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Social exclusion decreases risk-taking

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Social exclusion decreases risk-taking

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Content Running head: RISK AND SOCIAL EXCLUSION

1

Social Exclusion Decreases Risk-Taking
Vitaliya Droutman
University of Southern California

RISK AND SOCIAL EXCLUSION

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Table of Contents
Abstract ........................................................................................................................................... 3
Social Exclusion Decreases Risk-Taking ....................................................................................... 4
Neural correlates of decision-making .................................................................................... 5
Role of Insular cortex ............................................................................................................. 7
Developmental aspect ............................................................................................................ 7
Risk in Social Context ........................................................................................................... 8
Neural mechanisms of social exclusion ............................................................................... 10
Method .......................................................................................................................................... 11
Overview .............................................................................................................................. 11
Participants ........................................................................................................................... 12
Materials and Procedures ..................................................................................................... 12
Dependent Measures and Analysis ...................................................................................... 14
Results ........................................................................................................................................... 16
Discussion ..................................................................................................................................... 17
References ..................................................................................................................................... 21
Table 1 .......................................................................................................................................... 28
Table 2 .......................................................................................................................................... 29
Table 3 .......................................................................................................................................... 30
Appendix A. Need-Threat Scale (NTS) ........................................................................................ 32
Appendix B. Rejection Sensitivity (UPPS+P) .............................................................................. 33
Appendix C. Mindful Attention Awareness Scale ........................................................................ 36

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Abstract
Understanding risky behavior in social contexts is critical to developing a comprehensive theory of
adolescent decision making. This study examines the effect of social exclusion on risky decision
making and the relationships between risk taking and rejection sensitivity, urgency and mindfulness
personality traits. The Cyberball task was utilized to induce the experience of social exclusion.
Following this manipulation, risky decision-making was assessed with the CUPS Task, a simple
computer gambling game. The study concluded with an on-line, self-report questionnaire that measured
individual differences in the above personality traits and included a social exclusion manipulation
check. We found that subjects who were socially excluded were less likely to take gambles. The results
suggest that social exclusion reduces risk-taking, and that the trait of positive urgency is a significant
predictor of risk taking in the gain domain. We discuss the relationship of these findings to recent
findings that suggest that adolescents tend to be riskier when in the presence of peers.
Keywords: risk, social exclusion, risky decision-making, insula, insular cortex, risk taking

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Social Exclusion Decreases Risk-Taking
High school graduation is a significant milestone for adolescents. For many, it marks the end of
childhood in the parents’ home and a transition to autonomous living, often on a college campus miles
away. An important aspect of this newly found independence is the opportunity to make risky choices
that not everybody in this position is ready to handle. Statistics on automobile accidents, binge drinking,
contraceptive use and crime involvement indicate that adolescents and college-aged individuals are
more at risk than adults or children (Steinberg, 2004). For example, in 2005, young adults, aged 18-22
and enrolled full-time in college, were more likely than their peers not enrolled full-time to use alcohol
in the past month, to binge drink, and to drink heavily (Substance Abuse and Mental Health Services
Administration, 2006).
Risky decision making in adolescence has intrigued psychologists for decades. Recent
developments in neuroscience have shed some light on this phenomenon: different timetables of the
development of the neural components critical to reward-processing, impulsivity and self-control seem
to be partially to blame for heightened risk-taking in adolescence (Steinberg, 2010; Leijenhorst, et al.,
2010). Results of neuroimaging studies suggest that the Impulsive system focused on reward
processing is fully developed by adolescence, but that the Reflective system responsible for self-control
is not fully developed until the mid twenties (Steinberg, 2007; Galvan et al., 2006). Another
perspective, added by Reyna and Farley (2006), is the experience factor, or rather lack of experience of
the negative consequence of risky behavior by adolescents that also plays a role in increased risk taking
at this age. They argue, that due to less exposure to negative consequences, adolescents have not yet
been able to properly build the connections between negative consequences and affect that are critical
to decision making (Damasio, Tranel, & Damasio, 1991).
The above mentioned research is fundamental in our understanding of the developmental aspect
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of adolescent risky decision-making; however, it does not address the effect of the peer group or social
context. Peer relations take center stage in the adolescent world – they become more complex and take
up major portions of an adolescent’s time. They influence academic achievement and pro-social
behaviors, as well as problem behaviors such as substance use and delinquency (Steinberg & Morris,
2001). Therefore, understanding risky behavior in social contexts is critical to developing a
comprehensive theory of adolescent decision making. Significant work in this line of inquiry has been
performed by examining risky decision making in the presence of peers (Gardner & Steinberg, 2005;
Chein et al., 2011). Their findings indicate that adolescents take more risks when in the presence of
peers. The authors argue that the increased risk preference is due to an amplified focus on reward.
Alternatively, augmented risk in the presence of peers might be explained by conforming to perceived
social norms and fear of social exclusion. However, the effect of social exclusion, one of the most
powerful mechanisms of social influence, on risky behavior has not been previously studied; the
present work attempts to cover this gap.
Neural correlates of decision-making
A growing body of research indicates that motivated human behavior is the result of a dynamic
interplay between two systems: (1) an implicit or automatic appetitive Impulsive system, which
promotes cue-induced habitual behaviors,

