruent, and 30 trials were incongruent. At the beginning of each trial, participants saw a fixation cross for 350ms, followed by one of the 60 Stroop words

(randomly ordered) for 1000ms. Participants were instructed to respond to the color the word presented (within the 1000ms timeframe), ignoring all other information. On the incongruent trials, it is expected that participants need to self-regulate, such that they must inhibit what the word says (e.g. red) and respond to the color of the word (e.g. blue).

Therefore, our primary interest is how participants performed on the incongruent trials.

Mean accuracy on these incongruent trials (INCG-ACC) was used as the primary indicator of performance (Job et al., 2010).2

2 Each trail had a maximum time of 1000 ms and participants were forced to answer within this 21

Statistical Methods

IBM SPSS Statistics 19 was used to conduct the following analyses. Scales were calculated and scored in accordance with previous research. All descriptive statistics are expressed as means and standard deviations (SD; in brackets). Categorical data were dummy coded as 1 and 2. To examine differences on baseline variables between high and low HRV groups, median splits were conducted on baseline-RMSSD (median cut point:

3.868923). Planned contrasts using t-tests were conducted to examine baseline differences between groups (Rosnow & Rosenthal, 1995).

Zero-order Pearson’s correlations and multiple regression analyses were used to assess the strength of the relationship between resting HRV and variables of interest

(particularly implicit theories of SR). To test the moderating effect of lay theories of SR and HRV on the link between the experimental condition (non-depletion & depletion) and depletion effects (performance on the second task), PROCESS in SPSS was used

(Hayes, 2008). In the program PROCESS, model 1 (see Figure 2 and 3 for conceptual and statistical diagram) was used to test a main effect of each independent variable (IV) and the interaction of these IVs, giving the unique contribution of each term to the dependent variable (DV). PROCESS also determines the nature of the interaction term using conditional effects. Conditional effects show the strength, direction, and statistical significance of the link between the IV (Condition) and DV (INCG-ACC) at varying levels of the moderator (ITWS scores or resting HRV). Overall, a significant interaction

timeframe. In the event that participants did not answer within the allotted time, the trial was counted as “incorrect”. Thus, prolonged answers due to fatigue, inattention, or interference (due to the Stroop effect), in addition to interference that resulted in an incorrect answer, were reflected in accuracy rates. Overall, accuracy rates on this task best reflect overall performance. 22 term denotes significant moderation, and the condition effects analyses determines the nature of the interaction, that is, how the IV-DV relationship changes at different levels of the moderator.

Model 2 in PROCESS was also used to test the unique influence of one interaction term while controlling for the other (See Figure 4 and 5 for conceptual and statistical diagram). Using this model, we are able to determine if one interaction (IV x

Moderator, or, IV x Moderator 2) significantly accounts for variance above and beyond the other.

In all PROCESS models, incongruent reaction time (INCG-RT) was used as a covariate. In PROCESS models that include HRV as a predictor, variables that have been shown to correlate with HRV were entered as covariates. These variables include body mass index (BMI), respiratory frequency (as indexed by HFpeak), gender, ethnicity, and age (Hu, 2011; Koenig et al., 2014; Thayer et al., 2011). PROCESS statistics reported include unstandardized beta coefficients (standard error in brackets), confidence intervals

(CI), t-statistics, and p values.

All tests were two-tailed and significance levels were evaluated using an alpha of

.05. All moderation tests were two-tailed with confidence intervals (CI) set at 95%.

Chapter 3: Results

Ten participants’ yielded Stroop task accuracy rates greater than two standard deviations below the mean (<60%) and were removed from further analysis, leaving sixty-one participants (42 White, 42 females, see table 1 for full sample characteristics).

Of these 61 participants, 54 participants’ physiological data (HRV) were obtained; equipment failure prevented HRV analysis for the remaining seven participants. Table 2 summarizes means of baseline measures between individuals in each experimental condition. Analyses showed no significant differences between groups on all included baseline variables, including resting measures of cardiovascular activity (Table 2). Table

3 shows baseline measure differences between high and low HRV groups. High HRV individuals showed lower ITWS and STAI-T scores in comparison to the low HRV group.

