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a r t I C l e S
Humans display remarkable flexibility in their behavior. Like other
animals, we guide our behavior through direct experience, but we
can also infer the likely consequences of actions that have never been
taken
1,2
. Through generalizing principles and applying them to new
situations
3,4
, we can predict new relationships and statistical struc-
tures in our environment and use these to estimate the value of new
events
1,5,6
. Although some progress has been made in uncovering
the brain regions that underlie these complex abilities
1,3–7
, little or
no progress has been made in understanding how neuronal networks
support these complex computations, partly because it is unclear to
what extent such computations exist in species in which we can readily
measure single-cell activity.
One potential mechanism that allows for upcoming events to be
evaluated involves using past experience to predict consequences of
future possible scenarios. In rodents, hippocampal firing sequences at
choice points predict or ‘preplay’ the forthcoming environment
8
, and
the likely outcomes of their decision can later be decoded in the orbito-
frontal cortex
9
. In contrast, when choosing between novel options,
there is no direct experience from which to preplay and evaluate
future options. However, it is possible that the representation of an
upcoming novel outcome may be constructed by combining multiple
distinct relevant experiences, preplayed simultaneously.
To test these predictions, we required access to the information
content of neural populations underlying the representation of a novel
experience. Despite the poor spatial resolution of functional magnetic
resonance imaging (fMRI), there are well-validated strategies that can
reveal underlying cellular representations. For example, fMRI adapta-
tion takes advantage of the fact that activated cellular ensembles in
a voxel show a relative suppression in their activity in response to
repetition of a stimulus to which they recently responded. Despite
ambiguity in the biophysical mechanism underlying repetition sup-
pression
10,11
, when combined with careful experimental design the
technique allows for inferences to be made about the underlying neu-
ronal representations
12,13
.
We used fMRI adaptation to probe the neural representation of a
novel food reward. We hypothesized that, if the representation of a
novel food was constructed by explicit combination of multiple dis-
tinct experiences, we would observe fMRI adaptation when subjects
evaluated a novel reward immediately after evaluating a component
ingredient. Furthermore, if multiple experiences were replayed simul-
taneously, plasticity might result between the underlying neuronal
assemblies. Hence, experiences used to construct the same novel good
would later adapt to each other. Lastly, we hypothesized that this com-
plex construction process would not be required after an independent
neuronal representation of the novel good had been established. We
should therefore observe a reduction in each adaptation effect after
allowing the subjects to either experience the novel good directly
or simulate the novel good repeatedly. This repetition suppression
procedure allowed us to probe the neural mechanisms that underlie
human capacity for flexible, online, value construction.
RESULTS
Deciding between novel goods
We created 13 novel goods whose values were unknown to the sub-
jects (Fig. 1). However, each good was a novel combination of two
different familiar foods (Fig. 1a). Participants were given the oppor-
tunity to observe these novel goods without being allowed to sample
them by either taste or smell.
To first establish that these goods activate known value-related
brain regions, we measured fMRI activity in 19 subjects while they
evaluated and chose from pairs of these novel goods (Fig. 1b). After
the scan session, subjects performed a Becker-DeGroot-Marschak
(BDM) auction
14
that allowed us to measure subjects’ constructed
value for each good. Consistent with reports in simpler valuation
1
The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK.
2
The Oxford Centre for Functional Magnetic Resonance
Imaging of the Brain, University of Oxford, Oxford, UK. Correspondence should be addressed to H.C.B. (helen.barron.10@ucl.ac.uk) or T.E.J.B. (behrens@fmrib.ox.ac.uk).
Received 5 June; accepted 9 August; published online 8 September 2013; doi:10.1038/nn.3515
Online evaluation of novel choices by simultaneous
representation of multiple memories
Helen C Barron
1
, Raymond J Dolan
1
& Timothy E J Behrens
1,2
Prior experience is critical for decision-making. It enables explicit representation of potential outcomes and provides training
to valuation mechanisms. However, we can also make choices in the absence of prior experience by merely imagining the
consequences of a new experience. Using functional magnetic resonance imaging repetition suppression in humans, we examined
how neuronal representations of novel rewards can be constructed and evaluated. A likely novel experience was constructed by
invoking multiple independent memories in hippocampus and medial prefrontal cortex. This construction persisted for only a
short time period, during which new associations were observed between the memories for component items. Together, these
findings suggest that, in the absence of direct experience, coactivation of multiple relevant memories can provide a training
signal to the valuation system that allows the consequences of new experiences to be imagined and acted on.
