JulianaYacubian,1*JanGla ¨scher,1*KatrinSchroeder,2TobiasSommer,1DieterF.Braus,2andChristianBu ¨chel1
the local computation of expected value (EV). However, the ventral striatum only represented the gain-related part of EV (EV?). At
reward delivery, the same area shows a reward probability and magnitude-dependent prediction error signal, best modeled as the
difference between actual outcome and EV?. In contrast, loss-related expected value (EV?) and the associated prediction error was
generating adequate expectations under uncertainty. Prevalence of either part might render expectations more positive or negative,
In nonhuman primates, mesolimbic dopaminergic neurons are
involved in the representation of reward probability and reward
magnitude (Schultz et al., 1997; Fiorillo et al., 2003; Tobler et al.,
2005). In humans, these response properties have been observed
O’Doherty et al., 2003; Ramnani et al., 2004), a region known to
receive afferent input from midbrain dopaminergic neurons
(Haber et al., 1995). The ventral striatum responds to a condi-
O’Doherty et al., 2003) and shows a strong outcome-related re-
when an expected reward is omitted (Pagnoni et al., 2002; Mc-
Clure et al., 2003). These findings suggest that ventral striatal
activations resemble a prediction error signal similar to the do-
paminergic midbrain signal in the primate (Schultz and Dickin-
Reward processing in the human has also been investigated
using other incentive tasks containing a guessing or gambling
2000; Breiter et al., 2001; Delgado et al., 2003; Ernst et al., 2004;
Matthews et al., 2004; Abler et al., 2006). However, in contrast to
reinforcement learning, a proper model for the prediction signal
that combines reward magnitude and probability has not been
established. “Expected value” (EV), defined as the product of
likely basis for such a model (Knutson et al., 2005). In a guessing
the sum of the gain-related EV (EV?) and the loss-related EV
(EV?). The former is the probability of a gain times the magni-
ronal basis of EV have used tasks with gain versus no-gain out-
comes or loss versus no-loss outcomes (Knutson et al., 2005;
Dreher et al., 2006). In the former case, EV equals EV?, simply
because no loss can occur (i.e., EV?? 0), and in the latter case
EV equals EV?(i.e., EV?? 0).
We used a factorial design in combination with functional
magnetic resonance imaging (fMRI) in which volunteers could
gain and lose different amounts of money with different proba-
bilities in each trial. This allowed us to explicitly test whether
EV?and EV?are processed in the same or different brain areas.
Based on recent data (Bayer and Glimcher, 2005), showing a
limited dynamic range of dopaminergic midbrain neurons, we
expected that in such a task, the ventral striatum would only be
able to signal EV?but not EV?, and that an additional system
cated in the prediction of aversive events (Bu ¨chel et al., 1998;
LaBar et al., 1998; Breiter et al., 2001; Kahn et al., 2002; Glascher
for helpful suggestions on a previous draft of this manuscript. We declare that we do not have any competing
Correspondence should be addressed to Christian Bu ¨chel, NeuroImage Nord, Department of Systems Neuro-
J. Gla ¨scher’s present address: Caltech Brain Imaging Center, Department of Humanities and Social Sciences,
9530 • TheJournalofNeuroscience,September13,2006 • 26(37):9530–9537
and Buchel, 2005; Trepel et al., 2005), this structure is a possible
candidate for such a system.
Subjects. Forty-two healthy male volunteers, 27.3 ? 5.5 years of age
(mean age ? SD), participated in the main study. A second cohort of 24
healthy male volunteers, 24.9 ? 4.9 years of age (mean age ? SD), was
state during the menstrual cycle. Gonadal steroids have a regulatory in-
fluence on the reward system in female rats (Bless et al., 1994), and
estradiol in particular has been shown to modulate dopamine (DA) re-
lease, synthesis, and receptor binding in the striatum (Pasqualini et al.,
The local ethics committee approved the study and all participants
gave written informed consent before participating. Volunteers were
Neuropsychiatric Interview) (Sheehan et al., 1998) and with a gambling
questionnaire (Kurzfragebogen Glu ¨cksspielverhalten) (Petry, 1996) to
exclude psychiatric diseases and pathological gambling. All underwent a
urine drug screening to exclude cocaine, amphetamine, cannabis, and
Guessing task. The paradigm used was a simple guessing task subdi-
vided into two phases: anticipation and outcome. Each trial began with
the initial phase, volunteers had to place money on individual playing
cards. In some trials, they could place the money on the corners of four
adjacent cards (Fig. 1a) and in others on a single card (Fig. 1b). This
manipulation allowed us to control reward probability (low for a single
card and high for four cards). Altogether, volunteers played a series of
200 trials. Because of trial randomization, the probability for the low-
(i.e., 0.125) and four-eighths (i.e., 0.5), which was necessary to avoid a
rapid decrease in balance resulting from the unfortunate average gain/
and 50%. The inclusion of a third (i.e., very high) probability of seven-
increased the total number of conditions from 8 to 12.