and (2) the executive control/inhibitory prefrontal cortex
Reflective system (Bechara et al., 2000; Bechara, 2005). Fundamental in this literature is the Somatic
Marker Theory that “provides a systems-level neuroanatomical and cognitive framework for decision-
making and its influence by emotion” (Bechara & Damasio, 2005, p.336), originally proposed in 1991
by Damasio, Tranel & Damasio. This theory has been developed based on 15 years of clinical research
that studied “patients with lesions of the ventromedial prefrontal (VM) cortex who showed
impairments in judgment and decision-making in real-life settings, in spite of maintaining a normal
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intellect” (Bechara & Damasio, 2005, p.337) and it has been empirically confirmed in a range of
research. It incorporates and builds upon prior knowledge of mesolimbic and mesocortical
dopaminergic systems.
In 1978 Wise first proposed that the mesolimbic system, which projects from the ventro-
tegmental area to the nucleus accumbens, plays a key role in reward processing. This proposal was
later extensively studied and has been implicated in reward processing both for reward derived from
drugs, and for natural rewards, such as food and sex (Wise, 1988; Wise & Rompre, 1989; Robinson
and Berridge, 1993). Somatic marker theory suggests ‘that pleasant or aversive stimuli … trigger
quick automatic and obligatory affective responses through the amygdala system’ (Bechara, 2005,
p.1459). Thus, the amygdala is the critical neural structure in promoting automatic and habitual
behaviors. The mesolimbic system, amygdala and the process of strengthening cue-behavior-outcome
associations in habit formation together form the basis of the Impulsive system.
The mesocortical system that projects from the ventro-tegmental area to prefrontal cortex plays
an important role in decision making and inhibitory control. Somatic marker theory argues that the
ventro-medial prefrontal cortex (VMPFC) constitutes the core of Reflective system by triggering
affective states from recall of ‘prior experiences’ of similar decisions and its negative consequences .
The ‘prior experiences’ are not limited to personal involvement, but also include processing the
information about similar contexts. For example, the affective state patterns of negative consequences
of using drugs become represented in the brain when one learns about the dangers of drug use (Bechara,
2005).
In a normally functioning brain, the VMPFC driven reflective system controls the amygdala
focused impulsive system via several mechanisms, thus accomplishing the decision making process.

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Role of Insular cortex
More recent evidence suggests that a third neural system, the insular cortex, elicited by
homeostatic imbalance and deprivation states or by reward cues, also plays a key role in risky decision
making (Naqvi & Bechara, 2009; Naqvi et al., 2007). The impact of insular cortex activity in affective
decision making by adolescents has not yet been examined. However, imaging studies of young
healthy adults revealed that increased insula activation immediately prior to decision making has been
shown to be related to decreased preference for risk and vice versa
(Kuchen & Knutson, 2005; Xue et
al. 2010). Kuchen and Knutson designed an investment task in which participants had to select
between stocks (risky option with variable probability payoff combination) and a bond ('sure thing',
always pays $1). They found, that after the loss, insular cortex activation increased, which resulted in
risk avoidance behavior on the trial immediately following the loss. These findings were confirmed by
the work of Xue and colleagues (2010), who utilized a modified CUPS task (a simple gambling task),
that allowed them to isolate insular activity in different stages of the task. This approach differentiated
insula activation between decision making, waiting for feedback, and feedback processing. Their work
examines how the relationship between prior risk taking and risk preference during the current trial are
mediated by the insular cortex. They found that risk taking increases insular cortex activation, and,
more importantly, that increased insula activation leads to lower risk preference.

Developmental aspect
Smith, Xiao and Bechara (2010) experimentally tested children aged 8-17 using the Iowa
Gambling Task (IGT), in conjunction with a battery of established cognitive neuropsychological
assessments, and found a J- shaped curve: ‘Younger, more developmentally naive children performed
better on the IGT than older, early-adolescent individuals, with performance becoming advantageous
again towards the end of the teenage years’ (p.2). While analyzing possible mediators of this
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phenomenon, developmental psychologists proposed that different developmental trajectories of
critical neural components involved in reward processing and executive control may be misbalanced,
and thus mediate the increase in risky behavior observed in adolescence (Steinberg, 2007). Galvan et
al. examined ‘behavioral and neural responses to reward value manipulations across development’
(2006, p. 6885) using fMRI. Their study focused on Nucleus Accumbens (NAcc) and Orbito-Frontal
Cortex (OFC), because these regions were implicated in reward related learning by animal (Hikosaka
& Watanabe, 2000; Pecina et al., 2003) and in imaging studies (O’Doherty et al., 2001; Zald et al.,
2004). They found that adolescents demonstrated refined neural activity in NAcc similar to that of the
adults, and more diffused activity in OFC similar to children, and ‘interpret these data to suggest that
the NAcc development may precede that of the OFC during adolescence’ (p. 6889). In other words,
the Impulsive system, which is developed by adolescence, amplifies focus on reward, and the
Reflective system, not yet fully developed, is not yet capable of full behavioral control. Thus affective
decision making in adolescents is imbalanced toward risky behaviors.
Risk in Social Context
Decision-making in social situations was previously examined by Miller and Byrnes (2001),
Gardner and Steinberg (2005) and Chein et al (2011). Miller and Byrnes examined the relationships
between social-relational goals and socially competent behaviors. One of the questions they raised was
if adolescents who value social relationships more would exert more self-regulation in order to restrain
angry or violent behaviors, control impulses and behave with more responsibility and consideration
toward peers. Importance of social goals was assessed with a self-report questionnaire, and social
behaviors were assessed with a self-report questionnaire and a peer nomination questionnaire. The
results confirmed the hypothesis, ‘adolescents who reported that social–relational goals were important
to them also tended to report increased engagement in socially competent behaviors (2001)'.