First, the relationship between implicit theories of SR and resting HRV was assessed. Pearson correlations showed that RMSSD and ITWS scores were strongly related (r = -.536, p<.001). In a regression model that controlled for respiration, age, ethnicity, gender, and BMI, this relationship remained strong and significant (β= -.532, t=

-.4.32, p<.001), such that individuals who had lower resting HRV also held limited- resource views about SR (Figure 6). Table 4 shows correlation results between all variables.

Next, a moderated regression model (Model 1 in PROCESS) was used in an attempt to replicate results found in the Job et al (2010) study. Within this model, condition was used as the independent variable (IV), INCG-ACC is the dependent variable (DV), and ITWS scores as the moderating variable (see Figure 7 and 8 for conceptual and statistical representation). Analysis showed a main effect of condition

(b=.448 (.205), CI[.038, .859], t= 2.19, p= .031 but no main effect of ITWS (b= .013

(.008), CI[-.003, .030], t = 1.62, p= .112). Importantly, the interaction of condition and

ITWS was significant in this model (b= -.011 (.004), CI[-.0210, -.0016], t= -2.33, p=.022). Conditional effects show that the depletion manipulation predicts INCG-ACC in the limited-resource theorists (b= -.087(.037), CI[-.162,-.012], t= -2.33, p= .024) and not the unlimited-resource theorists (b= .037(.038), CI[-.038, .112], t= .989, p= .327) (Figure

9).

Next, moderated regression was used with INCG-ACC as the DV, RMSSD as the moderator, and condition as the IV (Figure 10 and 11 for conceptual and statistical diagram). Results showed a main effect of condition (b= -.674 (.242), CI[-1.16, -.191], t=

-2.81, p= .008) and RMSSD (b= -.218 (.098), CI[-.412,-.023], t= -2.26, p=.031), importantly, this model also showed a significant interaction (b= .165 (.062),

CI[.041,288], t= 2.68, p= .011)3. Conditional effects showed that the depletion manipulation predicts INCG-ACC in those with low HRV (b= -.127 (.045), CI[-.216,-

3 These results remain significant when controlling for trait anxiety (STAI-T) (b=.147 (.062), CI[.022, .273], t=2.370, p=.023).

.035], t= -2.80, p= .007) and not high HRV (b= .036(.039), CI[-.044, .116], t= .913, p=

.366) (Figure 12).

Model 2 in PROCESS was used to test if the above interactions were independent of one another. Results showed that with both interactions in the regression model, both become insignificant (ITWSxCondtion: b = -.007 (.005), CI[-.017, .004], t= -1.25, p=

.218; RMSSDxCondition: b = .100 (.054), CI[-.008, .209], t = 1.862, p= .070), suggesting significant overlap between these two interactions.

Lastly, PROCESS (model 1) was used to test an interaction between lay theories of SR and HRV in the manipulated group only (depletion) to examine how individuals high and low in either HRV and/or lay theories of SR responded to the manipulation

(Figure 13 and 14 for conceptual and statistical representation). Analysis revealed a marginal interaction between ITWS scores and RMSSD within the depletion group

(b=.009, CI[-.001, .020], t=1.73, p = .100) and not in the non-depletion group (b=.003,

CI[-.045, .051], t= .1302, p =.898) (Figure 15). Implications and interpretations of these effects are later discussed.

