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The Journal of Neuroscience, August 15, 2000, 20(16):6159-6165
Dissociable Neural Responses in Human Reward Systems
Rebecca
Elliott1,
Karl
J.
Friston1, and
Raymond J.
Dolan1, 2
1 Wellcome Department of Cognitive Neurology, Institute
of Neurology, London WC1N 3BG, United Kingdom, and 2 Royal
Free Hospital School of Medicine, London, NW3 2PF, United Kingdom
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ABSTRACT |
Reward is one of the most important influences shaping behavior.
Single-unit recording and lesion studies in experimental animals have
implicated a number of regions in response to reinforcing stimuli, in
particular regions of the extended limbic system and the ventral
striatum. In this experiment, functional neuroimaging was used to
assess neural response within human reward systems under different
psychological contexts. Nine healthy volunteers were scanned using
functional magnetic resonance imaging during the performance of
a gambling task with financial rewards and penalties. We demonstrated
neural sensitivity of midbrain and ventral striatal regions to
financial rewards and hippocampal sensitivity to financial penalties.
Furthermore, we show that neural responses in globus pallidus,
thalamus, and subgenual cingulate were specific to high reward levels
occurring in the context of increasing reward. Responses to both reward
level in the context of increasing reward and penalty level in the
context of increasing penalty were seen in caudate, insula, and ventral
prefrontal cortex. These results demonstrate dissociable neural
responses to rewards and penalties that are dependent on the
psychological context in which they are experienced.
Key words:
ventral striatum; hippocampus; orbitofrontal cortex; caudate; insula; gambling; decision-making
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INTRODUCTION |
Most adaptive behavior is driven by
basic survival needs such as food, drink, and sex that are experienced
as rewards and by avoiding aversive situations that are experienced as
punishments. The reinforcing effects of these behavioral outcomes are
mediated through distinct neural mechanisms. In animals, ascending
dopaminergic systems have been shown to be critically involved in
responses to various reinforcing stimuli, including food and drugs of
abuse. The ventral striatum, particularly the nucleus accumbens, is
probably the structure most reliably linked to reward-related processes (Wise, 1980 ; Robbins and Everitt, 1992; Schultz et al., 1993, 1996;
Stern and Passingham, 1996), but other structures are also involved,
including midbrain regions of ventral tegmental area (VTA) and
substantia nigra (Ljungberg et al., 1992; Schultz, 1997), the amygdala
(Cador et al., 1989; Everitt and Robbins, 1992), and regions of the
basal forebrain (Arvanitogiannis et al., 1996; Panagis et al.,
1997).
Reward in humans has been studied less extensively. However, functional
imaging studies using infusions of nicotine (Stein et al., 1998) or
cocaine (Breiter et al., 1997) have associated the rewarding effects of
these drugs with neural responses in regions including nucleus
accumbens, brainstem, amygdala, and prefrontal cortices. More abstract
reinforcers also exert powerful motivational effects in humans, as
societal preoccupation with gambling and other risk-taking behavior
testifies. Other functional imaging studies have associated financial
reward with activation of ventral striatum (Koepp et al., 1998),
midbrain, thalamic, and prefrontal regions (Thut et al., 1997). In
humans, the nonspecific excitement engendered by risk-taking behavior
may be as important in maintaining these behaviors as the potential
rewards, and it has not been clearly established how these nonspecific
effects are expressed in the human brain. Neuropsychological studies
suggest that ventral prefrontal regions may be an important interface between cognitive and emotional components of risk-taking behaviors (Damasio, 1994; Bechara et al., 1994). Furthermore, patients with lesions to ventromedial prefrontal regions show pronounced impairments on gambling tasks and fail to show normal task-related autonomic changes (Bechara et al., 1996).