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The other two research teams (Gardner & Steinberg, 2005; Chien et al., 2011), focused on risky
decision making in the presence of others. Gardner and Steinberg presented behavioral evidence that
participants in all age groups (adolescent, youth and young adults) 'made riskier decisions when in peer
groups than alone; and ...peer effects on risk taking and risky decision making were stronger among
adolescents and youths than adults (2005, p. 625). In this study participants played a driving
simulation computer game either alone or with peers. This game

requires participants to make decisions about whether to stop a car that is moving across the
screen once a traffic light turns from green to yellow. The appearance of the yellow light signals
the impending appearance of a red traffic light, as well as a potential crash if the car is still
moving when the red light appears (p.627).

The participants in the 'peers' condition played the game while being watched by two peers; the peers
were also instructed to talk to the participant, give advice, encouragement, etc. Chein and colleagues
conducted a neuroimaging study with a similar paradigm. They used the same driving task, and also
asked each participant to bring two peers to the lab. Unlike Gardner & Steinberg, Chein et al. utilized a
within subject design (all participants performed the task several times: some in an alone condition,
some with peers). In order to simulate peer presence while in the scanner, participants were told that
their friends would be watching their game performance on the monitor in the scanner control room;
the manipulation was strengthened by having participants communicate with peers through an intercom.
Behavioral findings mostly replicated Gardner & Steinberg (2005), with one exception: presence of
peers did not result in riskier behavior for youth and young adults, but only for adolescents. Examining
neuro-imaging results of this study the authors point to stronger activation of ventral striatum and PFC
areas in adolescents' while performing the 'peer' version of the task, and argue that 'the presence of
peers differentially sensitizes adolescents to the reward value of risky choices' (2011, p.7).

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Neural mechanisms of social exclusion
Social neuroscientists intrigued by the effects of social exclusion examined the neural activation
paths triggered by this condition in hopes of understanding its underlying neural mechanism
(Eisenberger, 2003; Eisenberger et al.2007; Masten et al., 2009). Their results were summarized by
Masten et al., 2009, p.144):
These studies have revealed a network of neural regions associated with the distress of social
exclusion, including the dorsal anterior cingulated cortex (dACC), involved in the ‘unpleasant’
experience of physical pain (Foltz and White, 1962; Rainville et al., 1997; Sawamoto et al.,
2000); the insula, associated with visceral pain and negative affective experience (Cechetto and
Saper, 1987; Lane et al., 1997; Philips et al., 1997; Aziz et al., 2000; Phan et al., 2004); and the
right ventral and right ventrolateral prefrontal cortex (VPFC/VLPFC), involved in the
regulation of distress associated with both physical pain and negative emotional experiences
more generally (Hariri et al., 2000; Petrovic and Ingvar, 2002; Lieberman et al., 2004, 2007).
The above mentioned work examines the effect of social exclusion on adult participants. Masten and
colleagues also examined the effect of peer rejection in the adolescent population (2009). They
simulated social exclusion with the Cyberball task (Williams et al., 2000),

reliably shown in the
literature to be a strong social exclusion manipulation, in order to examine specific patterns of neural
activity triggered by peer rejection. Their findings revealed that 'consistent with data from adult
samples, adolescents showed significant activity in the insula and the right VPFC. In addition,
significant activation was found in the subACC' (Masten et al, 2009). The study results also point to
the differences in the neural processing of rejection in the adolescent population comparable to adults:
no evidence of dACC activity was evident in adolescents; however, significant activation in the ventral
striatum (VS) was unique to this age group.
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The findings in regards to insular cortex activation (both in adult and adolescent samples) are of
particular interest for the present work, since the insular cortex activation was shown to influence risk
preference. That is, higher insula activation is related to lower risk taking. Thus, I hypothesize, that
increased insular cortex activation due to social exclusion would result in lesser risk-taking. The intent
of this research is to show behavioral evidence that is consistent with the mediating effects of insular
cortex activation on risky decision-making as a result of social exclusion. Subsequent work will
directly focus on the neural bases of this behavior.

