Dissociable Neural Processes Underlying Risky Decisions for Self Versus Other

Article (PDF Available)inFrontiers in Neuroscience 7(7):15 · March 2013with100 Reads
DOI: 10.3389/fnins.2013.00015 · Source: PubMed
Abstract
Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for oneself. The present study directly compared risky decisions for self with those for another person using functional magnetic resonance imaging (fMRI). Participants were asked to perform a gambling task on behalf of themselves (decision-for-self condition) or another person (decision-for-other condition) while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points) and a high-risk option (i.e., win or lose 90 points) with variable levels of winning probability. Compared with choices regarding others, those regarding oneself were more risk-averse at lower winning probabilities and more risk-seeking at higher winning probabilities, perhaps due to stronger affective process during risky decisions for oneself compared with those for other. The brain-activation pattern changed according to the target, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM) showed greater activation in the decision-for-other condition than in the decision-for-self condition. Parametric modulation analysis using individual decision models revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC) were associated with value computations for oneself and for another, respectively, during risky financial decisions. The results of the present study suggest that decisions for oneself and for other may recruit fundamentally distinct neural processes, which can be mainly characterized as dominant affective/impulsive and cognitive/regulatory processes, respectively.
ORIGINAL RESEARCH ARTICLE
published: 20 March 2013
doi: 10.3389/fnins.2013.00015
Dissociable neural processes underlying risky decisions
for self versus other
Daehyun Jung
1
, Sunhae Sul
2
and Hackjin Kim
3
*
1
Laboratory of Social and Decision Neuroscience, Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
2
Laboratory of Social and Decision Neuroscience, Wisdom Science Center, Korea University, Seoul, South Korea
3
Laboratory of Social and Decision Neuroscience, Department of Psychology, Korea University, Seoul, South Korea
Edited by:
Ming Hsu, University of California,
USA
Reviewed by:
Mathieu D’Acremont, California
Institute of Technology, USA
Songfa Zhong, National University of
Singapore, Singapore
*Correspondence:
Hackjin Kim, Department of
Psychology, Korea University, 145
Anam-ro, Seongbuk-gu, Seoul
136-701, South Korea.
e-mail: hackjinkim@korea.ac.kr
Previous neuroimaging studies on decision making have mainly focused on decisions on
behalf of oneself. Considering that people often make decisions on behalf of others, it is
intriguing that there is little neurobiological evidence on how decisions for others differ
from those for oneself. The present study directly compared risky decisions for self with
those for another person using functional magnetic resonance imaging (fMRI). Participants
were asked to perform a gambling task on behalf of themselves (decision-for-self condi-
tion) or another person (decision-for-other condition) while in the scanner. Their task was
to choose between a low-risk option (i.e., win or lose 10 points) and a high-risk option (i.e.,
win or lose 90 points) with variable levels of winning probability. Compared with choices
regarding others, those regarding oneself were more risk-averse at lower winning prob-
abilities and more risk-seeking at higher winning probabilities, perhaps due to stronger
affective process during risky decisions for oneself compared with those for other. The
brain-activation pattern changed according to the target, such that reward-related regions
were more active in the decision-for-self condition than in the decision-for-other condition,
whereas brain regions related to the theory of mind (ToM) showed greater activation in the
decision-for-other condition than in the decision-for-self condition. Parametric modulation
analysis using individual decision models revealed that activation of the amygdala and the
dorsomedial prefrontal cortex (DMPFC) were associated with value computations for one-
self and for another, respectively, during risky financial decisions.The results of the present
study suggest that decisions for oneself and for other may recruit fundamentally distinct
neural processes, which can be mainly characterized as dominant affective/impulsive and
cognitive/regulatory processes, respectively.
Keywords: fMRI, self–other decision, amygdala, dorsomedial prefrontal cortex, risky decision, prosocial behavior,
social neuroscience
INTRODUCTION
In daily life, we make decisions on behalf of others as often as
we make decisions on behalf of ourselves: we sometimes order
a lunch for a friend, choose presents for family, make decisions
for a company, or buy products or stocks for customers. These
other-regarding decisions, albeit not immediately targeted toward
ourselves, can be critical to the establishment and maintenance
of our social lives. Like decisions for oneself, decisions for oth-
ers ranging from mundane to profound also involve some level
of risk. Thus, it is important to understand the mental processing
that drives risky decisions for others as well as those for oneself.
Despite the significance of this issue, few neuroimaging studies
have directly compared decisions for oneself with those for others,
and only a small body of literature on the subject exists in the
field of social psychology. Thus, the goal of the present study is to
understand whether and how decisions (i.e., a risky decision in a
gambling task) for oneself and for others differ from each other at
the neural level through the use of functional magnetic resonance
imaging (fMRI).
An emerging body of literature on self–other decision mak-
ing has documented risky decisions in various domains, such
as surrogate decisions in medicine (Hare et al., 1992; Fagerlin
et al., 2001; Lipkus et al., 2001), public policy (Roszkowski and
Snelbecker, 1990; Reynolds et al., 2009), career choice (Kray and
Gonzalez, 1999), romantic relationships (Beisswanger et al., 2003;
Wray and Stone, 2005), and financial decisions in gambling tasks
(Hsee and Weber, 1997; Loewenstein et al., 2001; Stone et al., 2002;
Fernandez-Duque and Wifall, 2007). Although some progress
has been made, the findings of these studies have been rather
inconsistent. For instance, some studies have reported that peo-
ple behaved/thought in a more risk-seeking manner when they
decided for another person than for themselves (Hsee and Weber,
1997; Beisswanger et al., 2003), whereas others found that people
became more risk-averse in similar situations (Fernandez-Duque
and Wifall, 2007).
In order to reconcile the conflicting findings listed above,
recent studies have considered the potential mediating factors
of these observations (Fernandez-Duque and Wifall, 2007; Stone
and Allgaier, 2008). For example, Fernandez-Duque and Wifall
(2007) examined actor-observer asymmetry in risky decisions and
proposed that the self–other discrepancy could be mediated by
differential access to experiential and rational decision making
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Jung et al. Risky decisions for self versus other
systems. They suggested that when people decide for themselves,
the experiential system which involves intuitive and emotion-
ally based processes might weigh more heavily on the decision
making process than the rational system which engages effortful,
logical, and analytical processes (Denesraj and Epstein, 1994). This
could be the case because actors who make decisions for themselves
are more likely to be influenced by their own affective reactions
to the consequent rewards and punishments. Similarly, Hsee and
Weber (1997) showed that the level of description of the other
person accessible to participants mattered in the self–other deci-
sion making discrepancy. In their study, participants predicted that
others would be more risk-seeking than they would be in terms of
financial decisions when the other person for whom the decision
was made was described in anonymous and abstract terms. How-
ever, the self–other difference diminished when the other person
for whom the decision was made was described vividly and in con-
crete terms. The authors suggested that a vivid description of the
other person made the decisions for self and for other commen-
surate by eliciting strong affective reactions in subjects. To explain
the findings, they proposed a “risk-as-feelings hypothesis,” which
maintains that people rely on affective evaluations when making
decisions for themselves in risky situations (Hsee and Weber, 1997;
Loewenstein et al., 2001).
