REVIEW ARTICLE
published: 07 February 2012
doi: 10.3389/fnins.2012.00015
A neuropsychological approach to understanding
risk-taking for potential gains and losses
Irwin P. Levin1*, Gui Xue2, Joshua A.Weller 3, Martin Reimann2, Marco Lauriola4 and Antoine Bechara2,5,6*
1 Department of Psychology, University of Iowa, Iowa City, IA, USA
2 Department of Psychology, University of Southern California, Los Angeles, CA, USA
3 Decision Research, Eugene, OR, USA
4 Department of Developmental and Social Processes, University of Rome, Rome, Italy
5 Department of Psychiatry, McGill University, Montreal, QC, Canada
6 Clinical Research Division, Douglas Mental Health University Institute, Montreal, QC, Canada
Edited by:
Kerstin Preuschoff, University of
Zurich, Switzerland
Reviewed by:
Bruno B. Averbeck, National Institute
of Mental Health, USA
Philippe N. Tobler, University of
Zurich, Switzerland
Anna Van Duijvenvoorde, University of
Amsterdam, Netherlands
*Correspondence:
Irwin P. Levin, Department of
Psychology, University of Iowa, Iowa
City, IA 52242, USA.
e-mail: irwin-levin@uiowa.edu;
Antoine Bechara, Department of
Psychology, University of Southern
California, Los Angeles, CA 90089,
USA.
e-mail: bechara@usc.edu
Affective neuroscience has helped guide research and theory development in judgment and
decision-making by revealing the role of emotional processes in choice behavior, especially
when risk is involved. Evidence is emerging that qualitatively and quantitatively different
processes may be involved in risky decision-making for gains and losses. We start by
reviewing behavioral work by Kahneman andTversky (1979) and others, which shows that
risk-taking differs for potential gains and potential losses. We then turn to the literature
in decision neuroscience to support the gain versus loss distinction. Relying in part on
data from a new task that separates risky decision-making for gains and losses, we test a
neural model that assigns unique mechanisms for risky decision-making involving potential
losses. Included are studies using patients with lesions to brain areas specified as impor-
tant in the model and studies with healthy individuals whose brains are scanned to reveal
activation in these and other areas during risky decision-making. In some cases, there is
evidence that gains and losses are processed in different regions of the brain, while in other
cases the same region appears to process risk in a different manner for gains and losses.
At a more general level, we provide strong support for the notion that decisions involv-
ing risk-taking for gains and decisions involving risk-taking for losses represent different
psychological processes. At a deeper level, we present mounting evidence that different
neural structures play different roles in guiding risky choices in these different domains.
Some structures are differentially activated by risky gains and risky losses while others
respond uniquely in one domain or the other.Taken together, these studies support a clear
functional dissociation between risk-taking for gains and risk-taking for losses, and further
dissociation at the neural level.
Keywords: decision neuroscience, risky decision-making, gain/loss domain differences
INTRODUCTION
The combination of methods from the behavioral decision-
making literature such as risky decision-making tasks derived
from the classic work of Kahneman and Tversky (1979), and
methods of neuroscience such as functional magnetic resonance
imaging (fMRI) and lesion studies has led to breakthroughs in
both fields. Examples include how impairment in specific brain
functions translate into disadvantageous decision-making inside
and outside of the laboratory (Bechara et al., 1994, 1996, 1997,
1999) and how common decision-making biases and heuris-
tics can be understood at the neural level (Sanfey et al., 2003;
Hsu et al., 2005; Kuhnen and Knutson, 2005; De Martino et al.,
2006; Huettel et al., 2006; Tom et al., 2007). New areas of study
have emerged with titles such as neuroeconomics and decision
neuroscience.
A major contribution of this work has been a better under-
standing of how emotion, in combination with cognition, guides
our decisions, particularly in the realm of risky decision-making
where conflicts often arise in balancing the lure of reward and
the fear of loss. Evidence is accumulating that emotional reac-
tivity differs in response to risky gains and risky losses. Logical
questions are whether risk-taking for gains and risk-taking for
losses can best be understood as separate psychological processes,
and ultimately, whether they rely on different brain structures.
In this paper, we integrate findings from our own work and
that of others to come to conclusions that have some gener-
ality but also allow for differences between studies based on
methodology.