In summary, this can be seen as a 2 ? 2 ? 2 factorial design with the
factors probability (high or low), magnitude (one or five Euro) and out-
come (gain or loss), resulting in eight different conditions.
Initial credit was set to 20 Euro and continuously displayed on the
Euro bill (Fig. 1b). Volunteers were able to place their bet using a mag-
ing the bet, the display was kept constant during an additional anticipa-
tion period of 4207 ms, after which all cards were flipped, and the
This resulted in 171 trials with an interstimulus interval (ISI) of 12.26 s
and 29 trials with a longer ISI (21.46 s), introducing 14.6% null-events.
Seven of eight cards were black, the remaining one was a red ace (Fig.
(Fig. 1b, bottom). The order of trials was pseudorandomized and prede-
termined (i.e., the volunteer had no influence on the probability and the
magnitude of each individual trial).
Before entering the scanner, subjects received a standardized verbal
description of the task and completed a practice session, including all
possible combinations of probability, magnitude, and outcome.
Volunteers were told explicitly before the experiment that they would
that the amount would be deducted from the payment offered for par-
ticipating in this study. Volunteers ended the game with a negative bal-
ance of eight Euro, which was waived.
MRI acquisition. MR scanning was performed on a 3T MR Scanner
(Siemens Trio; Siemens, Erlangen, Germany) with a standard headcoil.
Thirty-eight continuous axial slices (slice thickness, 2 mm) were ac-
quired using a gradient echo echo-planar T2*-sensitive sequence (repe-
of view, 192 ? 192 mm). High-resolution (1 ? 1 ? 1 mm voxel size)
T1-weighted structural MRI was acquired for each volunteer using a
three-dimensional FLASH sequence.
A liquid crystal display video-projector back-projected the stimuli on
a screen positioned behind the head of the participant. Subjects lay on
their backs within the bore of the magnet and viewed the stimuli com-
fortably via a 45° mirror placed on top of the head coil that reflected the
images displayed on the screen. To minimize head movement, all sub-
jects were stabilized with tightly packed foam padding surrounding the
The task presentation and the recording of behavioral responses were
index.html) and Matlab 6.5 (MathWorks, Natick, MA).
Image processing. Image processing and statistical analyses were per-
only (b). This manipulation allowed us to control reward probability. The cards were flipped
money, and otherwise lost the money (b). c–g, Expected values and associated prediction
a better visual comparison with BOLD responses. The total expected value is shown in c. The
Yacubianetal.•DissociableSystemsPredictingGainandLoss J.Neurosci.,September13,2006 • 26(37):9530–9537 • 9531
formed using SPM2 (www.fil.ion.ucl.ac.uk/spm). All volumes were re-
aligned to the first volume, spatially normalized (Friston et al., 1995) to
an echoplanar imaging template in a standard coordinate system (Evans
et al., 1994), resampled to a voxel size of 3 ? 3 ? 3 mm and finally
Statistical analysis. All eight conditions of the paradigm were modeled
separately in the context of the general linear model as implemented in
ual hemodynamic responses (beginning of a trial and 7241 ms after trial
duration of 7241 ms, and the outcome-related response was modeled as
a single hemodynamic response. An additional covariate was incorpo-
rated into the model, representing the anticipation response modulated
by the total amount of mouse movements in the choice period of this
trial. This ensured that movement-related activation during the early
trial period is modeled independently from the regressors of interest
(Knutson et al., 2005).
To average the poststimulus BOLD response for display purposes, we
tion with a bin width of 2 s, modeling a total of 10 bins from 0 to 20 s
poststimulus. This results in 10 regressors for each condition and 80
bin after stimulus onset individually to model the BOLD response and
can capture any possible shape of response function up to a given fre-
quency limit. In this model, the parameter estimate for each time bin
represents the average BOLD response at that time. In Figures 2–5, we
therefore labeled the y-axis as “parameter estimates a.u.” Importantly,
these parameter estimates are directly proportional to the BOLD signal.
This additional analysis was only conducted to display activation time
Data were analyzed for each subject individually (first-level analysis)
and for the group (second level analysis). At the single-subject level, we
applied a high-pass filter with a cutoff of 120 s to remove baseline drifts.
All 16 parameter estimate images for the first analysis and all 80 param-
eter estimate images for the second analysis (FIR) were subsequently
entered into a random effects analysis. The problem of nonindependent
by performing a nonsphericity correction.
comparisons. For reasons of brevity, we focus our report on subcortical
and frontal areas. Based on previous data, correction for hypothesized
y, z: ?15, 9, ?9 mm (O’Doherty et al., 2004). Magnitude-dependent
tal cortex (Knutson et al., 2005), and correction was based on a 60-mm-
diameter sphere centered on x, y, z: ?21, 42, ?9 mm.
The involvement of the amygdala in predicting aversive events (i.e.,
losses) has been reported previously (Glascher and Buchel, 2005), and
correction for multiple comparisons was based on the amygdala regions
of interest provided by the Anatomical Automatic Labeling project at
http://www.cyceron.fr/freeware/ (Tzourio-Mazoyer et al., 2002). Cor-
rection for hypothesized ventromedial prefrontal cortex activation
(Knutson et al., 2003) was based on an anatomically defined 36-mm-
diameter sphere centered between the genu of the corpus callosum and
the anterior pole (center: x, y, z ? 0, 52, ?3).
We were interested in regions showing signal changes for prediction
phase. This commonality constraint was incorporated by using a con-
junction analysis comprising the contrasts for prediction and prediction
error. Intuitively, the ensuing conjunction analysis only shows areas in
which both contrasts individually reach significance (Nichols et al.,
Prediction error model. In fMRI studies of reinforcement learning, the
predictions and prediction errors have been used to model fMRI data
(O’Doherty et al., 2003). The prediction error represents the difference
between the actual outcome and the prediction. In reinforcement learn-
ing, this prediction error is then used to update future predictions. Al-
task with fixed probabilities, we can express the prediction error ? as
V ? p
? ? ?R ? V),
where V is the prediction, R is the actual outcome, and p is the gain
probability. This model can now be extended to also incorporate reward
magnitude x into the prediction term V, which then becomes the ex-
pected value as follows:
EV ? x ? p
? ? R ? EV,
where EV indicates the predicted outcome (i.e., expected value). The
prediction error ? is now the difference between actual outcome R and
expected value EV.
EV can be further divided in gain (EV?)- and loss (EV?)-related EV
EV?? xloss? ploss
EV?? xgain? pgain.
It should be noted that the concept of expected value was unable to
explain some phenomena in human choice behavior, and thus more
general forms of the value function have been derived (Edwards, 1955;
Kahneman and Tversky, 1991). In these models, x and p do not directly
enter into the estimation but rather nonlinear functions of both
(Machina, 1987; Kahneman and Tversky, 2000; Trepel et al., 2005).
However, it should be noted that the deviation from linearity of these
functions is most pronounced at the extremes. Analogous to previous
studies (Knutson et al., 2005), we assumed local linearity and based the
predictions on the expected value to explain BOLD responses in the
Dynamic model using trial-based probabilities. The true average prob-
ability of all trials was different from what could be guessed by the visual
card layout. We therefore created a model, which iteratively updates the
probabilities for the high- and low-probability conditions on a trial-by-
trial basis. For the beginning of the trial (i.e., before the first gain trial),
the graphically visible probabilities (12.5 and 50%) were used. Figure 6a
shows the traces of both (high and low) probabilities over the course of
the experiment. This dynamic probability trace was then used to calcu-
late trial-specific gain- and loss-related expected values and prediction
anticipation and the outcome regressor were identical to the original
model. However, in contrast to the original model, we entered gain- and
loss-related expected value and the respective prediction errors as para-
metric modulations. In analogy to the first analysis, the parameter esti-
mates for EV?, EV?, and the respective prediction errors were subse-
quently entered into a random effects analysis.
We continuously monitored all mouse movements during the
movements between different conditions. We observed a nega-
tive main effect of reward magnitude (Z ? 2.5; p ? 0.05) (i.e.,
more mouse-movement for one-Euro trials) (294.1 ? 18.0 pix-
els, mean ? SEM) compared with five-Euro trials (276.1 ? 17.5
were also observed (Z ? 9.1; p ? 0.05) for low-probability trials
(318.3 ? 17.3 pixels; mean ? SEM) compared with high-
9532 • J.Neurosci.,September13,2006 • 26(37):9530–9537Yacubianetal.•DissociableSystemsPredictingGainandLoss
to more degrees of freedom in placing the bet in low-probability
trials. No significant interaction was observed.
All eight conditions (all possible combinations of two reward
probabilities, two reward magnitudes, and two outcomes; i.e.,
gain/loss) of the paradigm were modeled separately. To test for
signal differences during anticipation, parameter estimates for
the first hemodynamic response (i.e., modeling the anticipation
of mouse movements was modeled as a condition-specific nui-
sance covariate removing movement-related signal changes.
stronger BOLD signal for trials with five Euro as opposed to one
ventral striatum (peak: x, y, z: ?12, 3, 0 mm, Z ? 5.6; peak: x, y,
z: 12, 6, 0 mm, Z ? 5.2; both p ? 0.05, corrected). Other cortical
areas showing a main effect of magnitude during anticipation
Z ? 5.5; peak: x, y, z: 33, 24, ?6 mm, Z ? 6.7; both p ? 0.05,
corrected) and bilateral anterior orbitofrontal cortex (peak: x, y,
z: ?39, 57, 3 mm, Z ? 4.0; peak: x, y, z: 36, 60, ?3 mm, Z ? 4.6;
both p ? 0.05, corrected).
(i.e., stronger BOLD signal for more likely gains) (Fig. 2b). The
(peak: x, y, z: ?12, 15, ?3 mm, Z ? 3.4; peak: x, y, z: 15, 15, ?6
mm; Z ? 4.2; both p ? 0.05, corrected). Additional reward
probability-related activation was observed in ventromedial pre-
frontal cortex (peak: x, y, z: 3, 51, ?6 mm; Z ? 3.3; p ? 0.05,
BOLD responses that strongly covaried with the linear model of
EV?(Fig. 1d), but not total EV (Fig. 1c), were observed in bilat-
y, z: ?12, 6, ?3 mm, Z ? 5.2; both p ? 0.05, corrected) (Fig. 3a)
an additional cohort of 24 volunteers. Peak signal changes that
correlate with EV?were observed in bilateral ventral striatum
Z ? 5.3; both p ? 0.05, corrected) (Fig. 3b).
The outcome phase was defined as a BOLD response evoked by
neuronal activity at the moment when the result of the trial was
revealed (i.e., the cards were flipped).
is related to the BOLD response elicited by reward anticipation. a, Main effect of magnitude
probability. Parameter estimates from the FIR model (i.e., time courses) are averaged across
Activation during the anticipation of monetary rewards overlaid on a template
Activations expressing gain-related expected value overlaid on a template T1-
Yacubianetal.•DissociableSystemsPredictingGainandLossJ.Neurosci.,September13,2006 • 26(37):9530–9537 • 9533
Gain-related activation (i.e., gain ? loss) was observed in bilat-
eral ventral striatum (peak: x, y, z: 12, 9, ?3 mm, Z ? 11.8; peak:
bilateral orbitofrontal cortex (peak: x, y, z: 48, 39, ?18 mm, Z ?
4.7; peak: x, y, z: ?45, 45, ?15 mm, Z ? 5.7; both p ? 0.05,
Because we observed ventral striatal responses during anticipa-
tion that were correlated with the linear model of gain-related
expected value, we tested the hypothesis that a prediction error
signal is computed as the difference between outcome and EV?
(see Materials and Methods) and therefore created a contrast
according to mean corrected predictions from this model (Table
1). Most importantly, we were interested in identifying areas co-
during the anticipation phase (Fig. 1d) and signal changes corre-
phase (Fig. 1e) as predicted by nonhuman primate data (Schultz
et al., 1997). A conjunction analysis was used to identify such
areas. Based on this conjunction analysis, we detected signal
mm, Z ? 5.2; peak: x, y, z, 12, 9, ?3 mm, Z ? 5.2; both p ? 0.05,
and an EV?-based prediction error signal during the outcome
phase (Fig. 4a). We replicated this important finding in an addi-
tional cohort of 24 volunteers (Fig. 4b). Voxels that coexpress
prediction error during outcome were observed in bilateral ven-
tral striatum (peak: x, y, z, 12, 9, ?3 mm, Z ? 5.8; peak: x, y, z,
?12, 6, ?3 mm, Z ? 5.3; both p ? 0.05, corrected) (Fig. 4b).
Interestingly, the time course in the left ventral striatum (Fig. 4c)
shows more pronounced deactivations for loss trials (cyan) than
based prediction error model (Fig. 1e). Because the actual gain
probabilities (26 and 66%) were slightly higher compared with
the graphically expected probabilities (12.5 and 50%), we repli-
trial-by-trial basis (see Materials and Methods for details). This
analysis showed activation patterns in the ventral striatum that
both p ? 0.05, corrected) (Fig. 6b).
Analogous to our model driven analysis for EV?and the associ-
ated prediction error, the same analysis was performed for loss-
related expected value, EV?. Areas showing both EV?-related
signal changes during anticipation (Fig. 1f) and an EV?-
associated prediction error response during the outcome phase
(Fig. 1g) were again identified using a conjunction analysis. In
y, z, ?27, ?3, ?18 mm, Z ? 3.9; both p ? 0.05, corrected) (Fig.
5a). Again, this finding was replicated in an independent cohort
(Fig. 5b). Compared with the prediction error based on EV?in
the ventral striatum, the time course in the amygdala (Fig. 5c)
shows less pronounced or no deactivations for loss trials (cyan),
in accordance with the EV?based prediction error model (Fig.
1g). Analogous to the analysis of EV?-related responses, we rep-
licated this analysis with an analysis using dynamic probabilities
on a trial-by-trial basis. This analysis showed activation patterns
0.26 0.660.260.66 0.260.66 0.26 0.66
nal T1-weighted MR image at p ? 0.001 (uncorrected). a, Bilateral ventral striatum coex-
Ventral striatal activation showing EV?-related activation during anticipation
9534 • J.Neurosci.,September13,2006 • 26(37):9530–9537Yacubianetal.•DissociableSystemsPredictingGainandLoss
in the amygdala that were similar to the original analysis (peak x,
3.3; both p ? 0.05, corrected) (Fig. 6c).
We systematically varied the characteristics of reward-related
processing using a factorial design that allowed for all possible
come in combination with fMRI. In two large cohorts of healthy
expected value (i.e., the product of reward probability and mag-
nitude during anticipation). Importantly, ventral striatal re-
sponses did not express the full range of expected value but only
gain-related expected value (EV?). At reward delivery, the same
diction error signal, parsimoniously modeled as the difference
between actual outcome and EV?. Conversely, loss-related ex-
pected value (EV?) and the associated prediction error were
identified in the amygdala.
(Knutson et al., 2000, 2001a,b; Delgado et al., 2003) or reward
combination of probability and magnitude (Rogers et al., 1999;
Ernst et al., 2004; Matthews et al., 2004; Coricelli et al., 2005;
Dreher et al., 2006). In most of these studies, volunteers had the
choice between different gambles and therefore did not include
cause this combination is least lucrative than the others, and
studies (Knutson et al., 2005) investigated different magnitudes
and probabilities but restricted the analysis to the anticipation
phase or did not use a full factorial design (Dreher et al., 2006).
Based on these studies, we decided to independently manipulate
anticipated reward magnitude and probability by presenting
previous studies (Elliott et al., 2004; Zink et al., 2004; Knutson et
al., 2005), volunteers were engaged in the task. Differences in
motor behavior were included in the statistical model and thus
are unlikely to confound the observed effects (Knutson et al.,
During the anticipation phase, we were able to demonstrate a
ing magnitude-dependent activation in the ventral striatum
(Knutson et al., 2003). In addition, we observed a weaker main
effect of probability showing more activation in the ventral stri-
ability (green; 4 cards selected) trials. The solid line depicts the overall probability (i.e., as
anticipation and EV?-related prediction error during outcome overlaid on a coronal T1-
a, Dynamic probabilities during the experiment. Each point on the dashed line
Yacubianetal.•DissociableSystemsPredictingGainandLoss J.Neurosci.,September13,2006 • 26(37):9530–9537 • 9535
atum during the anticipation of more probable rewards consis-
tent with a recent fMRI study (Abler et al., 2006). It is not sur-
rather than the midbrain, because the BOLD response reflects
fore, spiking activity of dopaminergic midbrain neurons is ex-
project, such as the ventral striatum.
The observation that ventral striatal responses are stronger after
role of the ventral striatum in encoding a reward-related predic-
tion error. Previous studies have suggested that ventral striatal
responses are correlated with a prediction error signal by either
using Pavlovian or instrumental conditioning tasks (McClure et
al., 2003; O’Doherty et al., 2003, 2004; Ramnani et al., 2004) or
ventral striatum (Pagnoni et al., 2002).
Our study confirms recent data (Abler et al., 2006) showing
that in the ventral striatum, the positive response after reward
occur. Importantly, our data also extend these findings by show-
ing a stronger deactivation in loss trials, when the loss was less
likely to occur. Second, our data show a decrease of the BOLD
signal below baseline in loss trials. As a consequence of omitted
but predicted rewards, a decrease in neuronal firing has been
observed in dopaminergic midbrain neurons in nonhuman pri-
mates (Schultz et al., 1997). The ventral striatum is one target of
presynaptic input and processing in the ventral striatum after
omitted rewards. This reduction of presynaptic input can lead to
a negative BOLD signal, as has been shown recently (Shmuel et
Primate data have suggested that dopaminergic midbrain neu-
rons should be able to signal the magnitude of a prediction error
(Tobler et al., 2005). In agreement with this data, we observed a
prediction error signal that was not only modulated by the prob-
ability of the reward but also by its magnitude. Intuitively, this
modulation is biologically plausible, because it is important for
small or a large reward. We noted that in a previous study, a
magnitude-related outcome signal was observed in the dorsal
rather than the ventral striatum (Delgado et al., 2003). However,
the investigation of a prediction error signal was not the goal of
We found a colocalization of EV?during anticipation and the
associated prediction error during outcome in the amygdala, in
accord with previous data (Breiter et al., 2001; Kahn et al., 2002;
Glascher and Buchel, 2005), showing that the amygdala was in-
volved in expressing predictions of aversive events.
Another study on classical conditioning using appetitive and
aversive outcomes has shown the amygdala to play a role in sig-
naling appetitive prediction errors and the lateral orbitofrontal
and genual anterior cingulate cortex in prediction errors con-
disagree with our findings.
In the study by Seymour et al. (2005), two specific conditioned
stimuli (CSs) were either predictive of an appetitive (i.e., pain
relief) or aversive (i.e., pain exacerbation) outcome, the alterna-
tive outcome was no change in state. In contrast, our paradigm
used mixed gambles, i.e., a certain stimulus configuration could
be considered as a single CS that can predict both an appetitive
(i.e., gain) or an aversive (i.e., loss) outcome. A gambling task
analogous to the learning paradigm by Seymour et al. (2005)
would have been if the outcome was either a gain versus nothing
or a loss versus nothing. Such a task has been used previously
(Knutson et al., 2005), and the ventral striatum was found to
express expected value. However, it should be noted that in de-
signs in which the alternative to an appetitive outcome is no
paradigm cannot be used to disentangle both possibilities.
Our data show that the same parts of the ventral striatum that
signal gain-related expected value during reward anticipation
code the prediction error at outcome. The peak activations for
activated clusters overlap at p ? 0.001. Moreover, our data lend
support to the notion that not total EV but only EV?represents
erate the ventral striatal prediction error signal.
a study on Parkinson’s disease (PD) patients (Frank et al., 2004),
tions and the associated prediction errors might be expressed in
the ventral striatum. The primate study showed that dopamine
spike rates in the postreward interval seem to only encode posi-
uted to the positive reward prediction error term of reinforce-
has been shown that PD patients, who have a dopaminergic def-
icit in the midbrain, are better at learning to avoid choices that
sensitive to positive than negative outcomes (Frank et al., 2004).
This finding might be related to our observation that the ventral
striatum, which receives dopaminergic inputs from the mid-
brain, is predominantly expressing gain-related predictions.
With respect to the neurotransmitter system involved in the
loss-related predictions, it has been advocated recently that the
serotonergic system, which directly projects to the ventral stria-
tum, is involved in this effect (Daw et al., 2002). However, an
data, are equally likely, given the presence of 5-HT receptors in
the amygdala (Aggleton, 2000).
In summary, our data represent evidence for two dissociable
value predictions based on possible gains against which actual
outcomes are compared. Conversely, the amygdala makes pre-
dictions concerning possible losses and, similar to the ventral
striatum, compares these predictions against actual outcomes.
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