The idea that risky decisions for oneself are mainly affected by
emotional reactions has been supported by a large body of neuro-
science literature. Most relevant is the finding that the amygdala,
a key structure for emotional processing during decision making
(Morrison and Salzman, 2010), plays a critical role in risky decision
making (Bechara et al., 1999; Hsu et al., 2005; De Martino et al.,
2006, 2010; Brand et al., 2007; Ghods-Sharifi et al., 2009; Smith
et al., 2009). For instance, De Martino et al. (2006, 2010) studied
the neural correlates of the framing effect, whereby people become
more risk-averse in a gain frame (i.e., when gains are made salient)
than in a loss frame (i.e., when losses are made salient). This effect
is a representative example of emotionally driven decision mak-
ing in risky situations and was strongly associated with activity
in the amygdala (De Martino et al., 2006); further, the effect was
significantly diminished in patients with amygdalar damage (De
Martino et al., 2010).
The amygdala forms extensive anatomical and functional con-
nections with the dorsomedial prefrontal cortex [DMPFC, which
includes Brodmann areas (BAs) 9, 32, 33, and part of the medial
prefrontal cortex (MPFC); Etkin et al., 2011] that show devel-
opmental progress (Hung et al., 2011). The amygdalas affective
reactions seem to be regulated via these connections (Banks et al.,
2007; Kim et al., 2011). Although relatively little is known about
the role of these amygdala–DMPFC connections in risky deci-
sions (Cohen et al., 2005), the DMPFC itself is also known as a key
structure for decision making in risky situations. For example,
Wu et al. (2011) found that activation of the MPFC, includ-
ing both dorsal and ventral regions, quantitatively reflected the
subjective value of monetary outcomes combined with probabil-
ity information about lottery tasks. Another study showed that
the DMPFC was specifically responsive to risk-related informa-
tion (Xue et al., 2009). Similarly, many previous studies using the
Iowa Gambling Task (IGT) have shown that risky decision mak-
ing is associated with increased activation of the DMPFC (Bolla
et al., 2003; Fukui et al., 2005; Tanabe et al., 2007). Further, the
DMPFC plays an important role in emotional regulation during
affective decision making (Banks et al., 2007), effort-based deci-
sion making (Rudebeck et al., 2006; Floresco and Ghods-Sharifi,
2007; Croxson et al., 2009), and perspective taking during other-
regarding processes (St. Jacques et al., 2010). In sum, while the
amygdala is responsible for affective reactions in risky decisions,
the DMPFC seems to control cognitive processes, such as weigh-
ing the probabilities and reward values of different options and
regulating emotion.
As reviewed above, evidence from the social psychology litera-
ture implies the existence of distinctive neural circuitry subserving
decisions on behalf of others as opposed to those made for oneself,
and unveiling this difference would greatly advance the current
theoretical account of prosocial decisions. In line with this idea,
a recent study showed that activity in the ventromedial prefrontal
cortex (VMPFC) was modulated by activity in the inferior parietal
lobule (IPL) a brain region close to the temporoparietal junction
(TPJ) that is involved in mentalization (Saxe and Powell, 2006)
when people made product purchase decisions for others, whereas
no such modulation effect of TPJ was found when people made
the same decisions for themselves (Janowski et al., 2012).
The present study aimed to examine the difference between
decisions made for oneself and those made for another in a risky
situation by using a gambling task paradigm with systematically
variable winning probability. On the bases of previous findings,
we predicted that affective processes would have stronger weight
in decisions made for oneself than for other. Thus, we hypoth-
esized that considering a risky choice on behalf of another may
employ the brain regions involved in cognitive/rational processes
(e.g., the prefrontal cortex) more than those associated with
affective/experiential processes (e.g., the amygdala), whereas the
opposite may be true when the risky decision is made for oneself.
MATERIALS AND METHODS
PARTICIPANTS
Twenty-three undergraduate students in South Korea [12 women;
mean age (SD) = 23.32 (2.59)] participated voluntarily and were
compensated an average of KRW 30000 (USD 25) for about
1 h of participation. Any potential health risks were carefully
screened via a self-report questionnaire, and informed consent
was obtained from all participants. All participants were right-
handed and reported having no chronic mental illness. Three
participants were excluded from analysis because they fell asleep
inside the scanner. The experimental procedures were approved
by the Institutional Review Board of Korea University.
TASK AND PROCEDURES
Participants performed a gambling task inside the MRI scanner.
We adopted the “modified risk task” developed by Knoch et al.
(2006), in which participants were asked to choose between two
options: one with lower risk (i.e., win or lose 10 points) and
another with higher risk (i.e., win or lose 90 points). The win-
ning probability of each option was 17–83% (the probabilities
used were 17, 33, 50, 67, and 83%). In each trial, participants were
presented with six boxes distinguished by pink and blue colors,
and they were asked to choose either pink or blue. The colors of
Frontiers in Neuroscience | Decision Neuroscience March 2013 | Volume 7 | Article 15 | 2
Jung et al. Risky decisions for self versus other
the boxes indicated the numbers of points that the participants
could win or lose: 10 for pink and 90 for blue (Figure 1A). Partic-
ipants were told that a yellow coin had an equal chance to appear
in any of the boxes and they would gain points if the coin was
contained in one of the boxes with the chosen color and that they
would lose the same number of points if the coin appeared in an
opposite-colored box. For example, if they chose pink and there
was a coin in one of the pink boxes, they won 10 points, but if
there was no coin in the chosen color, they lost 10 points. Like-
wise, if they chose blue, they won or lost 90 points if there was
or was not a coin among the blue boxes, respectively. Thus, pink
were the low-risk and blue were the high-risk options. The ratio of
pink and blue boxes determined the winning probability of each
option; for example, five pink boxes and one blue box meant that
the winning probabilities were 83% for pink and 17% for blue.
In each trial, participants had to make a decision on behalf of
either themselves (decision-for-self condition) or another person
(decision-for-other condition), according to a cue presented prior
to the task. The structure of a single trial is shown in Figure 1B.