In this experiment, we used functional magnetic resonance imaging
(fMRI) to measure neural responses to rewards while subjects performed
a simple gambling task. Correct and incorrect responses were associated
with financial rewards and penalties, and we assessed the relationship
between the level of accumulated gain or loss and regional hemodynamic
response. The design also allowed us to consider how this response was
modulated by the psychological context in which rewards or penalties
were experienced. Our general hypothesis, based on animal studies
(Koob, 1992; Robbins and Everitt, 1992, 1996; Schultz et al., 1993;
Aosaki et al., 1994) and previous imaging studies of financial reward
(Thut et al., 1997; Koepp et al., 1998) was that interconnected regions
of the midbrain, striatum, limbic system, and prefrontal cortices would
show reward-related activity. Specifically, we predicted responses in
ventral striatum, VTA, substantia nigra, amygdala, basal forebrain,
prefrontal cortex, or some subset of these regions.
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MATERIALS AND METHODS |
Experimental paradigm. Subjects were presented with
pairs of stimuli depicting playing cards, one red and one black, and
were told that on half the trials the red card was correct, and on the
other half the black card was correct. The task was to guess the
correct card on each trial and respond with a button press. Without the
subject's knowledge, feedback (correct or incorrect) was provided
according to a prespecified pseudorandom sequence, irrespective of the
actual choices made. At the side of the presentation screen a bar
displayed a cumulative "reward" score across all the trials. The
height of this bar had direct financial implications; subjects began
the experiment with a £10 "stake". Every correct response was
associated with an increase in height of one increment, representing
£1, whereas every incorrect response was associated with a decrease of
one increment (Fig. 1a). There
is no meaningful performance measure in this task because subjects are
simply guessing in the absence of information. The variable of interest
is the reward level; the task merely provides a realistic context in which rewards and penalties are experienced. At debriefing, subjects all described feelings of pleasure and disappointment in response to
rewards and penalties, respectively, although we acknowledge that there
were probably individual differences in the extent to which subjects
were motivated by reward and penalty, and this potential variation is
not explicitly addressed in the present design.
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Figure 1.
Cognitive activation paradigm.
a shows the task that was used; the pairs of black and
red cards to one side of the screen and the cumulative reward bar to
the other. b shows the sequence of outcomes,
corresponding to the actual rewards and penalties experienced, derived
from the constrained binomial random walk function. This same function
determined outcomes in all subjects, who consequently experienced the
same sequence of rewards and penalties. The red
asterisks mark the point at which blocks of rest were
intersposed. These rest blocks were used to model low frequency drift
in signal and were not involved in the subsequent regression
analysis.
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It is important at this point to define the terminology we use in
relation to this paradigm. The term "reward" can have various connotations; here "reward level" is defined as the height of the
bar (accumulated wins). High levels are likely to induce subjective feelings of pleasure and hedonism, which are facets of reward in this
experiment. Subjects reported such feelings at post-scan debriefing.
Low levels of the bar (accumulated loss) do not however reflect
punishment in the usual sense of the term, although loss of previously
gained rewards is clearly a form of negative outcome. We therefore use
the term "penalty " to define the removal of financial rewards. The
other experimental factor is change in level of the bar, as distinct
from absolute level. We use the term "increasing reward" to
describe positive changes and the term "increasing penalty" to
describe negative changes.
The task was divided into 24 test blocks. Each block had twelve trials
of 3.5 sec duration, so that the entire block lasted 42 sec. Test
blocks were separated by 42 sec periods of rest during which subjects
fixated centrally. At the start of each block, the height of the reward
bar maintained the level achieved at the end of the previous block. All
the subjects adopted a frequency matching strategy such that they
picked each color on ~50% of trials (red selected on 51.2% of
trials overall, black on 48.8%). In fact, the sequence of outcomes
(correct or incorrect) was predetermined, regardless of the subjects'
actual choices. This outcome sequence, and therefore also the height of
the bar, was generated using a binomial random walk (Fig.