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contexts, we observed a signal that correlated
with the value of the chosen option in a net-
work of brain regions that included ventral
and dorsal medial prefrontal cortex (vmPFC
and dmPFC, respectively), and posterior
cingulate cortex (mPFC: P = 0.001 familywise
error (FWE) corrected on cluster level, peak
t
17
= 6.30; Fig. 2a). The involvement of both vmPFC and dmPFC is
of particular interest given that the task required subjects to con-
struct and evaluate a model of a future outcome. This involvement
accords with recent evidence that vmPFC encodes value prefer-
ence for executable choices and dmPFC does so for choices that are
modeled abstractly
7
.
To evaluate these novel goods, subjects could not rely on pre-learnt
values. Thus, their only recourse was to construct, online, an expec-
tation of the compound’s value from knowledge of the individual
components. A key question is whether subjects constructed a novel
representation of the compound by explicitly combining the repre-
sentations of each component and, if so, which brain regions support
this construction process. We reasoned that this construction process
could be measured using fMRI adaptation. Activity relating to the
construction of the compound value would be suppressed when pre-
ceded by a related component if, and only if, the subject had engaged
the neuronal ensembles of the components when constructing a
representation of the compound.
Constructing representations of novel goods using memories
For every participant, we selected 2 of the 13 novel compounds, here
referred to as AB and CD, each consisting of two familiar individual
components (A and B, C and D) that subjects had tasted immediately
before the experiment (Fig. 1a). To avoid visual confounds in a later
analysis, we trained subjects to associate each of the six component
and compound foods (A, B, C, D, AB and CD) with two different
abstract shapes (Fig. 1c). Participants trained extensively on these
associations between food items and abstract shapes. In the final
block of trials, the mean accuracy was 97.8%, with a mean reaction
time of 845.2 ms.
On each trial in the scanner, we presented a distinct shape that
served as an instruction cue for subjects to elicit an explicit mental
representation of the associated food (Fig. 1d). The key comparison
of interest here was the brain activity elicited by novel goods when
preceded by related components (for example, A or B followed by AB)
compared with novel goods when preceded by unrelated components
(for example, C or D followed by AB).
Early in the experiment (block 1 of 3), we observed fMRI adapta-
tion between the representation of novel goods and their constituent
components in both mPFC (P < 0.001, FWE corrected on cluster
level, peak t
18
= 4.45; Fig. 2b) and bilateral hippocampus (t
18
= 2.55,
P = 0.010 using region of interest (ROI) analysis; Online Methods and
Fig. 2b). These two brain regions are components of a network that is
commonly activated in studies of value
7,15–18
, episodic memory
4,19,20
and spatial navigation
12
. Our result implies that these brain regions
construct a value representation of a novel item from component
memories, and do so by simultaneously engaging neuronal represen-
tations of these components.
Plasticity between simultaneously active memories
If this is the case then it follows that during the construction of the
compound good AB, the neuronal ensembles representing compo-
nents A and B should be simultaneously active. We reasoned that this
simultaneous activity, which first occurred during the stimulus-item
training phase before scanning, would induce experience-dependent
plasticity between cellular elements in these two ensembles—a plastic-
ity evident in the scanning trials as a shadow of this value construction
process. For example, after constructing a representation of ‘tea-jelly
dessert’, we reasoned that cellular representations of tea would induce
activity in jelly-preferring ensembles and vice versa. This can also be
tested using fMRI adaptation, which predicts a differential effect for
components that were part of the same compound compared with
components that were not. Indeed, when we compared early trials of
A that were preceded by B to those that were preceded by C, we again
found relative suppression in mPFC activity (P
= 0.014, FWE cor-
rected on cluster level, peak t
18
= 4.24; Fig. 2c), but not hippocampus
(t
18
= 0.34, P = 0.367 using ROI analysis, Online Methods).
Notably, across all three blocks, the extent to which individual par-
ticipants showed adaptation between related components in mPFC,
and in the hippocampus, was predicted by the average value of the
novel items (mPFC, r = 0.47 and P = 0.040; hippocampus, r = 0.58
and P = 0.010), but not component items (mPFC, r = −0.05, P = 0.833;
hippocampus, r = −0.09, P = 0.730). This suggests that the mechanism
underlying this suppression occurred during the earlier construction
of the novel good and not during the participant’s elicitation of the
component item at the time that this signal was measured. Indeed, in
both structures, the correlation with the value of the novel good sur-
vived the removal of any signal attributable to the component values
a
Components
Novel good
A
B
C
D
AB
CD
b
c
d
Evaluation
Choice
Feedback
ITI
+
tomjam
tomjam
tomjam
yogfee
yogfee
yogfee
4s...
?
?
4,000 ms
600 ms
800 ms
∼6,000 ms
Stim
800 ms
800 ms
Stim
ITI
ITI
+
+
∼2,500 ms
∼2,500 ms
Figure 1 Experimental design. (a) We made
13 novel goods from the combination of two
familiar food types that had not previously
been tasted together. Two examples are shown
here: avocado and raspberry smoothie (AB) and
tea-jelly (CD). (b) Participants made binary
decisions between the novel goods while in
the scanner. (c) Prior to entering the scanner,
two of the novel goods were chosen for each
participant. Participants learned to associate
each of these novel goods and their respective
components with two abstract stimuli. (d) In
the scanner, participants vividly imagined the
sensory properties of the food items in response
to each abstract stimulus presented.
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a r t I C l e S
(mPFC, r = 0.51, P = 0.015, Fig. 2d; hippocampus, r = 0.60, P = 0.004;
Fig. 2e and Supplementary Fig. 1). Together, these findings support
value-dependent plasticity in related components as a consequence
of coactivation during construction of the novel goods.
It is important to note that these three de facto tests of mPFC func-
tion (valuation, construction and plasticity) do not rely on the same
data. Despite slight differences in thresholded peak locations of the
two adaptation effects, they showed similar patterns of activity in
mPFC (Fig. 2f). mPFC can therefore evaluate novel goods by con-
structing explicit representations of expected outcomes from familiar
components, a process that engenders plasticity between simultane-
ously active component representations.
The influence of sensory experience upon construction
We then asked whether consummatory exposure to the novel goods
would reduce a need to construct value online. To test this idea, we
repeated the experiment in a second group of 20 subjects with one
important difference. This second group (familiar) was given a single
sample of each of the 13 novel compound goods to taste before the
experiment. Notably, both groups underwent the same item-stimulus
learning task before entering the scanner, and there was no significant
difference between groups in reaction time or accuracy on the final block
of trials during the learning task (P > 0.150; Supplementary Table 1).
Any difference between the two groups in the representation or evalu-
ation of novel goods could therefore be attributed to the effect of
sensory exposure.
We first assessed value effects during decision trials. Both groups
showed similar consistency in their choices (Supplementary Fig. 2).
As was the case for the unfamiliar group, the familiar group encoded
chosen value activity in a network of value-related brain regions that
included mPFC (Fig. 3a). In both groups, the neural activity observed
in mPFC was consistent with a role for this brain region in the evalu-
ation of compound goods (Fig. 3b,c).
To determine whether this single experience was enough to reduce
a need for online value construction, we compared adaptation effects
across the two groups. To avoid selection bias, we used ROIs derived
from whole-brain adaptation effects averaged across both adapta-
tion contrasts in the two groups (Online Methods and Fig. 4a,b).
A between-group comparison in these ROIs revealed significant
differences in the adaptation effects between the familiar and unfa-
miliar participants in both mPFC (group × condition interaction,
P = 0.018, F
1,144
= 5.76, three-way ANOVA, Online Methods) and
hippocampus (group × adaptation type × condition interaction,
P = 0.035, F
1,144
= 4.52, three-way ANOVA, Online Methods).
Figure 2 Neural correlates of constructing and
evaluating a novel good. (a) While participants
made binary choices between novel goods,
the mPFC (extending into dmPFC) encoded
chosen value. (b) The mPFC and hippocampus
showed repetition suppression to a novel good
when preceded by a related component (for
example, tea-jelly preceded by tea) compared
with when preceded by an unrelated component
(for example, tea-jelly preceded by avocado).
(c) The mPFC showed repetition suppression to
a component food item when preceded by the
related component (for example, tea preceded
by jelly) compared with when preceded by an
unrelated component (for example, tea preceded
by avocado). (d,e) In mPFC and hippocampus,
a significant positive correlation was revealed
between the amount of suppression between
related components (across all blocks) and the
average value participants assigned to the novel
goods (after removing effects attributable to the
value of the components; for mPFC: r = 0.51,
P = 0.015; hippocampus, r = 0.60, P = 0.004),
respectively. (f) Both adaptation effects showed
comparable effect size across the ventral-to-
dorsal gradient of mPFC (mean ± s.e.m. across
participants). The locations of the ROIs are shown and the effect size for both adaptation measures was scaled such that the peak value was equal to 1.
There was no significant difference between the two adaptation effects at any point on this gradient (P > 0.300 for all ROIs).
a b
c d e
f
x = –4
x = 24
y = –10
x = 4
x = –6
z = 12
(A→B) < (C→B)
(A→AB) < (C→AB)
“AB or CD?”
z = –8
z = 10
2
2
Adaptation effect size
Adaptation effect size
Adaptation effect size
1
1
0
0
Value of novel goods
Ventral to dorsal
–1
1
0
–1
1.5
0.5
–0.5
0
1.0
–1 210
Value of novel goods
–1
Figure 3 Sensory exposure to a novel good:
comparison between the unfamiliar and
familiar groups during the decision-making
task. (a) In the familiar group, the mPFC
correlated with chosen value during the
decision-making task (thresholded at
P < 0.01, uncorrected for visualization).
(b) ROI used to assess value signals in both
groups of participants during the decision
task. (c) During the decision-making task, the
unfamiliar and familiar groups showed comparable chosen value signals in mPFC (left, average of all task blocks for each group), and in the unfamiliar
group there was no change in the chosen value signal across time (right, block 1 versus blocks 2 and 3). Parameter estimates were extracted from ROI
shown in b (mean ± s.e.m. across participants). a.u., arbitrary units.
0.075
x = 2
x = –8
z = –12
a b c
0.050
0.025
0
Effect size (a.u.)
Unfamiliar
Familiar
B1
B2 and B3
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Using post hoc two-sample t tests to decompose these interactions,
we found that, relative to the unfamiliar group, the familiar group
showed reduced adaptation between the novel goods and their related
components in mPFC (group difference: trend, t
18
= 1.70, P = 0.053;
Fig. 4c) and in the hippocampus (group difference: t
18
= 3.11,
P = 0.003; Fig. 4c). Furthermore, the familiar group did not show
plasticity in mPFC between the representation of the constituent
components of a novel good (group difference: t
18
= 1.96, P = 0.033,
Fig. 4c). Crucially, there was no significant difference between groups
in their ability to accurately elicit the correct representations during
the imagination task (group comparison of accuracy, P = 0.82; reac-
tion time, P = 0.89) or in the average subjective value assigned to any
of the novel goods used in the adaptation task (P > 0.05 for all assigned
novel goods; Supplementary Fig. 3). This result therefore suggests
that even a single previous experience of a good is sufficient to reduce
a requirement for online value construction. This is particularly nota-
ble given that extensive experience is required to reduce goal-oriented
behavior and establish habitual actions
21
.
Temporal dynamics of the construction mechanism
If experiential and constructed valuation use distinct neural mecha-
nisms, it is possible that the value construction mechanism could
itself substitute for a direct experience and train experiential valuation
mechanisms. As the experiment progressed, subjects gained substan-
tial experience in constructing the representation of the novel good.
We asked whether, after multiple previous simulations of an experi-
ence, it was still necessary to construct and evaluate the representation
of novel goods anew on each trial. Alternatively, were values learned
despite participants never having experienced the novel good? As
our experiment extended over three separate blocks, we were able to
study changes in value construction–related adaptation effects over
time (Figs. 5 and 6).
Previous studies have found that goal-directed choice mechanisms
exhibit marked differences early and late in choice experiments
17
. We
used a three-way ANOVA (Online Methods) to identify attenuation
of adaptation effects in mPFC and hippocampus in the unfamiliar
group across the scanning session (block × condition interaction for
mPFC, P = 0.004, F
1,144
= 8.44; block × adaptation-type interaction
for hippocampus, P = 0.011, F
1,144
= 6.56). Post hoc t tests comparing
block 1 with all remaining blocks revealed a significant reduction in
adaptation over time of a novel good to its related component (mPFC,
t
18
= 2.12, P = 0.024; hippocampus, t
18
= 2.13, P = 0.024; Fig. 5a)
and in the plasticity between related components (mPFC, t
18
= 1.85,
P = 0.041; but not hippocampus, t
18
= 0.81, P = 0.785; Fig. 6a).
To ensure that sensitivity to the construction process was main-
tained across the duration of the experiment, we also considered
temporal dynamics of other adaptation effects and of value signals
encoded on decision trials. In the unfamiliar group, both adaptation
in mPFC to repetition of any item (but not stimulus) and adaptation
in visual areas to repetition of a stimulus did not show a reduction
over time (one-tailed paired t tests, t
18
= 0.46, P = 0.326; Fig. 5a;
t
18
= 0.50, P = 0.312; Supplementary Fig. 4a). Furthermore, the cho-
sen value signal encoded by mPFC also did not reduce over time, but
instead remained consistent across sessions (Fig. 3c). In addition, per-
formance on the imagination task improved across blocks (Fig. 5b,c).
Rather than a loss of sensitivity, this suggests that the diminishing
adaptation effects demonstrate that simulated experience is sufficient
x = 0
z = 10
x = 22
(A→AB) < (C→AB) (A→B) < (C→B) (A→AB) < (C→AB)
a b
c
Effect size (a.u.)
1.5
2.0
0.5
–0.5
0
1.0
Unfamiliar
Familiar
*
*
Figure 4 Sensory exposure to a novel good: comparison between the
unfamiliar and familiar groups during construction of a novel good.
(a) ROI used to compare mPFC adaptation effects. (b) ROI used to
compare hippocampus adaptation effects. (c) In mPFC, the familiar
group showed less adaptation between the novel goods and their related
components (left, P = 0.053, trend) and significantly less adaptation
between related components (middle, P = 0.033; both extracted from
the ROI shown in a). In the hippocampus, the familiar group showed
significantly less adaptation between the novel goods and their related
components (right, P = 0.008, extracted from ROIs shown in b).
*P < 0.05. Data are presented as mean ± s.e.m. across participants.
a
B1
B2 and B3
2.0
*
1.5
1.0
(A→AB) < (C→AB) (A→AB) < (C→AB) (A→A) < (C→A)
Effect size (a.u.)
0.5
0
*
b
100
80
60
40
Percentage accuracy
20
0
B1 B2 B3
Component Compound
B1 B2 B3
c
3
2
1
Reaction time (s)
0
B1 B2 B3
Component Compound
B1 B2 B3
Figure 5 In the absence of sensory exposure,
there was evidence for the construction
mechanism only in early trials: block 1 compared
with blocks 2 and 3 for unfamiliar subjects.
(a) There was significantly less adaptation in
blocks 2 and 3 between the novel goods and their
related components in mPFC and hippocampus,
respectively (left and middle, P = 0.024 each;
ROIs are shown). There was no significant
reduction across time in the mPFC adaptation
of a component item to itself when predicted
by two different stimuli (right, P = 0.326, ROI
is shown). (b,c) On the imagination task, the
unfamiliar group showed an increase in accuracy
(b) and a decrease in reaction time (c) across
blocks. *P < 0.05. Data are presented as mean ±
s.e.m. across participants.
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a r t I C l e S
to establish an independent representation of the novel good that no
longer needs to be reconstructed anew on each trial.
Despite the overall reduction of cross-component suppres-
sion over the course of the experiment, this was not true for com-
ponents that had been used to construct high-value novel goods.
When averaging across the final two blocks, both the mPFC and
hippocampus showed a significant positive correlation with the
value of the compound items (mPFC, r = 0.64, P
= 0.002; hippo-
campus, r = 0.63, P = 0.003, Fig. 6b,d and Supplementary Fig. 5),
after accounting for variance explained by the value of the compo-
nent items in both cases. A median split of participants according
to the value assigned to the novel goods subsequently verified that
there was long-lasting plasticity in mPFC and hippocampus in the
final two blocks for those participants who attributed high, but not
low, values to the novel goods (mPFC: high, t
8
= 2.84, P = 0.022;
high versus low, t
8
= 2.68, P = 0.028; hippocampus: high, t
8
= 3.52,
P = 0.008; high versus low, t
8
= 5.36, P < 0.001; Fig. 6c,e). Suggestive
evidence that value-dependent adaptation between related compo-
nent items emerged later in hippocampus relative to mPFC (Fig. 6c,e)
could not be verified statistically (t
8
= 1.30, P = 0.229). Together, these
results suggest that the plasticity is long-lasting if value is attributed
to the original association.
DISCUSSION
The role of memory in prospective evaluation and inference has been
emphasized in both animals
22
and humans
3,4,20
. Simulation and pre-
play can be used to explore an internal model of the environment
and evaluate anticipated outcomes
8,23
. However, the neural mecha-
nisms by which these processes are achieved have remained unclear,
particularly in circumstances in which anticipated outcomes have not
previously been experienced. We used repetition suppression in fMRI
to reveal a neuronal mechanism that supports prospective representa-
tion and evaluation of novel experiences.
Repetition suppression has been used extensively in sensory brain
regions to probe the information content of neural activations and,
more recently, in more frontal brain regions, including orbitofrontal
cortex
24
. However, a number of different hypotheses have been
proposed to explain the underlying physiological mechanisms behind
the phenomenon, including fatigue, sparse coding and predictive
coding
10,25–27
. Although there is not yet a consensus on which mecha-
nism provides the most appropriate explanation for the phenomenon,
the consequences of this ambiguity are mitigated when used in a care-
fully controlled experimental design, as all models make the same
prediction: if a neural population is sensitive to a particular feature
or dimension, then suppression will occur in response to a repetition
of this feature, but not others.
The repetition suppression procedure that we used was designed
to allow interrogation of the underlying representation of a novel
reward. By asking people to imagine and evaluate novel rewards in
the scanner, we found that the neural representation of a novel reward
was dependent on representations of multiple related and previously
experienced rewards. Our data suggest that neuronal networks can
construct a novel experience by simultaneous activation of multiple
previous memories so that this constructed experience may be evalu-
ated. Although signals in the anterior hippocampus were found to be
related to construction, those in mPFC were related to both construc-
tion and valuation.
Crucially, unlike other goal-directed decision mechanisms that have
been reported
21,23,28,29
, we only found evidence for a construction
mechanism when subjects had no direct experience of an outcome,
and even then only fleetingly. It is therefore possible that constructed
value can provide a substitute for direct experience and train the expe-
riential goal-directed systems that have been studied previously. This
training signal may be considered analogous to off-line training of
an habitual system that makes use of simulations from an internal
goal-directed model
23,30–32
. Whereas the teaching signal provided to a
habitual system replicates, or fine tunes, previous sensory experience,
the teaching signal provided to a goal-directed system may establish
an internal model of the future world by repeated imagination of a
novel experience.
During the construction process, a second repetition suppression
effect was observed between distinct and previously unassociated
memories that contributed to the construction. This effect implies that
the neural representation of related, compared with unrelated, compo-
nent items became more similar as a consequence of the pre-scan train-
ing task, during which the participants were first exposed to the novel
compounds. Notably, given that the suppression was not observed
in the familiar group, it seems highly unlikely that this suppression
Figure 6 In the absence of sensory exposure,
repetition suppression between related
components was maintained across the
duration of the experiment only if participants
assigned high value to the compound goods.
(a) Participants from the unfamiliar group
showed significant reduction in adaptation
between related components over time in
mPFC, but not hippocampus (P = 0.041 and
P = 0.785, respectively, ROIs are shown).
(b,d) The correlations shown in Figure 2 were
also significant in mPFC (b) and hippocampus
(d) when considering suppression effects
between related components in blocks 2 and
3 alone: the amount of suppression across
participants correlated positively with the
average value of the compound goods (mPFC,
r = 0.64, P = 0.002; hippocampus, r = 0.63,
P = 0.003). (c,e) A median split of participants
into those that assigned high and low values
to the compound goods revealed significant
suppression between related components in blocks 2 and 3 only in those participants who assigned high value (c, mPFC, High, P = 0.022; High versus
Low, P = 0.028; e, hippocampus, High, P = 0.008; High versus Low, P < 0.001). *P < 0.05. Data are presented as mean ± s.e.m. across participants.
a b c
d e
1.5
1.0
0.5
0
Effect size (a.u.)
(A→B) < (C→B)
B1
B2 and B3
2
1
0
–1
210–1
Value of novel goods
210–1
Value of novel goods
Adaptation effect size
1
0
–1
Adaptation effect size
1.5
1.0
0.5
0
–0.5
HighHigh LowLow
B1
High Low
B1
B2 and B3
High Low
B2 and B3
1.0
0.5
0
–0.5
Effect size (a.u.) Effect size (a.u.)
*
*
*
*
*
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