First, the cue indicating the decision condition (decision-for-self
or decision-for-other) was presented for 1–3 s, followed by the risk
task. After the six boxes were presented, the participants chose
between pink and blue by pressing the left or right button of
an MR-compatible mouse. They were asked to respond carefully
but as quickly as possible. Each participant performed 120 total
trials (60 in each of the decision-for-self and decision-for-other
conditions), which were divided into three sessions. Each session
consisted of 40 trials, with the same number of trials for each
condition. The order in which the different types of trials were
presented was determined pseudorandomly. Earned points were
accumulated separately for each condition. Participants were told
that the points accumulated in all trials would be converted into
real money. We informed participants that they would be endowed
with a base payment of 30,000 KRW; we also informed them that
25000–35000 KRW was an approximate range of final compen-
sation. Participants were kept blind to the exact ratio between
points and money, because we did not want them to focus on
calculating the exact amounts of money earned by themselves
or others. Subjects were also told that their task performance in
the decision-for-other condition would determine extra earnings
of another person who was randomly selected among the par-
ticipants of the same experiment. Participants understood that
the transfer would be completely anonymous so that neither the
participants themselves nor their counterparts would know each
other’s identities. They also knew that their decisions for others
would not affect their own profits, because the point totals for
self and other were calculated separately, and the tasks were per-
formed individually. In both conditions, participants started with
100 points; 4 s after the participants made a decision, the result
of the decision (i.e., win or lose) was presented on the feedback
screen (Figure 1B).
All instructions were given outside the scanner, and each par-
ticipant performed 10 practice trials before entering the scanner
to learn the task rules. After the completion of the task, the points
earned during the decision-for-self condition were converted into
money and added to the subject’s base payment (KRW 30000),
and the points earned during the decision-for-other condition
were actually transferred to another participant for whom the
participant made the decisions. Participants’ final earnings var-
ied 25000–35000 KRW, depending on their own performance and
that of the other randomly matched participant.
NEUROIMAGING PROCEDURES
fMRI data acquisition
We acquired data using an ISOL Forte 3T system with a
standard birdcage coil in the Brain Science Research Center
at the Korea Advanced Institute of Science and Technology.
T2
-weighted functional images were obtained using gradient–
echo echo-planar pulse sequences (TR = 2000 ms; TE = 30 ms;
FA = 80˚; FOV = 240 mm; 64 × 64 matrix; 24 slices; voxel
size = 3.75 mm × 3.75 mm × 4.0 mm). The stimuli were pre-
sented through an MR-compatible LCD monitor mounted on a
head coil (refresh rate: 60 Hz; display resolution: 640 × 480 pixels;
viewing angle: 30˚). Each functional run lasted 480–600 s, includ-
ing the first five TRs, which were discarded later due to unstable
magnetization.
FIGURE 1 | Schematic diagram of the experimental design. (A)
Five experimental conditions with variable probabilities and fixed
outcomes. Percentages in parentheses indicate winning
probabilities of the high-risk option (blue). (B) A schematic diagram
of the experimental design. Each trial began with a cue indicating
whether the decision is for self or for other, followed by a
gambling task. Participants were then asked to choose one of two
colors (pink or blue) by clicking the left or right mouse button.
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Jung et al. Risky decisions for self versus other
fMRI data analyses
Neuroimaging data were preprocessed and analyzed using Statis-
tical Parametric Mapping 8 (SPM8; the Wellcome Trust Centre
for Neuroimaging, University College London, UK). After tim-
ing correction for interleaved slice acquisition, correction for
head motion was performed through realignment of all func-
tional images to those of the first scan, and then a mean brain
image was created for each participant. The realigned images were
normalized to the Montreal Neurological Institute (MNI) echo-
planar imaging (EPI) template, resampled at a voxel resolution
of 2 mm × 2 mm × 2 mm, and spatially smoothed with an 8 mm
Gaussian filter. Anatomical localization was performed in SPM8
by reference to a T1 template image, and then the preprocessed
functional images were analyzed using the general linear model
(GLM; Friston et al., 1995). Regressors for the events of choice and
outcome delivery were convolved with a canonical hemodynamic
response function. Motion vectors obtained from the realignment
process were included as regressors in the GLM in order to reduce
noise. The resulting statistical parametric maps were first thresh-
olded stringently (significance level: FWE < 0.05, corrected for
multiple comparisons; cluster size threshold >5 voxels), but no
activation cluster survived this threshold.
To verify a pr iori hypotheses regarding several regions of inter-
est (ROI), we used the small-volume correction (SVC) method
for multiple comparisons (p < 0.05) in SPM8. We expected that
the ventral striatum (VS; left: x = 10, y = 6, z = 14; right:
x = 8, y = 6, z = 10), the ventral tegmental area (VTA; x = 4,
y = 14, z = 20), the anterior cingulate cortex (ACC; x = 4,
y = 24, z = 40), and the insula (x = 34, y = 20, z = 4) would be
involved in reward anticipation and feedback during risky choice
in the self–other contrast (Ernst et al., 2004) and that the TPJ
(left: x = 48, y = 57, z = 25; right: x = 53, y = 54, z = 17),
the posterior cingulate cortex (PCC; x = 2, y = 60, z = 27), and
the MPFC (x = 1, y = 63, z = 2) which are known to be related
to theory of mind (ToM) functions (Saxe and Powell, 2006)
would be involved in the same function in the other–self contrast.
In addition, we expected the amygdala (x = 22, y = 4, z = 18;
Smith et al., 2009) and the DMPFC (x = 8, y = 36, z = 30; Wu
et al., 2011) to encode the value of the risky option. The search vol-
umes for SVC were restricted to spheres with radii of 15 mm and
center coordinates obtained from corresponding studies. Addi-
tionally, we defined the ROIs in both hemispheres by mirroring
the coordinates obtained in previous studies. To reduce the risk of
false negatives and completely overview the clusters at which acti-
vation occurred, we also applied a less-stringent significance level
(p < 0.001, uncorrected; cluster size threshold 5 voxels); a table
with a list of activation clusters is included in the Supplementary
Material.
Brodmann areas and brain regions were identified in Talairach
space (Talairach and Tornoux, 1988) after converting the MNI
coordinates to Talairach ones using non-linear transformation
(Lancaster et al., 2007).
Contrasting decision-for-self versus decision-for-other
In order to explore which brain regions were more highly acti-
vated by the decision-for-self task than by the decision-for-
other task or vice versa, we estimated whole-brain contrast maps
from the periods when participants watched the six boxes and
received reward information during both tasks. The single-subject
whole-brain GLMs included the following regressors: (1) deci-
sion events at the time of task onset, when participants viewed
the stimuli for 10 types of trials (i.e., five levels of probability in
both the decision-for-self and decision-for-other conditions) and
made decisions, (2) button-pressing events, (3) feedback events
(at feedback onset, when participants watched two types of out-
comes: those for self and other), and (4) motion parameters. The
self–other and other–self contrasts were defined for all probability
conditions.