1b). This is a function derived from simulating a series of
trials of choosing between two outcomes, in which the outcomes have
equal probability of occurrence (for example tossing a coin a large
number of times). The particular function we chose was the same for all
subjects and was selected to ensure that the experimental variance fell
into the appropriate frequency range (lower than constraints imposed by
the hemodynamic response function, but high enough to avoid being
confounded with low frequency artifacts).
fMRI scanning. Neural responses were measured in nine
healthy volunteers scanned using a Siemens Vision system at 2 T to
acquire T1-weighted structural images and gradient echo, echoplanar
T2*-weighted images with blood oxygenation level-dependent
(BOLD) contrast. Functional images were acquired in two runs,
each of 240 volumes comprising 48 3 mm axial slices with 3 mm in-plane
resolution. For each run, six preliminary "dummy" volumes, to allow
for T1 equilibration effects, were acquired and subsequently discarded. Thereafter, volumes were acquired continuously every 4.2 sec so each
block of 12 behavioral trials corresponded to 10 scans. This temporal
asynchrony is important because time-locking trials to scans introduces
a systematic bias in sampling over peristimulus time.
Data analysis. Data were analyzed using statistical
parametric mapping (SPM98; Wellcome Department of Cognitive Neurology, London, UK) (Friston et al., 1995a,b,c). The procedure is summarized below; cited papers provide fuller mathematical detail. Before statistical analysis a series of spatial transformation stages are
required. First, images from each subject were realigned, using the
first as a reference (Friston et al., 1995a). They were then spatially
normalized (Friston et al., 1995a) by nonlinear transformation into the
standard space of Talairach and Tournoux (1988). Images were
smoothed with an 8 mm full width half maxium isotropic Gaussian kernel.
This experiment conforms to a factorial design with the level of the
bar and rate of change of level as independently varying factors. The
interaction between the two provides a measure of how responses to
reward level are modulated when that level occurs in the context of
increasing or decreasing reward. This design was motivated by the
important psychological consideration that the experience of reward or
penalty is a function of the context in which it occurs. Our paradigm
enabled us to distinguish activity associated with high reward levels
during a "winning streak", in which reward level is increasing,
from activity associated with the same reward levels in situations in
which reward level is static or decreasing. The analogous distinction
can also be made for the context of "losing streaks."
The actual statistical model used the height of the reward bar, its
rate of change of height, and the interaction between these two effects
to explain the evoked hemodynamic responses. The rest blocks were
important in that they allowed us to model subject-specific
low-frequency drift in signal, however comparisons with rest did not
form part of the statistical analysis. All scans entered into the
regression analysis were acquired while subjects were performing the
task, thus general behavioral activation and task-specific effects were
constant throughout the experimental scans and fully controlled for.
The variables being assessed were specifically those pertaining to the
experience of rewards and penalties. In simple terms, the function
displayed in Figure 1b was used as a model, and the analysis
involved determining in which regions the neural response was well
modeled by that function. The function was convolved with the
hemodynamic response to take account of the temporal properties of the
BOLD signal. This convolution provided a degree of temporal smoothing,
which meant that the asynchrony between trials and scans did not pose a
problem. A second function was also used to model the data, derived
from the mathematical differential of the height of the bar with
respect to trial (dH/dt where H is height, and
t is trial). Essentially this function represents the
gradient of the smoothed reward level function and provides a measure,
not of overall reward level, but of how fast that level is changing
over successive trials. It distinguishes between situations in which
the subjects experience a series of wins or losses leading to rapid
changes in reward level (a winning or losing streak in anecdotal terms)
and situations in which wins and losses alternate leading to a more
static overall reward level. Finally, we looked at the interaction
between the two functions (height and rate of change) to determine
those regions sensitive both to absolute reward level and to how fast
the level is changing.
The statistical model is thus based on trial-by-trial rewards and
penalties, and the analysis represents an event-related characterization of the neural responses to these. The critical extension here is that the response evoked by each event is allowed to
vary with reward level and rate of change of reward level, allowing us to look at responses to rewards and penalties specific to
different contexts: (1) high accumulated reward or high accumulated penalty: the main effects of level; (2) rapidly increasing or decreasing reward: the main effects of rate of change of level; and (3)
both extreme levels of reward (high or low) and rapid change: the
interaction term.
