Understanding the Source of Emotions:

Anxiety, Emotion Understanding Ability, and Risk-Taking

By

Jeremy A. Yip

A thesis submitted in conformity with the requirements

for the degree of

Doctor of Philosophy

Joseph. L. Rotman School of Management

University of Toronto

©Copyright by Jeremy Yip (2011)

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Understanding the Source of Emotions:

Anxiety, Emotion Understanding Ability, and Risk-Taking

JEREMY A. YIP

Doctor of Philosophy

Joseph. L. Rotman School of Management

University of Toronto

2011

Abstract: Can only a subset of individuals – those higher on the ability to understand the sources

of emotions – determine whether to disregard or allow the effects of emotions when making

decisions? I test two central predictions. First, I test whether individuals high on emotion

understanding ability (EUA), one of the key dimensions of emotional intelligence, are less

affected by incidental anxiety when making decisions involving risk than their lower ability

counterparts. The rationale for this prediction is that individuals who have high EUA are able to

correctly identify their source of anxiety and, based on perceived irrelevance, disregard

incidental anxiety when making risky decisions, whereas individuals who have low EUA are

confused about the source of anxiety and are more influenced by incidental anxiety when

making risky decisions. Second, I test whether individuals high on EUA are more affected by

integral anxiety when making risky decisions than their lower ability counterparts. The rationale

for this prediction is that individuals who have high EUA are able to correctly identify their

source of anxiety and, based on perceived relevance, use their integral anxiety to inform their

risky decision making whereas those who have low EUA misattribute their anxiety and are less

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likely to incorporate their integral anxiety into their decision making. In Experiment 1,

incidental anxiety reduced risk-taking among individuals with low EUA, but not among their

higher ability counterparts. In Experiment 2, the interactive effect of EUA and incidental

anxiety on risk-taking was eliminated when I identified the irrelevance of anxiety to the present

decision, but it remained when the irrelevance was not identified. To explore the role of EUA in

using the adaptive function of emotion in decision making, Experiment 3 assessed whether

emotionally intelligent individuals who have high EUA incorporate integral anxiety, as

measured by skin conductance responses, into their risk-taking, compared to those with low

EUA. Contrary to expectations, the results of Experiment 3 showed that, when EUA was high,

there was a negative effect of integral anxiety on risk-taking that was not significant. When

EUA was low, there was a significant positive effect of integral anxiety on risk-taking.

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ACKNOWLEDGEMENTS

I would like to thank my supervisor, Stéphane Côté, for his invaluable guidance in

developing my skills as a researcher and encouraging me to pursue my own research ideas with

intellectual rigour. I would also like to thank my supervisory committee members, Chen-Bo

Zhong and Dilip Soman, for their guidance and insightful feedback. Special thanks to Dana

Carney for making Experiment 3 possible by providing the training and resources for me to

conduct psychophysiology research. I also appreciate the comments and suggestions provided

by Glen Whyte and Markus Groth.

Special thanks to the following faculty members for helping me to refine my research

ideas: Sanford Devoe, Katy DeCelles, Nina Mazar, Lisa Kramer, Kevin Wang, Min Zhao, Terry

Amburgey, and Christopher Miners.

I am very grateful to the Social Sciences and Humanities Research Council of Canada,

Ontario Graduate Scholarship, Academy of Management (Human Resource Division), and

Society for Human Resource Management for their financial support in completing my

dissertation research. I also would like to thank Steve Stein at Multi-Health Systems (MHS) and

Curt Leslie at Wonderlic for providing access to some test materials.

Thank you to the following research assistants for their diligence in helping to carry out

the data collection procedures: Vivian Chan, Yunjie Shi, Man-On Tong, Joyce Wong, Jacky

Tam, and Jim McGee.

I am indebted to my parents, Garry and Susan Yip, and my brother, Leland Yip, and his

girlfriend, Danielle Pashkoff, for their ongoing love and support. And, most importantly, thank

you to Kelly Lee for her unconditional love, patience, and compassion.

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TABLE OF CONTENTS

TABLE OF CONTENTS ............................................................................................................. v

LIST OF TABLES ........................................................................................................................ vi

LIST OF FIGURES ...................................................................................................................... vii

INTRODUCTION......................................................................................................................... 1

MISATTRIBUTION OF INCIDENTAL EMOTION ................................................................ 4

INCIDENTAL ANXIETY AND RISK-TAKING ..................................................................... 6

EMOTION UNDERSTANDING ABILITY REDUCES THE EFFECT OF INCIDENTAL ANXIETY ON RISK-TAKING................................................................................................... 7

INTEGRAL ANXIETY AND RISK-TAKING…………………………………………… ... 11

EMOTION UNDERSTANDING ABILITY AMPLIFIES THE EFFECT OF INTEGRAL ANXIETY ON RISK-TAKING…………………………………………………………… ... 16

EXPERIMENT 1……………………………………………………………………………..20

EXPERIMENT 2…………………………………………………………………………….. 28

EXPERIMENT 3……………………………………………………………………………..40

GENERAL DISCUSSION………………………………………………………………… ... 55

APPENDIX 1. EXPERIMENTAL MANIPULATION IN EXPERIMENTS 1 AND 2……. 64

APPENDIX 2. MANIPULATION CHECK FOR EXPERIMENTS 1 AND 2……………... 65

APPENDIX 3. RISK-TAKING MEASURE FOR EXPERIMENT 1……….…………… .... 66

APPENDIX 4. AWARENESS OF SOURCE MANIPULATION FOR EXPERIMENT 2 .... 67

APPENDIX 5. RISK-TAKING MEASURE FOR EXPERIMENT 2……………………... .. 68

APPENDIX 6. SKIN CONDUCTANCE RESPONSE WAVEFORM………………… ........ 69

APPENDIX 7. SKIN CONDUCTANCE RESPONSE………………………………… ........ 70

APPENDIX 8. IOWA GAMBLING TASK…………………………………………… ......... 71

REFERENCES………………………………………………………………………… ......... 72

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LIST OF TABLES

TABLE 1. EXPERIMENT 1. PEARSON CORRELATIONS MATRIX………………… ... 25

TABLE 2. EXPERIMENT 1: LOGISTIC REGRESSION ANALYSIS FOR VARIABLES

PREDICTING RISK-TAKING ................................................................................................... 26

TABLE 3. EXPERIMENT 2: PEARSON CORRELATIONS MATRIX................................. 34

TABLE 4. EXPERIMENT 2: LOGISTIC REGRESSION ANALYSIS FOR VARIABLES

PREDICTING RISK-TAKING ................................................................................................... 35

TABLE 5. EXPERIMENT 2: LOGISTIC REGRESSION ANALYSIS FOR VARIABLES

PREDICTING RISK-TAKING CONTROLLING FOR COGNITIVE INTELLIGENCE ..... 39

TABLE 6. EXPERIMENT 3: PEARSON CORRELATIONS MATRIX................................. 52

TABLE 7. EXPERIMENT 3: HIERARCHICAL LINEAR REGRESSION ANALYSIS FOR VARIABLES PREDICTING RISK-TAKING ........................................................................... 53

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LIST OF FIGURES

FIGURE 1. RESULTS FROM EXPERIMENT 1………………………………………….....27

FIGURE 2. RESULTS FROM EXPERIMENT 2………………………………………….....36

FIGURE 3. RESULTS FROM EXPERIMENT 3………………………………………….....54

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INTRODUCTION

The interplay of emotion and judgment has fascinated philosophers and psychologists

for centuries. There are varied perspectives on the role of emotions in decision-making. On the

one hand, according to classical decision making theory (e.g., Simon, 1979), emotions are

capable of undermining our best efforts to be reasonable and rational when making decisions.

Classical theories of decision making have emphasized rationality, where decision makers are

thought to dispassionately evaluate alternatives based on potential consequences and then select

the optimal alternative (Loewenstein & Lerner, 2003). On the other hand, emotions can direct

our attention and inform our decision making (e.g., Frijda, 1986; Levenson, 1994). These

theories assert that emotions serve positive functions such as rapid orientation to significant

threats or opportunities in the environment, and information about conditions that require action

(Lowenstein & Lerner, 2003).

Whether an emotion facilitates or hinders decision making is determined, in part, by the

relevance of the emotional state to the judgment at hand (Han, Lerner, & Keltner, 2007;

Loewenstein & Lerner, 2003; Pham, 2007). There are two types of emotional influences on

decision making: incidental emotional states and integral emotional responses (Loewenstein &

Lerner, 2003; Pham, 2007). Incidental emotional states refer to immediate emotional

experiences that arise from environmental factors that are unrelated to the present decision.

Integral emotional responses refer to immediate emotional experiences that arise from

considering the consequences of the present decision itself. Incidental emotional states are

viewed as irrelevant to the present decision and as exerting a biasing effect, whereas integral

emotional responses are viewed as relevant to the present decision and as exerting a facilitating

effect (Pham, 2007).

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Incidental emotions, which are thought to constitute biases, influence some of our most

important decisions – with whom we fall in love (Dutton & Aron, 1974), in what investments

we place our money (Raghunathan & Pham, 1999), which governmental policies we support

(Fischoff, Gonzalez, Lerner, & Small, 2005), and how much we are willing to pay for products

(Lerner, Small, & Loewenstein, 2004). In particular, one of the most famous psychology

studies that demonstrates the biasing effect of incidental emotions on decision making was

conducted by Dutton and Aron (1974). The authors stationed a female interviewer at the end of

a bridge who approached young males as they crossed the bridge about whether they would be

interested in participating in a study on scenic attractions. In the emotional arousal condition,

the interviewer approached males who crossed a suspension bridge, which is 450 feet long and

suspended by steel cables, and tends to sway wildly 200 feet above a river in British Columbia.

In the comparison condition, the interviewer approached males who crossed a fixed wooden

bridge, which was very stable and only 10 feet above the river. The male participants were

asked to complete a questionnaire and then the interviewer provided her phone number in case

the participant wanted to talk further.

The results revealed that male participants who crossed the high suspension bridge were

more likely to place a phone call to the female interviewer compared to those who crossed the

low wooden bridge, suggesting that those participants who experienced emotional arousal from

crossing the suspension bridge unintentionally allowed their misattributed excitement (sexual

arousal) to carry over to their judgment about whether they find the female interviewer attractive.

A limitation of the design of this study was the lack of random assignment of participants

whereby adventurous participants would be more likely to cross the suspension bridge compared

to their less adventurous counterparts, but this limitation was addressed in subsequent lab

experiments (Dutton & Aron, 1974).

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Numerous studies have demonstrated that, by simply having participants view a film clip

(Lerner & Keltner, 2001), listen to music (Au, Chan, Wang, & Vertinsky, 2003), read a

newspaper article (Johnson & Tversky, 1983) or receive a gift (Isen & Geva, 1987), they

experience incidental emotions that carryover to unrelated decisions and judgments. Given the

ubiquity of these effects, it is important to know whether some individuals are less likely to

exhibit these incidental effects than others, and to identify the abilities that these individuals

may have that help them enhance their decision-making. Nonetheless, individual differences in

the propensity to exhibit effects of incidental emotions on decision-making have received scant

attention in the literature.

In contrast to the disruptive influence of incidental emotions on decisions, integral

emotions are thought to facilitate decisions by increasing the speed of decisions (Pham, Cohen,

Pracejus, & Hughes, 2001), directing attention to relevant decision cues (Pham, 2004), and

requiring less processing resources (Shiv & Fedorikhin, 1999). Integral emotions are emotional

reactions to the various options associated with a decision, and these integral emotional

reactions are used as a proxy for value and facilitate evaluation of the various options (e.g.,

integral happiness is indicative of a desirable option and integral anxiety is indicative of an

undesirable option) (Pham, 2007). The conceptualization of integral emotions is consistent with

the somatic marker hypothesis, which suggests that people naturally develop emotional

reactions to decision options, which become stored in memory as learned associations of

emotion and value, and these associations guide their decision process (Damasio, 1994).

Because integral emotions emanate from the options of a decision, integral emotions are more

difficult to manipulate and, therefore, have been measured using physiological equipment,

specifically skin conductance response.

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In order to understand whether a particular ability enables individuals to use the adaptive

function of emotion in their decision making, it is important to test whether the same ability

serves to amplify the effects of integral emotions on decision making. To date, previous research

has overlooked individual differences in the propensity to exhibit the effects of integral

emotions on decision making.