Method
Overview
The present study explores the effects of social exclusion on risky decision making in an older
adolescent population (ages 17-19). In order to reproduce the results of prior work I used the Cyberball
task to induce the experience of social exclusion. Following this manipulation, risky decision-making
was assessed with the CUPS Task (Xue et al., 2009) - a computer game in which participants make a
series of choices, each requiring selection from either a gain or loss domain with variable levels of risk,
in order to gain reward. To manipulate the social reward context, participants were told that the final
step of the experiment was a game to be played either individually or with a group. In order to ‘get into’
the group game, they had to reach a certain threshold in the CUPS task. As the final step of the study
individual difference in the following key personality traits were assessed using self-report
questionnaires.
Individual differences in rejection sensitivity may mediate the effect of social exclusion on risk
and therefore the rejection sensitivity measure (Downey & Feldman, 1996) was included at the end of
the study. Xue et al. (2010) found that the personality trait of negative urgency significantly correlated
with insular cortex activation. To examine if this effect would extend to risk taking I have included the
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UPPS+ urgency scale (Lynam, Smith, Whiteside, & Cyders, 2006) in the final questionnaire. This
revised version of the urgency scale measures both positive and negative urgency. Negative urgency
reflects difficulty in resisting urges and acting impulsively, especially when in a negative mood. It
includes items like ‘I have trouble resisting my cravings (for food, cigarettes, etc.)’ and ‘When I am
upset I often act without thinking’. Positive urgency captures the tendency for impulsive behavior
when in a positive mood, for example 'I tend to lose control when I am in a great mood' (for a full list
of items for both sub-scales see Appendix B).
Mindfulness is described as an acute, receptive and non-evaluative awareness of present
experiences (Brown, Ryan, & Creswell, 2007). It involves emotional disengagement that may reduce
an over-emphasis on the reward in a risky situation. In addition, clear focus and awareness of the
present (and own bodily reactions) would increase receptiveness to somatic cues and augment self-
control. This suggests that the trait of mindfulness may be related to risk taking and risk preference. To
evaluate this possibility, the mindful awareness and attention scale (MAAS, Brown & Ryan, 2003) was
also added to the final self-report questionnaire.
Participants
One hundred thirty three university students ages 17 to 19 at a U.S. West Coast university
participated in the study to fulfill course requirements. Participants were randomly assigned to a social
exclusion (70) or inclusion condition (63).
Materials and Procedures
Participants arrived at the lab in groups of four to six to take part in the group game
performance study. They were informed that they would play several games, some individually against
a computer, some with other participants remotely (on-line), and that the last task would be a face to
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face game. The experimenter led them to believe that they were participating in a large group study,
and that there were students in another computer lab that would join them later for the face to face task.
The experimenter further explained that in that last task (face to face game/competition) they could
participate either as an individual participant or as a part of the group. They were told that the
participants in the prior experiments preferred the group version; however, due to the restrictions of
this study, only the higher performing individuals would be able to join the team for the final round.
Thus in order to be included as part of the team their scores in the CUPS task must reach a certain
threshold.
As the first step of the study, participants played the Cyberball game, ostensibly with other
people from the on-line game room. In this task, participants (represented by an avatar) start by
playing a ball tossing game with two other players. At the end of the initial period, for Ss in the
exclusion condition, the two other players start throwing the ball to each other only, thus excluding the
participant. For participants in the control group, the game continues without change until the end.
Following the social exclusion manipulation, participants proceeded to play the CUPS game.
CUPS is a gambling task consisting of four blocks, two in the gain and two in the loss domains, offered
in counterbalanced order. In the beginning of each block the instructions specify if the goal is to 'gain
as much money as possible' (gain domain) or to 'lose the least possible amount of money' (loss domain).
For each trial the screen is divided into two parts and the participant has two choices: the 'sure choice' –
to win/lose $1, or the 'risky choice' to gamble, which could result in winning/losing more (Figure 1).
The gamble is represented by several cups with the money to win or lose concealed in one of the cups.
The amount of money is shown above the cups and it varies among $2, $3 and $5. When a participant
chooses to gamble the program selects the cup at random. The participant is then informed of the
outcome immediately. Since the number of cups determines the probability of winning or losing, and
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the number of cups varies from trial to trial between two, three or five; the probability also varies,
between 50%, 33% and 20%. The accumulated amount of winning or loss is shown only once at the
end of the task, in order not to influence participants’ risk preferences on each and every trial.
After the completion of the CUPS task participants were asked to fill out an on-line
questionnaire prior to proceeding to the group game. The questionnaire consisted of the Need-Threat
scale (Masten et al., 2009) to validate that the social exclusion manipulation was successful, the
Rejection Sensitivity scale (Downey & Feldman, 1996), the Urgency scale (Lynam, Smith, Whiteside,