Parametric modulation analysis based on individual decision
models
We conducted parametric modulation analysis to determine which
brain regions had activation levels that correlated with the decision
value that each participant placed on the risky choice. Each partic-
ipant made risky choices for self and other with varying probabili-
ties of a favorable outcome; we calculated the decision value using
optimal sigmoid functions fitted to the participant’s probability
(0–1) of choosing the high-risk option over the low-risk option
as a function of the probability of winning. The parameters of
the estimated models were calculated by using the least-squares
method for each participant (see the equation below).
f
(
x
i
)
=
1
e
a
(
b x
i
)
+ 1
In the equation above, the variable x is the winning probabil-
ity of the high-risk option, and f (x
i
) is the probability of a risky
choice on trial i. The parameter a indicates the slope of the sig-
moid function that reflects how drastically the probability of risky
choice changes according to the level of winning probability, and
b denotes an offset criterion for the winning probability of the
high-risk option when the participant is expected to choose the
risky option with 50% probability. We calculated the parameters
separately for the decision-for-self and decision-for-other condi-
tions. We generated separate single-subject GLMs for parametric
modulation analysis, which included the following regressors: (1)
decision events when a new configuration of colored boxes is dis-
played on the computer screen, along with individually estimated
decision values [i.e., f(x
i
)] as parameters for the decision-for-self
and decision-for-other conditions; (2) button-pressing events; (3)
feedback events at the time of feedback onset when participants
watched two types of outcomes (i.e., those for self and other); and
(4) motion parameters.
Psychophysiological interaction analysis
We conducted psychophysiological interaction (PPI) analysis
(Friston et al., 1997) to examine the functional connectivity
between the brain regions identified from the contrast analy-
ses. Specifically, we searched for brain regions whose activity
showed differential patterns of correlations with that of a source
region as a function of experimental condition (i.e., the decision-
for-self and decision-for-other conditions). We used the right
TPJ (rTPJ) as the source region, because it is the representative
area that reflects both perspective taking (Castelli et al., 2000;
Frontiers in Neuroscience | Decision Neuroscience March 2013 | Volume 7 | Article 15 | 4
Jung et al. Risky decisions for self versus other
Saxe and Wexler, 2005; Decety and Lamm, 2007) and other-
regarding behavior (Morishima et al., 2012), and because we
focused on examining how the brain regions activated during
decision-for-other communicated with other areas. We extracted
time-series data from the peak voxel of a cluster found in
rTPJ for each participant and then generated PPI regressors,
that is, the time-course of activity in the seed region modu-
lated by two levels of the psychological variable (i.e., decision-
for-other versus decision-for-self). We then estimated single-
subject whole-brain GLMs with the following regressors: (1)
time-course of activity in the seed region (rTPJ activity), (2) psy-
chological contrast (other–self contrast weight), (3) interaction
term (rTPJ activity × other–self contrast weight), and (4) motion
parameters.
RESULTS
BEHAVIORAL RESULTS
We first calculated the ratio of risky choices to the total number of
trials in each condition for each participant. Then, we conducted a
2 (conditions: decision-for-self and decision-for-other) × 5 (win-
ning probabilities of the high-risk option: 17, 33, 50, 67, and 83%)
repeated-measures ANOVA on the probability of choosing the
high-risk option (i.e., blue) over the low-risk option (i.e., pink).
Because Mauchly’s test indicated that the assumption of sphericity
had been violated (χ
2
= 34.070, p < 0.05), we used a multivariate
test, which revealed a significant two-way interaction effect, F (4,
16) = 3.150, p < 0.05. As shown in Figure 2, the difference between
the frequencies of high-risk decisions for self and for other varied
according to the probability of winning. Participants were more
likely to make risk-seeking decisions in the decision-for-self con-
dition than in the decision-for-other condition when the winning
probability of the high-risk option was higher, while the reverse
was true when it was lower.
To investigate this interaction further, we conducted post hoc
pairwise t -tests on the differences in the risky choice ratio
between the decision-for-self and decision-for-other conditions
at each level of wining probability of the higher risk option. We
found a significant difference between the conditions at 83%,
t(22) = 2.319, p < 0.05, and a marginally significant difference at
FIGURE 2 | Behavioral data showing the probability of risky choice as a
function of the probability of a more favorable outcome.
17%, t (22) = 2.01, p = 0.059 (Figure 2), although none of the
tests survived Bonferroni correction.
NEUROIMAGING RESULTS
Decision-for-self versus decision-for-other during the decision
event
To compare the brain regions associated with decisions for oneself
with those associated with decisions for another, the self–other
and other–self contrasts at the time of decision (i.e., task onset
time) were estimated. The self–other contrast revealed greater
activation in the decision-for-self condition than in the decision-
for-other condition in various regions, including the bilateral
VS (Figures 3A,C; left: x = 12, y = 2, z = 14; right: x = 18,
y = 12, z = 16), the VTA (x = 6, y = 24, z = 18), the ACC
(x = 8, y = 36, z = 34), and the right insula (x = 34, y = 24,
z = 12; all findings thresholded at p < 0.05, SVC FWE-corrected
unless otherwise stated). The other–self contrast showed that
the bilateral TPJ (left: x = 50, y = 62, z = 16; right: x = 58,
y = 66, z = 24) and the PCC (x = 6, y = 58, z = 30) were
more active in the decision-for-other condition than in the
decision-for-self condition (Figures 3B,D).
Neural responses to monetary outcomes for self versus other
The self–other contrast at the time of the monetary outcome
events revealed preferential activation of the right insula (x = 32,
y = 18, z = 16) in the decision-for-self condition. The other–
self contrast revealed the opposite pattern in the bilateral TPJ (left:
FIGURE 3 | Main contrast maps between self and other conditions.
Areas showing greater activity during decision events (A) in the
decision-for-self condition than in the decision-for-other condition and (B) in
the decision-for-other condition than in the decision-for-self condition. The
statistical threshold for the images was set at p< 0.005 (uncorrected). The
bar graphs in the lower panel show the beta coefficients (averaged across
all probabilities) of (C) the left VS (x = 12, y = 2, z = 14; Z = 4.19,
p < 0.05, SVC FWE-corrected) for the self–other contrast and (D) the left
TPJ (x = 50, y = 62, z = 16; Z = 4.34, p < 0.05, SVC FWE-corrected) for
the other–self contrast.
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Jung et al. Risky decisions for self versus other
x = 48, y = 62, z = 24; right: x = 48, y = 58, z = 22) and the
PCC (x = 2, y = 64, z = 38), which are similar to the areas of
activation observed at the time of decision events (Figure S1 in
Supplementary Material).
Parametric modulation analysis using individual decision models
The present study mainly aimed to examine the distinctive neural
structures involved in the computation of values of choices and the
prediction of risky choices on behalf of both oneself and others.
With this in mind, we conducted parametric modulation analysis
using the participants’individual decision models. Model parame-
ters were generated by fitting sigmoid functions to the probabilities
of choosing the high-risk option over the low-risk option. Eigh-
teen participants were subjected to the analysis, excluding two
participants whose behavioral data fit poorly to sigmoid functions
because of their atypical decisions (e.g., risky choices regardless of
probability). The individual decision models were estimated for
the decision-for-self and decision-for-other conditions separately.