In this investigation, I explore whether some individuals’ propensity to take risks are

more strongly shaped by incidental and integral anxiety than others. In Experiment 1, I examine

whether the decisions of individuals with high emotion understanding ability, who are skilled at

determining the source of their emotions, are less affected by incidental anxiety that is irrelevant

to current decisions than their less skilled counterparts. In Experiment 2, I examine whether

individuals with high emotion understanding ability exhibit weaker effects of incidental anxiety

because they correctly identify that the anxiety is irrelevant to the decision. In Experiment 3, I

examine whether individuals with high emotion understanding ability are more affected by

integral anxiety (as measured by skin conductance responses) that is relevant to current

decisions than their less skilled counterparts.

MISATTRIBUTION OF INCIDENTAL EMOTION

Past research has documented that incidental emotions have powerful effects on

judgments and decisions. The explanation for this pervasive effect of incidental emotions on

decision-making is that people reliably misattribute the physiological changes (arousal) to the

unrelated decision rather than to the actual source of their emotion (Schwarz & Clore, 1983).

To understand the importance of misattribution of emotion, it is necessary to understand the

nature of emotion. William James put forward an influential theory of emotion that suggested

that people’s perception of an emotionally exciting fact generates a specific pattern of

physiological changes that gives rise to a specific emotional state (James, 1890). While James’s

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theory identified an important component of emotion (i.e. physiological arousal as manifested

by the autonomic nervous system), his theory was criticized by Walter Cannon on the basis that

the autonomic nervous system gives rise to diffuse (instead of specific) patterns of physiological

changes that do not carry any meaning capable of generating a specific emotional state (Cannon,

1929).

In light of Cannon’s criticism, Schachter and Singer (1962) proposed a two-factor theory

of emotion, which suggests that people experience a general, undifferentiated pattern of

physiological arousal and then engage in a form of appraisal that attempts to attribute the

physiological arousal to a particular source. Schachter and Singer (1962) found empirical

evidence by injecting participants with epinephrine, which has an arousing effect, and having

them report their emotion based on their social context. In the misattribution condition, the

experimenter did not inform the participants about the arousing effect of the injection and then

had participants interact with a confederate who was acting happy and laughing. In the correct

attribution condition, the experimenter informed the participants about the arousing effect of the

injection and then had the participants interact with a confederate who was acting happy and

laughing. The results showed that participants who were uninformed about their emotional

arousal tended to report higher levels of happiness than those who were informed that their

emotional arousal would be a result of an injection of epinephrine. A similar set of results, in

terms of levels of anger, were found for participants who interacted with a confederate acting

angry. Therefore, emotions are thought to consist of two components: (1) physiological arousal

and (2) the attribution (or misattribution) of physiological arousal.

Building on these pioneering theories of emotion, Schwarz and Clore (1983) posited an

explanation for why people’s misattribution of incidental emotions can influence unrelated

decisions. In a seminal study, people reported greater satisfaction with their life when they were

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interviewed over the phone on sunny days and, thus, were in a pleasant emotional state, than

when they were interviewed on rainy days and, thus, experienced an unpleasant emotional state

(Schwarz & Clore, 1983). The explanation for these findings hinges on people’s reliance on

their momentary emotional states as a source of information in making decisions (Schwarz &

Clore, 2007). As a result, when people misattribute their incidental emotions to an unrelated

decision, they interpret their emotions as information about how they evaluate the options of a

decision, which results in their decision becoming biased (Barsade, Ramarajan, & Westen, 2009;

Loewenstein & Lerner, 2003).

INCIDENTAL ANXIETY AND RISK-TAKING

Anxiety, in particular, has been shown to influence decisions. Anxiety concerns

“subjective, consciously perceived feelings of apprehension and tension, which are accompanied

by or associated with activation (arousal) of the autonomic nervous system” (Spielberger, 1966,

p. 363). Anxiety encompasses unpleasantness, worry, tension, and nervousness (Gray, 1991).

People feel anxiety when they perceive that the world is uncertain and believe that they have

low control over the situation (Smith & Ellsworth, 1985). Therefore, when people are anxious,

they pay greater attention to potential threats in the environment (Eysenck, 1997), protect

themselves (Frijda, Kuipers, & ter Schure, 1989), and are more vigilant to preserve themselves

or their resources (Pacheco-Unguetti, Acosta, Callejas, & Lupianez, 2010).

As a result, when individuals feel anxiety, they tend to take less risk. Risk is defined “in

terms of uncertainty and consequences; perceived risk increases with higher levels of

uncertainty and/or the chance of greater associated negative consequences” (Campbell &

Goodstein, 2001, p. 440). Decision makers are described as low in risk-taking if they prefer a

certain outcome over an uncertain outcome with the same expected value, whereas decision

makers are described as high in risk-taking if they prefer an uncertain outcome over its certainty

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equivalent (Kahneman & Tversky, 1979). Past research has demonstrated a robust carryover

effect of incidental anxiety on less risk-taking (Raghunathan & Pham, 1999; Lerner & Keltner,

2001). In particular, Raghunathan and Pham (1999) experimentally induced incidental anxiety

in one group of participants by having them read a scenario that asked them to imagine that they

received a call from their doctor who wants to meet with them immediately to discuss an urgent

health concern. The authors induced incidental sadness in another group of participants by

having them read a scenario that asked them to imagine that they were returning home to see

their ailing mother. They found that, because incidental anxiety prompts implicit goals of

uncertainty reduction and risk avoidance, participants who experienced incidental anxiety had a

preference for investments with lower risk and lower reward. This effect was different than that

of incidental sadness; individuals who experienced incidental sadness preferred high-risk and

high-reward investments.

Furthermore, Lerner and Keltner (2001) found that, when participants wrote about five

things that made them most fearful, they reported lower levels of optimism in contrast to when

participants wrote about things that made them angry, they reported higher levels of optimism.

The justification for these opposing effects was that, compared to anger, fear activated lower

appraisals of certainty and lower levels of individual control. Therefore, when people

experience fear, they perceive greater uncertainty in the environment and become more

pessimistic in terms of the likelihood of negative events occurring to them.

Emotion Understanding Ability Reduces the Effect of Incidental Anxiety on Risk-Taking

Although past research has suggested that incidental anxiety reduces risk-taking, this

may not always be the case. The effect of incidental anxiety on risk-taking may vary according

to a factor that was not examined in previous studies: individual variation in the ability to

understand the source of emotions. This ability falls under the organizing framework of

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emotional intelligence, a set of abilities to use accurate reasoning about emotions and apply

emotional knowledge to enhance thought and performance (Mayer & Salovey, 1997; Salovey &

Mayer, 1990).

Emotional intelligence subsumes four abilities relating to perceiving emotion signals

from another’s expression (recognizing emotions), using emotional states to facilitate cognition

(using emotions), finding meaning in emotional states (understanding emotions), and managing

emotions in oneself and others (regulating emotions). The prevailing view among academic

researchers conceptualizes emotional intelligence as a distinct ability that focuses on a specific

area of problem solving, namely those involving emotions (Mayer, Roberts, & Barsade, 2008).

Previous research has demonstrated that emotional intelligence is a separate construct from

cognitive intelligence and personality traits (Brackett & Mayer, 2003; Côté & Miners, 2006).

Emotion understanding ability is considered the third branch of Mayer and Salovey’s

(1997) model of emotional intelligence, and it pertains to the skill of analyzing the cause and

effect relationship of events and emotions forwards (predicting future emotional states based on

current events) and backwards (pinpointing which past events elicited current emotions)

(MacCann & Roberts, 2008; Mayer et al., 2008; Mayer & Salovey, 1997). An example of

predicting future emotional states based on current events is as follows: An employee is

involved in a car accident in the parking lot, and so the employee is able to understand that he or

she will feel very anxious during the team meeting later in the day. An example of pinpointing

which past events elicited current emotions is as follows: An employee feels tense and anxious

during a meeting. After some introspection, he or she realizes that he or she is not nervous

based on participating in the meeting or being evaluated by others; instead, he or she realizes

that the nervousness arises from the uncertainty of the repair costs for his or her car.

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Emotion understanding ability is conceptually distinct from the other three emotional

intelligence abilities because it captures people’s ability to correctly appraise an emotion by

determining which events cause certain specific emotional states (Mayer & Salovey, 1997).

Recognizing emotions typically pertains to accurately identifying emotions in others based on

their nonverbal cues and expressions. Emotion understanding ability is distinct from

recognizing emotions because emotion understanding ability is not just concerned about what

people feel, but why they feel the way they do. Regulating emotions pertains to maintaining or

changing emotional states in the self or others. Emotion understanding ability is distinct from

regulating emotions because the goal of emotion understanding ability is determining the event-

emotion link instead of changing the emotional expressions to match particular norms or

expectations. Using emotions pertains to generating or using emotions to facilitate cognition or

performance. Emotion understanding ability is distinct from using emotions because emotion

understanding ability involves determining the causal chain between current events and future

emotions or past events and current emotions whereas using emotions involves matching current

emotions with current tasks.

Within the framework of the ability model of emotional intelligence, emotion

understanding ability is typically measured using two ways. First, the “changes” task presents

participants with a hypothetical scenario that describes a person who experiences a particular

emotional state. Then, an event occurs and participants are asked to indicate the emotional state

that the person would feel. Second, the “blends” task presents participants with a description of

several different basic emotions, and participants are asked to indicate which emotional state if

formed by combining the basic emotions.

As emotion understanding ability enables individuals to pinpoint the source of emotions,

it should help individuals identify whether the source of an emotion is irrelevant (i.e., the

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emotion is incidental) or relevant to the decision at hand (i.e., the emotion is integral). If the

emotion is incidental based on perceived irrelevance to the current decision, individuals high on

emotion understanding ability should be better able to block the carryover effect of the emotion

on their decision unlike their low ability counterparts. For instance, if an investor feels anxious

from a car accident that occurred on the way to work, he or she needs to determine whether the

car accident is irrelevant (incidental) to making a decision about purchasing a new stock. If the

investor has high emotion understanding ability and determines that this anxiety is irrelevant to

the investment decision, the carry-over effect of anxiety on investment decisions should be

diminished. In contrast, individuals with low emotion understand ability may be confused about

the source of their anxious state and should be more influenced by an incidental source of

anxiety on their decision making based on their misattribution of the source.

This hypothesis is strengthened by a previous demonstration (Schwarz & Clore, 1983)

that individuals who are unaware that they have positive feelings because of the nice weather

exhibit a stronger effect of positive emotions on judgments of life satisfaction than individuals

who are made aware of the source of their positive feelings (by asking them about the weather

prior to completing the life satisfaction questionnaire). Their findings showed that, on average,

people have a tendency to misattribute their incidental emotions to the current situation or

judgment, which allows their incidental emotions to carry over to the current judgment, but

when they correctly attribute their incidental emotion to the extraneous source, they do not

allow their incidental emotions to carry over to the current judgment. Similarly, emotion

understanding ability may moderate the negative effect of incidental anxiety on risk taking, so

that this effect is stronger among those with low rather than high emotion understand ability. In

Experiment 1, I tested this moderation.

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Moreover, my theorizing suggests that the moderating role of emotion understanding

ability is driven by the correct identification of whether anxiety is irrelevant to the present

decision. This suggests that the hypothesized interaction between emotion understanding ability

and incidental anxiety would dissipate of incidental anxiety to the present decision is explicitly

identified. In these additional conditions, individuals with low emotion understanding ability

should no longer be confused about the relevance of anxiety to the present decision. Like their

higher ability counterparts, they should not exhibit an effect of incidental anxiety on risk-taking,

and emotion understanding ability should not moderate the effect of incidental anxiety on risk-

taking. In Experiment 2, I tested this possibility in an effort to illuminate underlying

mechanisms.

INTEGRAL ANXIETY AND RISK-TAKING

Past research has demonstrated a robust carryover effect of integral anxiety on less risk-

taking. Damasio, Tranel and Damasio’s (1991) somatic marker hypothesis suggests that people

generally develop emotional reactions to decision alternatives and these emotional reactions

guide their choices. Building on the somatic marker hypothesis, in a notable set of studies,

Bechara and his colleagues found that normal individuals, in contrast to clinical patients,

generate anticipatory anxiety responses to a particular class of decision options, which serves as

an indication that they find these decision options to be more uncertain and, therefore, less

desirable. These physiological responses of anxiety reduce risky decision making (Bechara,

Damasio, Damasio, & Anderson, 1994; Bechara, Damasio, Tranel, & Damasio, 1997; Bechara,

Damasio, Damasio, & Lee, 1999; Loewenstein, Weber, Hsee, Welch, & 2001). The explanation

for this pattern of findings is that, unlike clinical patients who have suffered damages to the

areas of the brain responsible for emotion functioning (namely, the ventromedial prefrontal

cortex), normal individuals implicitly form associations between their integral anxiety (skin

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conductance responses) and evaluation of decision options, which becomes stored in memory as

somatic markers, and guides decisions about risk by allowing the integral anxiety to reduce risk-

taking.