& Cyders, 2006) and the Mindful Awareness scale (Brown & Ryan, 2003). When all the participants
of the session completed the questionnaire, the experimenter explained that the group game would not
take place after all, fully debriefed the participants and dismissed them.
Dependent Measures and Analysis
The Need-Threat scale (Masten et al., 2009) was used as a social exclusion manipulation check.
It is a 12 item scale that includes items like 'I felt rejected/invisible/liked' and it is measured on a 5-
point Likert scale, with the mean value calculated for each person (items 4-6 and 10-12 reverse-scored).
In the CUPS task, for each trial there is a choice between a sure thing (win/lose $1) and a
probabilistic choice. The subject's selection of a risky choice is an indication of risky behavior. In this
task there are three types of trials according to the probabilistic expected value (EV) theory (EV =
probability of positive outcome * outcome value): risk-equivalent (RE, EV = 1, for example: 2 cups
and $2 reward, EV=0.5*2), risk-disadvantageous (RD, EV<1, for example 3 cups and $2 reward,
EV=0.33*2) and risk advantages (RA, EV>1 for example 2 cups and $3 reward, EV=0.5*3). All
possible combinations of number of cups and dollar value are offered except for the two extreme cases
(5 cups and $2, 2 cups and $5) because prior work 'indicated that these types of trials exhibited no
sensitivity to individuals’ attitude toward risk' (Xue et al., 2009). Each of the four blocks consisted of
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35 trials, five repetition of each of the seven combinations. Xue and colleagues demonstrated that risk
preference was appropriately sensitive to EV, with RA> RE> RD and therefore they considered only
RE trials to be pure indicators of risk preference (2009). To be consistent with the literature most of the
results reported here are limited to RE trials (exceptions are noted). However, additional analysis
showed that our results and their significance do not change when all the trials are included in the
analysis. Since the existing literature indicated differences in the neural processing of positive and
negative reward (O’Doherty et al., 2001; Trepel et al., 2005; Knutson et al. 2007; Liu et al., 2007) and
lateralization of positive consequences to the left hemisphere (Bechara & Damasio, 2005) we examined
risk preference in gain and loss domain separately as well as combined together in one risk-preference
score.
The Rejection Sensitivity Questionnaire (RQS) was scored as follows (Downey & Feldman,
1996):
First, we obtained a rejection sensitivity score for each situation by weighting the expected
likelihood of rejection by the degree of concern over its occurrence. Specifically, we reversed
the score on expectancy of acceptance to index expectancy of rejection (expectancy of rejection
= 7 - expectancy of acceptance). We then multiplied the reversed score by the score for degree
of anxiety or concern. Second, we computed a total (cross-situational) rejection sensitivity
score for each participant by summing the rejection sensitivity scores for each situation and
dividing by 18, the total number of situations.
UPPS+ Impulsive Behavior scale (Lynam, Smith, Whiteside, & Cyders, 2006) is a 59 item 4-
point Likert type scale, consisting of five sub-scales: Negative Urgency, Positive Urgency, Lack of
Premeditation, Lack of Perseverance, and Sensation Seeking. Approximately half of the items in the
scale are reverse-scored (see full scale and scoring key in Appendix B).
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The Mindful Attention Awareness scale (MAAS, Brown & Ryan, 2003) is a 15 item 6-point
Likert type scale where all items are reverse scored (high score indicates low value of the measure).
Results
The manipulation check confirmed that the Cyberball task was successful in inducing the
feeling of social exclusion, as expected, with participants in the exclusion group scoring higher on NTS
(M=4.1, SD=.85 ) than participants in the inclusion group (M=3.2 , SD=.95), t(132)= 5.43, p <.0001.
Subjects in the current study were sensitive to the changes in EV, F(2, 396) = 126.11, p<.0001,
all three means significantly different: MRD=.4(.19), MRE=.55(.17), MRA=.69 (.21). This supports the
notion derived from the existing literature that the behavior in the RE trials is the best measure of risk
and that the only results for RE trials should be examined. The analysis confirmed the hypothesis that
the participants in the exclusion group took less risk (M=.5, SD=.17) than the participants in the
inclusion group (M=.59, SD=.15), t(132) = -3.17, p=.002 in RE trials. To examine the association of
domain (gain or loss), risk preference and inclusion-exclusion group I constructed a general linear
model for risk preference with inclusion-exclusion group, domain and their interaction as predictors.
The model revealed a marginal effect of the domain on risk preference (F(1,131)=3.85, p=.052),
participants took more risk in the gain domain compare to the loss domain; but no significant
interaction between domain and inclusion-exclusion group was evident.
Of additional interest is a more generalized model of risk preference in which risky behavior is
predicted by inclusion-exclusion group, type (RD/RE/RA), domain (gain/loss) and all interactions.
This model established that inclusion-exclusion group (F(1,131)=4.9, p=.03), risk type (F(2,131)=128,
p<.0001) and risk type * domain interaction (F(2,131)=3.38, p=.02) are significant predictors of risky
behavior, with marginal significance of domain effect (F(1,131)=3.18, p=.077) Specifically, excluded
participants took less risk than included participants, risk taking was sensitive to EV (RISKRD <
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RISKRE < RISKRA) ) and domain (RISKloss < RISKgain)(Table 1A&B).
Participants' rejection sensitivity scores (M=9.27, SD=2.54) ranged from 2 to 16.5. Linear
regression was used to predict risk taking in RE trials by rejection sensitivity scores for the participants
in exclusion group, but no significant relationship was revealed (F(1,68)=.5, p=.5). I also extended the