The analyses revealed that the chance of making a risky choice
for self was positively correlated with activation of the right ante-
rior amygdala (x = 16, y = 6, z = 16), whereas the chance of
making a risky choice for other was positively correlated with acti-
vation of the left DMPFC (x = 14, y = 34, z = 32). Activation
was not negatively correlated with the chance of making a risky
decision-for-self or for other in any brain region.
To investigate which brain regions drive the differences between
the models for self and other, we calculated the contrast between
the value computation models for self and other via parametric
modulation analysis. The self–other contrast showed that activa-
tion in the right amygdala (x = 24, y = 0, z = 22) was closer to
that predicted by the value computation model for self than that
for other, while activation in the left DMPFC (x = 14, y = 32,
z = 32) showed a stronger association with the decision model for
other than for self (Figure 4).
Parametric modulation analysis using expected value and outcome
We conducted additional parametric modulation analysis to
examine prediction error (PE)-related neural activity at the time
of the feedback events. The PE parameters were calculated by
subtracting the expected values (EVs) from the monetary out-
comes (10, 10, 90, or 90 points) separately for self and other
conditions. The EV of the low-risk option (i.e., choosing the
pink box) was calculated by adding the respective EVs for gain
(i.e., winning probability of the low-risk option × points gained
for winning) and loss (i.e., winning probability of high-risk
option × points lost for losing); the EV of the high-risk option
was calculated in an analogous manner. This analysis revealed
that in the decision-for-self condition, activity in the ACC (x = 8,
y = 32, z = 26) was correlated negatively with PE, whereas acti-
vation was not significantly correlated with PE in any brain area
in the decision-for-other condition (Figure S2 in Supplementary
Material).
Psychophysiological interaction analysis
We assessed the functional connectivity between brain regions
during the decision-for-self and decision-for-other conditions
using PPI analysis. We identified the brain regions in which
correlations between their activity levels and those of rTPJ
were modulated by psychological condition (decision-for-self
versus decision-for-other). The results revealed that rTPJ
showed stronger positive connectivity with the left DMPFC
(x = 4, y = 34, z = 34) in the decision-for-other condition
than the decision-for-self condition (p < 0.001, uncorrected;
Figures 5A,B). The coordinates of the DMPFC reported here
are immediately adjacent to those reported from the decision
FIGURE 4 | Main contrast maps between self and other decision
models. Significant correlations with the value parameters of risky choice
estimated by fitting sigmoid functions to actual decisions were found in (A)
the right amygdala (x = 24, y = 0, z = 22; Z = 3.85, p < 0.05, SVC
FWE-corrected) for the self versus other contrast and (B) the left DMPFC
(x = 14, y = 32, z = 32; Z = 3.94, p < 0.05, SVC FWE-corrected) for the
other versus self contrast. The statistical threshold for the images was set
at p < 0.005 (uncorrected). The bar graphs in the lower panel show the beta
coefficients of the (C) amygdala for the self versus other contrast and (D)
the DMPFC for the other versus self contrast.
FIGURE 5 | Psychophysiological Interaction (PPI) analysis. (A) Stronger
functional connectivity with rTPJ was found in the left DMPFC during
decisions for another than for oneself (x = 4, y = 34, z = 34; Z = 3.22,
p < 0.001, uncorrected). The statistical threshold for the images was set at
p < 0.005 (uncorrected). (B) The scatter plot representing a single-subject’s
data. It shows a stronger positive correlation between rTPJ and DMPFC
during the decision-for-other condition than the decision-for-self condition.
Frontiers in Neuroscience | Decision Neuroscience March 2013 | Volume 7 | Article 15 | 6
Jung et al. Risky decisions for self versus other
model for the other–self contrast (Figure S3 in Supplementary
Material).
Considering the widespread problem of non-independence
error in neuroimaging research (Kriegeskorte et al., 2009), we
were concerned about whether the present PPI findings in the
DMPFC were independent of seed-point selection. We did not
observe elevated DMPFC activity in the other–self contrast, even
at a low statistical-significance threshold (p < 0.05, uncorrected),
and careful examinations of individual subjects’ PPI GLM mod-
els revealed no evidence of a significant correlation between the
TPJ–time-course regressor and the psychological variable regres-
sor. Therefore, it seems more plausible that the variability in TPJ
activity not accounted for by the regressor for the other–self con-
trast contributed significantly to the PPI-related activity in the
DMPFC observed in the present study. This argument is further
supported by the relationship between the TPJ and DMPFC activ-
ities, as exemplified by a scatter plot of a representative individual
in Figure 5B. In addition, we performed cross-validation analy-
sis using a leave-one-subject-out method (Esterman et al., 2010),
in which single-subjects are iteratively left out of the first-stage
group analysis that localizes the TPJ. This analysis confirmed the
original results, although the size of the cluster in the DMPFC
became slightly smaller (x = 4, y = 34, z = 34; p < 0.001, uncor-
rected; Figure S4 in Supplementary Material). In sum, although
potential bias due to non-independent use of the data cannot be
completely excluded, we believe the possibility that it occurred is
minimal.
ADDITIONAL BEHAVIORAL EXPERIMENT
In order to explain the behavioral results, which were less dis-
tinguishable than the neural data in terms of self–other dif-
ferences, we conducted an additional behavioral experiment in
which we examined whether individual differences in prosocial-
ity explain the reduction in self–other behavioral discrepancies.
When we interviewed the participants about how they felt during
the task, some said that their decisions made for another person
felt the same as those made for themselves, whereas others said
that they could clearly distinguish between the two conditions
in terms of feelings. Thus, we hypothesized that individual dif-
ferences in prosociality (i.e., the ability or disposition to regard
another persons benefit as being as important as one’s own)
would affect the degree of self–other discrepancy in risky decision
making.
Nineteen participants performed the same risk tasks as we used
in the main experiment. The selection of high-risk options dur-
ing the task increased linearly as a function of the probability
of winning; this replicated the findings of the main experiment.
The statistical-analysis procedures were the same as those used
for the behavioral data in the main experiment. The interaction
between conditions and winning probabilities was significant,
F(4, 15) = 3.099, p < 0.05. To investigate the modulatory role
of individual variability, we measured each individual’s prosocial
tendency with the Triple Dominance Measure (TDM) task (see
Supplementary Material for details), which was adopted from a
previous study (Haruno and Frith, 2010). After removing two
participants who made inconsistent choices, which prevented
clear categorization, we categorized the participants into three
groups: prosocials (n = 6), individualists (n = 10), and competi-
tors (n = 1). We then combined individualists and competitors
into the proself group, following two previous related studies
(Van Lange and Liebrand, 1989; Sattler and Kerr, 1991). Figure 6
shows a greater self–other distinction in the probability of risky
choice for the proselfs (Figure 6A) compared with that for the
prosocials (Figure 6B), although no significant three-way inter-
action was observed among group (proselfs versus prosocials),
condition, and winning probability using a multivariate mixed
ANOVA, F(4, 12) = 1.252, p = 0.341. However, the three-way
interaction was significant when a mixed ANOVA was applied
after Greenhouse–Geisser correction, F(2.216, 33.245) = 3.724,
p < 0.05.