Bechara and his colleagues use an experimental paradigm that is well suited for

capturing integral anxiety in relation to risky decision making. In their series of studies,

participants complete a decision task known as the Iowa Gambling Task, which was created to

simulate real-life decision making in terms of uncertainty of reward and punishment. In the

gamble task, participants are given a $2,000 loan and are asked to earn as much money as

possible by choosing a card from one of four decks over 100 trials (decisions). Two of the

decks are considered “bad” decks, which carry a reward of $100 but large penalty cards, and

two of the decks are considered “good” decks, which carry a reward of $50 but small penalty

cards. Participants are unaware of which decks are “good” and which decks are “bad”, and must

figure this out over the course of the 100 decision trials. In the long run, if participants select

cards from the “bad” decks, they will earn an overall loss. However, if participants select cards

from the “good” decks, they will earn an overall gain.

As participants make decisions across the trials of the gamble task, they are connected to

an apparatus that makes continuous recordings of their skin conductance responses. Skin

conductance responses reflect changes in emotional arousal that are derived from the autonomic

nervous system (Dawson, Schell, & Filion, 2000). The autonomic nervous system serves a

regulatory function that enables the body to adapt to environmental demands (e.g., the “fight” or

“flight” response). Skin conductance is considered to be a common measure of autonomic

nervous system activity, which pertains to changes in eccrine sweating in the hands.

Traditionally, skin conductance has been found to be indicative of arousal, attention, and

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emotion (e.g., Boucsein, 1992; Dawson et al., 2000; Fowles, 1986; Mendes, 2009; Lykken &

Venables, 1971).

With respect to arousal, Boucsein (1992) have suggested that skin conductance is

influenced by the behavioural inhibition system, which is responsible for the avoidance response

to threats or punishment in the environment. If people are not able to engage in an active

avoidance response such as running away, increased levels of eccrine sweating occurs and can

be captured using skin conductance measurement. For example, Teper, Inzlicht, and Page-

Gould (2011) investigated the differences in physiological arousal between individuals who face

a real-life moral dilemma (behavioural condition) and a hypothetical moral dilemma

(forecasting condition). They found that participants experienced greater physiological arousal,

which was measured using skin conductance responses, when they had an option to cheat while

completing a math test (behavioural condition), than participants who forecasted whether they

would cheat while completing a math test (forecasting condition). The authors suggested that,

compared to participants in the forecasting condition, participants in the behavioural condition

had elevated skin conductance as a visceral signal to avoid cheating and, therefore, cheated

significantly less.

With respect to emotion, Landis (1930) concluded that skin conductance in isolation

cannot serve as a direct measure of a particular emotion. For example, it is not possible to

distinguish between an “anxiety” skin conductance response and a “happy” skin conductance

response. However, the psychological interpretation of skin conductance depends on the nature

of the stimulus that elicited the skin conductance response and the experimental design (Dawson

et al., 2000). Therefore, to interpret the skin conductance response as a particular emotion, the

experimenter must manipulate the stimulus (i.e. the source of the emotion) and determine

whether the skin conductance response is paired with the stimulus. Consistent with the

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Schachter and Singer’s (1962) two-factor theory of emotion, an emotional response occurs when

people experience physiological arousal, as measured by skin conductance, and make an

attribution, as manipulated by the source of emotion. For example, Bechara et al. (1997)

developed a discrete stimulus paradigm that elicits the emotion of anxiety among participants by

manipulating the uncertainty (probability of losses) associated with each of the card decks. In

their experimental paradigm, because the source of their physiological arousal emanates from

the uncertainty with the card decks, skin conductance response is not simply interpreted as

physiological arousal, but interpreted as a feeling of anxiety.

With respect to attention, Boucscein (1992) documented that skin conductance is likely

associated to alerting and orienting activity in the prefrontal cortex. Skin conductance signifies

an attentional response in which people attend to novel stimuli. For example, Lacey, Kagan,

Lacey, and Moss (1963) recorded participants skin conductance level at rest, in anticipation of

performing an attentional task, and performing an attentional task. The attentional task required

participants to attend to novel stimuli such as listening to and identifying changing sounds.

They found that skin conductance level increased significantly between each of those three

stages, suggesting that skin conductance reliably reflects mobilization of attentional resources.

Further evidence was found by Munro, Dawson, Schell, and Sakai (1987) by measuring skin

conductance level and the frequency of skin conductance responses of participants while they

completed a vigilance task, which involved seeing a different character each second and

pressing a button only when a “0” was presented. Compared to the period when participants

were resting, participants had higher levels of skin conductance and a higher number of skin

conductance responses when performing the vigilance task.

To summarize, skin conductance reflects arousal, emotion, and attention. The emotion

of anxiety is thought to involve physiological arousal, be a discrete emotion in response to

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uncertainty, and increase attention to threat-related stimuli (Eysenck, Derakshan, Santos, &

Calvo, 2007).

To measure skin conductance, a small electrical current is passed through the skin by

placing two sensors on adjacent fingers and measuring the resistance of the current (Mendes,

2009). Skin conductance responses are discrete and short modulations in skin conductance that

occur within seconds, whereas skin conductance level is the average level of skin conductance

over a long window of time that are defined in terms of minutes (Figner & Murphy, 2011).

Skin conductance responses are particularly useful in judgment and decision-making

research because they allow for the dynamic analysis of momentary emotional responses to

decisions. For a skin conductance response to be considered specific (as opposed to non-

specific), the skin conductance response should occur within five seconds either before or after

stimulus, event, or decision (Boucsein, 1992). In the research conducted by Bechara and his

colleagues, participants’ skin conductance responses are captured within five seconds prior to

decisions in the gamble task, which are referred to as anticipatory skin conductance (anxiety)

responses. These anticipatory skin conductance responses are explained by the somatic marker

hypothesis, which suggests that people automatically evaluate decision options (decks) and

develop anxiety responses to the decision options that are uncertain (Bechara et al., 1997). This

anxiety response results in less risky decision making in order to reduce the uncertainty and

avoid potential loss or danger. Therefore, anticipatory skin conductance responses are

considered to be the physiological manifestation of integral anxiety (i.e. anxiety that arises from

considering the consequences of a decision). This is consistent with what Figner and Murphy

(2011) concluded, “Anticipatory skin conductance responses are assumed to reflect affective

evaluation processes of different choice options” (p. 165).

16

Further support of equating anticipatory skin conductance responses with integral

anxiety is found from Loewenstein et al.’s (2001) risk-as-feelings hypothesis, which suggests

that risky decisions are based on the emotional reactions to the severity and likelihood of

possible outcomes of the decision. They define integral anxiety as the immediate physiological

reactions to risks and uncertainties that decision makers experience when contemplating the

relative desirability of decision options. The authors concluded that more empirical research

needs to use physiological measures such as skin conductance response to adequately capture

integral anxiety.

Emotion Understanding Ability Amplifies the Effect of Integral Anxiety on Risk-Taking

When anxiety is integral and is an emotional reaction to the present decision, individuals

with high emotion understanding ability should be more likely to integrate their anxiety into

their decision process. Building on the role of emotion understanding within the relationship

between emotions and decisions, the explanation for this prediction is that people with high

emotion understanding ability correctly identify that their anxiety emanates from considering

their decision options and perceive the anxiety to be relevant to their current decision. As a

result, individuals with high emotion understanding ability will be more likely to incorporate

their integral anxiety into their decision making by allowing the integral anxiety to carryover

and reduce their risk-taking. To draw a comparison, people with low emotion understanding

ability are unsure about the source of their anxiety and will be less likely to perceive any

relevance in their emotional state with respect to their decision making involving risk.

Therefore, these low emotion understanding ability individuals are less likely to let their integral

anxiety inform their decision-making and block the carryover effect of integral anxiety resulting

in higher risk-taking.

17

For example, if an investor feels anxious when considering whether to purchase a new

stock, he or she needs to determine whether the anxiety is relevant (integral) from evaluating the

two options: purchasing the stock or not purchasing the stock. For illustrative purposes, let us

assume that the stock is for a technology company that has delivered high return but with wild

fluctuations over one year. If the investor has high emotion understanding ability and determines

that this anxiety is relevant to the investment decision, the carryover effect of anxiety on

investment decisions should be amplified and the investor would not purchase the stock (low

risk-taking). In contrast, individuals with low emotion understand ability may be confused about

the source of their anxious state and will be less influenced by an integral source of anxiety on

their decision making based on their misattribution of the source, resulting in the purchase of the

stock (high risk-taking).

Research has replicated the robust effect of integral anxiety on risk-taking (e.g., Bechara

et al., 1994; Bechara et al., 1997; Bechara et al., 1999). For example, Bechara et al. (1994) had

participants complete the Iowa Gambling Task and found that patients who had damage to the

ventromedial prefrontal cortex were more likely to choose the risky options (Decks A and B),

compared to normal participants. No significant differences in cognitive functioning between

the groups were found. The explanation for this finding is that the ventromedial prefrontal

cortex is responsible for generating anxiety reactions to risky decision options and those who

suffered damage to that part of the brain were unable to develop anxiety reactions when

completing the Iowa Gambling Task.

Building on this finding, Bechara et al. (1997) conducted a very influential study on

integral anxiety and risky decision making in which they examined the specific mechanism

underlying the ability to make advantageous (less risky) decisions when the probabilities of

decision outcomes are ambiguous. They had a group of patients with bilateral damage to the

18

ventromedial prefrontal cortex and a group of normal participants complete the Iowa Gambling

Task and recorded their skin conductance responses. They found that, after encountering a few

losses, the normal participants generated anticipatory skin conductance responses before a

decision when they pondered the decision options, and the anticipatory skin conductance

responses enabled them to avoid selecting the risky card decks, resulting in overall gains. In

contrast, after encountering a few losses, the patients did not generate anticipatory skin

conductance responses and tended to select the risky card decks, resulting in overall losses. The

rationale for this finding was that, unlike patients with damage to the brain structures associated

with emotion, normal participants were able to automatically determine which decks were more

risky and less risky based on the integral anxiety that they experienced when considering which

deck to choose. Hence, the major contribution of this study was that emotions serve an adaptive

function in decision making by enabling people to implicitly judge their decision options before

being able to make the judgment of the desirability of the options explicit.

In Bechara et al.’s (1999) study, they employed the same experimental paradigm as in

the experiments mentioned previously; however, they were interested in the role that the

amygdala in contrast to the role of the ventromedial prefrontal cortex in the effect of integral

anxiety on risk-taking. The authors had three groups (normal participants, patients with damage

to the ventromedial prefrontal cortex, patients with damage to the amygdala) complete the Iowa

Gambling Tasks while skin conductance responses were continuously recorded. Similar to past

results, they found that, unlike normal participants, patients with either ventromedial prefrontal

cortex damage or amygdala damage were unable to develop anticipatory skin conductance

responses and, as a result, chose the more risky options and ended the task with an overall loss.

Moreover, the authors found support for their prediction that the lack of anticipatory skin

conductance responses were a result of the inability to experience emotion for patients with

19

amygdala damage and the inability to integrate somatic activity for patients with ventromedial

prefrontal cortex damage.

While the effect between integral anxiety and risk-taking is robust, the understanding of

individual differences in skill among normal people is limited. With respect to individual

differences in integral anxiety, Carter and Smith-Pasqualini (2004) found that some individuals

were more prone to develop anticipatory anxiety responses to making risky decisions than

others. However, the authors did not measure emotion understanding ability and did not test

whether perceived relevance of emotion, ultimately, determines whether that emotion carries

over to a decision. In Experiment 3, I theorize that, if anxiety is integral based on perceived

relevance to the current decision, individuals high on emotion understanding ability should

allow the carryover effect of the anxiety on their risky decisions unlike their low ability

counterparts.

20

EXPERIMENT 1

In Experiment 1, I tested whether there is a negative effect of incidental anxiety on risk-

taking among individuals with low emotion understanding ability, but not among individuals

with high emotion understanding ability.