model to the whole sample using linear regression to predict risk taking by inclusion-exclusion group
(t=3.25, p=.002) and rejection sensitivity (t=.32, p=.75) .
To evaluate if a relationship exists between risk preference and the personality trait of urgency,
I performed stepwise model selection in order to see if any of UPPS+ components would be significant
predictors for risk taking to be included in linear regression model (RE trials only), when controlling
for inclusion-exclusion group. This analysis did not reveal any significant relationships between
UPPS+ components and risk taking (see Table 2 for UPPS+P means, SD, F and p values). In addition, I
performed repeated measures ANOVA separately for gain and loss domain, with risk type as within
subjects and inclusion-exclusion group and UPPS+ components as between subjects factors (separate
analysis was performed for each UPPS+ component). The model for gain domain that included
positive urgency (F(2,396)=4.21, p=.04) exposed it as significant predictor of risk taking (Table 3). No
other significant relationship was found between any of the UPPS+ components and risk preference.
Participants' self-reported mindful attention and awareness scores (MAAS: M=3.3, SD=.64)
ranged from 1.4 to 4.67. Linear regression was used to examine if MAAS was a significant predictor
of risk taking when exclusion-inclusion group was also included as a predictor (only RE trials were
included in this analysis). Analysis did not reveal a significant relationship between trait of
mindfulness and risk taking (t=-1.36, p=.18).
Discussion
The study provides evidence that social exclusion leads to decreased risk taking in decision
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making. The neurobiological underpinnings of this process need to be examined in the course of a
neuroimaging study. From the psychological perspective there could be several explanations for this
finding. The simplest one being that social exclusion, as does any negative context, makes more salient
the negative consequence of taking risk. However, contradictory evidence exists: Mital and Ross (1998)
examined the effect of affective states on risk taking and found that people in negative mood took more
risks compared to the ones in positive mood.
Belonging is a fundamental human need, which likely has an evolutionary purpose – in the
early days of human development somebody excluded from the group would have a faint chance of
survival. Social exclusion threatens this need and thus may mobilize certain defense mechanisms that
make people more vigilant in order to prevent additional danger, which may result in a preference for
safer choices and reduced risk taking. This idea is supported by Cacioppo and Hawkley’s work that
found that perceived social isolation increases people's attention to negative or threatening aspects of
the social environment (Hawkley et al., 2012). Another interesting finding by Cacioppo and colleagues
is the negative correlation between participants' loneliness score and activation in ventral striatum (VS)
to socially rewarding stimuli, - participants with higher loneliness scores had lower activation in VS
(Cacioppo et al., 2009). Also an earlier study from the same lab found social interactions to be less
rewarding for individuals who are feeling lonely (Hawkley, Preacher & Cacioppo, 2007). Since the
goal of the gambling task in the present experiment was to gain social reward (join the group for the
face to face team game), it is possible that participants in the exclusion condition perceived lower value
of this reward than participants in the inclusion condition. This fits well with Chein at al.’s. work that
explains increased risk taking in the presence of peers by an amplified reward focus as evident by
increased activation in VS (Chein et al., 2011).
At first glance, it was somewhat surprising to find no relationship between rejection sensitivity
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and risk taking. However, when taking into consideration the neuroimaging findings of Masten et al.,
we find that rejection sensitivity was positively correlated with activation in dACC, precuneus and
anterolateral PFC (2009), but no relationship with insular cortex activation and rejection sensitivity was
evident. Therefore, it seems that the rejection sensitivity trait, although it significantly effects the
processing of social exclusion, may not mediate activity in risky decision-making circuitry and thus not
mediate risk taking in a social exclusion context.
More puzzling were the findings or lack thereof for UPPS scale, especially in the light of Xue et
al’s. work, that demonstrated that the negative urgency trait positively correlated with insular cortex
activation during risky decision making. However, when taking a closer look at their findings we see
that higher urgency scores were related to higher insular cortex activation increases only after 'the
insular activation was temporarily reduced' (2010). In their study insular cortex activation variability
was achieved by presenting subjects with gambles they would likely take (increased activation) or pass
(decreased activation) thus controlling insular activity on a trial by trial basis during the CUPS game.
In the present study, we attempted to increase insular cortex activation as a consequence of social
exclusion prior to the CUPS task, hoping that the increase would be strong enough to persist through
the CUPS game. We anticipated that the magnitude of the effect of social exclusion would over-
shadow the variability in the activation from trial to trial due to win/loss and gamble/pass. The
behavioral difference in risk preference between exclusion and inclusion groups is an indirect evidence
that the manipulation was successful; thus, it is possible, that due to design difference between our
study and Xue at al’s work (2010), we did not have similar enough conditions (insular cortex activation
reduction followed by increase in activation) to be able to reproduce the relationship between negative
urgency and risk preference that they have found.
Although no neuroimaging work was included in the present study, the results of a separate
RISK AND SOCIAL EXCLUSION