In mathematical terms, the statistical parametric maps (SPMs) were
based on a multiple-regression analysis, which can be thought of as
testing for partial correlations between the neurophysiological time
series and the regressors in question. This approach (Friston et al.,
1995b,e; Worsley and Friston, 1995) models observed changes at each
voxel in terms of the linear sum of a number of continuously varying
stimulus functions or regressors. Subject-specific low-frequency confounds were removed (Friston et al., 1995d) in the regression, using
the rest blocks to model out these confounds, and global differences
were controlled by proportional scaling (Friston et al., 1995a). The
significance of the association between the observed time series and
one, or a linear combination, of these regressors is tested with the
T statistic to give a SPM{T}. In this
instance the regressors were the height of the bar, the rate of change of height, and their interaction. The level of the bar and its rate of
change are, by definition, orthogonal factors.
Statistical inferences, corrected for the volume analyzed, were based
on the theory of random Gaussian fields (Friston et al., 1995c) and
used a fixed effects analysis, because we were only making inferences
about the normal subjects studied. Two levels of statistical inference
were used, in accordance with the established SPM conventions discussed
in detail elsewhere (Friston et al., 1995b,c,e). The data were
initially thresholded at p < 0.001 uncorrected, and
regions about which we had an a priori hypothesis were reported at this
threshold. For regions about which there was no clear a priori
hypothesis, a more stringent threshold of p < 0.05 corrected for multiple comparisons was used, and regions were only
reported if they survived at this threshold. The exception to this is
in the case of bilateral regions. If a region about which there was no
a priori hypothesis was activated at p < 0.05 corrected on one side and p < 0.001 uncorrected on the
other, both are reported. These are the only two thresholds used and
all voxels are reported at one or the other threshold, in line with
accepted practice. For the purposes of clarity, we use
p < 0.001 to indicate the uncorrected threshold and
*p <0.05 to indicate the corrected threshold throughout
this manuscript.
Finally, to facilitate the interpretation of interaction, we used a
masking procedure to look separately at interactions in voxels showing
(1) a positive relationship with reward level, (2) a negative
relationship with reward level, and (3) no relationship with reward
level. For masking purposes only, these main effects were thresholded
at an inclusive threshold of p < 0.05 uncorrected. It
should be noted that the voxels reported in the interaction terms were
significant at p < 0.001 uncorrected or
p < 0.05 corrected as usual: the masking procedure is
descriptive rather than statistical, allowing us to determine which of
the voxels significantly activated in the interaction are associated
with which main effects. Anatomical localizations were determined by
reference to the atlas of Duvernoy (1991) and to structural MRIs of the group.
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RESULTS |
Responses to high and low reward levels
Reward level, reflected by the overall height of the bar, was
significantly predictive of activity in a region of the right midbrain
(*p <0.05) (Fig.
2a, Table
1). With the resolution of our data
(smoothed to facilitate intersubject averaging), it was not possible to
attribute this activation to a particular structure. As can be seen in
Figure 2a, the activation is somewhat above the substantia
nigra, below the thalamic nuclei, and lateral to hypothalamic nuclei.
There was also a significant activation (p < 0.001) of right ventral striatum, lateral to nucleus accumbens (Fig.
2b, Table 1). These regionally specific responses are to high levels of reward and can be formulated in terms of a positive reward signal. A negative relationship between reward level and activity was observed in both hippocampi (*p < 0.05, left; p < 0.001, right) and
parahippocampal gyri (BA 35; *p < 0.05) (Fig. 2c, Table 1). These responses may reflect a negative reward
signal or a positive signal to financial penalty.
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Figure 2.
Responses to reward level. The figure
shows SPMs of the t statistic (after transformation to a
SPM{Z}) rendered onto a standard MRI template.
a shows the activation in right midbrain positively
associated with reward level (x = 9;
y =  12; z = 3), above the
substantia nigra and below the thalamus. b shows
activation in right ventral striatum (x = 18;
y = 9 or 12; z = 6) lateral
to the nucleus accumbens. Both activations were significant at
p < 0.001. c shows activation of
left hippocampus (x = 24; y = 18; z = 18; *p < 0.05) and
right hippocampus (x = 33; y = 12; z = 21; p < 0.001)
negatively associated with reward level.