Hypothesis 1: There is a negative effect of incidental anxiety on risk-taking among

individuals with low emotion understanding ability, but not among individuals with high

emotion understanding ability.

Method

Participants and Procedures. The sample was composed of 108 undergraduate

commerce students at the University of Toronto and recruited using a course credit participant

pool. The average age was 20 years (SD = 1.51) and 68 percent were female. Three participants

were removed because they were observed clicking through the measures in the testing session

without reading the questions.

Participants were randomly assigned to one condition of a one-factor (incidental emotion:

anxiety vs. neutral) between-participant design. Participants were scheduled for two separate

sessions in a research laboratory: a 60-minute group testing session and, within 10 days, a 30-

minute individual experimental session.

In the testing session, participants completed an emotion understanding ability test and

demographic questions. In the experimental session, participants were told that they would

complete different studies for different researchers. In the incidental anxiety condition, based on

past procedures to elicit anxiety (Tugade & Frederickson, 2004), participants were instructed

that they had 60 seconds to prepare a 3-minute speech on “why you are a good job candidate.”

The experimenter told participants in this condition that their speeches would be videotaped

using a camcorder in the room and then shown to peers in another study for evaluation of the

21

participants’ academic and social standing at the university. In past research, participants

reported feeling high levels of anxiety and experienced significant physiological changes

associated with anxiety (Tugade & Fredrickson, 2004). In the neutral condition, participants

were instructed to prepare a mental list of grocery items for 60 seconds.

Following the emotion induction, the experimenter told participants in the anxiety

condition that he or she had to go down the hall to retrieve a memory stick for the video-

recording, and those in the neutral condition that he or she had to go down the hall to obtain

sheets of paper for writing the grocery list. The experimenter then asked the participants to

complete some unrelated decision-making tasks for another researcher at the University of

Toronto while he or she retrieved the memory stick or the sheets of paper. After participants

completed the decision-making tasks, they were told to complete the next study, which

consisted of a manipulation check for anxiety. Participants were then told that the study was

over, debriefed about the purpose of the experiment, and told that they did not have to deliver a

speech or write a grocery list. Participants were compensated with two course credits (see

Appendix 1).

Measures.

Emotion understanding ability. The understanding emotions branch of the Mayer-

Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey, & Caruso, 2002) is a

32-item performance-based assessment that was administered via computer to test the emotional

skill of correctly identifying how events generate particular emotions. The test publisher does

not authorize reproduction of actual items from the MSCEIT. In some test items, hypothetical

situations describe a person who experiences a specific emotion and, then, encounters an event,

and respondents identify the emotion that results from the event. Other items consist of

questions about how various emotions blend together to form a specific emotion, and

22

respondents identify the resulting emotion. Respondents must select the best answer among a

set of options of varying degrees of correctness based on the judgments of expert emotion

researchers. Raw scores are transformed into normalized standard scores with a mean of 100

(SD = 15). Split-half reliability of this measure was at .77 in past research (Mayer et al., 2002).

The mean score was 91.69 (SD = 13.72, 𝛼 = .72).

Previous research has shown that emotion understanding ability has been positively

related to verbal intelligence (r = .38, p < .05) (Roberts, Schulze, & MacCann, 2007), and

positively related to academic achievement (r = .23, p < .05) (Brackett & Mayer, 2003). There

appears to be some evidence of overlap between emotion understanding ability and cognitive

intelligence. One possible explanation for this overlap is that emotion understanding ability

involves logically reasoning ability that likely underlies both emotion problem solving (i.e.

causal relationships between events and emotions) and cognitive problem solving (i.e. causal

relationships between mathematical functions and verbal arguments). Past research has also

demonstrated that emotion understanding ability positively predicts a negotiation partner’s

satisfaction with an outcome (r = .23, p < .05) (Mueller & Curhan, 2007).

Manipulation Check. The manipulation check consisted of the average of three self-

report items: nervous, anxious, and tense (Raghunathan & Pham, 1999). Participants rated the

extent to which they felt each term on a 7-point response scale from 1 (strongly disagree) to 7

(strongly agree) (see Appendix 2). The mean score was 3.26 (SD = 1.60, 𝛼 = .91).

Risk-Taking. I adapted a risk-taking measure from past research (Raghunathan & Pham,

1999). Participants were asked whether they preferred Gamble A that offers a 100% chance of

winning $1, or Gamble B that offers a 10% chance of winning $10 and a 90% chance of

winning $0 (see Appendix 3). Gamble B is more risky because it involves more uncertainty and

a greater likelihood of negative consequences (Campbell & Goodstein, 2001; Highhouse, 2001).

23

In a pilot between-person study (n= 87), Gamble B was rated as more risky (M=8.46,

SD=2.75) than Gamble A (M=2.15, SD=2.40), t(85)=11.34, p<001, on a scale of 1 (not risky at

all) to 11 (very risky).

Results and Discussion

Participants in the anxiety condition (M=3.91, SD=1.59) reported higher anxiety than

those in the neutral condition (M=2.53, SD=1.26), t(105)=4.98, p<.01, revealing that the

manipulation was successful.

Table 1 presents the Pearson correlation coefficients for each of the variables. While

none of the correlations were significant, the associations between variable were in the expected

direction. For example, emotion understanding ability was positively associated with risk-

taking, (r = .18, n.s.), suggesting that people with higher levels of emotion understanding ability

result in higher risk-taking because they are less biased by the negative effect of incidental

anxiety on risk-taking.

Because the emotion understanding ability is a continuous variable and risk-taking is a

dichotomous variable, I used binary logistic regression to test my hypothesis that emotion

understanding ability moderates the effect of incidental anxiety (relative to neutral emotion) on

risk-taking. I regressed risk-taking on incidental anxiety condition, emotion understanding

ability, and their interaction. The results displayed in Table 1 show a significant interaction. As

expected, incidental anxiety had a negative effect on risk-taking, B = -6.53, SE = 2.98, Wald(1)

= 4.79, p < .05. As expected, there was no effect of emotion understanding ability on risk-

taking. The explanation for no significant differences between high and low emotion

understanding ability on risk-taking is that those participants with low emotion understanding

ability who were in the neutral condition should have take the same amount of risk as

participants with high emotion understanding in both the neutral and anxiety conditions. More

24

important, as expected, the interaction between incidental anxiety condition and emotional

understanding ability was significant, B =.07, SE = .03, Wald(1) = 4.19, p < .05.

Following Aiken and West (1991), I decomposed the interaction by examining the effect

of anxiety on risk-taking at two conditional values: one SD above and one SD below the mean

on emotion understanding ability. As expected, incidental anxiety had a negative effect on risk-

taking at one standard deviation in emotion understanding ability below the mean, B = -1.47, SE

= .64, Wald(1) = 5.30, p < .05. However, incidental anxiety had no effect on risk-taking at one

standard deviation in emotion understanding ability above the mean, B = .31, SE = .57, Wald(1)

= .29, p = .59. This interaction is shown in Figure 1.

This finding supports my prediction: Individuals who are skilled at determining the

source of emotions did not allow their incidental anxiety to carry over to their risk-taking,

whereas individuals with lower emotion understanding ability, who were presumably confused

about the source of their anxiety, allowed incidental anxiety to reduce their risk-taking.

25

Table 1.

Experiment 1: Pearson correlation matrix (N = 108)

Variable 1 2 3

1. Anxiety condition

2. Emotion understanding ability -.16

3. Risk-taking -.14 .16

Note. Anxiety condition was coded as 1 = incidental anxiety and 0 = neutral emotion. Emotion understanding ability was represented

as a continuous score. Risk-taking was coded as 1 = higher-risk decision (Gamble B) and 0 = lower-risk decision (Gamble A).

26

Table 2.

Experiment 1: Logistic Regression Analysis for Variables Predicting Risk-taking (N = 108)

Variable B SE Wald p-value

Anxiety condition -6.53 2.98 4.79 .03

Emotion understanding ability -.01 .02 .20 .66

Anxiety condition × emotion understanding ability .07 .03 4.19 .04

Constant .62 2.04 .09 .76

Note. Anxiety condition was coded as 1 = incidental anxiety and 0 = neutral emotion. Emotion understanding ability was measured

on a continuous scale. Risk-taking was coded as 1 = higher-risk decision (Gamble B) and 0 = lower-risk decision (Gamble A).

27

Figure 1. Experiment 1: The effect on incidental anxiety on risk-taking is stronger among

participants below the median on emotion understanding ability than among those above the

median. The median split on emotion understanding ability was performed for illustrative

purposes; all analyses were conducted with continuous scores.

38.5%

48.0%

14.3%

44.8%

0%

10%

20%

30%

40%

50%

60%

Low EUA High EUA

% C

hoos

ing

the

risky

opt

ion

Emotion Understanding Ability

Neutral

Anxiety

28

EXPERIMENT 2

Experiment 1 found that, when emotion understanding ability is low, there is an effect of

incidental anxiety on risk-taking, but when emotion understanding ability is high, there is no

effect. I designed Experiment 2 to examine the mechanism by which individuals with high

emotion understanding ability do not exhibit an effect of incidental anxiety on risk-taking:

knowledge about the relevance of anxiety to the present decision.

In my theoretical development, I proposed that one reason why emotion understanding

ability reduces the effect of incidental emotions is that it helps people identify whether an

emotion is irrelevant (i.e., the emotion is incidental) or relevant to the decision (i.e., the emotion

is integral). Accordingly, if it is explicitly mentioned that incidental anxiety is irrelevant, the

moderating effect of emotion understanding ability should be eliminated. I tested this possibility

in Experiment 2 by adding experimental conditions in which the experimenter helped

participants identify that the elicited anxiety is irrelevant to the decision.

I predicted that, when individuals are not made aware of the relevance of incidental

anxiety to the decision, as in Experiment 1, the effect of incidental anxiety on risk-taking

becomes stronger as emotion understanding ability decreases. When individuals are made aware

of the relevance of incidental anxiety to the decision, however, emotion understanding ability

should not moderate the effect of incidental anxiety on risk-taking; neither those high nor those

low on emotion understanding ability should exhibit an effect of incidental anxiety on risk-

taking.

In addition, whereas risk-taking was assessed with responses to a hypothetical gamble in

Experiment 1, I examined a real decision in Experiment 2. The replication of the findings from

Experiment 1 using a real decision is important because a real decision is more personally

relevant and involves actual consequences. I employed a realistic risk-taking task with the

29

following features: a) the task entails a choice between two outcomes that are personally

relevant; b) the probabilities of the outcomes are ambiguous; and c) the choice requires

behaviour on behalf of participants (Mandel, 2003).

I also added a control for cognitive ability to rule out the possibility that the effect of

emotion understanding ability is driven by this related ability (Côté & Miners, 2006; Mayer et

al., 2002), which dampened decision-making biases in past research (Frederick, 2005; Funder &

Block, 1989). Frederick (2005) found that cognitive reflection, which is the ability to resist the

temptation to report the first response that comes to mind, predicts whether people will be less

biased in their decision making. Specifically, he asked participants three questions like the one

presented here: “A bat and a ball cost $1.10 in total. The bat costs a dollar more than the ball.

How much does the ball cost?” Fewer than half of the sample of 3,000 university students gave

the correct answer, which is five cents. The students who answered this question correctly were

considered high in terms of cognitive reflection ability and tended to be more patient, preferring

larger rewards that would be received later over smaller rewards that would be received sooner,

in contrast to the low cognitive reflection ability students. Cognitive reflection ability was

found to be positive associated with cognitive intelligence (r = .22 to .46). Similar to emotion

understanding ability, higher cognitive reflection ability participants also preferred more risky

options compared to their lower ability counterparts.

As further evidence of cognitive ability reducing biases in decision making, Funder and

Block (1989) found that teenagers with higher cognitive intelligence tend to prefer a larger

payoff ($4.80) in a later session than a smaller payoff ($4) in an earlier session.

With respect to the current experiment, it is plausible that people with high cognitive

intelligence disassociate all irrelevant factors, including emotions, to remain focused on the task

at hand when making a decision, whereas people with low cognitive intelligence lack focus and

30

are distracted by incidental anxiety when making decisions about risk. I address this alternative

explanation in Experiment 2.

