by jeremy a. yip - university of toronto t-space · 2013. 11. 21. · jeremy a. yip . doctor of...
Post on 23-Jan-2021
1 Views
Preview:
TRANSCRIPT
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)
ii
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
iii
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.
iv
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.
v
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
vi
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
vii
LIST OF FIGURES
FIGURE 1. RESULTS FROM EXPERIMENT 1………………………………………….....27
FIGURE 2. RESULTS FROM EXPERIMENT 2………………………………………….....36
FIGURE 3. RESULTS FROM EXPERIMENT 3………………………………………….....54
1
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).
2
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).
3
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.
4
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
5
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
6
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
7
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
8
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.
9
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
10
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.
11
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
12
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
13
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
14
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
15
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
ing
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
58
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
61
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
66
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%
67
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
REFERENCES
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions.
Newbury Park, CA: Sage.
Au, K., Chan, F., Wang, D., & Vertinsky, I. (2003). Mood in foreign exchange trading:
Cognitive processes and performance. Organizational Behavior and Human Decision
Processes, 91, 322–338.
Barsade, S. G., Ramarajan, L., & Westen, D. (2009). Implicit affect in organizations. In B.
Staw & A. Brief (Eds.), Research in Organizational Behaviour, 135-162.
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future
consequences following damage to human prefrontal cortex. Cognition, 50, 7-15.
Bechara, A., Damasio, H., Tranel, D., Damasio, A. R., & Lee, G. P. (1999). Different
Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-
Making. The Journal of Neuroscience, 19(13), 5473–5481
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously
before knowing the advantageous strategy. Science, 275, 1293-1295.
Boucsein, W. (1992). Electrodermal activity. New York, NY: Plenum Press.
Brackett, M. A., & Mayer, J. D. (2003). Convergent, Discriminant, and Incremental Validity of
Competing Measures of Emotional Intelligence. Personality and Social Psychology
Bulletin, 29(9), 1147-1158.
Campbell, M. C., & Goodstein, R. C. (2001). The moderating effect of perceived risk on
consumers’ evaluations of product incongruity: Preference for the norm. Journal of
Consumer Research, 28, 439-449.
73
Cannon, W. B. (1929). Bodily changes in pain, hunger, fear and rage (2nd ed.) New York:
Appleton.
Carter, S., & Smith-Pasqualini, M. C. (2004). Stronger autonomic response accompanies better
learning: A test of Damasio’s somatic marker hypothesis. Cognition and Emotion, 18,
901-911.
Côté, S., & Miners, C. T. H. (2006). Emotional intelligence, cognitive intelligence, and job
performance. Administrative Science Quarterly, 51, 1-28.
Damasio, A. R. (1994). Descartes’ error. New York: Putnam.
Damasio, A. R., Tranel, D., & Damasio, H. C. (1991). Somatic markers and the guidance of
behavior: Theory and preliminary testing. New York, NY: Oxford University Press.
Dawson, M. E., Schell, A. M., & Filion, D. L. (2000). The electrodermal system. In J. T.
Cacioppo, L. G. Tassinary, & G. G. Berntson (Ed.), Handbook of Psychophysiology, 2nd
ed. (pp. 200 -223). New York, NY: Cambridge University Press.
Dutton, D. G., & Aron, A. P. (1974). Some evidence for heightened sexual attraction under
conditions of high anxiety. Journal of Personality and Social Psychology, 30, 510-517.
Eysenck, M. W. (1997). Anxiety and cognition: A unified theory. Hove, England: Psychology
Press.
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive
performance: Attentional control theory. Emotion, 7, 336-353.
Figner, B., & Murphy, R. O. (2011). Using skin conductance in judgment and decision making
research. In M. Schulte-Mecklenbeck, A. Kuehberger, & R. Ranyard (Eds.), A handbook
of process tracing methods for decision research (pp. 163-184). New York, NY:
Psychology Press.
74
Fischhoff, B., Gonzalez, R. M., Lerner, J. S., & Small, D. A. (2005). Evolving judgments of
terror risks: Foresight, hindsight, and emotion. Journal of Experimental Psychology:
Applied, 11, 124-139.
