Differential Fairness Decisions and Brain Responses After Expressed Emotions of Others in Boys with Autism Spectrum Disorders
Differential Fairness Decisions and Brain Responses After Expressed Emotions of Others in Boys with Autism Spectrum Disorders
Eduard T. Klapwijk 0 1 2 3 4 5 6
Moji Aghajani 0 1 2 3 4 5 6
Gert‑Jan Lelieveld 0 1 2 3 4 5 6
Natasja D. J. van Lang 0 1 2 3 4 5 6
Arne Popma 0 1 2 3 4 5 6
Nic J. A. van der Wee 0 1 2 3 4 5 6
Olivier F. Colins 0 1 2 3 4 5 6
Robert R. J. M. Vermeiren 0 1 2 3 4 5 6
0 Department of Psychiatry, VU University Medical Center , Amsterdam , The Netherlands
1 Leiden Institute for Brain and Cognition (LIBC) , Leiden , The Netherlands
2 Department of Child and Adolescent Psychiatry, Curium - Leiden University Medical Center , Postbus 15, 2300 AA Leiden , The Netherlands
3 Department of Psychiatry, Leiden University Medical Center , Leiden , The Netherlands
4 Institute of Criminal Law & Criminology, Faculty of Law, Leiden University , Leiden , The Netherlands
5 Department of Child and Adolescent Psychiatry, VU University Medical Center , Amsterdam , The Netherlands
6 Institute of Psychology, Leiden University , Leiden , The Netherlands
Little is known about how emotions expressed by others influence social decisions and associated brain responses in autism spectrum disorders (ASD). We investigated the neural mechanisms underlying fairness decisions in response to explicitly expressed emotions of others in boys with ASD and typically developing (TD) boys. Participants with ASD adjusted their allocation behavior in response to the emotions but reacted less unfair than TD controls in response to happiness. We also found reduced brain responses in the precental gyrus in the ASD versus TD group when receiving happy versus angry reactions and autistic traits were positively associated with activity in the postcentral gyrus. These results provide indications for a Electronic supplementary material The online version of this article (doi:10.1007/s10803-017-3159-4) contains supplementary material, which is available to authorized users.
Social decision-making; Autism spectrum disorders; Interpersonal effects of emotions; Dictator game; fMRI
* Eduard T. Klapwijk
Difficulties in reciprocal social interactions and
communication are among the core features of autism spectrum
disorders (ASD), along with a restricted repertoire of
activities and interests (American Psychiatric Association 2013).
These social deficits have been documented in numerous
studies showing that individuals with ASD have
impairments in the ability to represent other people’s mental states
(i.e., mentalizing; Baron-Cohen et al. 1985; Kaland et al.
2008) and in processing emotions of others (Adolphs et al.
2001; Hobson 1986; Uljarevic and Hamilton 2013).
Neuroimaging studies have also revealed differences between
individuals with ASD compared to typically developing
(TD) individuals in brain areas relevant for
social-affective functioning (Di Martino et al. 2009; Fishman et al.
2014; Frith 2001; Pelphrey et al. 2011; Philip et al. 2012;
White et al. 2014). These studies suggest that social
deficits in ASD are associated with atypical activation in brain
areas involved in mentalizing, such as hypoactivation in
the medial prefrontal cortex (mPFC) and temporoparietal
junction (TPJ) (e.g., Castelli et al. 2002; Wang et al. 2007;
Watanabe et al. 2012), as well as in brain areas relevant for
processing and resonating with others’ emotions such as
hypoactivation in the inferior frontal gyrus and both
underand overactivation in the amygdala (e.g., Greimel et al.
2010; Klapwijk et al. 2016a; Monk et al. 2010; Pelphrey
et al. 2007; Swartz et al. 2013).
In most of the neuroimaging studies on social processing
in ASD, participants are merely required to observe
others or to think about their mental states (e.g., Kana et al.
2015; Schulte-Ruther et al. 2011; Vander Wyk et al. 2014).