Hypothesis 2: When individuals are not made aware of the relevance of incidental

anxiety to the decision, the effect of incidental anxiety on risk-taking becomes stronger

as emotion understanding ability decreases. When individuals are made aware of the

relevance of incidental anxiety to the decision, emotion understanding ability should not

moderate the effect of incidental anxiety on risk-taking

Method

Participants and Procedures. The sample consisted of 132 undergraduate commerce

students at the University of Toronto. The average age was 21 years (SD = 2.2) and 67% were

female.

Experiment 2 used almost the same procedures as Experiment 1 with the exception of

three notable differences. First, participants were randomly assigned to one condition of a 2

(incidental emotion: anxiety vs. neutral) × 2 (source of emotion: aware vs. unaware) between-

participants design. The source of emotion was manipulated by presenting participants in the

aware conditions with a special note that read, “You may feel anxious because people often get

anxious when preparing to deliver a speech” (in the incidental anxiety condition) or “You may

feel no emotion because people often feel no emotion when mentally preparing a grocery list”

(in the neutral emotion condition). Participants in the unaware conditions did not receive these

instructions (see Appendix 4). Second, I used a behavioural measure described below, rather

than a hypothetical measure of risk-taking. Third, I measured cognitive ability in the testing

session.

Measures.

31

Emotion understanding ability. As in Experiment 1, I measured the ability to

understand emotion with the relevant branch of the MSCEIT. The mean score was 92.77 (SD =

15.55, 𝛼 = .73).

Manipulation check. I used the same items as in Experiment 1. The mean score was

3.45 (SD = 1.55, 𝛼 = .86).

Risk-taking. As part of a study on real-life decision making, participants were asked to

read an excerpt from an official statement issued by the provincial Ministry of Health, which

read as follows: “Influenza, commonly known as “the seasonal flu”, is a respiratory illness that

usually circulates throughout the fall and winter. People of any age can get the flu and sickness

usually lasts two to seven days. Most people who get the flu are sick for only a few days. In

others, the symptoms can last for weeks. In extreme cases, some people can develop

complications and become very ill, requiring hospitalization. The flu shot can substantially

reduce your chances of getting the flu. The university offers flu shot clinics each year.” (see

Appendix 5). The experimenter then asked participants: “Would you like to put your email

down on a list to attend an upcoming flu shot clinic that will be held on campus?” I measured

risk-taking by recording whether a participant wrote his or her email address on a sign-up sheet

(low risk-taking) or chose not to provide his or her email address (high risk-taking). For those

participants who signed up, I contacted participants about where and when the flu clinics were

scheduled to take place. Choosing not to sign up for the clinic is considered risky because it

entails more uncertainty (about whether participants will catch the flu) and a greater likelihood

of negative consequences (catching the flu) (Campbell & Goodstein, 2001; Highhouse, 2001).

In a pilot between-person study, not signing up was rated as more risky (M=4.54,

SD=2.31) than signing up (M=3.24, SD=2.89), t(85)=2.38, p<.05, on a scale of 1 (not risky at all)

to 11 (very risky). While the option to not sign up for the flu shot clinic was rated more risky

32

than the option to sign up for the flu clinic, it was below the midpoint of the scale. However, I

am primarily interested in the relative risk (i.e. more risky option compared to a less risky option)

as opposed to absolute risk (i.e. risky option must exceed a rating of 6).

Cognitive ability. I administered the Wonderlic Personnel Test (Wonderlic, 1992)

(M=104.19, SD=10.93). In this test, respondents are given 12 minutes to respond to 50 verbal,

mathematical, and analytical problems. Each correct response is counted and then a total raw

score is computed. The raw score is then transformed into an interpretable normalized standard

IQ score with a mean of 100 and a standard deviation of 15. In past research, the reliability

was .87 (Wonderlic, 1992).

Results and Discussion

Participants in the incidental anxiety condition reported higher levels of felt anxiety (M =

3.92, SD = 1.42) compared to those participants in the neutral condition (M = 3.01, SD = 1.56),

t(128) = 3.48, p < .01.

Table 3 presents the Pearson correlation coefficients for each of the variables. Of

particular interest, risk-taking was positively correlated with emotion understanding ability (r

= .18, p < .05) and cognitive intelligence (r = .23, p < .05). These results are consistent with my

prediction and suggest that people with higher levels of emotion understanding ability and

cognitive intelligence result in higher risk-taking because they are less biased by the negative

effect of incidental anxiety on risk-taking. Emotion understanding ability was positively

correlated with the source condition (r = .31, p < .01) but this is not important because the result

is due to the source condition being coded as 1 for awareness of the source and 0 for no

awareness of the source, and emotion understanding ability was recorded on a continuous scale.

I conducted binary logistic regression to test my hypothesis by regressing risk-taking on

emotion understanding ability, anxiety condition, and source of emotion condition, and the two-

33

way and three-way interactions terms. The results of this analysis, displayed in Table 4, revealed

a significant three-way interaction between incidental anxiety condition, emotion understanding

ability, and source of emotion condition, B = -.12, SE = .06, Wald(1) = 4.24, p < .05. This three-

way interaction is decomposed in Figure 1. Of special note, emotion understanding ability did

not significantly predict risk-taking, B = -.00, SE = .03, Wald(1) = .01, p < .91. This is expected

given that the awareness of source manipulation would diminish the positive effect of emotion

understanding on risk-taking.

34

Table 3

Experiment 2: Pearson correlations matrix (N = 132)

Variable 1 2 3 4 5

1. Anxiety condition

2. Source condition .00

3. Emotion understanding ability -.03 .31**

4. Cognitive intelligence -.06 .13 .45**

5. Risk-taking -.14 .11 .18* .23*

* p < 0.05, **p < 0.01

Note. Anxiety condition was coded as 1 = incidental anxiety and 0 = neutral emotion. Source condition was coded as 1 = aware and 0

= not aware. Emotion understanding ability and cognitive intelligence were measured on a continuous scale. Risk-taking was coded

as 1 = higher-risk decision (deciding not to sign up for the flu clinic) and 0 = lower-risk decision (deciding to sign up for the flu

clinic).

35

Table 4

Experiment 2: Logistic Regression Analysis for Variables Predicting Risk-taking (N = 132)

Variable B SE Wald p-value

Anxiety condition -10.84 4.51 5.78 .02

Source condition -2.33 3.22 .52 .47

Emotion understanding ability -.00 .03 .01 .91

Anxiety condition × emotion understanding ability .11 .05 4.74 .03

Anxiety condition × source condition 12.22 5.54 4.86 .03

Source condition × emotion understanding ability .02 .03 .46 .50

Anxiety condition × source condition × emotion understanding ability -.12 .06 4.24 .04

Constant .31 2.22 .02 .89

Note. Anxiety condition was coded as 1 = incidental anxiety and 0 = neutral emotion. Source condition was coded as 1 = aware and 0

= not aware. Emotion understanding ability was measured on a continuous scale. Risk-taking was coded as 1 = higher-risk decision

(deciding not to sign up for the flu clinic) and 0 = lower-risk decision (deciding to sign up for the flu clinic).

36

Figure 2. Experiment 2: When participants were not made aware of the source of emotion, the

association between incidental anxiety and risk-taking was stronger among participants below

the median on emotion understanding ability than among those above the median (top panel).

When participants were made aware of the source of emotion, neither participants above nor

those below the median in emotion understanding ability exhibited an effect of anxiety on risk-

taking (bottom panel). The median split on emotion understanding ability was performed for

illustrative purposes; all analyses were conducted with continuous scores.

52.9%50.0%

6.3%

43.8%

.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Low EUA High EUA

% c

hoos

ing

the

risky

opt

ion

Emotion Understanding Ability

Unaware of Source Condition

Neutral

Anxiety

41.2%

58.8%

43.8%

52.9%

.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Low EUA High EUA

% C

hoos

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the

risky

opt

ion

Emotion Understanding Ability

Aware of Source Condition

Neutral

Anxiety

37

I interpreted the interaction statistically by examining whether the interactive effect of

emotion understanding ability and incidental anxiety on risk-taking occurred in the unaware

condition, but not in the aware condition. To test the contrast effects within the aware versus

unaware conditions, I decomposed this interaction by examining the effect of incidental anxiety

on risk-taking at both higher and lower levels of emotion understanding ability.

In the unaware condition, incidental anxiety had a negative effect on risk-taking at one

standard deviation in emotion understanding ability below the mean, B = -2.64, SE = .93,

Wald(1) = 8.08, p < .01. However, incidental anxiety had no effect on risk-taking at one

standard deviation in emotion understanding ability above the mean, B = .66, SE = 1.02, Wald(1)

= .42, p = .52. These results replicate the findings of Experiment 1: anxiety reduced risk-taking

among low emotion understand ability participants but not among their higher ability

counterparts.

Participants in the aware condition exhibited a different pattern. There was no main

effect of incidental anxiety on risk-taking at one standard deviation in emotion understanding

ability below the mean, B = .26, SE = .82, Wald(1) = .10, p = .75. Similarly, there was no main

effect of incidental anxiety on risk-taking at one standard deviation in emotion understanding

ability above the mean, B = -.19, SE = .61, Wald(1) = .10, p = .75. These results are different

than those of the aware condition and those of Experiment 1: when I identified the relevance of

the anxiety to the decision, anxiety did not reduce risk-taking among the high nor the low

emotion understanding ability participants.

These results suggest that correctly identifying the source and relevance of their anxiety

explains why individuals with high emotion understanding ability disregard incidental anxiety

from risk-taking.

38

Subsidiary analyses controlling for cognitive ability revealed the same conclusions,

which are presented in Table 5. In particular, the three-way interaction of emotion

understanding ability, anxiety condition, and source of emotion condition held, B=-.12, SE=.06,

Wald(1)=3.71, p=.05, as did the two-way interaction between emotion understanding ability and

anxiety condition in the unaware condition, B=.11, SE=.05, Wald(1)=4.74, p<.05.

39

Table 5

Experiment 2: Logistic Regression Analysis for Variables Predicting Risk-taking Controlling for Cognitive Intelligence (N = 132)

Variable B SE Wald p-value

Anxiety condition -13.95 6.36 4.82 .03

Source condition -1.20 3.48 .12 .73

Emotion understanding ability -.02 .03 .37 .55

Cognitive ability .04 .02 2.15 .14

Anxiety condition × emotion understanding ability .11 .05 4.73 .03

Anxiety condition × source condition 11.82 5.81 4.15 .04

Source condition × emotion understanding ability .01 .04 .13 .71

Anxiety condition × cognitive ability .03 .05 .32 .57

Anxiety condition × source condition × emotion understanding ability -.12 .06 3.71 .05

Constant -2.21 2.98 .55 .46

Note. Anxiety condition was coded as 1 = incidental anxiety and 0 = neutral emotion. Source condition was coded as 1 = aware and 0 = not aware. Emotion understanding ability and cognitive ability were measured on a continuous scale. Risk-taking was coded as 1 = higher-risk decision (deciding not to sign up for the flu clinic) and 0 = lower-risk decision (deciding to sign up for the flu clinic).

40

EXPERIMENT 3

Taken together, Experiments 1 and 2 suggest that individuals with high emotion

understanding ability block the biasing effect of incidental anxiety on risky decisions because

they have the capacity to determine the irrelevance of their incidental anxiety to their risky

decisions. In contrast, individuals with low emotion understanding ability are influenced by

incidental anxiety when making risky decisions because of their confusion of the source of their

anxiety and inability to determine the irrelevance of their incidental anxiety. Therefore, emotion

understanding ability enables individuals to disregard the disruptive influence of incidental

anxiety when making risky decisions.

However, very little is understood in terms of the individual differences in ability that

enable individuals to leverage the adaptive function of integral emotions when making decisions.

In contrast to incidental anxiety that arises from environmental factors that are irrelevant to the

present decision, integral anxiety arises from considering the consequences of the present

decision and are relevant to the present decision (Loewenstein & Lerner, 2003; Pham, 2007).

Since relevant information about the desirability and consequences of a risky decision is

conveyed in integral anxiety (Loewenstein et al., 2001), it is important to understand whether

emotion understanding ability is able to extract the relevant information about risky decision

options by allowing integral anxiety to reduce risk-taking.

In Experiment 3, I assess whether those individuals with high emotion understanding

ability allow for integral anxiety to carry over to related risky decisions in contrast to those with

low emotion understanding ability. My theory asserts that people with high emotion

understanding ability are able to obtain useful information associated with integral anxiety that

emerges from considering the uncertainty associated with options for a decision because they

view the integral anxiety as relevant to the decision at hand, which facilitates their decisions by

41

reducing their risk-taking. People with low emotion understanding ability feel emotional

arousal but do not reliably attribute their anxiety to the correct source, which in this case is the

decision. By misattributing their anxiety, they regard their anxiety as irrelevant and do not use

their anxiety as information, resulting in higher risk-taking.