Fowles, D. C. (1986). The eccrine system and electrodermal activity. In M. G. H. Coles, E.
Donchin, & S. W. Porges (Eds.), Psychophysiology: Systems; Processes, and Applications
(pp. 51-96). New York: Guilford.
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic
Perspectives, 19, 24-42.
Frijda, N. H. (1986). The emotions. New York, NY: Cambridge University Press.
Frijda, N. H., Kuipers, P., & ter Schure, E. (1989). Relations among emotion, appraisal, and
emotional action readiness. Journal of Personality and Social Psychology, 57(2), 212-228.
Funder, D. C., & Block, J. (1989). The role of ego-control, ego-resiliency, and IQ in delay of
gratification in adolescence. Journal of Personality and Social Psychology, 57, 1041-1050.
Gray, J. (1991). Fear, panic, and anxiety: What’s in a name? Psychological Inquiry, 2 (1), 77-78
Han, S., Lerner, J. S., & Keltner, D. (2007). Feelings and consumer decision making: An
appraisal-tendency framework. Journal of Consumer Psychology, 17, 158-168.
Highhouse, S. (2001). Judgment and decision making research: Relevance to industrial and
organizational psychology. In N. Anderson, D.S. Ones, H.K. Sinangil, & C. Viswesvaran
(Eds.), Handbook of industrial, work and organizational psychology (pp. 314-332). Sage.
Hofmann, D.A. (1997). An overview of the logic and rationale of hierarchical linear models.
Journal of Management, 23, 723-744.
Hofmann, D.A., & Gavin, M.B. (1998). Centering decisions in hierarchical linear models:
Theoretical and methodological implications for organizational science. Journal of
Management, 24, 623-641.
75
Isen, A. M., & Geva, N. (1987). The influence of positive affect on acceptable level of risk: The
person with a large canoe has a large worry. Organizational Behavior and Human
Decision Processes, 39, 145-154.
James, W. (1890). What is an emotion? Mind, 9, 188-205
Johnson, E. J., & Tversky, A. (1983). Affect, generalization, and the perception of risk. Journal
of Personality and Social Psychology, 45(1), 20-31.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under
risk. Econometrica, 47(2), 263-291.
Lacey, J. I., Kagan, J., Lacey, B. C., & Moss, H. A. (1963). The visceral level: Situational
determinants and behavioral correlates of autonomic response patterns. In P. H. Knapp
(Ed.), Expression of the Emotions in Man (pp. 161-196). New York: International
Universities Press.
Landis, C. (1930). Psychology of the psychogalvanic reflex. Psychological Review, 37, 381-398.
Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social
Psychology, 81, 146-159.
Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carry-
over effects of emotions on economic transactions. Psychological Science, 15, 337-341.
Levenson, R.W. (1994). I. Human emotion: A functional view. II. The search for autonomic
specificity. III. Emotional control: Variation and consequences. In P. Ekman & R.
Davidson (Eds.). The nature of emotion. Fundamental questions. New York: Oxford
University Press.
Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson,
H. Goldsmith, & K. Scherer (Eds.), Handbook of Affective Science (pp. 619-642). Oxford:
Oxford University Press.
76
Loewenstein, G., Weber, E., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological
Bulletin, 127(2), 267-286.
Lykken, D. T., & Venables, P. H. (1971). Direct measurement of skin conductance: A proposal
for standardization. Psychophysiology, 8, 656-672.
MacCann, C., & Roberts, R. D. (2008). New paradigms for assessing emotional intelligence:
Theory and data. Emotion, 8(4), 540-551.
Mandel, N. (2003). Shifting selves and decision making: The effects of self-construal priming
on consumer risk-taking. Journal of Consumer Research, 30(1), 30–40.
Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human abilities: Emotional intelligence.
Annual Review of Psychology, 59, 507-536.
Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter
(Eds). Emotional development and emotional intelligence: Implications for educators (pp.
3-31). New York: Basic Books.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2002). Manual for the MSCEIT: Mayer-Salovey-
Caruso Emotional Intelligence Test. Toronto, ON: Multi-Health Systems.