In order to frame this investigation, we start with a model put
forth to support the findings from two studies we conducted
with patients with lesions to areas of the brain known to be
critical to risky decision-making, namely the ventromedial pre-
frontal cortex (VMPFC), the amygdala, and the insula (Bechara
et al., 1999; Clark et al., 2008). As summarized in Figure 1 (from
Weller et al., 2007), we propose that risky decision-making is
influenced by the opposing forces of lure of gain and fear of
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Levin et al. Neuropsychology of risky gains/losses
FIGURE 1 | Illustration of the expanded neural model of
decision-making under uncertainty. Processing of primary inducers,
mediated by the amygdala, triggers the ventromedial prefrontal cortex
(VMPFC) system, which, in turn, conducts a more deliberative analysis of
uncertainty. However, decisions involving potential losses may trigger
redundant neural responding from structures such as the insula (anterior,
posterior, or both) and the adjacent primary and secondary somatosensory
cortices (SI and SII), which are independent of the amygdala; these backup
processes are represented here by dotted lines.
risk1. We operationalize the “lure” of rewards as either the poten-
tial for a relatively large gain in the gain domain (in comparison
to the small sure gain from a riskless choice) or the potential for
avoiding a loss altogether in the loss domain, and the “fear” of risk
as arising from risking a relatively large loss in the loss domain
(in comparison to the small sure loss from a riskless choice) or
not winning anything in the gain domain. These two forces act in
opposite directions in exciting or inhibiting risk-taking. We sug-
gest that theVMPFC subregions, the amygdala, and the insula each
contribute in different ways to the processing and utilization of
these two critical pieces of emotional information. The mere pres-
ence of uncertainty induces a primary “fear” response elicited by
the amygdala, which has been associated specifically with fear pro-
cessing and avoidance behavior (LeDoux, 2000; Trepel et al., 2005;
Phelps,2006). This fear response activates theVMPFCwhose func-
tion it is to mediate decision-making and allows for more careful
deliberative processes by linking together working memory and
emotional systems (Damasio, 1994).
While the amygdala has been studied extensively and shown to
be a key substrate for triggering emotional responses, especially
in connection with fear (LeDoux, 2000), the fact remains that the
triggering of emotional responses involvesmultiple neural regions,
and not just the amygdala. Thus, structures such as the insula,
which are independent of the amygdala, are also likely to impact
decision-making under uncertainty (Kuhnen and Knutson, 2005;
Clark et al., 2008; Weller et al., 2009). In particular, we propose
that the insula and the amygdala provide complementary systems
for dealing with potential losses, which we attribute to the evolu-
tionary significance of dealing with potential losses. Our ancestors
1Some definitions of risk include loss as a component. However, in order to incor-
porate risk-taking for gains and losses, we use a more general definition of risky
choice as involving choice options of differing outcome variability. In the typical
task described here, the choice is between a “sure thing” or “riskless” option with
fixed outcome and a “risky” option with variable possible outcomes.
learned to avoid situations that risked the loss of things essential
for survival and it is reasonable to assume that our brains have
been primed for avoiding losses.
This account parallels the proposed dual systems approach
of System 1 (experiential) and System 2 (deliberative) for
decision-making (Kahneman, 2003). The neural underpinnings
of these mechanisms have also been addressed in the “somatic
marker” framework. According to the “somatic marker hypothe-
sis” (Bechara and Damasio, 2005; Reimann and Bechara, 2010),
after the amygdala triggers an automatic emotional response (or
primary induction), the VMPFC subsequently prompts a more
careful deliberative analysis that triggers secondary emotional
responses (secondary induction) that help guide advantageous
decision-making. Findings in support of the somatic marker
hypothesis were key to new behavioral theories in which emo-
tions play a pivotal role in decision-making (Mellers et al., 1999;
Loewenstein et al., 2001; Slovic et al., 2002).
In the following sections of this paper, we review the evidence
for our model based on studies involving the VMPFC, amygdala,
and insula, but we also include studies involving other areas that
have implications for addressing the basic question of whether
there is evidence at the neural level of a distinction between risky
decision-making in the gain and loss domains. We will provide
evidence that separate psychological processes are involved in risk-
taking for gains and losses in terms of both behavioral and neuro-
logical reactions that discriminate between risk-taking to achieve
a gain and risk-taking to avoid a loss. We then address the more
complex issue of whether distinct neural structures support these
different reactions. In the case of fMRI studies, we will see that
results depend on when during the decision-making process the
recordings are made. We start, however, with some more straight-
forward and well-known behavioral phenomena that motivate the
search for neurological dissociations between risk-taking for gains
and losses.