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There were no regions showing a significant neural response associated
with a main affect of change in height.
Context-dependent responses to financial rewards and penalties
A key aspect of rewarding situations is how reward-related
responses are modulated by psychological context. Context-dependent neural responses of this nature were assessed using the interaction between the absolute height of the bar and rate of change. By testing
for this interaction in regions that correlated positively with reward
level, we identified neural responses uniquely associated with reward
level in the context in which reward was increasing rapidly, a
so-called winning streak, relative to situations in which it was not
(i.e. stable or decreasing reward levels). These responses were in
right anterior medial thalamus (*p < 0.05), bilateral
pallidum (p < 0.001), and bilateral subgenual
cingulate (p < 0.001) (Fig.
3).
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Figure 3.
Responses to reward in the context of a winning
streak. The transverse slice in a displays activations
of right anteroventral thalamus (area VA; x = 6;
y = 12; z = 0;
*p < 0.05), bilateral globus pallidus
(x = +15; y = 3;
z = 6; p < 0.001), and
subgenual cingulate (x = +9; y = 36; z = 3; p < 0.001)
associated with reward occurring during a winning streak. These group
activations appear somewhat spread because of the spatial smoothing
necessary for intersubject averaging. Therefore it should be noted that
we cannot be certain that the putative thalamus and global pallidus
activations indeed represent regionally distinct neural responses,
although this is how we have chosen to interpret them. b
shows enhanced neural response in the bilateral hippocampus associated
with the experience of high levels of penalty during a losing streak.
It should be noted that the activation in the right hippocampal region
is contaminated by the large blood vessel.
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Similarly, we assessed interactions occurring in regions showing that
correlated negatively with reward level. Enhanced activation of both
hippocampi (p < 0.001) was associated with a
high level of penalty in the context of increasing penalty (Table
2). Thus the hippocampal response to
removal of reward, or accumulated penalty, became more pronounced when
that level was experienced in the context of a losing streak.
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Table 2.
Maximally activated voxels in areas where significant
evoked activity to reward level was context-dependent
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Context-dependent responses to both rewards and penalties
Although our data show specific neural responses to either reward
or penalty that are modulated by psychological context, we also
identified associated with both high reward levels during a winning
streak and high penalty levels during a losing streak. This analysis
identified regions responsive to emotionally salient experiences, in
which the salience is congruent, but blind to the valence (i.e., good
or bad) of the experience (Table 2). The regions encompassed bilateral
orbitofrontal cortex (BA 47), insula, and head of caudate (all
*p < 0.05) (Fig. 4).
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Figure 4.
Responses to generic risk-taking
processes. Activations of right (x = 51;
y = 15; z = 12;
p < 0.001) and left (x = 33;
y = 15; z = 15;
*p < 0.05) orbitofrontal cortex, right
(x = 42; y = 9;
z = 6; *p < 0.05) and left
(x = 36; y = 6;
z = 6; *p < 0.05) insula, and
right (x = 21; y = 24;
z = 0; *p < 0.05) and left
(x = 9; y = 12;
z = 12; p < 0.001) caudate
associated with both reward during a winning streak and penalties
during a losing streak. These activations may represent responses to
risk-taking, independent of outcome.
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DISCUSSION |
Our key finding is a demonstration of neural responses to abstract
financial reinforcers. Crucially, we have shown that these responses
are dissociable with respect to the psychological context, determined
by subjects' recent experience of changes in reward or penalty. Thus,
these data suggest that certain neural responses are associated with
reward level per se, whereas others are associated with an interaction
between actual reward level and changes in level.
Reward level per se was related to activity in the midbrain and ventral
striatum, a crucial component of dopaminergic projection systems.
Although the midbrain activation could not be localized to either the
substantia nigra or the VTA, key sites of origin of ascending dopamine
systems, recent evidence, suggests that dopaminergic projections to the
prefrontal cortex may be more widespread in origin than has previously
been believed (Williams and Goldman-Rakic, 1998). Our results are
consistent with, and extend findings from, single neuron and
excitotoxic lesion studies in animals. Midbrain dopamine neurons have
been shown to respond in a relatively homogeneous way to primary
reinforcers (Ljungberg et al., 1992; Mirenowicz and Schultz,
1996), whereas numerous studies have identified the ventral striatum as
critical in reward-related processing (Apicella et al., 1991; Schultz
et al., 1993) (for review, see Koob, 1992; Robbins and Everitt, 1996).