Additionally, we performed a correlation analysis between self–
other indices (generated by squaring the difference in probability
of making a risky choice between the self and other condi-
tions) and individuals’ TDM scores (obtained by counting the
number of prosocial choices across eight sets of decision tri-
als). We found a negative relationship between prosocial tendency
and self–other indices (Pearson correlation coefficient r = 0.523,
p < 0.05), indicating that more prosocial participants showed
smaller differences in risky choice between the self and other
conditions.
FIGURE 6 | Data from the additional behavioral study showed distinct behavioral response patterns between (A) proself participants and (B) prosocial
participants.
www.frontiersin.org March 2013 | Volume 7 | Article 15 | 7
Jung et al. Risky decisions for self versus other
DISCUSSION
The present study investigated the differences between the neural
correlates of risky decision making on behalf of oneself and that
on behalf of others via fMRI. The behavioral results showed
that participants were more sensitive to risk-related informa-
tion (i.e., probability of winning) in the decision-for-self con-
dition than the decision-for-other condition. When participants
decided for themselves, they became more risk-averse and risk-
seeking when the winning probability of the high-risk option
was lower and higher, respectively. This tendency became weaker
when people decided for others; this might suggest diminished
affective responses to the risky situation in this condition. We
sought to test this possibility by contrasting the neural corre-
lates of risky decision making for oneself with those for oth-
ers. The brain-activation pattern changed according to the tar-
get of the decision, such that reward-related regions were more
active in the decision-for-self condition than in the decision-
for-other condition, whereas regions related to the ToM showed
the opposite association. Parametric modulation analysis describ-
ing each individual’s decision model revealed that the amygdala
and DMPFC were involved in computing decision values tar-
geting self and other, respectively. These findings indicate that
fundamentally distinct neural processes subserve value compu-
tations when making risky choices for oneself versus for other
people.
SELF–OTHER DIFFERENCES IN RISKY DECISION MAKING
Participants were more likely to vary their choices according to
winning probability in the decision-for-self condition than in the
decision-for-other condition. More specifically, participants made
risk-seeking and risk-averse choices when the winning probability
of the risky option was high and low, respectively. This may indi-
cate greater involvement of emotional processes in biasing risky
choices for self versus other. The fact that this pattern was not
observed in the decision-for-other condition may indicate weak
emotional intrusion or effective cognitive regulation while making
choices for other. Moreover, as is the case for other types of other-
regarding behavior, risky decisions for others may also require
the ability to understand others’ minds. Indeed, we found in the
additional behavioral experiment that the self–other difference in
risk-seeking choices was affected by subjects’ levels of prosociality.
That is, prosocial participants made decisions for themselves and
for others in the same manner, whereas proself participants made
the two types of decisions in distinguishable ways. This result hints
that the ToM function may contribute to risky decision making
for others, given that prosocial orientation is tightly related to per-
spective taking and mentalization (Underwood and Moore, 1982).
In addition, the amount of effort expended deciding for another
person could be another determinant of individual differences
in decision making regarding self versus other. In our additional
experiment, proself participants were less sensitive to probability
information that is critical for successful decisions, when making
choices for others than for themselves. This suggests that people
who are indifferent to others benefit put less effort into decisions
for others than those for themselves. In other words, making a
decision for another person would be a painstaking task to some-
one who acts in the best interests of others (i.e., a prosocialist),
because he/she would feel the need to reduce the fundamental
self–other difference.
Overall, decision making on behalf of others seems to be a
demanding process that entails expending more effort and cog-
nitive resources than making decisions for oneself: it requires
different psychological and physiological mechanisms and is more
difficult. When people make decisions on behalf of others, cogni-
tive processes are weighted more heavily than affective processes
are (Fernandez-Duque and Wifall, 2007), and subjects tend to
make such decisions in norm-based ways, such that they consider
what they think is “right” rather than what they “feel like doing
(Stone and Allgaier, 2008). Subjects making decisions on behalf of
others also seem to value their reputations (i.e., the impressions
they convey to the people for whom they make the decisions; Jonas
et al., 2005). At the same time, other-regarding behavior might
require self-regulatory processes to deal with the conflict between
selfish and prosocial motivations: subjects feel a need to regu-
late their emotional reactions and inhibit their selfish impulses to
minimize cost, but if they do nothing, they neglect the other per-
sons interests (DeWall et al., 2008). Thus, it would be reasonable
to think that the self–other discrepancy in risky decision mak-
ing observed in the present study reflects the different types of
psychological processes (i.e., affective versus cognitive processes)
associated with making decisions on behalf of oneself versus oth-
ers, respectively. Further, the self–other difference in the amount of
cognitive resources required during the risky decision task might
have resulted in behavioral differences.
BRAIN REGIONS ASSOCIATED WITH DECISIONS FOR SELF AND FOR
OTHER
One of the main findings of the present study is that people
seem to use different modes of decision making when they decide
for themselves and for others; this is particularly emphasized by
the neuroimaging results. In the decision-for-self condition, the
VS, caudate, VTA, insula, and ACC were more active than in the
decision-for-other condition. Given the large number of previous
studies that reported strong associations between these regions and
both reward processing (Breiter et al., 2001; Knutson et al., 2001;
Baxter and Murray, 2002; Ernst et al., 2004; Yacubian et al., 2006;
Carter et al., 2009; Ghods-Sharifi et al., 2009; Smith et al., 2009)
and risk processing (Kuhnen and Knutson, 2005; Preuschoff et al.,
2006), people might be more sensitive to reward and perceived
risk when they make decisions for their own profit than for that
of others. On the other hand, the TPJ, PCC, and MPFC showed
greater activation in the decision-for-other condition than in the
decision-for-self condition. Given that these regions are regarded
as parts of the ToM network, which is central to understanding
others’ intentions through mentalization and perspective taking
(Fletcher et al., 1995; Gallagher et al., 2000; Walter et al., 2004;
Saxe and Wexler, 2005; Amodio and Frith, 2006; Frith and Frith,
2006; Saxe and Powell, 2006), it seems that people might activate
their ToM systems in order to take another’s perspective and thus
perform the risky choice task for another’s benefit. Supporting
this idea, a recent study (Janowski et al., 2012) found that VMPFC
activity during decision making for others – but not for oneself
was modulated by TPJ,one of the important brain regions involved
in mentalization. These differences between decision making for
Frontiers in Neuroscience | Decision Neuroscience March 2013 | Volume 7 | Article 15 | 8
Jung et al. Risky decisions for self versus other
oneself and for others may lead to self–other distinctions in the
value computation and decision processes, which are discussed
below.