One study that lends partial support to the role of emotion understanding in the

relationship between integral emotions and decision making is by Seo and Barrett (2007). The

authors found that investors who were better at differentiating negative emotions in response to

stock market feedback were able to adjust their decision making and, consequently, realized

higher investment returns. However, although the study used an experience sampling procedure,

their study did not measure participants’ ability to differentiate emotions until after they made

their decisions. Furthermore, there is a difference in how they defined and operationalized

emotion understanding ability in comparison to the present set of experiments. The authors

define emotion understanding ability in terms of the ability to distinguish different types of

emotional experiences by labelling different emotional states. The index of emotion

differentiation reflects variation between self-report items of emotion, which could indicate

greater emotional variability rather than the ability to appraise different emotions. In contrast,

the present paper defines emotion understanding ability in terms of the ability to determine the

cause and effect relationship between events and emotions and I measure this ability with a task

on how emotions progress from events and a task on how emotions blend together to form new

emotions. Experiment 3circumvents these shortcomings by measuring anxiety that occurs

before the decision (integral anxiety) and measuring emotion understanding ability.

It should be emphasized that integral anxiety concerns the immediate feelings of anxiety

that are experienced when contemplating the options or consequences of a decision

(Loewenstein & Lerner, 2003). Integral anxiety is distinct from expected anxiety, which

42

pertains to the cognitive forecasts about the emotional consequences of decision outcomes

(commonly known as affective forecasting). Therefore, integral anxiety is how a decision

maker feels right now, and expected anxiety is how the decision maker will feel in the future.

Experiment 3 employs a realistic risk-taking task called the Iowa Gambling Task, which

involves a decision between two personally relevant outcomes by compensating participants on

how much they earn or lose on the gamble task. In addition, the probabilities of the outcomes

are ambiguous. Finally, each decision requires behaviour on behalf of participants, which

carries consequences.

Hypothesis 3: There is no effect of integral anxiety on risk-taking among individuals

with low emotion understanding ability, but there is a negative effect of integral anxiety

on risk-taking among individuals with high emotion understanding ability.

Method

Participants and Procedures. The sample consisted of 52 undergraduate students at

Columbia University. The average age was 23 years (SD = 3.7) and 58% were female.

Participants were scheduled for two separate sessions in a research laboratory: a 60-

minute group testing session and, within 10 days, a 45-minute individual experimental session.

In the testing session, participants completed an emotion understanding ability test and

demographic questions.

In the experimental session, participants were seated in front of a desktop computer

terminal and, after participants gave informed consent, they were asked to indicate their non-

dominant hand. The experimenter had participants clean their hands with a disposable alcohol

napkin to eliminate any oils from the skin that would prevent the electrodes from maintaining

contact with the skin. An electrolyte gel was applied to each electrode and then one electrode

was placed on the medial phalanges of the index finger and the medial phalanges of the middle

43

finger. The placement of electrodes was used for all participants. The participants were then

asked to sit still for five minutes in order obtain baseline skin conductance levels and ensure that

the skin conductance was being recorded properly. By having the participants take a deep

breath, the experimenter could ensure that the skin conductance hardware and software was

recording properly since skin conductance responds to respiration. The baseline skin

conductance level data can be used to eliminate people who are unresponsive or be entered as a

control variable to account for individual differences of general levels of arousal.

The electrodes that are placed on the participants’ non-dominant hand are connected by

wires to the Biopac hardware, which consists of the base module (MP150) and the skin

conductance module (GSR100C). The Biopac hardware is also connected to a laptop that is

running the AcqKnowledge software, which is used to record waveforms of skin conductance.

As skin temperature and eccrine sweat production increases, the electrical current that flows

through the electrodes can pass more freely through the skin.

On the Biopac hardware, the acquisition parameters were set to 5 Siemens with the

low-pass filter set to 1 Hz. The low-pass filter is a smoothing technique to eliminate any

artifacts that are caused by participants’ movements or the conductivity of the electrodes.

After the five-minute baseline period, participants were given instructions about the

Iowa Gambling Task. The Iowa Gambling Task was administered on a desktop computer that

was connected to the laptop computer with the Acknowledge software, enabling the skin

conductance data to be acquired and synchronized with data for the decisions in the Iowa

Gambling Task. On the desktop computer screen, participants saw four decks of cards, a credit

of $2,000, and bar graphs of their overall winnings or losses. Participants were instructed that

their goal was to win as much money as possible and avoid losing as much money as possible.

To make the Iowa Gambling Task more personally relevant, participants were told that they

44

would be paid 1/200 of the outcome of the gamble task (i.e. $2,000 = $10) for the experimental

session. Participants were led to believe that, since they started with a $2,000 credit, they would

start with $10 for their participation, but depending on their performance, they would be

compensated less or more than their starting amount. In actuality, for the Iowa Gambling Task,

participants would receive $10 at a minimum as well as any gains that they had accumulated at

the end of the task. The purpose of deceiving the participants to believe that they could receive

less compensation based on their performance on the Iowa Gambling Task was to make the

consequences of their decisions to be as personally relevant as possible. Not only would

participants take the task more seriously and be motivated to perform at their best, they would

also have strong emotional reactions knowing that they would have to suffer the consequences

of poor decision making. Participants completed the Iowa Gambling Task, lasting 20 minutes.

In the end, all participants received $10 for their participation in the testing session and at least

$10 for their participation in their experimental session. Participants will be debriefed about the

actual purpose of the experiment.

Measures.

Emotion understanding ability. As in Experiment 1 and Experiment 2, I measured the

ability to understand emotion with the third branch of the MSCEIT. The mean score was 103.82

(SD = 16.87, 𝛼 = .78). The mean score of emotion understanding ability for Experiment 3 was

higher than the mean score for Experiments 2 and 3 because the sample consisted of

undergraduate students at Columbia University. One possible explanation for the higher mean

score is that the admission standards for undergraduate students at Columbia University are

higher than those at University of Toronto, not only in terms scholastic achievement test (SAT)

scores, but also in terms of emotional and social abilities pertaining to leadership in extra-

curricular activities.

45

Integral anxiety. Participants completed the Iowa Gambling Task, which is designed to

elicit integral anxiety responses and measure risk-taking (Bechara et al., 1997). The Iowa

Gambling Task was created to simulate real-life decision making in terms of rewards,

punishments, and uncertainty and test the somatic-marker hypothesis. Participants were told to

select cards one at a time from any of the four card decks and try to win as much money as

possible. Although participants were not aware of the outcomes associated with each of the

decks, there were two “bad” decks (Decks A and B), which carry reward cards of $100 but have

severe penalty cards such as losses of $1,500, and two “good decks”(Decks C and D), which

carry reward cards of $50 but have less severe penalty cards such as losses of $300. Over 100

trials, choosing cards from the “bad” decks will lead to an overall loss, whereas choosing the

“good” decks will lead to an overall gain. Bechara and colleagues (1997) found that after

encountering a few losses with each of the decks, individuals begin to develop integral anxiety

responses that become connected to evaluating.

Bechara and his co-authors (1997) found that there are four periods of emotion learning:

pre-punishment (between the 1st card and the 10th card, no penalty cards are received), pre-

hunch (between the 10th card and the 50th card, integral anxiety becomes associated to the bad

decks), hunch (between the 50th card and the 80th card, participants report liking or disliking

certain decks based on the riskiness), and conceptual (between the 80th card and 100th card,

participants are able to accurately articulate the which decks are good and bad). The authors

outlined these stages in order to demonstrate that people are not able to explicitly explain why

they prefer certain decks over others, suggesting that people are relying on their emotional

responses rather than computational abilities. I examined skin conductance responses between

the 1st and 100th card because previous research has examined skin conductance responses in

46

relation to risk-taking during the entire Iowa Gambling Task (Bechara et al., 1997; Bechara et

al., 1999).

Skin conductance responses pertain to immediate changes in skin conductance and can

be quantified in several different ways. The two most common ways of quantifying skin

conductance response in judgment and decision making research are in terms of amplitude and

area bounded by the curve (see Appendix 6). First, amplitude captures the intensity of

emotional arousal (anxiety) by computing the difference between the skin conductivity at the

onset of a skin conductance response and the peak of the skin conductance response. The

typical rise time between the onset and peak of skin conductivity is one to three seconds

(Boucsein, 1992; Figner & Murphy, 2011). A skin conductance response must exceed a

threshold of .02 𝜇s and reach the peak within the five seconds preceding a decision to be

counted as a skin conductance response in order for an amplitude value to be recorded.

Amplitude is the most reliable and widely accepted quantification of skin conductance response

in the literature (Dawson et al., 2000; Figner & Murphy, 2011).

Second, the area bounded by a curve or area under the curve captures the amplitude over

time. Some researchers have suggested that the area bounded by a curve is more useful in

quantifying skin conductance response than in terms of amplitude or time alone because the skin

conductance response is typically represented graphically in terms of both the intensity of

arousal and the duration of arousal (see Appendix 7) (Boucscein, 1992; Figner & Murphy, 2011).

The Acqknowledge software calculates the area bounded by the curve by drawing a line

between two endpoints of the skin conductance response waveform within a five-second

window prior to each decision and calculating the area. Area is expressed in terms of amplitude

units, which is measured in terms of volts of electricity, multiplied by time, which is measured

in seconds (volts-sec).

47

There are three reasons for why skin conductance responses and the Iowa Gambling

Task are better suited for capturing the effect of integral anxiety on risk-taking than

conventional psychological experiments. First, the measurement of skin conductance responses

are unobtrusive and do not require introspection on the part of participants as required for self-

report psychological measures (Fowles, 1986). Second, skin conductance responses can be

measured reliably and in synchronicity with multiple decisions over time (Figner & Murphy,

2010). Third, skin conductance responses represent actual physiological changes as opposed to

perceived physiological changes when making decisions (Dawson et al., 2000).

Risk-taking. I measured risk-taking by recording whether a participant chose Deck A

and Deck B (high risk-taking) or chose Deck C and Deck D (low risk-taking) for each decision

trial (see Appendix 8). Choosing Decks C and D are considered risky because it entails more

uncertainty (about whether participants will lose money) and a greater likelihood of negative

consequences (losing more money).

Results and Discussion

Hierarchical linear modeling was used to examine the moderating effect of emotion

understanding ability (Level 2 between-persons predictor) on the relationship between

momentary integral anxiety (Level 1 within-person predictor) and risk-taking (Level 1

momentary outcome).

There are several advantages of using hierarchical linear modeling (Hofmann, 1997).

First, hierarchical linear modeling provides a more complete understanding of the dynamic

nature of integral anxiety and risk-taking. Second, hierarchical linear modeling is well suited

for examining within-person variation, between-person variation, and the cross-level interaction

between the two. Third, hierarchical linear modeling accounts for the partial interdependence of

48

within-person events that does not lead to systematic bias associated with ordinary least squares

regression.