Mendes, W. B. (2009). Autonomic nervous system. In E. Harmon-Jones and J. Beer (Eds.),
Methods in Social Neuroscience (pp. 118-147). New York: Guilford Press.
Mueller, J., & Curhan, J. (2006). Emotional intelligence and counterpart mood induction in a
negotiation. International Journal of Conflict Management, 17, 110-128.
Munro, L.L., Dawson, M. E., Schell, A. M., & Sakai, L. M. (1987) . Electrodermal lability and
rapid performance decrement, in a degraded stimulus continuous performance task.
Journal of Psychophysiology, 1, 249-257.
77
Pacheco-Unguetti, A. P., Acosta, A., Callejas, A., & Lupianez, J. (2010). Attention and anxiety:
Different attentional functioning under state and trait anxiety. Psychological Science, 21, 2,
298-304.
Pham, M. T. (2004). The logic of feeling. Journal of Consumer Psychology, 14, 360-369.
Pham, M. T. (2007). Emotion and rationality: A critical review and interpretation of empirical
evidence. Review of General Psychology, 11, 155-178.
Pham, M. T., Cohen, J. E., Pracejus, J. W., & Hughes, G. D. (2001). Affect monitoring and the
primacy of feelings in judgment. Journal of Consumer Research, 28(2), 167-188.
Raghunathan, R., & Pham, M. T. (1999). All negative moods are not equal: Motivational
influences of anxiety and sadness on decision making. Organizational Behaviour and
Human Decision Processes, 79, 56-77.
Raudenbush, S.W., & Bryk, A. S. (2002). Hierarchical Linear Models (Second Edition).
Thousand Oaks: Sage Publications.
Roberts, R. D., & Schulze, R., & MacCann, C. (2008). The measurement of emotional
intelligence: A decade of progress? In G. Boyle, G. Matthews, & D. Saklofske (Eds.), The
Sage handbook of personality theory and assessment (pp. 461-482). New York: Sage.
Roberts, R. D., Schulze, R., O'Brien, K., MacCann, C., Reid, J., & Maul, A. (2006). Exploring
the validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with
established emotions measures. Emotion, 6, 663-669.
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition, and
Personality, 9, 185-211.
Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being:
Informative and directive functions of affective states. Journal of Personality and Social
Psychology, 45, 513-523.
78
Schwarz, N., & Clore, G. L. (2007). Feelings and phenomenal experiences. In A. W. Kruglanski,
& E. T. Higgins (Ed.), Social psychology: Handbook of basic principles (2nd ed.) (pp.
385- 407). New York, NY: Guildford Press.
Schachter, S., & Singer, J. (1962). Cognitive, social, and physiological determinants of
emotional state. Psychology Review, 69(5), 379-399.
Seo, M-G., & Feldman Barrett, L. (2007). Being emotional during decision making – good or
bad? An empirical investigation. Academy of Management Journal, 50, 923-940.
Shiv, B., & Fedorikhin, A. (1999). Heart and mind in conflict: The interplay of affect and
cognition in consumer decision making. Journal of Consumer Research, 26, 278-292.
Simon, H. A. (1979). Rational decision making in business organizations. Washington, DC:
American Psychological Association.
Singer, J. D., (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models,
and individual growth models. Journal of Educational and Behavioral Statistics, 24(4),
323-355.
Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of
Personality and Social Psychology, 48, 813-838.
Spielberger, C. (1966). The effects of anxiety on complex learning and academic achievement.
In Spielberger (Ed.), Anxiety and Behavior (pp. 361-398). New York: Academic Press.
Teper, R., Inzlicht, M., & Page-Gould, E. (2011). Are we more moral than we think? Exploring
the role of affect in moral behavior and moral forecasting. Psychological Science, 22, 553-
558.
Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to
bounce back from negative emotional experiences. Journal of Personality and Social
Psychology, 86, 320-333.
79
Yerkes, R. M, & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-
formation. Journal of Comparative Neurology and Psychology,18, 459-482.
Wonderlic, E. F. (1992). Wonderlic Personnel Test User’s Manual. Libertyville, IL: E. F.
Wonderlic.
top related