NEURAL CORRELATES OF VALUE COMPUTATION IN RISKY DECISIONS
FOR THE SELF VERSUS FOR OTHERS
The most noteworthy finding of the present study is revealed by
the contrast between the decision models for self-targeted versus
for other-targeted decisions. The parametric modulation analysis
of each individual’s decision models elucidated the distinct neural
correlates of value computation for self and for other in risky
decision making, revealing negative coupling between activations
of the amygdala and the DMPFC whose magnitudes depended
on the target of the decision. Activations in the amygdala and
DMPFC were associated with value computation in the modified
risk task, replicating the results of previous studies (Ghods-Sharifi
et al., 2009; Smith et al., 2009; Morrison and Salzman, 2010).
Importantly, the direct contrast between self-regarding and other-
regarding decision making in terms of the computed value of the
risky option revealed that the amygdala was more strongly associ-
ated with the value computation for self than that for other; this
result is in line with the “risk-as-feelings hypothesis,” which pro-
poses that affective responses play a relatively greater role in risky
decision making for oneself than for others (Loewenstein et al.,
2001). On the other hand, the DMPFC was more engaged in the
value computations regarding decisions for other than those for
self; this result supported our prediction that cognitive processes
might outweigh affective processes in risky decision making for
others.
As we reasoned above, decision making for another without
regard to one’s own benefit could require effort and additional
cognitive resources. In this respect, recent evidence on the role of
the ACC which is immediately adjacent to the DMPFC in effort-
based decision making might provide an interesting explanation
for our findings. For example, severing the connection between
the amygdala and the ACC impaired rats’ decision making abil-
ities, such that they no longer chose a high-reward option that
required more effort than the corresponding low-reward option
(Floresco and Ghods-Sharifi, 2007). Studies in both animals and
humans have shown that the ACC is sensitive to the amount of
effort exerted during decision making and shows increased activa-
tion during increased effort to earn larger rewards in both animals
and humans (Rudebeck et al., 2006; Croxson et al., 2009). Thus,
it seems plausible that the stronger association between DMPFC
activation and the value computation in the decision-for-other
condition than in the decision-for-self condition may reflect the
fact that people tend to expend greater amounts of effort during
risky decision making for others than for themselves. In addition,
the regulatory function of the DMPFC over amygdalar activity
may play a role in creating the self–other distinction between the
neural correlates of value computation. The DMPFC forms strong
connections with the amygdala (Roy et al., 2009; Salzman and Fusi,
2010; Etkin et al., 2011; Hung et al., 2011; Robinson et al., 2011)
and regulates its emotional reactions (Banks et al., 2007). Indeed,
decision making for others requires self-regulatory processes in
order to deal with the conflict between selfishness and prosocial-
ity (DeWall et al., 2008). The role of the DMPFC as part of the
ToM network (Fletcher et al., 1995; Gallagher et al., 2000; Wal-
ter et al., 2004; Amodio and Frith, 2006; Frith and Frith, 2006)
provides another possible explanation for the self–other distinc-
tion in the neural correlates of value computation, especially in
relation to perspective taking, mentalizing, and inferring others’
intentions (St. Jacques et al., 2010). Consistent with this idea, a
recent study showed that DMPFC activity during the judgment
of others’ opinions (Waytz et al., 2012) or during the observa-
tion of others’ distress (Masten et al., 2011) predicted subsequent
prosocial behavior. Thus, consideration of risky options for others
may require inference of their mental states, which then in turn
recruits the ToM network, including the DMPFC.
Consistent with the previous ToM literature, activity in the
rTPJ a major area for mentalization (Castelli et al., 2000; Saxe
and Wexler, 2005; Decety and Lamm, 2007) was greater dur-
ing the decision-for-other than the decision-for-self condition in
the present study. This region also showed heightened functional
connectivity with DMPFC in the other–self contrast; the activ-
ity of DMPFC increased as a function of the computed values of
risky option during choices for other more than during choices
for self. The findings make it tempting to speculate that rTPJ may
send a signal to DMPFC and contribute to its control of amyg-
dalar activity when considering choices for others, enabling us to
choose options with diminished emotional biases in risky decision
making.
In summary, the results of the parametric modulation analy-
sis support our prediction that risky decision making on behalf
of another person may involve additional cognitive processes,
including effort-based decision making, self-regulation, and ToM
functions. Alternatively, the cognitive/rational system might out-
weigh the affective/experiential system in risky decision making
on behalf of others, given the evidence that links the DMPFC to
cognitive processes and the amygdala to affective processes.
This study also unfolds important questions that need to be
addressed in future projects. First, we could not determine the
relationship between individual differences in brain activity and
behavioral responses. We computed a self–other difference index
score for each participant and examined the relationship between
neural activity and behavioral results. In contrast with our predic-
tions, however,we failed to find statistically significant correlations
between them. Although the exact reason for this failure is cur-
rently unknown,it may be that few participants showed sufficiently
large self–other difference indices. This may have caused lim-
itations in individual variability that obscured the relationship
between participants’ decisions and neural responses. Given the
role of the prosocial trait in this task, as revealed in the second
behavioral study, it would be interesting for a future fMRI study
to select participants with a wide range of prosociality. Similarly,
it would be interesting to investigate the neural underpinnings of
prosocial orientation during self–other decision making, consid-
ering our finding from the additional behavioral experiment that
increased prosocial orientation reduced self–other differences. We
envision future studies to address this important issue.
Second, in this experimental design, we kept the magnitude
of gain/loss for each option constant to minimize noise due to
variable reward magnitude; we varied only the reward’s attainabil-
ity (via manipulation of winning probability), which modulated
www.frontiersin.org March 2013 | Volume 7 | Article 15 | 9
Jung et al. Risky decisions for self versus other
the attractiveness of the risky option. Therefore, the difference
between the EVs of the high-risk and low-risk options changed
with the winning probability, whereas the risk of each option
(defined as outcome variance; Markowitz, 1952) remained con-
stant across different levels of winning probability (see Table S2
in Supplementary Material). This feature of our experimental
design may leave room for alternative interpretation of the behav-
ioral results. More specifically, the greater sensitivity to winning
probability in the decision-for-self condition may simply reflect
choices based on the EV of the risky option. Likewise, we can-
not completely rule out the possibility that people may have
chosen the high-risk option for others less than for themselves
out of spite, that is, with the intention of lowering the bene-
fits of others. In this sense, particular caution may be necessary
in interpreting the observed correlations between neural activ-
ity and the model parameters, and future study should allow for
more-systematic manipulation of the EV and risk values for each
option.