In the Iowa Gambling Task, there are a total of 100 decision trials for each participant

and, as a result, 100 measurement windows for integral anxiety. With 52 participants and 100

measurement windows, there are approximately a total of 5,200 measurement windows across

participants. Level 1 consists of 100 integral anxiety episodes and 100 risk-taking decision

trials within each participant and Level 2 consists of one emotion understanding ability for each

participant. To analyze the data, I used SAS PROC MIXED (Singer, 1998). Integral anxiety is

operationalized in two ways. First, integral anxiety was operationalized by the amplitude for

each skin conductance response that occurs in a five-second window that precedes each risky

decision. The integral anxiety amplitude score was person-centered by subtracting the mean of

all the integral anxiety amplitude scores for each person from every integral anxiety score

(Hofmann & Gavin, 1998). Second, integral anxiety was operationalized by the area bounded

by the curve for each skin conductance response that occurs in a five-second window that

precedes each risky decision. The integral anxiety area score was person-centered. The Level 1

model is as follows:

𝑅𝐼𝑆𝐾 − 𝑇𝐴𝐾𝐼𝑁𝐺 = 𝛽0𝑗 + 𝛽1𝑗𝐴𝑁𝑋𝐼𝐸𝑇𝑌𝑗 + 𝑒𝑖𝑗

𝛽0𝑗is a random coefficient for person j, 𝛽1𝑗 is a random coefficient for integral anxiety,

and 𝑒𝑖𝑗 represents error. The Level 2 model is as follows:

𝛽0𝑗 = 𝛾00 + 𝛾01𝐸𝑀𝑂𝑇𝐼𝑂𝑁 𝑈𝑁𝐷𝐸𝑅𝑆𝑇𝐴𝑁𝐷𝐼𝑁𝐺 𝐴𝐵𝐼𝐿𝐼𝑇𝑌𝑗 + 𝑢0𝑗

𝛽1𝑗 = 𝛾10 + 𝛾11𝐸𝑀𝑂𝑇𝐼𝑂𝑁 𝑈𝑁𝐷𝐸𝑅𝑆𝑇𝐴𝑁𝐷𝐼𝑁𝐺 𝐴𝐵𝐼𝐿𝐼𝑇𝑌𝑗 + 𝑢1𝑗

𝛾00is the average risk-taking for the low emotion understanding ability individuals, 𝛾01

is the average risk-taking difference between low emotion understanding ability and high

emotion understanding ability individuals, and 𝑢0𝑗 is the unique effect of person j on mean risk-

49

taking (error) after accounting for emotion understanding ability. 𝛾10 is the average integral

anxiety – risk-taking slope within low emotion understanding ability individuals, 𝛾11 is the

difference in integral anxiety – risk-taking slopes between low emotion understanding ability

individuals and high emotion understanding ability individuals, and 𝑢1𝑗 is the unique effect of

person j on the integral anxiety – risk-taking slope (error) after accounting for emotion

understanding ability.

The random error terms, 𝑢0𝑗 and 𝑢1𝑗, that predict the 𝛽 coefficients at Level 2 are

important because they represent unknown sources of variance in risk-taking at Level 1 due to

differences among emotion understanding ability at Level 2. Therefore, the error indicates

whether there are random contextual effects on risk-taking that are not specified (Raudenbush &

Bryk, 2002).

When integral anxiety was operationalized using area bounded by the curve, no

significant results were found. However, when integral anxiety was operationalized using

amplitude, significant results were obtained. The results of the Pearson correlation coefficients

are shown in Table 6. Emotion understanding ability was negatively correlated with risk-taking,

r = -.03, p < .01, suggesting that higher emotion understanding ability is associated with lower

risk-taking. The baseline skin conductance level during the rest period was negatively related to

emotion understanding ability, r = -.17, p < .01, and positively related to risk-taking, r = .03, p

< .05. Given the significant associations of baseline skin conductance level with emotion

understanding ability and risk-taking, I entered it as a covariate.

When hierarchical linear modelling was conducted, the results show a significant

interaction. As expected, the interaction between integral anxiety and emotional understanding

ability was significant, B = -.0004, t(5289) = -2.54, p < .05.

50

As presented in Table 3, when the mean baseline skin conductance level from the five-

minute relaxation period was entered as a control variable, the interaction integral anxiety and

emotional understanding ability remains significant, B = -.0004, t(5091) = -2.49, p < .05. It is

customary to enter mean baseline skin conductance level as a covariate to account for the

variance due to individual differences in general levels of arousal (Dawson et al, 2000). This

interaction is shown in Figure 3.

Following Aiken and West (1991), I decomposed the interaction by examining the effect

of anxiety on risk-taking at two conditional values: one SD above and one SD below the mean

on emotion understanding ability. Contrary to my prediction, integral anxiety did not have a

significant effect on risk-taking at one standard deviation in emotion understanding above the

mean, B = -.0047, t(5091) = -1.26, p = .20. However, also unexpectedly, integral anxiety had a

significant effect on risk-taking at one standard deviation in emotion understanding below the

mean, B = .0072, t(5091) = 2.36, p < .05.

The results did not support my hypothesis for Experiment 3, which proposed that

individuals with high emotion understanding ability who are skilled at determining the source of

emotions allow their integral anxiety to carry over to their risk-taking, whereas individuals with

lower emotion understanding ability, who have difficulty pinpointing the source of their anxiety,

did not allow integral anxiety to reduce their risk-taking. Instead, the results showed that

individuals with high emotion understanding did not allow their integral anxiety to carry over

their risk-taking, but individuals with low emotion understanding ability allowed their integral

anxiety to increase their risk-taking. Therefore, the interaction effect of integral anxiety and

emotion understanding ability on risk-taking is driven by the low emotion understanding ability

individuals. One possible explanation for this pattern of results is that participants with low

emotion understanding ability misattributed their physiological arousal (as measured by skin

51

conductance response amplitude) to the uncertainty (fluctuations) in decision outcomes instead

of the uncertainty of losses. As a result, they misinterpreted the physiological arousal as

excitement, instead of anxiety, which resulted in an increase in risk-taking. Perhaps, they

treated the Iowa Gambling Task as a game with real monetary outcomes and found the variation

in payoffs after each decision to be exciting. In contrast, participants with high emotion

understanding ability, for the most part, did not allow their integral anxiety to reduce their risk-

taking because they were able to decouple anxiety from their risk-taking in order to maintain a

state of objectivity.

As pointed out earlier, skin conductance response reflects changes in physiological

arousal and emotion can be interpreted based on the source manipulation. In other words, it is

not possible to distinguish an “anxiety” skin conductance response from an “excitement” skin

conductance response (Dawson et al., 2000). Certainly, past research by (Bechara et al., 1994;

Bechara et al., 1997; Bechara et al., 1999) has suggested that, after encountering a very small

number of losses in the Iowa Gambling Task, participants develop anticipatory skin

conductance responses and reduce their risk-taking, which can be inferred as integral anxiety

reducing risk-taking. However, given the pattern that participants with higher skin conductance

responses tended to take more risk, it is plausible to interpret the skin conductance responses as

excitement.

52

Table 6

Experiment 3: Pearson correlations matrix (N = 52)

Variable 1 2 3 4

1. Average baseline skin conductance level

2. Skin conductance response amplitude .01

3. Emotion understanding ability -.17** -.00

4. Risk-taking .03* .03 -.03**

* p < 0.05, **p < 0.01

Note. Risk-taking was coded as 1 = higher-risk decision (Decks A and B) and 0 = lower-risk decision (Decks C and D).

53

Table 7

Experiment 3: Hierarchical Linear Regression Analysis for Variables Predicting Risk-taking (N = 52)

Variable B SE DF t-value p-value

Baseline mean SCR -0.0021 0.0051 49 -0.40 .97

SCR amplitude 0.0372 0.0141 5091 2.64 .01

Emotion understanding ability -0.0013 0.0012 49 -1.03 .31

SCR amplitude × emotion understanding ability -0.0004 0.0001 5091 -2.49 .01

Constant 0.4861 0.1364 49 3.56 .00

Note. Risk-taking was coded as 1 = higher-risk decision (Decks A and B) and 0 = lower-risk decision (Decks C and D). SCR refers to

skin conductance response.

54

Figure 3. Experiment 3: The effect on integral anxiety on risk-taking is stronger among

participants above the mean on emotion understanding ability than among those below the

mean. All analyses were conducted with continuous scores.

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Neutral Integral Anxiety

Ris

k-Ta

king

Low EUAHigh EUA

55

GENERAL DISCUSSION

My central prediction was that the decisions concerning risk of individuals who are

adept at understanding the source of their emotions are less affected by incidental anxiety and

more affected by integral anxiety than their less able counterparts. In two experiments, I found

that emotion understanding ability reduces the negative effect of incidental anxiety on risk-

taking. Specifically, I found that individuals who have high emotion understanding ability are

more likely to disregard incidental anxiety from their risk-taking, whereas those who have low

emotion understanding ability more likely to allow their incidental anxiety to carry over to their

risk-taking (Experiments 1 and 2). I also found that the negative effect of incidental anxiety on

risk-taking can be attenuated among individuals with low emotion understanding ability by

providing these individuals with information about the relevance of anxiety to the decision

(Experiment 2), suggesting that becoming aware of the relevance of emotion to the decision (i.e.,

whether emotion is incidental or integral) drives the effect of emotion understanding ability.

In the third experiment, I found that emotion understanding ability moderated the effect

of integral anxiety on risk-taking, but in an unexpected direction. In particular, I found that

individuals with high emotion understanding ability are less likely to allow integral anxiety to

carryover and reduce their risk-taking whereas individuals with low emotion understanding

ability are more likely to allow integral anxiety to carryover and increase their risk-taking

(Experiment 3). This finding suggests that people with high emotion understanding ability have

a tendency to diminish emotional influences from their decisions. More interestingly, I found

that people with low emotion understanding ability have a tendency to misinterpret their

physiological arousal as excitement, which has a positive effect on their risk-taking. While

those with high emotion understanding ability are more likely to decouple emotional influences

from their risky decision making, those with low emotion understanding ability tend to

56

misattribute their arousal to the uncertainty in outcomes, rather than uncertainty in losses, and

are more likely to allow their excitement to increase their risky decision making.

Taken together, this series of experiments provides some evidence for how a subset of

individuals with a specific ability (namely, emotion understanding ability) is likely to exert

control using cognitive appraisals of emotion when making decisions. Experiment 1 and 2

demonstrated that individuals with high emotion understanding ability are more skilled at

identifying the source of their anxiety and determining the relevance of their anxiety in relation

to a risky decision as opposed to individuals with low emotion understanding ability who

regularly misattribute their anxiety to the wrong source and are unable to perceive the relevance

of their anxiety in relation to a risky decision. Experiment 3 is consistent with my theoretical

assertion with respect to those low in terms of emotion understanding ability tend to mistakenly

overreact to emotional influences when making decisions. Low emotion understanding ability

individuals may misinterpret their physiological arousal as excitement in response to the

decision and allow their excitement to increase their risk-taking. However, the findings from

Experiment 3 suggest that individuals with high emotion understanding may perceive the

relevance of their anxiety, but do not allow it to influence their risk-taking.

In light of the results from the three experiments, let us revisit my earlier example about

the investor who needs to make a risky decision about purchasing a new stock. If an investor is

involved in a car accident on the way to work, he or she will naturally feel anxious in response

to the uncertainty of how much the repairs will cost, whether his or her insurance rate will rise,

how his or her spouse may react to the accident, among other factors. An emotionally intelligent

investor who has high emotion understanding ability is able to determine that the source of the

anxiety does not emerge from risky decision, but from the car accident (Experiment 1). He or

she can then conclude that the anxiety is incidental and, therefore, irrelevant to the risky

57

decision and is more likely to disregard any cognitive and behavioural tendencies that are

associated with the anxiety (Experiment 2). In contrast, an emotionally unintelligent investor

who has low emotion understanding ability mistakenly attributes his or her anxiety to the

decision, instead of the car accident. He or she then concludes that the anxiety is integral and,

therefore, relevant to the risky decision and is more likely to become biased when evaluating the

possible outcomes of the decision.

On the other hand, if an investor has an uneventful drive to work, sits down at his or her

desk, and starts considering a risky decision about whether to purchase a stock, he or she will

feel anxiety based on the uncertainty about whether the stock will rise or fall in value, how long

he or she should hold the investment, whether the underlying value is representative of the stock

market price as well as other factors. An emotionally intelligent investor who has high emotion

understanding ability is able to determine that the source of the anxiety emanates from the risky

decision, not from any other extraneous factors such as worries about a promotion or uncertainty

about his or her children performing well in school. He or she can then conclude that the

anxiety is integral and, therefore, relevant to the risky decision but is less likely to include the

anxiety in his or her risky decision making in order to maintain a state of objectivity and not

overreact to immediate feedback or outcomes (Experiment 3). In contrast, an emotionally

unintelligent investor who has low emotion understanding ability mistakenly attributes his or her

arousal to variation in outcomes of the risky decision. He or she then misinterprets his or her

arousal as linked to the risky decision and is unable to accurately evaluate the possible outcomes

of the decision, resulting in increased risk-taking.

As this example illustrates, emotion understanding ability enables individuals to

determine which emotions are relevant and irrelevant to decisions. Using a robust effect of

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anxiety on risk-taking, three experiments provide some evidence for how emotion understanding

ability can have contrasting effects of anxiety to help or hinder risky decision making.

Theoretical Implications

These findings extend our understanding of emotion and judgment in three specific ways.

First, emotional intelligence researchers have made the call to sharpen the predictions among the

branches of emotional intelligence (Mayer et al., 2008; Roberts, Schulze, O'Brien, MacCann,

Reid, & Maul, 2006). Previous research on emotional intelligence has typically been concerned

with the global emotional intelligence of individuals, because of the positive manifold among

the four emotional abilities (i.e., perceiving, using, understanding, and regulating). Instead, my

model focuses on a specific emotional intelligence ability that is the most theoretically relevant.

Subsidiary analyses revealed that none of the other emotional abilities (perceiving, using, or

regulating emotions) measured with the MSCEIT moderated the effect of incidental anxiety on

risk-taking in Experiment 2 (p = .12 to .92). These auxiliary results suggest that my findings are

driven by emotion understanding ability specifically, rather than other dimensions of emotional

intelligence. However, in Experiment 1, I found that using emotions had a significant

moderating effect of incidental anxiety on risk-taking (p = .00), but perceiving emotions (p = .71)

and regulating emotions (p = .45) did not have a significant moderating effect. In Experiment 3,

I found that perceiving emotions (p = .04) and regulating emotions (p = .02) moderated the

effect of integral anxiety on risk-taking, but using emotions did not (p = .32).

Second, my investigation introduces a specific mechanism by which emotion

understanding ability shapes decisions: Individuals who do not exhibit a biasing effect of

incidental anxiety on risk-taking are able to identify whether the emotion is relevant or

irrelevant to the decision at hand. In comparison, individuals who are influenced by incidental

anxiety on their risk-taking appear to be confused about what triggered their anxiety and,

59

therefore, allow irrelevant anxiety to shape their decisions. This specific mechanism builds on

our understanding about people’s tendency to misattribute their incidental emotional states to

the current situation from Schachter & Singer’s (1962) two-factor theory of emotion, and

people’s tendency to extract information associated with their incidental emotional states based

on their misattribution from Schwarz and Clore’s (1983) feelings-as-information theory.

Third, although the majority of emotional intelligence research has investigated the

social function of emotional intelligence (see Mayer et al., 2008 for a review), this paper speaks

to the intra-psychic function of emotional intelligence by specifying how the ability to

understand emotions can influence the effect of incidental anxiety on risk-taking. Past research

has emphasized the role of emotional intelligence in facilitating the communicative function of

emotion, but my research studies shed light on the role of emotional intelligence in facilitating

the informational function of emotion.

Fourth, the examination of the role of emotion understanding ability in relation to

integral anxiety and risk-taking extends the boundaries of emotional intelligence, suggesting the

possibility that emotional intelligence underlies automatic processes of emotion. Bechara et al.

(1997) found that anticipatory skin conductance responses that precede risky decisions occur

without consciousness awareness in the early decision trials of the Iowa Gambling Task and,

therefore, participants are unable to provide to articulate which decks are risky. Traditionally,

emotional intelligence has been conceptualized as set of abilities that pertains to deliberate,

conscious processes of emotion problem solving. However, the results of Experiment 3 suggest

people with high emotion understanding ability are able to exert substantial control over

automatic processes of emotion by blocking emotions into their decision making.

Finally, one of the strengths of this set of experiments is that I test the contrasting effects

of incidental anxiety and integral anxiety on risk-taking. By employing the same emotion and

60

same decision making task but manipulating the source of the emotion, emotion understanding

ability moderates the effect of anxiety on risk-taking. This paper provides empirical support to

the theoretical distinctions between incidental and integral emotions.

Practical Implications

This series of experiments also have two practical implications. First, the findings

suggest an important selection criterion for financial decision makers such as investment

managers, financial traders, and commercial banking. By assessing the level of an individual’s

emotion understanding ability, employers and investors will have greater insight into whether a

candidate would be unduly influenced by incidental emotions when making decisions. Also,

because financial decision makers are tasked with making risky decisions under time pressure, it

would be useful to know which decision makers can properly use intuitive processes to make

decisions. Beyond the realm of finance, emotion understanding ability may also prove to be an

important criterion for managerial positions because, as employees rise in an organization, they

are afforded more power and responsibility and their decisions have widespread consequences.

To screen candidates for emotional control, employers will have a better sense of which

managers will make decisions based on a state of objectivity instead of extraneous factors.

Second, the findings point to a particular mechanism that can alleviate the effects of

incidental emotions on decision making. As demonstrated in Experiment 2, when people who

have low emotion understanding ability are made aware of the event that is the cause of their

emotional state, they are able to reduce the carryover effect of the incidental emotion on their

decision making. Therefore, by understanding the source of an individual’s emotional state, the

individual can essentially choose whether it makes sense to become emotionally aroused when

making a decision. The powerful effect of this simple mechanism can inform management

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practice by having managers remind decision makers before making a decision, “How do you

feel and what is causing you to feel this way?”

Limitations and Future Research

The present experiments explore the effects of only one specific emotion (anxiety) on

decision making. I chose to focus this investigation of emotion understanding ability on the

anxiety and risk-taking relationship because the robust effect of anxiety on risk-taking has been

substantiated in previous research and provides a conservative test of my proposed model. That

is, several studies have suggested that people generally tend to be overwhelmed by contextual

anxiety when making risky decisions (Ragunathan & Pham, 1999; Lerner & Keltner, 2001).

However, as predicted, Experiment 1 and Experiment 2 showed that higher levels of emotion

understanding ability attenuate the negative effect of incidental anxiety on risk-taking.

Given that research on emotion has shifted to the study of the distinct effects of discrete

emotions on decisions and behaviours, determining whether the moderating effect of emotion

understanding ability applies to other emotion-behaviour relationships warrants future research.

For example, building on the empirical evidence of the negative effect of incidental disgust on

selling prices and buying prices (Lerner et al., 2004), I would predict that emotion

understanding ability would eliminate the negative carryover effect of incidental disgust on

prices.

Another line of future research would examine emotion understanding ability in relation

to incidental emotions with a positive valence such as the effect of incidental happiness on risk-

taking. Isen and Geva (1987) found that the relationship between happiness and risk-taking is

more complicated than relationship between anxiety and risk-taking whereby the effect of

happiness and risk-taking is moderated by the magnitude of the potential loss. In particular, the

authors found that there is a negative effect of incidental happiness on risk-taking when the

62

potential loss is large, but there is a positive effect of incidental happiness on risk-taking when

the potential loss is small. While it is possible that emotion understanding ability should

eliminate both the negative and positive effects of incidental happiness on risk-taking, it may be

that those who are induced with incidental happiness and have high emotion understanding

ability may exhibit less risk-taking when they are made aware that the potential loss is large

because of an integral anxiety response or a cognitive bias. Future research is required to

disentangle the effect positive emotions on risk-taking.

The current model concentrates on only one facet of emotional intelligence, emotion

understanding ability, in illuminating the linkage between emotions and judgments, but serves

as a good starting point to explore whether other emotional intelligence abilities play a

significant role in governing our decision making under emotional influences. While emotion

understanding ability can be viewed positively as an ability that guards against emotional biases

and extracts relevant information from emotions, it is plausible that other emotional intelligence

abilities may have a negative influence on emotions carrying over to decisions. For instance,

emotion recognition ability, which is defined as the ability to recognize emotional sensations in

the self and emotional expressions in others, may amplify the effect of incidental anxiety on

risk-taking. The rationale for this prediction is that people with high emotion recognition ability

may be overly sensitive to incidental emotions generated from their environment and become

unduly influenced when making decisions as opposed to people with low emotion recognition

ability who are unable to detect emotional sensations accurately and are not influenced by

incidental emotion when making decisions. If this moderating effect of emotion recognition

ability is found, it would suggest that we can expand our model of emotionally intelligent

decision making to include both emotion recognition ability and emotion understanding ability,

63

and examine how the two abilities interact to delineate the emotional processes underlying

decision making.

Lastly, another area of inquiry would be to extend the current model to not only other

discrete emotions but also to other outcomes such as performance. The relationship between

anxiety and performance has traditionally garnered interest about the effects of different levels

of arousal and performance (Yerkes & Dodson, 1908). However, it is particularly interesting

because the effects of incidental anxiety and integral anxiety on performance need to be

disentangled. Based on the present paper, emotion understanding ability may provide some

insight into the independent effects of incidental anxiety and integral anxiety as well as the

combination of both effects on performance.

Conclusion

My experiments showed that emotion understanding ability can diminish the negative

effect of incidental anxiety on risky decision making. When people understand the source of

their emotions, they are able to determine relevance of those emotions to the decision at hand,

and disregard incidental emotions from carrying over to decision. Because attribution is a

necessary component of the emotional experience, it is important to identify a subset of low

emotion understanding ability individuals who are more likely to fall prey to incidental

emotions when making risky decisions and their higher ability counterparts who are not

susceptible to the biasing effect of incidental emotions.

64

APPENDIX 1. EXPERIMENTAL MANIPULATION IN EXPERIMENTS 1 AND 2

Anxiety: The first task is testing your ability to prepare a speech in a short amount of time. You will have 60 seconds to prepare a 3-minute speech on "Why you are a good job candidate." While you are delivering your speech, we will videotape you, and your speech will be shown to your peers for evaluation. When making your speech, please look directly into the camera [point to camera] and begin speaking. Remember, your video-taped speech will be shown to your peers for evaluation, and your speech will be used to determine your academic and social standing at the university. Are you ready to begin mentally preparing your speech? Please begin now – you have 60 seconds [Experimenter start stopwatch]. Neutral: The first task is about consumer preferences. Please think about what items you purchase when you go grocery shopping. You have 60 seconds to create a mental list of grocery items. Are you ready to begin? [Experimenter uses stopwatch]. When 60 seconds are over: Anxiety: (just as you are about to start videotaping, realizes that the memory stick is full. Initially look surprised, and then flustered.) The video camera is working, but the memory stick seems to be full. Just give me a second (frantically do a quick search of your purse and/or the camera bag). I'm going to have to get another memory stick from the RA next door. Before I do so, in the interest of time, I am going to have you complete some unrelated tasks by another researcher at Rotman, and then we will videotape your speech at the end. (For participants in the anxiety condition and source manipulation condition, you should casually say the following while making eye contact). Neutral: I'll give you some forms near the end of the session to jot down your mental grocery list. (For participants in the neutral condition and source manipulation condition, you should casually say the following while making eye contact) Anxiety: These tasks are on the computer. Please have a seat here and follow the instructions on the screen. I am going next door/down the hall to get the memory stick for the speech task. Neutral: I am going to have you complete some unrelated tasks by another researcher at Rotman. These tasks are on the computer. Please have a seat here and follow the instructions on the screen. Anxiety: Unfortunately I couldn't find a new memory stick, so you will not have to deliver a speech on video camera. Neutral: You will not have to write down the grocery list.

65

APPENDIX 2. MANIPULATION CHECK FOR EXPERIMENTS 1 AND 2

Please click on the number on the scale that best describes your current feelings. On this scale, 1 does not describe my current feelings at all and 7 describes my current feelings very well.

I feel ANXIOUS/NERVOUS/TENSE.

1 - Does NOT describe my current feelings at all

2

3

4

5

6

7 - Describes my current feelings very well

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APPENDIX 3. RISK-TAKING MEASURE FOR EXPERIMENT 1

You must choose one of two gambles. Gamble A offers a 100% chance of winning $1. Gamble B offers a 10% chance of winning $10 and 90% chance of winning $0. Please click on the gamble that you would choose.

A

Win $1100%

B

Win $090%

Win $1010%

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APPENDIX 4. AWARENESS OF SOURCE MANIPULATION FOR EXPERIMENT 2

Special instructions for the incidental anxiety condition:

You may feel anxious because people often get anxious when preparing to deliver a speech.

Special instructions for the neutral condition:

You may feel no emotion because people often feel no emotion when mentally preparing a grocery list.

68

APPENDIX 5. RISK-TAKING MEASURE FOR EXPERIMENT 2

The Seasonal Flu

Influenza, commonly known as “the seasonal flu,” is a respiratory illness that usually circulates throughout the fall and winter. People of any age can get the flu and sickness usually lasts two to seven days. Most people who get the flu are sick for only a few days. In others, the symptoms can last for weeks. In extreme cases some people can develop complications and become very ill, requiring hospitalization. The flu shot can substantially reduce your chances of getting the flu. The University of Toronto offers flu shot clinics each year.

69

APPENDIX 6. SKIN CONDUCTANCE RESPONSE WAVEFORM

70

APPENDIX 7. SKIN CONDUCTANCE RESPONSE

Note. This figure depicts the key components of a skin conductance response. The figure was

duplicated from the paper by Figner and Murphy (2010).

71

APPENDIX 8. IOWA GAMBLING TASK

72

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