Although these studies have greatly advanced the
understanding of the neurocognitive mechanisms associated
with social deficits in ASD, most do not take more
interactive elements of social exchange into account. Studying
such elements, however, is essential, as responding towards
others involves different cognitive processes than merely
observing others’ behavior (Schilbach et al. 2013). This
is especially important because a discrepancy has been
reported between potentially normative performance on
explicit social tasks in ASD versus difficulties in applying
social abilities during social interactions (Klin et al. 2003).
For example, although adults with ASD do not
spontaneously attribute mental states to others, they are able to
understand mental states of others when they are explicitly
encouraged to mentalize (Moran et al. 2011; Senju et al.
Paradigms inspired by behavioral economics are
increasingly used to investigate social cognitive processes
underlying social interactions in psychiatric populations (Hasler
2012; Sharp et al. 2012) including ASD (Chiu et al. 2008;
Sally and Hill 2006; Yoshida et al. 2010). These paradigms
not only offer simplicity and experimental control, but also
have the advantage that they model interactive elements of
social exchanges (King-Casas and Chiu 2012; Rilling and
Sanfey 2011). Previous experiments using economic games
suggest that people with ASD are indeed impaired in
executing mentalizing abilities during interactive games. For
example, adolescents with ASD show a different response
in the middle cingulate cortex compared to controls when
deciding to reciprocate investments in the trust game,
suggesting problems with mentalizing during online social
interaction (Chiu et al. 2008; Frith and Frith 2008). In a
different strategic game, the stag hunt game, players can
cooperate to hunt highly valued stags or act alone and hunt
rabbits of lower value. Yoshida et al. (2010) used this game
to estimate participants’ representations of the other
player’s intentions for cooperation. They found that adults with
ASD made less use of these representations than control
participants when playing the game (Yoshida et al. 2010).
Further evidence comes from a study in which children
with ASD had to judge others’ morality and subsequently
played a cooperative game both with the child they judged
to be morally ‘nice’ and ‘bad’. This study showed that
children with ASD (in contrast to TD children) did not
distinguish between morally good and bad partners in the
cooperative game but did correctly judge others’ morality in
basic moral judgment stories (Li et al. 2014). These studies
using economic games thus also suggest that individuals
with ASD are able to make explicit inferences about others’
intentions but are less effective in using this information
when making interactive decisions.
Although it has been suggested that individuals with
ASD are impaired in processing emotions of others
(Adolphs et al. 2001; Baron-Cohen et al. 1997; Harms et al.
2010), studies using economic games among individuals
with ASD did not focus on the role of emotions in social
interactions. However, many studies in healthy populations
have shown that emotions expressed by others during
interactions can influence subsequent behavior of the observer
(van Kleef et al. 2010). For example, disappointed
reactions of others might lead to fairer subsequent responses
in observers than angry reactions of others (Lelieveld et al.
2012, 2013b), whereas during negotiations displays of
happiness might signal satisfaction leading to lower offers
(van Kleef et al. 2004). Currently, evidence suggests that
individuals with ASD are less likely to integrate emotional
contextual cues into their decision-making (De Martino
et al. 2008). Yet little is known about how they make social
decisions in response to emotions during social interaction.
Therefore, in the current study we examined if emotions
expressed by others influence fairness decisions and
associated brain responses in boys with ASD compared with TD
controls. While being scanned, participants were presented
with written expressions of anger, disappointment and
happiness by peers in response to an earlier decision about
dividing tokens, after which they were given the
opportunity to divide tokens again. A previous study using this
paradigm found that TD adolescents reacted with more fair
allocations after they read disappointed reactions compared
with angry and happy reactions from their peers (Klapwijk
et al. 2013). Neuroimaging studies that used this paradigm
found that when TD participants received happy reactions
they showed increased responses in the TPJ, a brain area
that is important for mentalizing and attention (Klapwijk
et al. 2016b; Lelieveld et al. 2013a).
Based on previous work showing that individuals with
ASD made less use of social information when making
decisions (Izuma et al. 2011; Li et al. 2014; Yoshida et al.