Third, we did not measure the various psychological factors
that could have affected the self–other difference. For instance,
it is possible that the subjective social distance between a par-
ticipant and another person for whom the participant made
the decision could have affected his/her decisions, although we
explicitly told the participants that they were making decisions
for an anonymous person. It would be interesting to test the
effect of social distance by comparing decisions made for indi-
viduals with whom the subject is close with decisions made
for strangers. In addition, we could not confirm which of the
psychological processes discussed above is the most prominent
driver of the self–other difference. Future studies using different
types of tasks or including additional behavioral and physiolog-
ical measurements, such as eye movements, skin conductance
response, or glucose consumption levels, could further elucidate
the mechanisms underlying the self–other discrepancy in risky
decision making.
The present study included direct comparisons between risky
decisions for self and other in a single experiment and provided
the first evidence of differences in neural processes between risky
financial decisions on behalf of oneself and those on behalf of
other. Reward systems were activated when people decided for
themselves, whereas the ToM network became more active when
subjects made decisions for another person. Most importantly,
activity in the neural loci of value computation differed between
risky decisions for oneself and for others: the amygdala and
DMPFC were associated with decisions on behalf of oneself and
others, respectively. Our findings suggest that affective processes
have greater weight than cognitive processes in risky decision mak-
ing for self. On the other hand, decision making for others seems
to be a more difficult and effortful process that engages cogni-
tive systems and emotional regulation, in which ToM functions
might also participate. We expect future research to follow up on
the present findings with the aim of providing a more-complete
understanding of the neural mechanisms underlying prosocial and
other-regarding behaviors.
ACKNOWLEDGMENTS
This study was supported by the Cognitive Neuroscience Pro-
gram of the Korean Ministry of Science and Technology
(M10644020003-06N4402-00310) and the National Research
Foundation of Korea Grant funded by the Korean Government
(NRF-2011-327-H00038).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at http://www.frontiersin.org/Decision_Neuroscience/10.
3389/fnins.2013.00015/abstract
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential con-
flict of interest.
Received: 30 March 2012; accepted:
28 January 2013; published online: 20
March 2013.
Citation: Jung D, Sul S and Kim
H (2013) Dissociable neural processes
underlying risky decisions for self ver-
sus other. Front. Neurosci. 7:15. doi:
10.3389/fnins.2013.00015
This article was submitted to Frontiers
in Decision Neuroscience, a specialty of
Frontiers in Neuroscience.
Copyright © 2013 Jung, Sul and Kim.
This is an open-access article distributed
under the terms of the Creative Com-
mons Attribution License, which p er-
mits use, distribution and reproduction
in other forums, provided the orig inal
authors and source are credited and sub-
ject to any copyright notices concerning
any third-party graphics etc.
Frontiers in Neuroscience | Decision Neuroscience March 2013 | Volume 7 | Article 15 | 12
    • "In fact, the majority of the studies in the vicarious reward meta-analysis did not directly involve the participant and only asked that participants observe others receive rewards – which may explain the lack of NAcc activation across studies. However, a few studies in this meta-analysis asked participants to win rewards for another person (Braams et al., 2014; Jung et al., 2013; Varnum et al., 2014 ), linking participants' direct actions to others' rewarding outcomes. Due to the limited number of studies, however, we could not determine if vicarious reward tasks that involve direct action (vs. "
    [Show abstract] [Hide abstract] ABSTRACT: Individuals experience reward not only when directly receiving positive outcomes (e.g., food or money), but also when observing others receive such outcomes. This latter phenomenon, known as vicarious reward, is a perennial topic of interest among psychologists and economists. More recently, neuroscientists have begun exploring the neuroanatomy underlying vicarious reward. Here we present a quantitative whole-brain meta-analysis of this emerging literature. We identified 25 functional neuroimaging studies that included contrasts between vicarious reward and a neutral control, and subjected these contrasts to an activation likelihood estimate (ALE) meta-analysis. This analysis revealed a consistent pattern of activation across studies, spanning structures typically associated with the computation of value (especially ventromedial prefrontal cortex) and mentalizing (including dorsomedial prefrontal cortex and superior temporal sulcus). We further quantitatively compared this activation pattern to activation foci from a previous meta-analysis of personal reward. Conjunction analyses yielded overlapping VMPFC activity in response to personal and vicarious reward. Contrast analyses identified preferential engagement of the nucleus accumbens in response to personal as compared to vicarious reward, and in mentalizing-related structures in response to vicarious as compared to personal reward. These data shed light on the common and unique components of the reward that individuals experience directly and through their social connections.
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    [Show abstract] [Hide abstract] ABSTRACT: One route to understanding the thoughts and feelings of others is by mentally putting one's self in their shoes and seeing the world from their perspective, i.e., by simulation. Simulation is potentially used not only for inferring how others feel, but also for predicting how we ourselves will feel in the future. For instance, one might judge the worth of a future reward by simulating how much it will eventually be enjoyed. In intertemporal choices between smaller immediate and larger delayed rewards, it is observed that as the length of delay increases, delayed rewards lose subjective value; a phenomenon known as temporal discounting. In this article, we develop a theoretical framework for the proposition that simulation mechanisms involved in empathizing with others also underlie intertemporal choices. This framework yields a testable psychological account of temporal discounting based on simulation. Such an account, if experimentally validated, could have important implications for how simulation mechanisms are investigated, and makes predictions about special populations characterized by putative deficits in simulating others.
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    • "Received: 01 May 2013; accepted: 29 October 2013; published online: 26 November 2013. Citation: Kang P, Lee J, Sul S and Kim H (2013) Dorsomedial prefrontal cortex activity predicts the accuracy in estimating others' preferences. Front. "
    [Show abstract] [Hide abstract] ABSTRACT: The ability to accurately estimate another person's preferences is crucial for a successful social life. In daily interactions, we often do this on the basis of minimal information. The aims of the present study were (a) to examine whether people can accurately judge others based only on a brief exposure to their appearances, and (b) to reveal the underlying neural mechanisms with functional magnetic resonance imaging (fMRI). Participants were asked to make guesses about unfamiliar target individuals' preferences for various items after looking at their faces for 3 s. The behavioral results showed that participants estimated others' preferences above chance level. The fMRI data revealed that higher accuracy in preference estimation was associated with greater activity in the dorsomedial prefrontal cortex (DMPFC) when participants were guessing the targets' preferences relative to thinking about their own preferences. These findings suggest that accurate estimations of others' preferences may require increased activity in the DMPFC. A functional connectivity analysis revealed that higher accuracy in preference estimation was related to increased functional connectivity between the DMPFC and the brain regions that are known to be involved in theory of mind processing, such as the temporoparietal junction (TPJ) and the posterior cingulate cortex (PCC)/precuneus, during correct vs. incorrect guessing trials. On the contrary, the tendency to refer to self-preferences when estimating others' preference was related to greater activity in the ventromedial prefrontal cortex. These findings imply that the DMPFC may be a core region in estimating the preferences of others and that higher accuracy may require stronger communication between the DMPFC and the TPJ and PCC/precuneus, part of a neural network known to be engaged in mentalizing.
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