20
preliminary pilot study corroborated this idea (Droutman et al., 2012). That experiment used the same
materials as the present study, and only slightly modified procedure (a within subjects design was
utilized, so that all participants played the Cyberball game followed by the CUPS task twice, once in
the included and once in the excluded condition, in counterbalanced order). The results suggest that
social exclusion is associated with elevated activation in insular cortex, and that insular cortex
activation during Cyberball is negatively correlated with risk preference during the CUPS task (R=-.81,
p<.05).
Understanding the effect of social influence on decision making is crucial to answering the
question why risk taking increases in adolescence and how to reduce the adverse consequences of this
phenomena. Gardner and colleagues examined the effect of presence of peers and found that it
increases risk taking and this increase was stronger for adolescents when compared with kids and youth
(2005). The present line of research could begin to fill a significant gap in the adolescent decision
making literature by exploring neural mechanisms of social influence. This knowledge could be used to
develop more successful training and interventions programs targeted towards this vulnerability in
adolescents.

RISK AND SOCIAL EXCLUSION

21
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Table 1
Predicting risk taking by inclusion-exclusion group, risk type and domain
A.Parameter estimates.
Parameter F p
Inclusion-exclusion group 4.92 .03
Risk type 128 <.0001
Gain or loss 3.18 0.08
Incluson-exclusion * type 2.16 0.12
Incluson-exclusion * gain_loss .002 0.97
Gain_loss *type 3.98 .02
Incluson-exclusion * gain_loss *type 0.12 0.87

B. Means (SD) by domain, risk type and inclusion-exclusion group.
Risk type
Gain/Loss Excluded M(SD) Included M(SD) Total M(SD)
RD Loss 0.396(.25) 0.41(.24) 0.4(.25)
RD Gain .398(.25) .417(.24) .407(.24)
RE Loss .482(.26) .567(.22) .522(.24)
RE Gain .525(.21) .62(.16) .57(.19)
RA Loss .621(.31) .692(.26) .655(.29)
R A Gain .695(.24) .755(.23) .723(.24)

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Table 2
UPPS+P descriptive statistics
Mean SD Min Max F p
Negative Urgency 2.26 0.55 1.1 3.9 0.17 0.68
Lack of premeditation 2.6 0.35 1.18 2.91 0.05 0.83
Lack of perseverance 1.85 0.43 1 2.9 1.11 0.29
Sensation Seeking 2.95 0.6 1.42 3.92 0.42 0.51
Positive Urgency 1.89 0.52 1 3.31 0 0.96

Note: F and p values calculated by model selection process attempting to regress risk preference on UPPS+P components
controlling for exclusion-inclusion group

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Table 3
Repeated measures (by risk type) ANOVA predicting risk taking by inclusion-exclusion group and
positive urgency.

Gain Domain Loss Domain
Variable F p F p Effect type
Include-exclude 5.09 .03 2.1 .15 Between subjects
Positive urgency 4.21 .04 .14 .7 Between subjects
Type of risk 5.97 .006 4.2 .02 Within subjects
Type * incl-exc 1.43 .24 1.22 .29 Within subjects
Type * pos_urgency .59 .51 .38 .62 Within subjects

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Gain Domain Loss Domain

Figure 1. CUPS task

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Appendix A. Need-Threat Scale (NTS)
Items introduced by the following:
Please indicate the extent to which you felt the following feelings during the Cyberball (ball-throwing)
game that you completed.

The accompanying 5-point scale:

1 2 3 4 5
Not at all Moderately Very much so

1. I felt “disconnected.”
2. I felt rejected.
3. I felt like an outsider.
4. I felt good about myself.
5. My self-esteem was high.
6. I felt liked.
7. I felt invisible.
8. I felt meaningless.
9. I felt non-existent.
10. I felt powerful.
11. I felt I had control over the course of the interaction.
12. I felt superior.

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Appendix B. Rejection Sensitivity (UPPS+P)
Items introduced by the following:
Below are a number of statements that describe ways in which people act and think. For each
statement, please indicate how much you agree or disagree with the statement. Be sure to indicate your
agreement or disagreement for every statement below. Also, there are questions on the following pages.

The accompanying 4-point scale:

1 2 3 4
Agree Strongly Agree Somewhat Disagree Somewhat Disagree Strongly

Negative Urgency
2. I have trouble controlling my impulses.
7. I have trouble resisting my cravings (for food, cigarettes, etc.).
12. I often get involved in things I later wish I could get out of.
17. When I feel bad, I will often do things I later regret in order to make myself feel better now.
22. Sometimes when I feel bad, I can’t seem to stop what I am doing even though it is making me feel
worse.
29. When I am upset I often act without thinking.
34. When I feel rejected, I will often say things that I later regret.
39. It is hard for me to resist acting on my feelings.
44. I often make matters worse because I act without thinking when I am upset.
51. In the heat of an argument, I will often say things that I later regret.
54. I always keep my feelings under control.
58. Sometimes I do impulsive things that I later regret.
Positive Urgency
5. When I am very happy, I can’t seem to stop myself from doing things that can have bad
consequences.
10. When I am in great mood, I tend to get into situations that could cause me problems.
15. When I am very happy, I tend to do things that may cause problems in my life.
20. I tend to lose control when I am in a great mood.
25. When I am really ecstatic, I tend to get out of control.
30. Others would say I make bad choices when I am extremely happy about something.
35. Others are shocked or worried about the things I do when I am feeling very excited.
40. When I get really happy about something, I tend to do things that can have bad consequences.
45. When overjoyed, I feel like I can’t stop myself from going overboard.
RISK AND SOCIAL EXCLUSION