Chapter 4: Discussion

Individuals can experience ego-depletion, a state where SR is at less than full capacity

(Muraven & Baumeister, 2000). However, SR is not limited to concepts such as cognition, behavior, and emotion, but has been shown to be closely related to underlying physiological autonomic processes (Brosschot et al., 2006, 2010; Thayer et al., 2012,

2010; Thayer & Lane, 2009). Therefore, the present investigation examined two possible individual differences in ego-depletion; (1) a physiological indicator of SR capacity and

(2) the motivation to engage in SR. Resting-HRV was used as an index of SR capacity, and lay theories of SR were used to index the motivation to engage in SR. The present study replicates results from previous work, suggesting that unlimited-resource theorist do not experience ego-depletion in comparison to limited-resource theorists (Job, Dweck,

& Walton, 2010). Further evidence supports the idea set forth by the NIM, suggesting that HRV, a biomarker of health, is also a marker of SR ability. Moreover, this study provides evidence that vagally mediated HRV, a proxy of frontal brain activity, is related to lay theories of SR, that is, if one thinks SR is limited or unlimited. Therefore, this investigation suggests that resting HRV is related to SR ability, also shaped by the willingness to engage in SR. The present findings have several implications for existing theories of ego-depletion.

Research has noted the high likelihood of the ego-depletion phenomenon; the current study supports this previous work, showing that those who first complete a SR task become depleted and subsequently, show performance decrements on a second task.

This main effect supports the SM-SR, however this relationship is moderated by lay theories of SR such that individuals who think SR is unlimited do not become depleted whereas limited-resource theorists show worse performance following depletion (Figure

7). This moderated relationship supports the MM-SR, suggesting that the motivation

(good expectations) to engage in SR plays a pivotal role in the experience of depletion

(Job et al., 2010; Molden et al., 2012).

Additionally, evidence from the current study supports the NIM suggesting that

HRV is a physiological marker of SR ability – resting HRV predicts when individuals experience depletion on tasks that uses the same brain areas associated with the regulation of HRV. Specifically, cortical brain regions such as the medial prefrontal cortex and anterior cingulate cortex are involved with inhibitory tasks such as the Stroop

(Matthews et al., 2004; Pardo, Pardo, Janer, & Raichle, 1990). These same brain areas are connected with the periphery through the vagus nerve, which innervates various organs and regulates peripheral functions (Thayer et al., 2012; Thayer & Lane, 2000).Therefore, this psychophysiological pathway underlying SR activity can be indexed using resting

HRV (Thayer et al., 2009), and the current results support this notion. Furthermore, one study found that resting HRV predicted task fatigue and persistence, such that those with low resting HRV persisted less at a task in comparison high HRV individuals after undergoing a fatigue manipulation (Segerstrom & Nes, 2007). While the concepts of

“fatigue” and “depletion” are related, the present investigation is the first that directly assess the relationship between resting HRV and ego-depletion. Thus, current results support and extend previous research, showing that HRV predicts when individuals will become depleted following consecutive SR tasks. Those with low resting HRV show ego-depletion in comparison to those with high HRV (Figure 10).

Moreover, this is the first investigation to examine the relationship between SR capacity (indexed by HRV) and the motivation to engage in SR (as indexed by lay theories of SR). Evidence shows a strong relationship between resting HRV and the motivation to engage in SR (Figure 4) beyond possible confounds (see methods).

Interestingly, within the depletion group, results show a marginal interaction between resting HRV and ITWS scores. This interaction can be interpreted in two ways: (1) Those who had lower HRV and scored higher on the ITWS show better performance in comparison to those with low HRV and low ITWS scores (Figure 13a); (2) those who scored low on the ITWS and had higher HRV show better performance in comparison to those who scored low on the ITWS and had lower HRV (Figure 13b). These results suggest that when having low rMSSD or ITWS scores, being high in the other variable

(ITWS or rMSSD) can be beneficial for performance. While research suggests that holding a limited theory of SR or having lower resting HRV is detrimental for SR success, the interaction between these two variables suggests that having one, but not the other, may serve as a protective factor when faced with depletion. Furthermore, these findings have important cognitive and health implications beyond a proof of theory.

Further Implications

Ego-depletion can create deficits in tasks that require SR. These deficits can be harmful in many areas of life (e.g. goal pursuit). The MM-SR suggests that without the proper motivation, one will experience ego-depletion. Current evidence extends this notion, suggesting that without the proper motivation, having high HRV or high cognitive ability may serve as a protective factor when facing depletion, such that limited-resource theorists with high HRV will not experience performance decrements with repeated SR.

Along these lines, previous research suggests that those with high HRV are better able to perform and persist at cognitive tasks in comparison to those with low HRV (Segerstrom

& Nes, 2007b; Thayer et al., 2009). The present results also extend this notion, suggesting that holding unlimited theories of SR may protect against ego-depletion in individuals with low in HRV. Taken together, these results suggest that having high

HRV, but not motivation, and vice versa, may protect an individual from decrements in cognitive SR. Moreover, these results suggest that HRV is an indicator of SR capacity, but also the willingness to use this capacity.

Moreover, resting HRV is also associated with overall health (Brosschot, Van

Dijk, & Thayer, 2007; Thayer & Sternberg, 2010; Thayer et al., 2012; Thayer & Lane,

2000; Thayer & Sternberg, 2006; Weber et al., 2010). Specifically, resting HRV has been associated with endocrine, inflammatory, immune, and cardiovascular function – systems crucially responsible for maintaining a healthy individual (Jarczok et al., 2013; Thayer et al., 2011; Thayer & Sternberg, 2006, 2010). The current results show a strong between relationship resting HRV and lay theories of SR, such that the greater the HRV, the more

30 a person thinks SR is unlimited (Figure 4). In other words, physiological indicator of health and SR is related to metacognitions of SR – this relationship remains strong and significant when controlling for possible confounds such as respiration, age, ethnicity, gender, and BMI. One possible explanation of these results may be that individuals with low resting HRV experience more SR failures in comparison to high HRV individuals.

These failures can lead to poorer expectations of a desired outcome and may negatively influence the motivations to engage in these behaviors, leading to ego-depletion.

Empirical evidence also shows that when an individual is engaged in SR, and has knowledge of future SR tasks, individuals are often motivated to save SR resources for future use, leading to decreased SR performance on the current task (Muraven, Shmueli,

& Burkley, 2006). Particularly in individuals who think that SR is a limited resource, this concern and worry of becoming depleted of SR resources may be stressful, leaving the individual in vigilant mental state. It is theorized that a number of maladaptive psychological states, for example chronic worry, vigilance, and rumination, can decrease resting levels of HRV (Brosschot et al., 2010). Thus, these limited-theorists’ chronic resting HRV may be lower because of the constant and vigilant monitoring of SR resources. In other words, holding a limited theory of SR may be a psychological state that is deleterious to autonomic function, as indexed by HRV. Overall, combining both prior research (Hagger et al., 2010; Job et al., 2010; Segerstrom & Nes, 2007) and current psychophysiological evidence, having low HRV and/or limited-resource theories may be maladaptive for general SR and autonomic function, which can have downstream consequences for health.

Limitations and Future Directions

One limitation of the current study is that it is correlational – the causal nature between lay theories of SR and resting HRV cannot be determined with these data.

Although it is proposed that resting HRV and lay theories of SR can influence one another to maintain their negative association (i.e. the more limited, the lower HRV), research is needed to examine this possibility. Future studies should address this limitation by manipulating lay theories of SR. Job et al. (2010) show that when participants complete a questionnaire favoring either limited or unlimited theories, individuals are influenced by the questionnaire and hold theories consistent with the manipulation. Therefore, those in the unlimited manipulation do not show ego-depletion effects in comparison to the limited manipulation group. Future studies should use similar methodology to examine how this manipulation affects both HRV and task performance.

Additionally, ego-depletion studies often replicate results with a different set of

SR tasks. Conceptual replications are helpful here to ensure that participants are examining SR, and effects are not an artifact of the tasks used. Therefore, it is important that the current results be replicated using different SR tasks (e.g. handgrip and cold pressor task; for example see Gailliot et al., 2007; Hagger et al., 2010; Mark Muraven &

Baumeister, 2000).

SR, including autonomic regulation, has been shown to decline with age (Choi et al., 2006; Thayer, Sollers, et al., 2009). One limitation of the current study is that this is a college age sample and thus, the present results may not extend to other age ranges.

Future studies should examine how HRV and motivation play a role in SR in older adults and other age groups (e.g. adolescences). Lastly, we cannot exclude potential bias due to a small sample size; future studies should examine this in phenomenon in a larger sample.

Conclusions

Ego-depletion is a state where an individuals’ SR is operating at less than full capacity. However, the current evidence, coupled with previous research, shows that this only occurs in individuals who lack both SR ability, as indexed by HRV, and motivation to use this ability, as indexed by lay theories of SR. Moreover, lay theories of SR are strongly related to resting-HRV. This investigation suggests that high HRV and/or holding unlimited theories of SR is essential for adaptive SR whereas the respective alternative can be detrimental to SR and possibly, overall health.

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Appendix A: Tables

Sample Size (n) 61

Men 19 Women 42 Caucasian American 42 Ethnic Minorities 19 Age, years 19.02 (1.61) BMI, kg/m2 22.71 (2.92)

HR, bpm 73.57 (11.09) lnRMSSD (ms) 3.81 (.490) ITWS Scores 41.93 (5.50)

Table 1. Sample Demographics.

Age, BMI, HR, lnRMSSD, and ITWS scores are represented as means (standard deviation in brackets).

Age, HR, lnRMSSD n BMI kg/m2 HFpeak ITWS STAI-T DERS RRS CIS SCS BIF years bpm , ms

Non- 19.00 22.81 74.36 3.77 .281 42.24 39.93 90.04 42.96 57.96 139.48 37.04 29 Depletion (1.95) (2.90) (11.53) (.472) (.055) (5.16) (8.75) (13.84) (11.77) (9.87) (18.74) (2.37)

19.03 22.62 72.85 3.85 .273 41.66 39.09 89.19 41.19 58.53 145.94 37.53 Depletion 32 (1.26) (2.98) (10.82) (.511) (.043) (5.86) (9.34) (11.65) (10.37) (8.50) (18.57) (2.28) p .940 .799 .621 .528 .579 .682 .813 .799 .540 .813 .194 .420

Table 2. Baseline Characteristics by Depletion Group Manipulation.

41 Mean (standard deviation in brackets) values on baseline measures. Age was calculated in years, heart rate (HR) in beats per

4 minute, Body mass index (BMI) was calculated in kg/m2, and High Frequency peak (HFpeak) is calculated using spectral analyses

and is regarded as a measure of respiratory frequency. These comparisons suggest that this investigation had successful

randomization.

n Age, years BMI kg/m2 HR, b/min HFpeak ITWS STAI-T DERS RRS CIS SCS BIF

Low HRV 27 18.96 22.85 77.66 .275 44.22 42.66 92.96 45.00 58.04 138.04 37.33 (1.22) (3.49) (10.32) (.048) (4.93) (9.86) (13.96) (12.50) (9.01) (19.76) (2.43) High HRV 27 19.26 22.53 69.49 .279 39.85 36.80 86.20 39.12 57.80 147.83 37.48 (2.05) (2.56) (10.46) (.051) (4.70) (7.64) (10.95) (9.08) (9.97) (18.44) (2.25)

p .522 .701 .006** .781 .002** .021* .059† .060† .929 .074† .823

Table 3. High and Low HRV Group Comparisons on Baseline Variables.

2 Mean values (standard deviation shown in brackets) shown for each variable by high and low HRV group. Age was calculated in years, heart rate (HR) in beats per minute, Body mass index (BMI) was calculated in kg/m2, and High Frequency peak (HFpeak) is calculated using spectral analyses and is regarded as a measure of respiratory frequency. All self-report data (ITWS, STAI-T, DERS, RRS, CIS, SCS, and BIF) were calculated in accordance with prior research (see methods for full description of questionnaires). Statistical significance using p values are show for each comparison. †p<.10 *p<.05 ** p<.01

INCG- INCG- lnRMSSD ITWS BIF SCS DERS STAI-T CIS RRS ACC RT lnRMSSD --

ITWS -.536*** --

BIF .029 .023 --

SCS .293* -.396** -.050 --

DERS -.271* .432** .097 -.306 --

STAI-T -.454** .432** .023 -.630*** .447*** --

CIS .041 -.215† -.132 .557*** -.213† -.402** --

RRS -.263† .424** .089 -.364** .549*** .652*** .244† -- 43

4 INCG- ACC .142 -.244† .167 .161 -.086 -.259* .239† -.194 -- 3

INCG-RT .067 .060 -.295* .015 -.169 .017 .203 .068 -.045 --

Table 4. Correlation Matrix of Variables.

This table shows the relationship between all independent variables collected in the study using Pearson’s r correlation coefficients. † p<.10 * p<.05 **p<.01 ***p<.001

Appendix B: Figures

Inhibitory Control Self-Control Avoidance Self-Regulation Self-Regulation Inhibitory Control

Figure 1. Conceptual Diagram of Self-RegulationSelf-Regulation Note: Self-regulation (SR) is defined as regulating the self in a manner consistent with goals, motivations, and values. One type of SR is self-control, defined as pursuing a larger-later reward in the face of an immediate and smaller option. Different mechanisms such as inhibitory control (inhibit a pre-potent response and respond in a more desirable way) and avoidance (to avoid a threatening or harmful situation) can be used to achieve adaptive self- control. As the graph depicts, inhibitory control can service both self-control and SR independently (Fujita, 2011).

Figure 2. Conceptual Representation of Model 1 in PROCESS Note: Model 1 in processes tests the effect of M (moderator) on the link between X and Y. The interaction effect of M and X is significant if, at varying levels of M, the strength and/or direction of the X and Y relationship changes.

Figure 3. Statistical Representation of Model 1 in PROCESS Note: X is the independent variable, M is the moderating variable, XM is regarded as the interaction between X and M, Y is the dependent variable, and e is the error term. This model tests the main effect of X, M, and the interaction between the two on the dependent variable (Y).

M W

Figure 4. Conceptual Representation of Model 2 in PROCESS

Note: Model 2 in processes tests the effect of two moderators (M and W) on the link between X and Y. The interaction effect of M and X is significant if, at varying levels of M, the strength and/or direction of the X and Y relationship changes, after accounting for the WX interaction. Similar to the MX interaction, the WX interaction is significant if, at varying levels of M, the strength and/or direction of the X and Y relationship changes, after accounting for the variance of the MX interaction.

Figure 5. Statistical Representation of Model 2 in PROCESS

Note: X is the independent variable, M is the moderating of conditional variable, W is a second moderating variable, XM is regarded as the interaction between X and M, XW is regarded as the interaction between X and W, Y is the dependent variable, and e is the error term. This model tests the main effect of X, M, W, and the interaction of XM and XY on the dependent variable (Y).

5 β = -.532***

4.5

3.5

3 lnRMSSD

2.5

2 20 30 40 50 60 ITWS Scores

Figure 6. Scatter Plot of Resting HRV and Lay Theories of SR Note: This scatterplot of ITWS scores and Resting HRV controls for BMI, age, gender, ethnicity, and respiration. *** p<.001

ITWS

Figure 7. Conceptual Diagram of Moderation Test between Lay Theories of SR, Depletion, and Performance

Note: Conceptual model tested to examine the moderation effect of ITWS scores on the link between depletion and performance.

Figure 8. Statistical Diagram of Moderation Test between Lay Theories of SR, Depletion, and Performance

Note: Statistical model tested to examine the moderation effect of ITWS scores on the link between depletion and performance.