The nucleus accumbens has probably been most consistently related to
reward, but responses have been observed throughout the ventral third of the striatum in animals (Apicella et al., 1991; Schultz et al.,
1993; Schultz, 1997). The region observed here is lateral to the
nucleus accumbens but falls clearly within the ventral striatum. The
positive relationship between response in midbrain and ventral striatum
and reward level accords with evidence of preferential response in
midbrain dopamine neurons to appetitive rather than aversive stimuli
(Mirenowicz and Schultz, 1996). In animals these responses are
to biologically salient reinforcers such as food and addictive drugs.
Our findings suggest that similar systems mediate effects of more
abstract rewards, further suggested by recent evidence for endogenous
dopamine release in ventral striatum during performance of a
financially rewarded video game (Koepp et al., 1998). In many animal
studies, responses in ventral striatum have been to individual
rewarding events rather than accumulated reward. In as far as the
concept of "accumulated reward" is applicable to the animal
literature, in which rewards are typically consumed immediately,
accumulated reward and occurrence of rewarding events tend to be
confounded. The design used here allows a dissociation between
different contexts in which reward is experienced, allowing us to
specify the nature of ventral striatal response to financial rewards in
humans. The region is responsive to rewarding events particularly when
a high level of reward has accumulated.
In contrast to regions in which activations reflected absolute reward
levels, responses in other regions were dependent on rapid changes in
reward level, suggesting a critical differentiation within human reward
systems. There were no neural responses associated with a main effect
of winning or losing streak. At first sight this is somewhat
surprising, however it is important to note that a winning streak can
occur when the overall "score" remains negative, such that a series
of rewards has served only to reduce the extent of the deficit.
Significant responses to winning or losing streak were seen only in
contexts in which there was a congruence between rate of change and
reward level. Greater reward levels, occurring specifically in the
context of a winning streak, activated pallidum, anteroventral
thalamus, and subgenual cingulate, all of which receive projections
from striatal and limbic regions implicated in reward and punishment
(Alexander et al., 1986; Swerdlow and Koob, 1987; Cador et al., 1989;
Everitt and Robbins, 1992). These regions in turn project to prefrontal
and premotor areas and may provide an important link between basic
reward signals and processes related to higher cognition and behavioral
output. For example, during a winning streak these systems may enhance
incentive motivation to maintain behavioral responses. Responses in
these regions may also reflect an increased expectation of reward
associated with being "on a roll." This interpretation accords with
animal studies that implicate globus pallidus, albeit ventral to the
region we observed, in the expression of incentive-related behavior and reward expectation (Schultz et al., 1992; McAlonan et al., 1993; Inglis
et al., 1994). The involvement of the subgenual cingulate is of
particular interest because this region is implicated in the
pathogenesis of clinical depression (Drevets et al., 1997), a disorder
characterized by reduced experience and expectation of reward and
impaired motivation (Lewinsohn et al., 1979).
These findings partially confirmed our a priori hypothesis concerning
regions implicated in reward. We did not observe activations in
amygdala or basal forebrain that have been associated with reward-related processes in animals (Cador et al., 1989; Everitt and
Robbins, 1992; Arvanitogiannis et al., 1996; Panagis et al., 1997). One
possible reason for this discrepancy is that responses in these regions
may be specific to biologically salient and do not generalize to more
abstract financial reinforcers. fMRI studies of the rewarding effects
of drugs (nicotine or cocaine) in humans have reported neural responses
in amygdala and basal forebrain (Breiter et al., 1997; Stein et al.,
1998), whereas previous studies of financial reward have, like ours,
failed to observe such responses (Thut et al., 1997; Koepp et al.,
1998). However, other lesion (Bechara et al., 1999) and functional
imaging (Zalla et al., 2000) studies have implicated the amygdala in
response to financial rewards, and therefore the role of this region
remains unclear. Another possible account is that amygdala response to
reward habituates very rapidly and, therefore, is not necessarily seen
in contexts in which the experiences of reward are sustained over a
relatively extended time period.
The context-dependent dissociation in neural responses to reward
can be contrasted with a less differentiated response to financial
penalties. Higher levels of penalty, defined in terms of
accumulated loss, were associated with activation in bilateral hippocampi, and these activations were further enhanced in the context of a losing streak. Whereas the hippocampus has been widely shown to play a crucial role in memory (Zola-Morgan and Squire, 1990;
Dolan and Fletcher, 1997), our findings suggest its functions extend
beyond the purely mnemonic. Previous evidence for nonmnemonic functions
include animal data showing that hippocampal lesions enhance
self-stimulation responding (Zimmermann et al., 1997), increase
the hedonic properties of food reward (Schmelzeis and Mittelman, 1996),
and increase resistance to extinction (Jarrard et al., 1986). These
findings suggest that hippocampal activity may have an inhibitory
effect on experience of reward. Our finding of hippocampal response to
penalty would be in line with this evidence for an inverse relationship
between hippocampal activity and experience of reward. Theoretical
accounts have also posited a role for the hippocampus in mediating
nonreward or punishment in humans, which has been elaborated as a
"behavioral inhibition system" (Gray, 1982, 1995).
Further context-dependent neural responses were observed in regions
closely connected to reward-related striatal and limbic structures.
These responses were seen both to high levels of reward and to high
levels of penalty in the congruent contexts of winning and losing
steaks, respectively. These responses were thus specific to congruent
situations but blind to the valence of outcomes. They may reflect more
generic processes associated with risk-taking, such as the excitement
that is an essential component in maintaining risky behaviors. Subjects
described the situations in which the height of the bar was at an
extreme, either positive or negative, and also changing rapidly, as the
most exciting; this was when they experienced the "buzz" of
gambling. It is thus possible that the neural response seen under these
conditions was associated with this subjective experience of
excitement. Both the insula (Casey et al., 1995; Buechel et al., 1998)
and orbitofrontal cortex (Bechara et al., 1996, 1997) have been
previously implicated in representing changes in body state associated
with emotional response. The orbitofrontal cortex is also a key region
in mediating emotional influences on decision-making behavior in humans
(Damasio, 1994; Elliott et al., 1997) and adapting responses to
different behavioral contingencies (Rolls et al., 1994; Rolls, 1996),
components of both winning and losing situations. It may also,
therefore, subserve the processes of decision making and behavioral
guidance that are critical in gambling situations, such as that used here.
The findings we report here provide evidence for dissociable functions
within an extended human reward system. Our results suggest that
activity in ascending dopaminergic systems projecting from midbrain to
ventral striatum reflects the overall level or value of reward.
However, key projection sites of this system (globus pallidus,
thalamus, and subgenual cingulate) respond to reward level in a context
sensitive way, showing activation only when reward is both high and
increasing. By contrast with these dissociable responses to reward,
responses to financial penalties are not anatomically differentiated,
although they do show context-dependent enhancement. Finally, we
demonstrated certain neural responses that may mediate generic
processes maintaining risk-taking behavior regardless of outcome.
Overall the findings, particularly those suggesting context dependency
in reward systems, have implications for the development of theoretical
models of reward-dependent behavior in humans.
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FOOTNOTES |
Received March 30, 2000; revised May 23, 2000; accepted May 23, 2000.
K.J.F. and R.J.D. are supported by the Wellcome Trust. We are grateful
to Professor Richard Passingham for anatomical advice.
Correspondence should be addressed to Dr. Rebecca Elliott, Neuroscience
and Psychiatry Unit, Room G907, Stopford Building, University of
Manchester, Oxford Road, Manchester M13 9PT, UK. E-mail:
rebecca.elliott{at}man.ac.uk.
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TOP
ABSTRACT
INTRODUCTION