34
50. When I am really excited, I tend not to think of the consequences of my actions.
53. I tend to act without thinking when I am really excited.
55. When I am really happy, I often find myself in situations that I normally wouldn’t be comfortable
with
57. When I am very happy, I feel like it is ok to give in to cravings or overindulge.
59. I am surprised at the things I do while in a great mood.
Lack of Premeditation
1. I have a reserved and cautious attitude toward life.
6. My thinking is usually careful and purposeful.
11. I am not one of those people who blurt out things without thinking.
16. I like to stop and think things over before I do them.
21. I don't like to start a project until I know exactly how to proceed.
28. I tend to value and follow a rational, "sensible" approach to things.
33. I usually make up my mind through careful reasoning.
38. I am a cautious person.
43. Before I get into a new situation I like to find out what to expect from it.
48. I usually think carefully before doing anything.
49. Before making up my mind, I consider all the advantages and disadvantages.
Lack of Perseverance
4. I generally like to see things through to the end.
9. I tend to give up easily.
14. Unfinished tasks really bother me.
19. Once I get going on something I hate to stop.
24. I concentrate easily.
27. I finish what I start.
32. I am able to pace myself so as to get things done on time.
37. I am a person who always gets the job done.
42. I almost always finish projects that I start.
47. Sometimes there are so many little things to be done that I just ignore them all.
Sensation Seeking
3. I generally seek new and exciting experiences and sensations.
8. I'll try anything once.
13. I like sports and games in which you have to choose your next move very quickly.
18. I would enjoy water skiing.
RISK AND SOCIAL EXCLUSION

35
23. I quite enjoy taking risks.
26. I would enjoy parachute jumping.
31.I welcome new and exciting experiences and sensations, even if they are a little frightening and
unconventional.
36. I would like to learn to fly an airplane.
41. I sometimes like doing things that are a bit frightening.
46. I would enjoy the sensation of skiing very fast down a high mountain slope.
52. I would like to go scuba diving.
56.I would enjoy fast driving.
Scoring Instructions
(Negative) Urgency (all items except 1 are reversed)
items 2 (R), 7(R), 12 (R), 17 (R), 22 (R), 29 (R), 34 (R), 39 (R), 44 (R), 51 (R), 54, 58 (R)

(lack of) Premeditation (no items are reversed)
items 1, 6, 11, 16, 21, 28, 33, 38, 43, 48, 49.

(lack of) Perseverance (two items are reversed)
items 4, 9 (R), 14, 19, 24, 27, 32, 37, 42, 47 (R)

Sensation Seeking (all items are reversed)
items 3 (R), 8 (R), 13 (R), 18 (R), 23 (R), 26 (R), 31 (R), 36 (R), 41 (R), 46 (R), 52 (R), 56 (R)

Positive Urgency (all items are reversed)
items 5 (R), 10 (R), 15 (R), 20 (R), 25 (R), 30 (R), 35 (R), 40 (R), 45 (R), 50 (R), 53 (R), 55 (R), 57 (R),
59 (R)

(R) indicates the item needs to be reverse scored such 1=4, 2=3, 3=2, and 4=1.
RISK AND SOCIAL EXCLUSION

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Appendix C. Mindful Attention Awareness Scale

Items introduced by the following:
Below is a collection of statements about your everyday experience. Using the 1–6 scale below,
please indicate how frequently or infrequently you currently have each experience. Please
answer according to what really reflects your experience rather than what you think your
experience should be.

The accompanying 6-point scale:

1 2 3 4 5 6
Almost
Always
Very
Frequently
Somewhat
Frequently
Somewhat
Infrequently
Very
Infrequently
Almost
Never

1. I could be experiencing some emotion and not be conscious of it until
some time later.
2. I break or spill things because of carelessness, not paying attention, or
thinking of something else.
3. I find it difficult to stay focused on what’s happening in the present.
4. I tend to walk quickly to get where I’m going without paying attention
to what I experience along the way.
5. I tend not to notice feelings of physical tension or discomfort until they
really grab my attention.
6. I forget a person’s name almost as soon as I’ve been told it for the first
time.
7. It seems I am “running on automatic” without much awareness of what
I’m doing.
8. I rush through activities without being really attentive to them.
9. I get so focused on the goal I want to achieve that I lose touch with
what I am doing right now to get there.
10. I do jobs or tasks automatically, without being aware of what I’m doing.
11. I find myself listening to someone with one ear, doing something else at
the same time.
12. I drive places on “automatic pilot” and then wonder why I went there.
13. I find myself preoccupied with the future or the past.
14. I find myself doing things without paying attention.
15. I snack without being aware that I’m eating.

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Asset Metadata

Creator Droutman, Vitaliya (author)

Core Title Social exclusion decreases risk-taking

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 11/28/2012

Defense Date 11/28/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag insula,insular cortex,OAI-PMH Harvest,risk,risk taking,risky decision-making,Social exclusion

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Read, Stephen J. (committee chair), Bechara, Antoine (committee member), Monterosso, John R. (committee member)

Creator Email droutman@usc.edu,vdroutman@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-121842

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA