CharlottePre ´vost,1,2MathiasPessiglione,3EliseMe ´te ´reau,1,2Marie-LaureCle ´ry-Melin,2,3andJean-ClaudeDreher1,2
Bron,France,2UniversitéClaudeBernardLyon1,69003Lyon,France,and3InstitutduCerveauetdelaMoe ¨llee ´pinie `re,INSERMUnitémixtederecherche
enSanté975,Motivation,Brain,andBehaviorTeam,Ho ˆpitalPitie ´-Salpe ˆtrie `re,75013Paris,France
Decision making consists of choosing among available options on the basis of a valuation of their potential costs and benefits. Most
theoretical models of decision making in behavioral economics, psychology, and computer science propose that the desirability of
systems for rewards (erotic stimuli) associated with different types of costs, namely, delay and effort. We show that humans devalue
rewards associated with physical effort in a strikingly similar fashion to those they devalue that are associated with delays, and that a
the anterior insula, represent the decreasing value of the effortful option, coding the expected expense of energy. Together, these data
Decision making can be seen as a process of maximizing utilities
ical formalization of utility function has proven particularly effi-
cient in describing choice behavior is delay discounting. “Delay
discounting” refers to the empirical finding that both humans
and animals value immediate rewards more than delayed re-
wards. A large number of behavioral studies have demonstrated
that the subjective value of a delayed reward may be discounted
hyperbolically (Ainslie, 1975; Frederick et al., 2002). Similarly,
because effort carries a cost, a reward may carry a higher value if
those associated with delays.
delay or effort costs (Kahneman and Tversky, 1979). A choice is
made after a valuation stage, regardless of the nature of the cost.
However, lesion studies in rodents suggest at least partial disso-
ciations between the neural structures used to assess delay- and
effort-based decision making (Rudebeck et al., 2006; Walton et
al., 2006; Floresco et al., 2008). Despite the fundamental impor-
neural substrates involved in making decisions about delay and
effort costs, it is unknown whether these circuits can be general-
ized to humans and whether they specifically concern the valua-
tion stage. Indeed, specifying the roles of brain structures
specifically involved during the valuation stage, and not during
the subsequent waiting/effort periods, has proven difficult be-
decision-making process a lesioned animal is impaired. Yet, a
number of them have shown that it is neither the ability to wait
nor the exertion of effort, per se, that is impaired by the use of
control conditions (Rudebeck et al., 2006).
to extend the framework of subjective utility functions to the
domain of effort discounting, allowing us to assess whether
the human brain computes subjective values related to rewards
associated with different types of costs in distinct sets of brain
structures. We designed similar delay- and effort-discounting
paradigms using erotic pictures as rewards, involving passive de-
lay periods in the second range and real physical effort using a
hand grip. On each trial, subjects made a choice based on an
incentive cue (fuzzy erotic image) between a variable costly op-
tion (waiting or exerting more effort), leading to viewing the
erotic picture in clear for a “long” time (large reward), and a
14080 • TheJournalofNeuroscience,October20,2010 • 30(42):14080–14090
default option, having a minimal cost but leading to viewing the
picture in clear for a “short” time (small reward) (Fig. 1).
as money because monetary rewards neither allow subjects to
experience the reward at the time of delivery inside the scanner
nor allow them to experience the delay period, often ranging from
days to months (McClure et al., 2004; Kable and Glimcher, 2007;
Wittmann et al., 2007; Ballard and Knutson, 2009). Moreover, the
subjective rating of each fuzzy erotic picture was used to compute
subjective values without assuming that subjective value increases
linearly with the objective amount of money (Kable and Glimcher,
Participants. Eighteen young, healthy, heterosexual men (mean age:
23.1 ? 1.8 years), participated in the study. All the participants were
males because men are generally more respon-
sive to visual sexual stimuli compared with
women, both in terms of behavioral arousal
and brain response (Hamann et al., 2004;
Sescousse et al., 2010), and to avoid potential
her et al., 2007). Two participants initially en-
rolled were excluded from data analysis because
of inappropriate calibration of the task for the
preferences of these subjects. The study was
approved by the Paris Pitie ´-Salpe ˆtrie `re Hos-
pital ethics committee, and written informed
consent was obtained from all subjects. All
participants were right-handed, as assessed
by the Edinburgh Handedness Question-
naire (mean score: 0.83 ? 0.17) and were
atric or neurological illness, as assessed by the
Mini-International Neuropsychiatric Inter-
view and the Hospital Anxiety and Depression
Scale (HAD) (mean HAD score: 3.32 ? 1.46).
None of the subjects showed impulsivity pat-
terns, as assessed by the Barratt impulsiveness
using the French analysis of sexual behavior
questionnaire (Spira et al., 1993) and sexual
arousability was measured with the Sexual
Arousability Inventory (SAI) (Hoon and
Chambless, 1998) (mean score SAI: 92.76 ?
12.34), which ensured that subjects showed a
“standard” sexual arousability. Subjects were
asked to avoid any sexual relationship for 24 h
before the scanning session.
Delay/effort discounting task. Subjects were
first asked to read the instructions of the task.
To ensure that subjects understood the task
and to familiarize them with the hand grip,
they were trained on a practice version outside
the scanner room with fuzzy cues and clear
outcome pictures that were different from
those subsequently used in the scanner (to
avoid any habituation effect). During training
and inside the scanner, subjects’ maximal
strength was measured using a magnetic reso-
nance imaging (MRI)-compatible handgrip
(designed by Eric Featherstone, Wellcome
They were then taken inside the scanner and
were invited to find an optimal body posi-
tion, while lying down with the power grip in
their right hand, the arm resting over the belly. The power grip was
made up of two molded plastic cylinders, which compressed an air tube
signal was fed to the stimuli presentation computer (PC) via a signal
conditioner (CED 1401, Cambridge Electronic Design). The task was
programmed on a PC using the matlab toolbox Cogent 2000
There were five sessions (lasting ?9 min) composed of 48 trials each,
leading to a total of 240 trials. The behavioral task was composed of the
following two conditions: a delay condition and an effort condition,
which were presented in a random order. Each trial started with the
presentation of a cue (0.5 s) showing an erotic fuzzy picture of a naked
woman. The screen then displayed the instruction “Wait?” or
picture briefly appeared on a screen and was followed by the instruction “Wait?” or “Squeeze?,” together with a thermometer
from 15 to 90% of subjects’ maximal strength for the effort). Depending on the incentive cue and the proposed level of cost,
Pre ´vostetal.•ValuationofEffortandDelayCostsJ.Neurosci.,October20,2010 • 30(42):14080–14090 • 14081
could be displayed at six different heights (Fig. 1). This level indicated
either the proposed delay period, which could last between 1.5 and 9 s
90% (increment: 15%) of the subject’s maximal strength. In the delay
condition, subjects had to decide whether they chose the costly option
(i.e., wait the longer delay period indicated by the height of the level on
1.5 s) to view the picture for 1 s (small reward). Similarly, in the effort
condition subjects decided whether it was worth investing in a stronger
effort to see the picture clearly for 3 s or simply to invest in a small effort
corresponding to 15% of their maximal strength to see the cue for 1 s.
Subjects made their choice with a response pad in their left hand (accept
costly option, forefinger; reject costly option, middle finger).
to visually indicate which option was chosen. Then, subjects were in-
volved in waiting or in exerting an effort proportional to the level indi-
cated on the thermometer. In the delay condition, subjects passively
waited until the required time had elapsed and the thermometer was
filled up to the indicated level on the thermometer. In the effort condi-
tion, subjects squeezed the hand grip until they reach the indicated level
on the thermometer. Finally, at the outcome, according to their choice
(costly vs default option), they viewed the erotic picture clearly for 3 or
1 s. The duration of the display of the cue plus the proposition (instruc-
his decision during this time, the trial was aborted and the instruction
“Pay attention” was displayed for 2 s.
The trial ended with an intertrial interval of 1.5 s plus a jitter of ?1 s
when subjects accepted the proposition and 3.5 s plus a jitter of ?1 s
when they rejected it. In this way, the outcome and the intertrial interval
subjects adopt the strategy of choosing more the default option to see
overall because this would have considerably extended the duration of
the case in the current version of the experiment, since short delayed
options would lead to waiting overall the exact same duration as the
delayed rewarded option. Subjects were explicitly asked to make their
choices according to both the fuzzy cue and the proposed level of delay/
effort and to weigh the cost and benefit of each option. They were also
told that systematically choosing the less costly option would not allow
them to see more pictures or finish the experiment earlier and that the
sexual intensity of the fuzzy pictures was not linked to the level of pro-
posed delay/effort since these images were presented in a random order.
The six different levels were randomly presented across sessions with an
average of 20 trials per level and per condition.
Stimuli. Each picture was presented twice: once with a fuzzy appear-
ance at the beginning of each trial (incentive cue) and once in a clear
form at the outcome. Erotic pictures of women were used because—
contrary to monetary rewards—they allowed us to include waiting
periods in the second range and to have subjects really experience the
biological importance, erotic stimuli have not been studied as reinforc-
ers, but rather as arousing stimuli in passive viewing paradigms focusing
on sexual function.
Five hundred fifty erotic pictures were selected from the World Wide
face visible. These pictures underwent a glass effect in Adobe Photoshop
type, frosted; scaling, 100; and without invert texture.
The use of fuzzy cues had several advantages. First, displaying them at
the beginning of each trial allowed preserving the saliency of the clear
could have occurred if only the clear pictures had been repeated (Agmo,
1999). Second, they had the power to motivate the subject by giving an
of the clear picture. Hence, they allowed us to partially guide subjects’
choices. Moreover, performing postscan ratings of each fuzzy cue, fur-
ther used in subjective value computation, allowed us to avoid the as-
with an objective amount of money (Pine et al., 2009).
Five hundred sixteen of these pictures were rated by 30 men in a pilot
experiment using the software Presentation 9.9 (Neurobehavioral Sys-
tems) (the other 34 pictures were used as examples). The 240 best rated
pictures considered as the most rewarding pictures were selected for our
delay/effort discounting task. The rationale for using different viewing
durations for the clear picture was based on the fact that subjects work
harder to see an attractive face longer because it is more rewarding
(Hayden et al., 2007).
The duration of the erotic pictures seen clearly and the various delays
and efforts used were initially piloted in several behavioral experiments,
ber of immediate and delayed options were chosen by the subjects.
Ratings of the fuzzy cues. At the end of the scanning session, subjects
were asked to rate the 240 fuzzy cues they saw during the experiment.
They had to answer the question: “How much would you like to see this
measure of each picture was used to compute the subjective values (cor-
responding to the reward intensity, called A in Eq. 1 below). The ratings
of the cues were also used in behavioral analysis (see Fig. 2) and in
functional MRI (fMRI) region of interest (ROI) analyses (see below,
Computational model, and Figs. 5b, 6b), after normalizing and sorting
them into one of four categories (category 1 being the lowest rated pic-
tures and category 4 being the highest rated pictures). For the latter
analyses, we collapsed the original nine levels of ratings into four bins to
ensure a sufficient number of repetitions in each bin and to generate
thermometer were not rated after the scan and were assumed to be per-
ceived in a linear fashion by the subjects.
Computational model. We used a hyperbolic function to compute the
subjective values of delayed rewards because it has previously been
value of the reward associated with the costly option and the default
option in the delay condition (SVD) and in the effort condition (SVE).
These subjective values were computed as follows:
SVD? AD? xD/(1 ? CD? kD) (1)
SVE? AE? xE/(1 ? CE? kE) (2)
the clear picture for 3 s (large reward) and viewing it for 1 s (small
reward), C corresponds to the proposed level of the cost, and k is a
subject-specific constant corresponding to the discount factor. This
model was then used to create a parametric regressor corresponding to
the estimated subjective value of the rewards associated with the costly
option in a given condition for analysis of brain images.
The associated probability (or likelihood) of choosing the costly op-
tion was estimated by implementing the softmax rule, as follows:
P(delayed option) ? 1/(1 ? exp(?SVD/?D)) (3)
P(effortful option) ? 1/(1 ? exp(?SVE/?E)) (4)
This standard stochastic decision rule allowed us to compute the proba-
bility of choosing the costly option according to its associated subjective
value (supplemental Figs. 4, 5, available at www.jneurosci.org as supple-
mental material). The temperature ? is a free parameter concerning the
randomness of decision making. The parameters x, k, and ? were ad-
justed using the least square method to minimize the distance between
the behavioral choice and the probability of choice estimated by the
model, across all sessions and subjects.
14082 • J.Neurosci.,October20,2010 • 30(42):14080–14090Pre ´vostetal.•ValuationofEffortandDelayCosts
fMRI data acquisition. Imaging was performed on a 3 tesla TRIO TIM
(EPIs) were acquired in an interleaved order with blood oxygen-
dependent level (BOLD) contrast. Whole-brain functional images were
acquired in 35 slices (128 ? 128 voxels, 2 mm slice thickness, 2 mm
interslice gap, 30° off of the anterior commissure-posterior commissure
line at a repetition time of 1.98 s). We used a tilted plane acquisition
sequence to optimize functional sensitivity in the orbitofrontal cortex
(Weiskopf et al., 2006). T1-weighted structural images were also ac-
quired, coregistered with the mean EPI, segmented, and normalized to a
standard T1 template. EPI images were analyzed in an event-related
manner, within a general linear model (GLM), using the statistical para-
metric mapping software SPM5 (Wellcome Department of Imaging
discarded to allow for T1 equilibration effects. Before the analysis, the
ized using the same transformation as structural images, and spatially
smoothed with an 8 mm full-width at half maximum Gaussian kernel.
GLM1: main fMRI data statistical analysis. We used one main linear
regression model to account for our data (GLM1). Each trial was mod-
eled as having three different phases, corresponding to the decision-
making phase, the cost-enduring phase and the outcome phase. Trials
were sorted according to the condition (delay or effort) and distributed
into separate regressors. Two regressors were used to account for the
decision phase: one for the delay condition and one for the effort condi-
was included as a parametric modulation on these two regressors. Two
effort investment period (one for each condition). Two additional re-
gressors accounted for the outcome phase, one for the small reward and
the design matrix contained eight regressors of interest, all convolved
with a canonical hemodynamic response function (HRF) and modeled
for motion artifact, subject-specific realignment parameters were mod-
were computed at the individual subject level and then taken to a group
level random-effects analysis (one-sample t test).
We applied a threshold of p ? 0.001 (uncorrected) with a cluster-
the results concerning this model exclusively concern the decision-
Additional control analyses: GLM2 and GLM3. Because the subjective
value reflects a ratio between the rating of the cue and the level of pro-
posed delay or effort, an increased BOLD response with subjective value
the incentive, a negative correlation with the proposed level of delay or
effort, or a combination of both the rating and the level of the cost.
Conversely, a decreased BOLD response with subjective value of the
costly reward could reflect a negative correlation with the ratings of the
or a combination of both the rating and the level of the cost. To investi-
gate whether the brain regions showing activity correlating with subjec-
tive value of the costly reward were better accounted by the subjective
cost in a given condition at the time of choice, we performed two addi-
tional fMRI analyses (GLM2 and GLM3) with these single parameters as
parametric regressors (rating of the cue alone and level of the cost alone
modeled in separate regressors for the delay and effort conditions), as-
ter size in millimeters) of the activity of the cluster correlating with
subjective value were larger than those of these single parameters (sup-
plemental Fig. 7, supplemental Tables 1–3, available at www.jneurosci.
org as supplemental material). These two analyses were similar to our
main GLM1 except that the subjective value regressor was replaced by a
(GLM2) and the level of the cost in the second one (GLM3).
ROI analyses. To gain more insight into the correlational analysis ob-
tained with subjective value, we performed additional ROI analyses
with the parameters of the task (rating and level of cost) to plot the
respective influences of the proposed level of effort or delay and the
influence of the ratings of the incentive in different brain regions.
Regions of interest, conducted with the extension of SPM MarsBaR
(http://marsbar.sourceforge.net/), were defined functionally from the
intersection of the functional cluster of interest and an 8-mm-radius
the Montreal Neurological Institute coordinates reported in Table 1. In
the delay condition, the functional clusters of interest were the ventro-
medial prefrontal cortex (vmPFC) and the ventral striatum because of
Pre ´vostetal.•ValuationofEffortandDelayCosts J.Neurosci.,October20,2010 • 30(42):14080–14090 • 14083
their reported role in coding the subjective value of delayed rewards in
the monetary domain (Tanaka et al., 2004; Kable and Glimcher, 2007;
Peters and Bu ¨chel, 2009). In the effort condition, for the positive corre-
lation with subjective value of the effortful reward, the only ROI was the
primary motor cortex (M1) because it was the main activation found in
(ACC) and anterior insula cortex because a previous study in rodents
reported during anticipation of higher reward in the context of effort-
based cost–benefit valuation in humans (Croxson et al., 2009) and be-
cause previous fMRI studies also reported an important role for the
(Huettel et al., 2005; Grinband et al., 2006). Figures 3b and 4b show the
parameter estimates obtained from GLM1, GLM2, and GLM3 in each
ROI. Moreover, to illustrate the activities correlating with subjective
value of the high reward in the delay and the effort condition, the ?
estimates of SVDand SVEwere plotted (Figs. 3c, 4c). Note that no statis-
tical test was performed on these SVs because the ROI analysis is not
independent from the whole-brain analysis.
To further illustrate the shape of the correlational analysis with the
subjective value, the rating, and the level of proposed cost, we estimated
three additional GLMs (GLM4, GLM5, and GLM6). This allowed us to
extract and isolate the percent signal change (averaged across subjects)
according to the subjective value of the reward associated with the costly
delay and effort costs (GLM6) (see Figs. 5, 6). In GLM4, each trial was
making phase, the cost-enduring phase, and the outcome phase. Trials
were sorted according to the condition (delay/effort). Twelve regressors
6 for the delay condition and 6 for the effort condition. Two regressors
were used to account for the experienced delay period and the effort
investment period (one for each condition). Two additional regressors
one for the large reward. Therefore, the design matrix contained 16 re-
gressors of interest, all convolved with a canonical HRF. To correct for
motion artifacts, subject-specific realignment parameters were modeled
as covariates. For each session of each subject, the subjective values were
sixth (being the high category). This allowed us to extract the percent
signal change for each of these categories, which was averaged across
sessions for each subject and then averaged across all subjects (Figs. 5a
the level of proposed delay (costs) in the brain regions correlating posi-
tively with the subjective value of the delayed option, we performed
GLM5 and GLM6. GLM5 had the same three different phases, with the
four categories of rating of the cue as parametric modulation at the time
of choice instead of the six categories of subjective values (Figs. 5b and
BOLD change as a function of the levels of costs also had the same three
cue (Figs. 5c, 6c).
Cost-enduring phase and outcome phase. Finally, we took advantage of
the valuation processes concomitant with the decision from the brain
regions recruited during the cost-enduring phase and those activated by
the large versus small rewards in each of the two conditions. For this
analysis, we used a similar model to our main GLM1, except that this
model included a parametric modulation by the level of the cost during
the delay period and during the effort exerted, and also separated the
large and small rewards for the delay and effort conditions. These func-
tional data were analyzed by constructing a set of boxcars having the
duration of each corresponding event (i.e., decision phase lasting the
response times (RTs), delay/effort phase lasting the delay effectively
reward and 3 s for the large reward), all convolved with a canonical
0.05 corrected for multiple comparisons. For the outcome contrasts, we
used a threshold of p ? 0.005, uncorrected, because of our very specific
focus on ventromedial prefrontal cortex and striatum, which have both
number of studies (Knutson et al., 2001; Smith et al., 2010).
Finally, in a last analysis, we investigated whether the activation ob-
our main GLM1, except that this model included a parametric modula-
tion by the level of the cost during the delay period and during the effort
exerted, and also during the outcome period for the delay and effort
decision phase lasting the RTs, delay/effort phase lasting the delay effec-
old of p ? 0.001, uncorrected with a cluster-level threshold of p ? 0.05
corrected for multiple comparisons.
Subjects provided clear evidence of both delay and effort dis-
counting in their preferences, choosing more frequently the
costly option for lower proposed levels of delay (F(5,75)? 46.30;
p ? 0.001) and effort (F(5,75)? 54.67; p ? 0.001) (Fig. 2a,c), as
well as for higher ratings of the incentive (assessed by postscan
ratings of the fuzzy cues) in both the delay (F(3,45)? 7.21; p ?
0.001) and the effort conditions (F(3,45)? 14.71; p ? 0.001) (Fig.
incentive were observed in both the delay (F(15,25)? 1.84, p ?
the fact that increasing ratings of the fuzzy cue more strongly
influenced the choice toward the costly option at intermediate
levels of delay/effort (supplemental Figs. 1, 2, available at www.
jneurosci.org as supplemental material).
Response times increased with the proposed level of delay
(F(5,75)? 4.14; p ? 0.01) or effort (F(5,75)? 6.46; p ? 0.001),
effort (supplemental Fig. 3, available at www.jneurosci.org as
supplemental material). These results show that subjects inte-
grated in their decision both the benefit associated with the cue
and the cost indicated by the proposed level of delay/effort. An
alternative interpretation of our RTs findings in terms of diffi-
point of subjective equivalence between the two options (where
subjects are equally likely to choose the costly or noncostly op-
tions), compared with RTs obtained for other levels. This is not
equivalence in the delay condition) did not significantly differ
alence in the effort condition) did not significantly differ from
p ? 0.1, paired t tests).
Figure 2, e and f, show the subjective value of rewards associ-
ated with the costly and noncostly options in both conditions,
and supplemental Figs. 4, 5, available at www.jneurosci.org as
supplemental material). As expected, the subjective value of the
14084 • J.Neurosci.,October20,2010 • 30(42):14080–14090Pre ´vostetal.•ValuationofEffortandDelayCosts
reward associated with the costly option decreased as the associ-
ated proposed level of delay or effort increased, demonstrating
that delay and effort were effectively perceived as costs. This re-
sult also demonstrates, to the best of our knowledge for the first
time, that the subjective value of a high reward associated with a
larger effort is discounted hyperbolically (see also below, Model
tive to have subjects devalue primary rewards in a few seconds.
Finally, we investigated how much time was needed to exert
each of the six levels of effort (Fig. 2g). The time difference be-
was shorter than a second, and the time required to exert all the
efforts lasted ?2 s, and were all significantly ?3 s (i.e., the time
corresponding to the second smallest level of proposed delay)
valuation of effort and delay (occurring during the decision
phase) are dissociable in the brain.
The behavioral fits of the probability of choosing the costly
options are shown in supplemental Figures 4 and 5 (available
at www.jneurosci.org as supplemental material) for each sub-
ject. To further ensure that a hyperbolic function was a better
fit to subjects’ behavior, we compared hyperbolic and expo-
the modeled and observed data, estimated by the least-square
method, revealed that the hyperbolic fit was better than the
exponential fit to explain subjects’ behavior ( p ? 0.001).
Moreover, the hyperbolic function fitted equally well subjects’
behavior in the delay and effort conditions because the dis-
tance ? between the hyperbolic fit and the behavioral data did
not differ between these two conditions (?delay? 0.1393 ?
0.049, ?effort? 0.1412 ? 0.063; p ? 0.93).
Subjective valuation of delayed/effortful erotic rewards
We used parametric regression analyses to identify the brain re-
the decision-making phase. First, we found that activity in the
ventral striatum and vmPFC increases with higher subjective
yses (see Materials and Methods) confirmed that the activity of
these two brain regions was better accounted by the subjective
value (GLM1) than by the rating of the incentive (GLM2) or the
level of delay (GLM3) taken independently (Fig. 3b, Table 1;
option. Error bars represent SEM. g, Time required to exert each level of effort in the effort-
0.01, one-sample t tests comparing the effect sizes to zero. Note that no statistics were per-
bar plot is simply shown to illustrate the results presented in the regression analysis with
Pre ´vostetal.•ValuationofEffortandDelayCosts J.Neurosci.,October20,2010 • 30(42):14080–14090 • 14085
supplemental Fig. 7a, supplemental Table
1, available at www.jneurosci.org as sup-
plemental material). Other brain regions,
such as the lateral prefrontal cortex, also
showed increasing activities with higher
the rating alone than by the subjective
mental Table 1, available at www.jneurosci.
org as supplemental material). No brain
region showed activity negatively correlat-
When searching for brain regions
showing activity correlating with the sub-
with a more substantial effort, a positive
correlation was found only in the left pri-
mary motor (M1) cortex (Fig. 4a, Table
1), contralateral to the right hand squeez-
better explained by the rating of the cue
(GLM2) than by the increasing subjective
value of the reward associated with a
larger effort (GLM1) (supplemental Fig.
7b, supplemental Table 2, available at
www.jneurosci.org as supplemental ma-
terial), suggesting that it reflects prepara-
tion to engage in the subsequent effort
with higher incentive value of the fuzzy
cue, rather than a valuation signal. More-
over, increased left M1 activity was also observed when more
effort had to be exerted in the cost-enduring phase, showing that
the same brain region is involved in motor preparation and in
execution of the action (supplemental Fig. 6b, available at www.
jneurosci.org as supplemental material).
In contrast, a negative correlation with the subjective value of
the large reward associated with a more substantial effort was
the ACC was better accounted by the subjective value of the ef-
effort level (GLM3), whereas the anterior insula activity was bet-
ter explained by the proposed level of effort (GLM3) than by
other parameters (Fig. 4c; supplemental Fig. 7c, supplemental
Table 3, available at www.jneurosci.org as supplemental mate-
rial). Thus, the ACC may be in a particular position to integrate
both the benefit associated with the fuzzy cue and the effort cost
while the anterior insula assesses the level of energy expenditure
required to reach a proposed effort.
effort condition. Categorical analyses allowed us to extract the
signal percentage change in these regions for different catego-
of rating (GLM5) (Figs. 5b, 6b), and the different levels of pro-
6c show that activity of the ventral striatum/vmPFC and the
ACC/anterior insula valuation systems code in opposite fashion
delayed rewards and future energetic expenses: activity of the
ACC/insula coding the decreasing subjective value of effortful
rewards increase with larger proposed efforts whereas ventral
striatal/vmPFC activity coding the increasing subjective value of
delayed rewards show decreased activity with longer delays. This
indicates that delay and effort costs engage distinct neural
Separate systems for subjective valuation of delayed/effortful
To test whether the brain networks identified with subjective
valuation of delay and devaluation of effort engage separate neu-
ral systems, we also performed direct comparisons of the activi-
ties of brain regions in which the positive correlation with
subjective value of the delayed reward was significantly greater
(respectively lower) than the negative correlation with subjective
value of the effortful reward (supplemental Tables 4, 5, available
at www.jneurosci.org as supplemental material). These direct
whole-brain statistical comparisons of the effects of subjective
value in the effort and delay conditions, demonstrated the spec-
reward and in the devaluation of effortful reward.
Finally, we performed two conjunction analyses, one search-
ing for brain regions positively correlating with the subjective
value of the costly reward during both the delay and effort con-
with the subjective value during the delay condition and nega-
14086 • J.Neurosci.,October20,2010 • 30(42):14080–14090Pre ´vostetal.•ValuationofEffortandDelayCosts
tively correlating with subjective value during the effort condi-
tion. No voxel survived in these conjunctions ( p ? 0.05, cluster
corrected at p ? 0.05), supporting the view that there is neither a
common neural system tracking the value of both delayed and
effortful rewards nor valuing delayed reward and devaluing ef-
Cost-enduring and outcome phases
When investigating which brain regions were modulated by the
level of cost that subjects experienced during the delay and effort
longer only activated the ACC sulcus (supplemental Fig. 6a,
available at www.jneurosci.org as supplemental material, green-
blue). In contrast, exerting more effort activated the adjacent
midbrain, and the left motor cortex (supplemental Fig. 6b, avail-
able at www.jneurosci.org as supplemental material).
were specifically more active at the outcome for the large reward
compared with the small reward (1 s). In the delay condition, we
caudate and the vmPFC compared with the small reward (sup-
plemental Fig. 8a, available at www.jneurosci.org as supplemen-
tal material). Similarly, in the effort condition these same brain
regions were also found to be more active for the large reward
relative to the small reward (supplemental Fig. 8b, available at
www.jneurosci.org as supplemental material).
In a separate analysis, we found, in the delay condition, a
positive correlation between the time waited and activity in the
ventral striatum and ventromedial PFC at the time of outcome
(supplemental Fig. 8c, available at www.jneurosci.org as supple-
mental material). There was no significant correlation between
This study provides the first evidence that humans devalue re-
with delays, as accounted for by the representation of subjective
values used in revealed preference theories (Bernoulli, 1982; Gul
ceived as costly, at the neural level a striking dissociation was
observed between mesolimbic regions (e.g., ventral striatum and
of the delayed reward, and between the anterior cingulate cortex
and the anterior insula, showing a negative correlation with the
subjective value of effortful reward (therefore appearing to de-
value rewards that require greater effort). Critically, although
decreased as the imposed delay to a reward increased (Figs. 3b,
5b,c), the ACC–anterior insula network, coding devaluation of
the effortful reward (Fig. 4b), showed increasing (Fig. 6c)–rather
than decreasing–activity with higher levels of proposed effort,
supporting the hypothesis that the computations of the subjec-
different neural mechanisms.
Thus, our neuroimaging data indicate that distinct valuation
systems compute the subjective value of rewards associated with
opposite fashion delayed and effortful rewards, although similar
behavioral choices and discount utility functions were observed
for delay and effort costs. The fact that the valuation processes
underlying decisions associated with different types of costs can
be fractionated at the cerebral level is consistent with the exis-
in the brain (Rangel et al., 2008; Dreher, 2009), including neuro-
reward, risk, and probabilistic reward (Kuhnen and Knutson,
2005; Dreher et al., 2006; Preuschoff et al., 2006; Peters and
Bu ¨chel, 2009). These different valuation systems may lead to di-
receive a high reward.
Our delay-discounting findings are in accordance with a
number of previous works in animals (Cardinal et al., 2001) as
etary rewards in humans (Kable and Glimcher, 2007; Wittmann
et al., 2007; Ballard and Knutson, 2009; Gregorios-Pippas et al.,
2009). In particular, they are broadly consistent with a recent
fMRI study revealing that the ventral striatum and anterior me-
dial PFC track the subjective value of delayed monetary rewards
(Kable and Glimcher, 2007). Although our vmPFC was more
posterior than the anterior medial PFC reported in this study, it
overlapped with the vmPFC region associated with valuation in
other paradigms (Plassmann et al., 2007; Behrens et al., 2008;
column). a, The subjective value of the delayed reward with trials binned into six categories
Linear regressions were performed to test the linear relationship between percentage BOLD
a–c, Percent signal change in ventral striatum (left column) and vmPFC (right
Pre ´vostetal.•ValuationofEffortandDelayCostsJ.Neurosci.,October20,2010 • 30(42):14080–14090 • 14087
logical manipulation of the ventral stria-
tum and orbitofrontal cortex of rodents
induce behavioral deficits during delay-
based paradigms (Cardinal et al., 2001;
Mobini et al., 2002; Winstanley et al.,
2004; Rudebeck et al., 2006), and in-
often been reported with expected value
and its basic components, reward proba-
bility and magnitude (Knutson et al.,
2005; Dreher et al., 2006; Yacubian et al.,
suggest that subjective valuation signals of
erotic rewards really experienced inside the
scanner are computed in similar limbic
frontostriatal networks than nonexperi-
enced secondary (monetary) rewards,
delayed from minutes to month/years.
Therefore, the neural response to both
primary and secondary reinforcers fol-
lows similar delay-discounting functions,
suggesting that valuation of delayed re-
of neuronal computation, regardless of
the reward nature and the delay duration
incurred before reward delivery.
It is still unclear whether there are dif-
ferences between the valuation of differ-
ent types of primary rewards that are
physiologically necessary (e.g., juice/wa-
ter) or not (erotic pictures). In a previous
cludes the ventral striatum and the
vmPFC, was preferentially activated by choices between an im-
mediate reward and a delayed juice reward in intertemporal
choices (McClure et al., 2007). These findings may appear con-
increased, subjects were more likely to choose the option leading
pare to this previous study because a different type of computa-
tional modeling approach was used to account for the data.
cesses during intertemporal choice: the impatient ?-process,
steeply discounting all nonimmediate rewards; and a more pa-
tient ? process, active in both immediate and delayed trials, less
steeply discounting all delayed rewards. In contrast, our study,
which adopted the approach proposed by Kable and Glimcher
bolically discounted value of both delayed and immediate re-
wards. Moreover, our report that activity in the ventral striatum
and vmPFC vary when only the delay to reward changes pro-
vides direct evidence that these regions do not exclusively
value immediate rewards.
A very different pattern of results was observed in the effort
with higher subjective value of the effortful reward (GLM1), but
(supplemental Fig. 7b, available at www.jneurosci.org as supple-
in the subsequent effort with higher incentive value of the
fuzzy cue, rather than a valuation signal. Consistent with this,
M1 activity was also observed when exerting the effort (sup-
plemental Fig. 6b, available at www.jneurosci.org as supple-
mental material), showing that the same brain region is actually
involved in motor preparation and in execution of the action.
More importantly, a negative correlation with the subjective
was found in the ACC–anterior insula network. This decreasing
activity reflects that when the proposed effort cost increases, the
effortful reward is devalued. This devaluation reflects that the
proposed effort is perceived in terms of engagement of energy
since higher ACC/anterior insula activity was also observed with
contrast, no brain region showed increased activity with higher
by the subjective value of the effortful option than by the level of
proposed effort or the rating of the cue, indicating that the ACC
opposed to a more basic motor preparatory signal.
These results demonstrate a critical role of the ACC–anterior
insula network for evaluating whether or not it is worth produc-
ing a given effort for the reward at stake. Importantly, ACC ac-
tivity did not correlate with the subjective value of the delayed
reward. This implies that the ACC is not merely involved when-
specifically when evaluating the benefits of exerting more effort
for a higher reward compared with a less rewarding option that
requires less energy expenditure. These findings may explain the
subjective value of the effortful reward with trials binned into six categories such that each bin has an equal number of trials
14088 • J.Neurosci.,October20,2010 • 30(42):14080–14090 Pre ´vostetal.•ValuationofEffortandDelayCosts
impairment of ACC-lesioned animals in effort-based decision
making (Walton et al., 2003; Rudebeck et al., 2006) as being the
result of a dysfunction in evaluating the reward associated with
the costly effort and additionally predict that anterior insula le-
sions in humans may lead to deficits in assessing effort expenses.
codes the interaction between the expected reward amount and
the effort cost (Croxson et al., 2009). However, in this study
subjects did not decide between more or less effortful options.
Overall, the present findings show that the ACC plays a pivotal
role in evaluating whether the proposed level of effort is worth
being engaged, considering the benefit of one course of action
(Walton et al., 2006; Rushworth et al., 2007). Our ACC finding
also converges with monkey electrophysiological recordings in-
dicating that ACC neuronal activity reflects the integrated value
of a course of action, encoding a combination of several deci-
(Kennerley et al., 2009) and with the fact that ACC neurons en-
code a monkey’s progression through the series of work steps
toward reward—although in this task effort and delay were not
separated (Shidara and Richmond, 2002). However, our results
are not a mere confirmation of animal studies suggesting that
there are different brain systems for effort and delay costs. In-
valuation, the decision, or the delay/effort execution stages. Our
which brain structures showed activity positively or negatively
correlating with the subjective value of effortful or delayed pri-
isolate this valuation stage from the delay period and effort exer-
tion phases (supplemental Fig. 7, available at www.jneurosci.org
as supplemental material).
The ventral striatum, the ACC, and the vmPFC are strongly
implicated in cost/benefit decision making. Yet, their relative
roles have never been directly simultaneously compared using a
similar design for decisions concerning delay and effort costs.
Our paradigm, which separately manipulates the benefit (cue)
and the cost, indicates that during the effort condition ventral
striatal and vmPFC responses did not correlate with the subjec-
tive value of the effortful reward or with the level of proposed
effort. This result demonstrates that the ventral striatal value sig-
come to a similar conclusion (Walton et al., 2009; Gan et al.,
2010). In particular, ventral striatal phasic dopamine release has
been reported to reflect the magnitude of the benefit, but not the
expected effort (Gan et al., 2010). Consistent with this finding,
ventral striatal activity positively correlated with the rating of the
cue (benefit) in both the delay and effort conditions, but was not
modulated by the proposed level of effort (supplemental Fig. 9,
available at www.jneurosci.org as supplemental material). Al-
though the relationship between striatal dopamine release and
the BOLD signal is still unclear, analyses of variations in genes
involved in striatal dopamine transmission establish a link be-
tween higher striatal BOLD signal and dopamine synaptic avail-
ability during reward processing (Dreher et al., 2009). A direct
relationship between midbrain dopamine synthesis and BOLD
signal changes in the reward system has also been demonstrated
(Dreher et al., 2008). Together, our current results help to pin-
point the specific roles of brain regions specifically involved dur-
ing the valuation stage of decisions related to delay and effort
making signals in the human brain by revealing that distinct val-
uation subsystems are engaged for different types of costs, and
code in opposite fashion delayed rewards and future energetic
expenses. From an evolutionary perspective, separate valuation
systems may have evolved through the need of responding to
distinct types of costs in different environments. For example,
reward, whereas the opposite is true for other species (Stevens et
al., 2005). Finally, our demonstration that separate neural sys-
tems track the subjective value of rewards associated with differ-
ent types of costs may prove useful for understanding impulsive
(delay aversion) and apathetic (effort aversion) behavior in a
number of neuropsychiatric disorders known to impair the ca-
pacity to select between available options based on an evaluation
of their potential costs and benefits (Paulus, 2007).
Agmo A (1999) Sexual motivation–an inquiry into events determining the
occurrence of sexual behavior. Behav Brain Res 105:129–150.
AinslieG (1975) Speciousreward:abehavioraltheoryofimpulsivenessand
impulse control. Psychol Bull 82:463–496.
Ballard K, Knutson B (2009) Dissociable neural representations of future
reward magnitude and delay during temporal discounting. Neuroimage
Behrens TE, Hunt LT, Woolrich MW, Rushworth MF (2008) Associative
learning of social value. Nature 456:245–249.
Bernoulli D (1982) The works. Boston: Birkhauser.
Impulsive choice induced in rats by lesions of the nucleus accumbens
core. Science 292:2499–2501.
Croxson PL, Walton ME, O’Reilly JX, Behrens TE, Rushworth MF (2009)
Effort-based cost-benefit valuation and the human brain. J Neurosci
Dreher J-C (2009) Decomposing brain signals involved in value-based de-
Tremblay L, eds), pp 137–164. New York: Academic, Elsevier.
Dreher JC, Kohn P, Berman KF (2006) Neural coding of distinct statistical
properties of reward information in humans. Cereb Cortex 16:561–573.
Dreher JC, Schmidt PJ, Kohn P, Furman D, Rubinow D, Berman KF (2007)
Menstrual cycle phase modulates reward-related neural function in
women. Proc Natl Acad Sci U S A 104:2465–2470.
Dreher JC, Meyer-Lindenberg A, Kohn P, Berman KF (2008) Age-related
changes in midbrain dopaminergic regulation of the human reward sys-
tem. Proc Natl Acad Sci U S A 105:15106–15111.
DreherJC,KohnP,KolachanaB,WeinbergerDR,BermanKF (2009) Vari-
ation in dopamine genes influences responsivity of the human reward
system. Proc Natl Acad Sci U S A 106:617–622.
Floresco SB, Tse MT, Ghods-Sharifi S (2008) Dopaminergic and glutama-
tergic regulation of effort- and delay-based decision making. Neuropsy-
Frederick S, Loewenstein G, O’Donoghue T (2002) Time discounting and
time preference: a critical review. J Econ Lit 40:351–401.
GanJO,WaltonME,PhillipsPE (2010) Dissociablecostandbenefitencod-
ing of future rewards by mesolimbic dopamine. Nat Neurosci 13:25–27.
Gregorios-PippasL,ToblerPN,SchultzW (2009) Short-termtemporaldis-
counting of reward value in human ventral striatum. J Neurophysiol
Grinband J, Hirsch J, Ferrera VP (2006) A neural representation of catego-
rization uncertainty in the human brain. Neuron 49:757–763.
Gul F, Pesendorfer W (2005) The case for mindless economics. Working
paper. Princeton, NJ: Department of Economics, Princeton University.
HamannS,HermanRA,NolanCL,WallenK (2004) Menandwomendiffer
in amygdala response to visual sexual stimuli. Nat Neurosci 7:411–416.
Hare TA, Camerer CF, Rangel A (2009) Self-control in decision-making
involves modulation of the vmPFC valuation system. Science
Hayden BY, Parikh PC, Deaner RO, Platt ML (2007) Economic principles
motivating social attention in humans. Proc Biol Sci 274:1751–1756.
HoonEF,ChamblessD (1998) Sexualarousabilityinventory(SAI)andsex-
Pre ´vostetal.•ValuationofEffortandDelayCostsJ.Neurosci.,October20,2010 • 30(42):14080–14090 • 14089
related measures (Davis CM, YWL, Bauserman R, Schreer G, Davis SL,
eds), pp 71–74. Washington, DC: American Psychiatric Association.
Huettel SA, Song AW, McCarthy G (2005) Decisions under uncertainty:
probabilistic context influences activation of prefrontal and parietal cor-
tices. J Neurosci 25:3304–3311.
Kable JW, Glimcher PW (2007) The neural correlates of subjective value
during intertemporal choice. Nat Neurosci 10:1625–1633.
Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision
under risk. Econometrica 47:263–291.
Kennerley SW, Dahmubed AF, Lara AH, Wallis JD (2009) Neurons in the
frontal lobe encode the value of multiple decision variables. J Cogn Neu-
Knutson B, Fong GW, Adams CM, Varner JL, Hommer D (2001) Dissocia-
tion of reward anticipation and outcome with event-related fMRI. Neu-
Knutson B, Taylor J, Kaufman M, Peterson R, Glover G (2005) Distributed
neural representation of expected value. J Neurosci 25:4806–4812.
Kuhnen CM, Knutson B (2005) The neural basis of financial risk taking.
McClureSM,LaibsonDI,LoewensteinG,CohenJD (2004) Separateneural
systems value immediate and delayed monetary rewards. Science
McClure SM, Ericson KM, Laibson DI, Loewenstein G, Cohen JD (2007)
Time discounting for primary rewards. J Neurosci 27:5796–5804.
(2002) Effects of lesions of the orbitofrontal cortex on sensitivity to de-
layed and probabilistic reinforcement. Psychopharmacology (Berl)
Patton JH, Stanford MS, Barratt ES (1995) Factor structure of the Barratt
impulsiveness scale. J Clin Psychol 51:768–774.
Paulus MP (2007) Decision-making dysfunctions in psychiatry–altered ho-
meostatic processing? Science 318:602–606.
Peters J, Bu ¨chel C (2009) Overlapping and distinct neural systems code for
subjective value during intertemporal and risky decision making. J Neu-
Pine A, Seymour B, Roiser JP, Bossaerts P, Friston KJ, Curran HV, Dolan RJ
(2009) Encoding of marginal utility across time in the human brain.
J Neurosci 29:9575–9581.
Plassmann H, O’Doherty J, Rangel A (2007) Orbitofrontal cortex encodes
willingness to pay in everyday economic transactions. J Neurosci
Preuschoff K, Bossaerts P, Quartz SR (2006) Neural differentiation of ex-
pected reward and risk in human subcortical structures. Neuron
Rangel A, Camerer C, Montague PR (2008) A framework for studying the
neurobiology of value-based decision making. Nat Rev Neurosci
Rudebeck PH, Walton ME, Smyth AN, Bannerman DM, Rushworth MF
(2006) Separate neural pathways process different decision costs. Nat
Rudebeck PH, Behrens TE, Kennerley SW, Baxter MG, Buckley MJ, Walton
ME,RushworthMF (2008) Frontalcortexsubregionsplaydistinctroles
in choices between actions and stimuli. J Neurosci 28:13775–13785.
RushworthMF,BehrensTE,RudebeckPH,WaltonME (2007) Contrasting
iour. Trends Cogn Sci 11:168–176.
Schweighofer N, Shishida K, Han CE, Okamoto Y, Tanaka SC, Yamawaki S,
Doya K (2006) Humans can adopt optimal discounting strategy under
real-time constraints. PLoS Comput Biol 2:e152.
Sescousse G, Redouté, Dreher JC (2010) The architecture of reward value
coding in the orbitofrontal cortex. J Neurosci 30:13095–13104.
Shidara M, Richmond BJ (2002) Anterior cingulate: single neuronal signals
related to degree of reward expectancy. Science 296:1709–1711.
Smith DV, Hayden BY, Truong TK, Song AW, Platt ML, Huettel SA (2010)
Distinct value signals in anterior and posterior ventromedial prefrontal
cortex. J Neurosci 30:2490–2495.
Spira A, Bajos N, Giami A, Laurent R, Leridon H (1993) Les comporte-
ments sexuels en France. Paris: La Documentation Franc ¸aise.
Stevens JR, Rosati AG, Ross KR, Hauser MD (2005) Will travel for food:
spatial discounting in two new world monkeys. Curr Biol 15:1855–1860.
Tanaka SC, Doya K, Okada G, Ueda K, Okamoto Y, Yamawaki S (2004)
Prediction of immediate and future rewards differentially recruits
cortico-basal ganglia loops. Nat Neurosci 7:887–893.
Walton ME, Bannerman DM, Alterescu K, Rushworth MF (2003) Func-
for evaluating effort-related decisions. J Neurosci 23:6475–6479.
Walton ME, Kennerley SW, Bannerman DM, Phillips PE, Rushworth MF
of effort-related decision making. Neural Netw 19:1302–1314.
Walton ME, Groves J, Jennings KA, Croxson PL, Sharp T, Rushworth MF,
Bannerman DM (2009) Comparing the role of the anterior cingulate
cortex and 6-hydroxydopamine nucleus accumbens lesions on operant
effort-based decision making. Eur J Neurosci 29:1678–1691.
Weiskopf N, Hutton C, Josephs O, Deichmann R (2006) Optimal EPI pa-
rameters for reduction of susceptibility-induced BOLD sensitivity losses:
a whole-brain analysis at 3 T and 1.5 T. Neuroimage 33:493–504.
Winstanley CA, Theobald DE, Cardinal RN, Robbins TW (2004) Contrast-
ing roles of basolateral amygdala and orbitofrontal cortex in impulsive
choice. J Neurosci 24:4718–4722.
Wittmann M, Leland DS, Paulus MP (2007) Time and decision making:
differential contribution of the posterior insular cortex and the striatum
during a delay discounting task. Exp Brain Res 179:643–653.
Yacubian J, Gla ¨scher J, Schroeder K, Sommer T, Braus DF, Bu ¨chel C (2006)
of prediction in the human brain. J Neurosci 26:9530–9537.
14090 • J.Neurosci.,October20,2010 • 30(42):14080–14090Pre ´vostetal.•ValuationofEffortandDelayCosts
Supplemental material for
Distinct valuation subsystems in the human brain
for effort and delay
Charlotte Prévost, Mathias Pessiglione, Elise Météreau, Marie-Laure Cléry-Melin and
This PDF file includes :
- Supplemental Figures 1-9.
- Supplemental Tables 1-5.
Activations related to the cost-enduring phase
Waiting longer induced higher activity only in the ACC sulcus (green-blue,
Supplemental fig. 6a) while exerting more effort led to increase activity in the adjacent ACC
gyrus (ACCg) (Supplemental fig. 6a), the right amygdala, the dopaminergic midbrain (in the
possible vicinity of the substantia nigra) and the left motor cortex (Supplemental fig. 6b).
Thus, the two types of cost, delay and effort, are represented by different cortical regions not
only during the valuation processes but also during the cost-enduring phase when the level
of cost effectively increases.
Our results concerning the effort exertion phase indicate that ACC activity increases
while exerting higher effort, consistent with previous fMRI studies showing that the ACC is
involved during physical efforts (Critchley et al., 2003; Croxson et al., 2009) and additionally
indicate that ACC activity increases with higher proposed level of effort, before any effort is
exerted (Fig. 4). Thus, the ACC codes both future and actual energy expenditure, providing a
critical link between motivation and action.
Activity in the dopaminergic midbrain and right amygdala also increased as subjects
exerted more efforts, likely reflecting activity related to effort at stakes, consistent with both
animal and modeling approaches suggesting that dopamine neurotransmission exert a
powerful influence over the vigor and strength of responding (Niv et al., 2007; Assadi et al.,
2009; Ghods-Sharifi et al.) and with the implication of the amygdala in effort-based decision-
making and in representing information about how more effort is needed before reward
delivery (Floresco and Ghods-Sharifi, 2007; Ghods-Sharifi et al., 2009). In both monkeys and
humans, the ACCg is connected with brain regions concerned with the processing of reward
and emotion, such as the amygdala (Van Hoesen et al., 1993; Beckmann et al., 2009).
Previous delay-discounting studies in humans using monetary rewards were not
designed to investigate which brain regions respond to the actual experience of a delay
period because they used delays ranging from days to months, well beyond the range of a
few seconds used in animals (but see Gregorios-Pippas et al., 2009). The fact that distinct
human ACC regions may be responsible for experiencing delay and effort costs may shed
lights on inconsistent findings in rodents reporting either no influence of ACC lesions on
delay-discounting (Rudebeck et al., 2006) or ACC lesions leading to disinhibition or
‘execution’ impulsivity (motor impulsivity), over-responding to unrewarded stimuli (Bussey et
al., 1997; Parkinson et al., 2000) and prematurely responding in situations where they are
required to wait (Muir et al., 1996).
Outcome-related activity for large versus small rewards
In the delay condition, we found that the large reward induced higher activity in the
ventral caudate and the ventromedial PFC as compared to the small reward (Supplemental
Fig. 8a). In the effort condition, similar brain regions were also found to be more active for the
long reward relative to the short reward (Supplemental Fig. 8b), reflecting that the
experienced value of the large reward induces similar effects regardless of the type of cost
previously experienced, and confirming the role of these brain regions in experiencing reward
value (Knutson et al., 2001; Sescousse et al., 2010).
In a separate analysis, we used a similar model to our main GLM1, except that this
model included a parametric modulation by the level of the cost during the delay period and
during the effort exerted, and also during the outcome period for the delay and effort
conditions separately. We found a positive correlation between the time waited and activity in
the ventral striatum and ventromedial PFC at the time of outcome in the delay condition
(Supplemental fig. 8c) (p<0.001, uncorrected; cluster-level threshold of p<0.05 corrected for
multiple comparisons). There was no significant correlation between the effort exerted and
BOLD response at the outcome in the effort condition. Thus, the neural representation of the
outcome value of erotic stimuli are related to delay costs but not with efforts associated with
obtaining these rewards, contrary to the assumption that such outcome value are unrelated
to different types of costs (Peters and Buchel, 2010).
4 Download full-text
Supplemental figure 1. Probability of choosing the costly option according to the rating of
the cue and the proposed level of delay. Subjects discounted the value of the delayed
reward, choosing more frequently the costly option for lower proposed levels of delay (F(5,75)
= 46.30; p<0.001), as well as for higher ratings of the incentive (assessed by post-scan
ratings of the fuzzy cues) (F(3,45) = 7.21; p<0.001). Interactions between the level of delay and
ratings were observed (F(15,25)=1.84, p<0.05), due to the fact that increasing rating of the
fuzzy cue more strongly influenced the choice towards the costly option at intermediate
levels of delay.
Supplemental figure 2. Probability of choosing the costly option according to the rating of
the cue and the proposed level of effort. Subjects provided clear evidence of effort-
discounting in their preferences, choosing more frequently the costly option for lower
proposed levels of effort (F(5,75) = 54.67; p<0.001), as well as for higher ratings of the
incentive (F(3,45) = 14.71; p<0.001). Interactions between the level of effort and ratings of the
cue were observed (F(15,25)=2.68, p<0.001), due to the fact that increasing rating of the fuzzy
cue more strongly influenced the choice towards the costly option at intermediate levels of