100

95 *** 90

85 ACC ACC - 80 Unlimited-Resource

INCG Limited-Resource 75

60 Non-Depletion Depletion

Figure 9. Interaction between Lay Theories of SR and Depletion on Performance Note: This graph depicts the moderating effect of ITWS scores on the link between the depletion manipulation and performance on the Stroop task. Those who held limited theories of SR and were in the depletion condition show ego-depletion effect in comparison to unlimited theorists and those in the non-depletion condition. *** p<.001

RMSSD

Figure 10. Conceptual Diagram of Moderation Test between Resting HRV, Depletion, and Performance Note: Above shows the conceptual model used to test if the relationship between condition and Stoop task accuracy changed at varying levels of resting RMSSD.

Figure 11. Statistical Diagram of Moderation Test between Resting HRV, Depletion, and Performance Note: Model 1 in PROCESS was used to examine the effect of condition (non-depletion (1) and depletion (2)), resting RMSSD, and the interaction between the two, on Stroop task performance (INCG-ACC).

***

105

100 *** 95

85 ACC

- High HRV

ICG 80 Low HRV

60 Non-Depletion Depletion

Figure 12. Interaction between Resting HRV, Depletion, and Performance Note: Figure 10 shows the moderating effect of resting HRV on the link between the depletion manipulation and performance on the Stroop task. Those who had low HRV, and were in the depletion condition, show ego-depletion effect in comparison to individuals with low HRV and all participants in the non-depletion condition. ** p <.01

RMSSD

Figure 13. Conceptual Diagram of Moderation Test between Lay Theories of SR, Resting HRV, and Performance in the Depletion Group Note: Model 1 was used to test if the relationship between lay theories of SR and Stoop task accuracy changed at varying levels of Resting HRV in those who were depleted.

Figure 14. Statistical Diagram of Moderation Test between Lay Theories of SR, Resting HRV, and Performance Note: Model 1 in PROCESS was used to examine the effect of ITWS scores, resting RMSSD, and the interaction between the two, on Stroop task performance (INCG-ACC).

96 94

88 ACC

- ** 86 High HRV

84 Low HRV INCG 82 80 78

76 Unlimited-Resource Limited Resource

B: 96 94 92

90 88

ACC ** - Unlimited-Resource 86 84 Limited Resource INCG 82 80 78 76 High HRV Low HRV

Figure 15. Interaction between Resting HRV and Depletion on Performance Note: The above graphs (A and B) show the interaction between lay theories of SR and Resting HRV in the Depletion group only. * p<.05 ** p <.01 ***p<.001

Appendix C: Implicit Theories of Willpower Scale

Strenuous mental activity 1. Strenuous mental activity exhausts your resources, which you need to refuel afterwards (e.g. through taking brakes, doing nothing, watching television, eating snacks). (R) 2. After a strenuous mental activity, your energy is depleted and you must rest to get it refueled again. (R) 3. When you have been working on a strenuous mental task, you feel energized and you are able to immediately start with another demanding activity. 4. Your mental stamina fuels itself. Even after strenuous mental exertion, you can continue doing more of it. 5. When you have completed a strenuous mental activity, you cannot start another activity immediately with the same concentration because you have to recover your mental energy again. (R) 6. After a strenuous mental activity, you feel energized for further challenging activities.

Resisting temptations

1. Resisting temptations makes you feel more vulnerable to the next temptations that come along. (R) 2. When situations accumulate that challenge you with temptations, it gets more and more difficult to resist the temptations. (R) 3. If you have just resisted a strong temptation, you feel strengthened and you can withstand any new temptations. (R)

4. It is particularly difficult to resist a temptation after resisting another temptation right before. 5. Resisting temptations activates your willpower and you become even better able to face new upcoming temptations. 6. Your capacity to resist temptations is not limited. Even after you have resisted a strong temptation you can control yourself right afterwards.

Note: R = reversed items