It is typical to consider risk-taking as a unified behavioral con-
cept when we talk about a person in terms such as “She is a
risk-taker”or “He likes to play it safe.”However, it has been shown
that risk-taking within the same individual varies across content
domains such as monetary, health, and social risks (Weber et al.,
2002). Within each of these domains, we may talk about an action
as being “risky” because of the uncertainty of its outcome without
differentiating between the potential for achieving benefits ver-
sus the potential for avoiding aversive consequences. Kahneman
and Tversky (1979) demonstrated a fundamental principle that
sparked decades of later research: individuals were more likely
to take a risk to avoid a loss than to achieve a gain of the same
magnitude2. Later work by the same authors revealed a fourfold
pattern of risk-aversion for gains and risk-seeking for losses of
2Following Kahneman and Tversky (see Kahneman, 2003), most framing studies
have employed between-subjects designs. However, it is important to note that reli-
able risky choice framing effects have been reported in within-subject designs where
procedural precautions have been taken to avoid recognition of repeated problems
by using multiple problems and presenting gain and loss versions of the same prob-
lem in separate sessions spaced widely apart (Levin et al., 2002). Emphasis in this
paper will be on tasks involving actual gains and losses where separate gain and
loss trials can be administered to the same decision makers without concern for
consistency demands.
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Levin et al. Neuropsychology of risky gains/losses
high probability but risk-seeking for gains and risk-aversion for
losses of low probability (Tversky and Kahneman, 1992). This was
explained in terms of underweighting the likelihood of high prob-
ability but overweighting the likelihood of low probability events.
The tasks described in this paper will primarily be of the for-
mer type. This paper describes a relatively new component of this
research: neuroscientific studies that provide additional sources of
data that separate risk-taking to achieve a gain and risk-taking to
avoid a loss.
In presenting the most recent research in our laboratory, we
focus on the “cups task” (Levin et al., 2007), which we devel-
oped specifically to separate risky decision-making for actual gains
and losses, both in terms of overall riskiness and sensitivity to
expected value (EV) differences between choice options. The cups
task includes a gain domain and a loss domain. Gain trials involve
some probability of an addition to the decision-maker’s account
while loss trials involve a possible reduction. Decision makers
choose between one array of cups inwhich the outcome is constant
(the riskless choice) and one array of cups in which the outcomes
vary (the risky choice). Outcomes are displayed immediately after
choices are made. By varying the number of cups and the amount
to be won or lost, we create gain and loss trials with contingencies
that either do or do not favor a risky choice (see Figure 2). For
example, a one-out-of-three chance of winning five coins is better
in the long run than a sure gain of one coin but a one-out-of-three
chance of losing five coins is worse in the long run than a sure loss
of one coin. A key component of data analysis for the cups task is
the extent to which an individual makes choices based on the con-
sideration of relative EV between choice options, for both gain-
and loss-related decisions. EV sensitivity represents an index of
advantageous decision-making because consistently choosing the
option with a more favorable EV will yield more positive outcomes
in the long run. As will be described later, a somewhat simpler
version of the task was adapted for use in scanner research. Across
many data sets, we demonstrated that Kahneman and Tversky’s
(1979) original finding of more risk-taking to avoid a loss than
to achieve a gain of the same magnitude is reproduced in the
cups task. Beyond the initial demonstration of greater risk-taking
for losses than for gains, our recent research with the cups task
showed age-related differences in risk-taking as a function of deci-
sion domain (risk-taking to achieve a gain versus to avoid a loss).
Risk-taking in the domain of gains decreased monotonically from
early childhood to older adulthood whereas overall risk-taking to
avoid losses was remarkably constant across age groups (Weller
et al., 2011). Within both domains, EV sensitivity increased from
early childhood through adulthood with a slight decline for older
adults.
EVIDENCE FROM DECISION NEUROSCIENCE
We turn to neuroscience for an exploration of brain functions
that may help explain these gain/loss behavioral differences. Our
approach in this paper is to provide a body of evidence that is
consistent with the proposition that risky decision-making is sep-
arable in the gain and loss domains rather than providing a single
“critical” test.
Historically, the most fundamental functional division of the
brain was thought to be the one that distinguished between
approach and avoidance behaviors.However,many years of animal
research failed to identify anatomically separate neural substrates–
neural systems underlying pain and pleasure seem to overlap
considerably (e.g., Craig, 2009). Later human behavioral stud-
ies found equivocal support for a separation of neural systems
whereby the left hemisphere is predominantly concerned with
approachbehaviors and the lure of reward,whereas the right hemi-
sphere is critical for avoidance behaviors and the fear of uncer-
tainty (Davidson et al., 1990). More recently, neuropsychological
FIGURE 2 | Samples of trial types on the cups task. Note: in each case the “riskless” side is depicted on the left and the “risky side” is depicted on the right.
In the experiments these were counterbalanced over trials.
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Levin et al. Neuropsychology of risky gains/losses
research on the approach–avoidance conflict evolved into studies
of risky decision-makingwhere the shiftwas to amoremicroscopic
analysis of neural systems.
Neuroimaging data have been used to gain new insights con-
cerning risky decision-making. In particular, fMRI studies use
changes in blood flow that accompany neural activity in different
parts of the brain to associate these areas to particular behav-
iors. For instance, in a recent meta-analysis of fMRI studies of
risky decision-making using young, healthy adults, Mohr et al.
(2010) found evidence common to all studies that risk process-
ing is associated with activation of specific emotional systems in
the brain such as the anterior insula, especially when potential
losses are involved. The dorsolateral prefrontal cortex and pari-
etal cortex are also activated when making decisions involving
risk. Using fMRI in conjunction with a paradigm in which indi-
viduals decided whether to accept or reject gambles offering a
50/50 chance of gaining or losing varying amounts of money, Tom
et al. (2007) found that activity in the ventral striatum and the
VMPFC increased as potential gains increased but decreased as
potential losses increased. Also, in the anterior insula, activity was
foundmore strongly associatedwith the anticipation of losses than
with anticipation of gains (Knutson et al., 2007). Earlier research
showed increased arousal following losses than following gains
(Bechara et al., 1999). Such results motivated us to classify study
results based on whether activation was measured before, during,
or after a decision was made.
In order to get amore complete picture,we conducted a focused
literature search. Using the keywords “fMRI,” “gains,” “losses,”
“risk,” and“uncertainty,”Table 1 summarizes the results of a num-
ber of fMRI studies in terms of which areas of the brain were
studied and at what point in time, and whether the study pro-
vided support for distinct mechanisms involved in risky decision-
making for gains and losses.While the results are“mixed,”a pattern
emerges when the studies are separated based on whether brain
activation was measured before, during, or after a risky choice was
made. Most noteworthy, while different regions were the focus of
different studies, in 14 studies in which activation was assessed
prior to a choice (i.e., anticipation), support for separate mech-
anisms was found in eight studies, four studies did not support
separate structures, and two studies did not make claims about
separate structures because they focused on a specific region only.
For example, studies by Kuhnen and Knutson (2005) and Knut-
son et al. (2008b) each found that the nucleus accumbens was
activated in anticipation of a risky gain, whereas the insula was
activated in anticipation of a risky loss. We think these results are
particularly compelling because they suggest that different parts of
the brain drive risky decision-making in anticipation of uncertain
gains versus uncertain losses. Whereas activation during or after
a risky choice can influence subsequent risky choices, activation
prior to a choice is unique in its potential to influence the current
choice.
Beside the dissociation at the pre-decision stage, recent evi-
dence suggests that experienced gains and lossesmight also activate
different regions, which then affect subsequent decisions mak-
ing. In a recent study using the cups task, we found that at the
feedback stage,experienced rewardwas associatedwith strong acti-
vation in the VMPFC and the ventral striatum, and the stronger
reward-related responses in theVMPFC were positively associated
with risk-taking (Xue et al., 2009). In a follow up study, we explic-
itly examined how neural and behavioral responses to gains and
losses were associated with subsequent decisions. We developed a
modified version of the cups task in which a single array of cups
was presented on a given trial where one coin would be lost for all
but one randomly selected cup, but multiple coins would be won
if the other cup was drawn (Xue et al., 2011). The decision-maker
indicated whether to take or not take the gamble. In one analysis,
we focused on how an experienced gain versus an experienced loss
could modulate subsequent risky decision-making, both behav-
iorally and neurally. We found that subjects took more risk after
losing a gamble than after winning a gamble. At the neural level,
we again found that at the feedback stage, win was associated
with stronger activation than loss in the anterior cingulate cor-
tex, the posterior cingulate cortex, the ventral striatum, and the
insula. More importantly, decisions after loss were associated with
stronger activation in the frontoparietal network, which was posi-
tively correlated with individuals’ increased tendency to take more
risk. These results thus suggest that experienced gains and losses
not only involve different brain regions, but also trigger differential
neural responses and behaviors in subsequent decisions.
Despite this suggested anatomical separation, the fact remains
that the same structure, for example, the insula, has sometimes
been implicated in the processing of both painful and pleasurable
stimuli (e.g., Craig, 2009). Indeed, when compared to a base-
line of activation following trials on which the decision-maker
decided not to take the gamble, both experienced gains and losses
elicited strong insular activation, which then modulated subse-
quent decision-making (Xue et al., 2010). This calls for caution
when making absolute determination about the anatomical sepa-
ration of these pleasure (gain)–loss (pain) systems. In particular,
a proper baseline should be included in this analysis since the
same regions might show opposite modulation by gains and losses
(Tom et al., 2007). Thus, the stronger activation for gains or losses
in some regions might not necessarily reflect distinct neural struc-
tures for gains and losses. Another reason for these difficulties in
establishing absolute anatomical separations is that cellular physi-
ological evidenceof neurons responding topositive versusnegative
valence stimuli, at least within the amygdala, indicates separation,
while anatomical evidence is highly inter-mixed (e.g., Paton et al.,
2006). This explains why the neural systems for risky gains versus
losses can be functionally separate, but finding clear-cut separa-
tion viewed at the global anatomical level is more difficult, given
the proximity and overlap of these two systems.
Next, we turn to lesion studies which are smaller in number
in terms of addressing this issue but which should align with the
“anticipatory” fMRI studies because, of course, pre-existing brain
damage would likewise serve to influence revealed choices. While
neuroimaging studies argue whether a particular brain region is
involved in a particular function, lesion studies test whether that
brain region is necessary for that function, and thus form more
direct tests of the model in Figure 1 and our earlier reference
to anatomically separate neural substrates. The logic here is that
if a particular function is impaired in individuals with a local-
ized lesion, then the affected neural region must play a crucial
role in executing that function. Lesion studies seem to reveal little
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Levin et al. Neuropsychology of risky gains/losses
Table 1 | Functional magnetic resonance imaging studies of risk-taking for gains and losses.
Authors and
year
Focal topic Time of
measurement
Conditions Active regions
identified
Sample
size
Result
TIME OF MEASUREMENT BEFORE DECISION-MAKING
Knutson
et al. (2001)
Anticipation of
monetary
reward
Before
decision-making
(anticipation of
choice phase)
Gains (reward
anticipation versus
neutral)
Nucleus accumbens 8 Study supports separate
structures because addi-
tional other regions were
activated for gains com-
pared to losses
Caudate
Putamen
Anterior thalamus
Amygdala
Anterior cingulate cortex
Medial prefrontal cortex
Supplementary motor area
Posterior cingulate cortex
Cerebellar vermis
Losses (punishment
anticipation versus
neutral)
Caudate
Anterior thalamus
Matthews
et al. (2004)
Risky decision-
making
Before
decision-making
(prior to selection
phase)
Gains (risky
response minus safe
responses)
Medial frontal gyrus 12 Study supports separate
structures because differ-
ent regions were activated
for gains versus losses
Nucleus accumbens
Caudate tail
Middle occipital gyrus
Losses (safe minus
risky responses)
Superior temporal gyrus
Middle temporal gyrus
Inferior frontal gyrus
Kuhnen and
Knutson
(2005)
Risky decision-
making in a
financial
context
Before
decision-making
(anticipation of
choice phase)
Gains (risky choices
and risk-seeking
mistakes)
Nucleus accumbens 19 Study supports separate
structures because differ-
ent regions were activated
for gains versus lossesLosses (riskless
choices and risk-
aversion mistakes)
Insula
Gains versus losses Medial prefrontal cortex
Orbitofrontal cortex
Nucleus accumbens
Anterior cingulate cortex
Precuneus
Posterior cingulate
Yacubian
et al. (2006)
Decision-
making under
uncertainty
Before
decision-making
(anticipation of
choice phase)
Gains (computation
of expected value for
gains)
Ventral striatum 66 Study supports separate
structures because differ-
ent regions were activated
for gains versus lossesLosses (computation
of expected value for
losses)
Amygdala
Knutson
et al. (2007)
Decision-
making in
purchasing
context
Before
decision-making
(anticipation of
choice phase)
Gains (purchasing a
preferred product)
Anterior cingulate cortex 26 Study supports separate
structures because differ-
ent regions were activated
for gains versus losses
Dorsolateral prefrontal cortex
Medial frontal gyrus
Superior frontal gyrus
Anterior insula
Nucleus accumbens
Caudate
Globus pallidus
Posterior cingulate
Losses (spending
money)
Frontopolar cortex
Medial prefrontal cortex
Anterior cingulate cortex
Parahippocampal gyrus
(Continued)
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Levin et al. Neuropsychology of risky gains/losses
Table 1 | Continued
Authors and
year
Focal topic Time of
measurement
Conditions Active regions
identified
Sample
size
Result
Seymour
et al. (2007)
Risky decision-
making in a
financial
context
Before
decision-making
(prediction error
phase)
Gains (rewards) Anterior striatum 20 Study supports separate
structures because differ-
ent regions were activated
for gains versus lossesLosses Posterior striatum
Knutson
et al.
(2008a)
Decision-
making in a
buying and
selling context
Before
decision-making
(anticipation of
choice phase)
Gains (buying versus
selling at low prices)
Medial prefrontal cortex 24 Study supports separate
structures because differ-
ent regions were activated
for gains versus losses
Buying and selling of
preferred products
Nucleus accumbens
Losses (selling
product)
Insula
Knutson
et al.
(2008b)
Risky decision-
making in a
financial
context
Before
decision-making
(anticipation of
choice phase)
Gains (high-risk shift
versus low-risk shift)
Anterior insula 15 Study supports separate
structures because differ-
ent regions were activated
for gains versus losses
Caudate
Nucleus accumbens
Gains versus losses Medial prefrontal cortex
Caudate
Putamen
Inferior frontal gyrus
Precentral gyrus
Posterior cingulate
Lingual gyrus
Breiter
et al. (2001)
Expectancy
and
experience of
monetary
gains and
losses
Before
decision-making
(expectancy
phase)
Gains (good spinner) Frontal lobe 12 Study does not sup-
port separate structures
because same key regions
were activated for both
gains and losses
Amygdala
Nucleus accumbens
Sublenticular extended
amygdala
Hypothalamus
Losses (bad spinner) Frontal lobe
Amygdala
Nucleus accumbens
Sublenticular extended
amygdala
Fukui et al.
(2005)
Risk
anticipation
during Iowa
GamblingTask
Before
decision-making
(anticipation of
choice phase)
Gains (risky
response minus safe
responses)
Medial frontal gyrus 14 Study does not make
claims about separate
structures because the
medial frontal cortex was
the region of focus
Paulus and
Frank
(2006)
Comparison of
high versus
low probability
prospects
Before
decision-making
(from onset of the
presentation of
the options until
the subject had
made a response)
Losses (high
probability
prospects) versus
gains (low probability
prospects)
Precuneus 16 Study does not sup-
port separate structures
because same regions
were activated for both
gains and losses
Cingulate gyrus
Insula
Middle frontal gyrus
Middle occipital gyrus
Precuneus
Superior parietal lobule
Insula
Thalamus
Postcentral gyrus
Inferior parietal lobule
Middle frontal gyrus
Superior temporal gyrus
Precentral gyrus
Caudate
(Continued)
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Levin et al. Neuropsychology of risky gains/losses
Table 1 | Continued
Authors and
year
Focal topic Time of
measurement
Conditions Active regions
identified
Sample
size
Result
Tom et al.
(2007)
Risky decision-
making and
loss aversion
Before
decision-making
(anticipation of
potential gains or
losses phase)
Gains (potential gain
effects)
Nucleus accumbens 16 Study does not sup-
port separate structures
because same regions
were activated for both
gains and losses
Caudate
Thalamus
Ventromedial prefrontal cortex
Orbitofrontal cortex
Frontal pole
Middle frontal gyrus
Middle/superior frontal gyrus
Posterior cingulate
Midbrain
Losses (potential
loss effects)
Nucleus accumbens
Caudate
Thalamus
Ventromedial prefrontal cortex
Orbitofrontal cortex
Frontal pole
Middle frontal gyrus
Middle/superior frontal gyrus
Posterior cingulate
Midbrain
Preuschoff
et al. (2008)
Risk prediction
error and risk
in decision-
making
Before
decision-making
(prediction phase)
Risk prediction error Insula 19 Study does not make
claims about separate
structures because the
insula was the region of
focus
Tobler et al.
(2009)
Risk and
expected value
in decision-
making
Before
decision-making
(prediction phase)
Gains (increased
risk-seeking)
Lateral prefrontal cortex 15 Study does not sup-
port separate structures
because same regions
were activated for both
gains and losses
Losses (increase
risk-aversion)
Lateral prefrontal cortex
TIME OF MEASUREMENT DURING DECISION-MAKING
O’Doherty
et al. (2001)
Reversal
learning task
of monetary
reward and
punishment
During
decision-making
(acquisi-
tion/reversal
phase)
Gains Orbitofrontal cortex 9 Study does not clearly sup-
port separate structures
because same key region
(here: orbitofrontal cortex)
was activated for both
gains and losses
Medial prefrontal cortex
Posterior inferior prefrontal
sulcus
Losses Orbitofrontal cortex
Posterior inferior prefrontal
sulcus
Dorsal anterior cingulate
cortex
Gottfried
et al. (2002)
Appetitive and
aversive
olfactory
learning
During learning
phase
Gains (appetitive
olfactory learning)
Medial orbitofrontal cortex 15 Study does not sup-
port separate structures
because same key regions
were activated for both
gains and losses
Anterior orbitofrontal cortex
Ventral striatum
Nucleus accumbens
Pallidum/dorsomedial
amygdala
Uncus/ventromedial amygdala
Dorsomedial amygdala
Ventromedial prefrontal cortex
Cerebellar hemisphere
Anterior hippocampus
(Continued)
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Levin et al. Neuropsychology of risky gains/losses
Table 1 | Continued
Authors and
year
Focal topic Time of
measurement
Conditions Active regions
identified
Sample
size
Result
Losses (aversive
olfactory learning)
Lateral orbitofrontal cortex
Medial orbitofrontal cortex
Nucleus accumbens
Temporal pole/piriform cortex
Paulus et al.
(2003)
Risky decision-
making
During
decision-making
Gains (risky
response versus
safe response)
Insula 17 Study does not clearly sup-
port separate structures
because same key region
(here: insula) was activated
for both gains and losses
but to a greater extent for
risky versus safe responses
Cuneus
Precuneus
Middle frontal gyrus
Losses (risky
response versus
punishment
response)
Inferior frontal gyrus
Insula
Superior parietal lobule
Huettel
et al. (2005)
Uncertain
decision-
making
During
decision-making
Gains/losses
(increasing
uncertainty)
Insula 12 Study does not clearly sup-
port separate structures
because same regions
were activated for both
gains and losses
Inferior frontal gyrus
Middle frontal gyrus
Thalamus
Inferior parietal lobule
Intraparietal sulcus
Plassmann
et al. (2010)
Processing of
appetitive
versus
aversive goal
values
During
decision-making
(decision-making
phase)
Gains (appetitive
goal values)
Medial orbitofrontal cortex 19 Study does not sup-
port separate structures
because same key regions
were activated for both
gains and losses
Dorsolateral prefrontal cortex
Losses (aversive
goal values)
Medial orbitofrontal cortex
Dorsolateral prefrontal cortex
TIME OF MEASUREMENTAFTER DECISION-MAKING (OUTCOME PROCESSING)
Hsu et al.
(2005)
Ambiguous
decision-
making
After
decision-making
(response to risk)
Gains (gamble
versus certainty)
Occipital cortex 16 Study supports separate
structures because addi-
tional other regions were
activated for gains com-
pared to losses
Medial frontal gyrus
Brodmann area 6
Precentral gyrus
Insula
Caudate head
Brodmann area 18
Insula
Middle temporal gyrus
Losses (certainty
versus gamble)
Precentral gyrus
Occipital cortex
Fujiwara
et al. (2009)
Monetary
reward and
punishment
After
decision-making
(presentation of
chosen outcome)
Gain-specific regions Anterior cingulate cortex 17 Study does not sup-
port separate structures
because same key regions
were activated for both
gains and losses
Posterior cingulate cortex
Superior frontal gyrus
Inferior operculum
Insula
Midbrain
Inferior temporal gyrus
Inferior parietal lobule
Cerebellum
Loss-specific regions Anterior cingulate cortex
Inferior operculum
Insula
Common gain and
loss regions
Anterior cingulate cortex
Posterior cingulate cortex
Postcentral gyrus
(Continued)
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Levin et al. Neuropsychology of risky gains/losses
Table 1 | Continued
Authors and
year
Focal topic Time of
measurement
Conditions Active regions
identified
Sample
size
Result
Inferior operculum
Insula
Midbrain
Middle temporal gyrus
This table is sorted by time of measurement (before, during, or after decision-making) and by result (supportive of separate structures or not). In each category, the
table is sorted first in chronological order, then in alphabetical order.
dissociation between the domains of gains and losses within the
prefrontal cortex region, but such dissociations are more likely
to be revealed when one considers two other neural systems, the
insula and amygdala, which feed information into the prefrontal
cortex. Indeed, within the prefrontal cortex, patients with dam-
age to the VMPFC show deficits for both risky gains and risky
losses (Weller et al., 2007). Compared to healthy controls,VMPFC
patients showed increased levels of risk-taking and decreased sen-
sitivity toEVdifferences in both gain and loss domains. In contrast,
amygdala patients showed impaired decision-making and exag-
gerated levels of risk-taking to achieve gains. However, in the loss
domain amygdala damage was not associated with significantly
increased risk-taking or decreased EV sensitivity. Given the abun-
dance of literature suggesting that the amygdala is involved with
avoidance of punishment, this finding suggests that other struc-
tures may act in concert with the amygdala to produce a signal that
engages theVMPFC.Whenpatientswith insula damagewere com-
pared to controls, a different pattern emerged (Weller et al., 2009).
Consistent with research suggesting that the insula is important
for risk processing (Preuschoff et al., 2008), insula lesion patients
like VMPFC and amygdala patients showed decreased sensitiv-
ity to EV differences between choice options for both risky gains
and risky losses. However, these individuals showed lower levels
of risk-taking compared to healthy controls, especially on gain
trials. Thus the insula, with connections to the amygdala, ventral
striatum, and the VMPFC, may serve the purpose of providing a
“gate” to determine the effectiveness of excitatory and inhibitory
motivational circuits, signaling approach or danger. Subsequently,
insula damage may result in a blunted response toward risk, and
would lead to insensitivity to changes in environmental contin-
gencies signaling the approach or avoidance of a risk, regardless of
domain.
Because the amygdala and insula have long been implicated
in the processing of negative emotions, evoked from stimuli that
are particularly aversive and perhaps even a threat to survival (e.g.,
LeDoux, 2000; Paulus and Stein, 2006; Phelps, 2006),we argue that
these emotional reactions may be processed by multiple neural
structures and are thus more difficult to disrupt as a result of a
focal lesion to the amygdala or the insula alone3. Specifically, a
person with a damaged amygdala but an intact insula can still
3It should be noted that redundancy has also been found in learning and mem-
ory systems, which allow learning to occur in multiple parallel memory systems; see
Pinker and Ullman (2002) for an example of how multiple memory systems support
the generation of verb past-tense.
make reasoned decisions in the domain of losses even when they
cannot in the domain of gains. While a separation in processing
gains and losses is achieved at the level of the amygdala versus
insular cortex, the two neural systems may come closely together
(and become more difficult to dissociate) by the time information
reaches the prefrontal cortex, which responds similarly to risky
gains and risky losses. Nevertheless, when considering the evi-
dence from both insula and amygdala lesions, support for separate
processes for risky decision-making in the gain and loss domains
seems to emerge. Consistent with our model, the insula, in addi-
tion to its general role in processing risk, serves to especially aid
in recruitment of the VMPFC to guide risky decisions in the more
emotion-laden loss domain.
SUMMARY AND CONCLUSION
Taken individually, each of the neuroimaging and lesion studies
reviewed here has its limitations. Lesion studies are limited to the
small sample of available participants who meet the criteria of
damage to a targeted area. Furthermore, some of those included
may have collateral damage to other adjacent areas. fMRI stud-
ies also typically have small sample size due to financial and time
constraints. Furthermore, the complexity and length of tasks that
can be conducted in a scanner are limited. Also, because differ-
ent studies focus on different areas (see Table 1), comparisons,
and integration of findings can be difficult. Finally, for present
purposes, the tasks used in the different studies differed in their
ability to separate the gain and loss domains.
Nevertheless,we believe that we can provide a meaningful sum-
mary of the findings reviewed here. Behavioral studies suggest
differences in decision-making for risky gains and risky losses. A
study comparing different age groups suggests different develop-
mental trajectories for risk-taking in the gain and loss domains.
Neuroimaging studies are sometimes inconclusive in mapping
brain systems to differential reactions to risky gains and losses.
For example, while there is evidence that a system such as the
VMPFC or the striatum is involved in both risky gains and losses,
different parts of the system may be differentially sensitive to gains
and losses (Xue et al., 2009). In such cases, the more general
hypothesis of separate processes underlying risk-taking for gains
and losses is still supported. With regard to the stricter hypothesis
of separate structures, a breakdown of fMRI studies in Table 1
shows the strongest evidence for this hypothesis when recordings
capture pre-decisional or anticipatory processes. We believe that
the lesion studies provide the most direct evidence implicating
separate structures.
www.frontiersin.org February 2012 | Volume 6 | Article 15 | 9
Levin et al. Neuropsychology of risky gains/losses
Although a more detailed meta-analysis is clearly warranted,
Table 1 shows that a wide variety of structures are involved in
risky decision-making beyond those depicted in Figure 1. Never-
theless, we feel that the relatively simple depiction of the model
represents a good start in capturing the different neurological
underpinnings of risk-taking for gains and losses. The comple-
mentary roles of the VMPFC, amygdala, and insula depicted in
the model are consistent with both the general hypothesis that
separate processes underlie risk-taking for gains and losses, and the
stricter hypothesis of separate neural structures coming together
in different ways to guide risky decision-making in the gain and
loss domains. In conclusion, we find that evidence of differ-
ent neural responses underlying risk-taking for gains and losses
favors the hypothesis that decision makers react differently to risky
gains and losses, both in terms of overt risk-taking and neural
activation.
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 06 July 2011; accepted: 19 Jan-
uary 2012; published online: 07 February
2012.
Citation: Levin IP, Xue G, Weller JA,
Reimann M, Lauriola M and Bechara
A (2012) A neuropsychological approach
to understanding risk-taking for potential
gains and losses. Front. Neurosci. 6:15.
doi: 10.3389/fnins.2012.00015
This article was submitted to Frontiers
in Decision Neuroscience, a specialty of
Frontiers in Neuroscience.
Copyright © 2012 Levin, Xue, Weller ,
Reimann, Lauriola and Bechara. This is
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