2010), we expected that they would be less likely to
integrate emotional contextual information into their
decisionmaking processes. This would be reflected in less
differences in fairness decisions between the three emotions in
the ASD versus TD group. Predictions for neuroimaging
results were based on previous studies in ASD that revealed
altered activation compared to controls in brain regions
involved in social cognition. Whereas most previous studies
used facial emotions, the current study used written
emotions, and we therefore expected to find differences in
frontotemporal brain regions involved both in social cognition
and language processing. For example, reduced activation
in the inferior frontal gyrus has been reported in ASD when
presenting emotional faces (e.g., Baron-Cohen et al. 1999;
Greimel et al. 2010; Holt et al. 2014) and altered
activation in ASD in this region during mentalizing and social
cognition has been identified in two meta-analyses (Di
Martino et al. 2009; Philip et al. 2012). Furthermore, prior
studies that used the same paradigm as in the current study
showed that the TPJ is sensitive to happy reactions in TD
controls (Klapwijk et al. 2016b; Lelieveld et al. 2013a).
Given reports of reduced TPJ activation in social tasks in
ASD (Castelli et al. 2002; Lombardo et al. 2011), we also
expected group differences here.
Male adolescents with ASD were recruited from
specialized child psychiatric centers providing both inpatient and
outpatient care for persons with ASD; TD control
adolescents were recruited through local advertisement. All
participants were aged 15–19 years (see Table 1 for participant
characteristics). Exclusion criteria were (central)
neurological abnormalities, a history of epilepsy or seizures,
head trauma, left-handedness, and IQ less than 75.
Intelligence was estimated using the Wechsler Adult Intelligence
Scale—third edition (WAIS-III) or Wechsler Intelligence
Scale for Children—third edition (WISC-III) subscales
Vocabulary and Block Design.
The ASD group consisted of 23 adolescent boys with a
clinical ASD diagnosis of whom 21 completed both phases
of the task (see Experimental Task section below). Data
from two ASD participants were discarded due to
excessive motion, leaving a final sample of 19 participants with
Table 1 Group characteristics
ASD. Two of the 19 boys were diagnosed with autistic
disorder, nine with Asperger’s syndrome, and eight with
pervasive developmental disorder not otherwise
specified (PDD-NOS) according to the DSM-IV-TR criteria. In
addition, according to diagnostic information from their
clinicians, two participants also met DSM-IV-TR criteria
for ADHD, two for dysthymia, and one for major
depression. The autism diagnostic observational schedule-generic
(ADOS-G; Lord et al. 2000) and autism diagnostic
interview-revised (ADI-R; Lord et al. 1994) were administered
besides clinical judgment. Seventeen participants met the
criteria for autism or ASD on the Social Interaction and
Communication domains of the ADOS-G, and two scored
above the cut-off point only in one of these domains.
However, these two participants fulfilled the ADI-R criteria
for autism. We were able to administer the ADI-R for 17
participants and all 17 fulfilled the autism criteria on the
ADI-R Social Interaction and Communications domains.
Review of the medical charts of the other two indicated that
autistic features were already present from an early age.
Nine participants with ASD took medication at the time of
testing (N = 1 atypical antipsychotics, N = 2
psychostimulants, N = 3 selective serotonin re-uptake inhibitors, N = 3
multiple medications). The social responsiveness scale
self-report version (SRS-A) (Constantino and Gruber 2002;
Constantino and Todd 2005) was used as a quantitative
measure of autistic traits.
Thirty-seven TD control boys participated of whom 34
completed both phases of the task (see Experimental Task
section below). Data from one TD participant was
discarded due to excessive motion and another 14 for
groupwise matching for age and IQ, leaving a final sample of 19
TD participants. All TD participants were screened using
the SRS-A in order to exclude participants with heightened
autistic traits (i.e., SRS-A T-score > 60). The youth self
Autism spectrum disorders
(ASD) (N = 19)
(TD) (N = 19)
*Significantly different at p < 0.001
aSelf-report of affective and cognitive empathy was measured using the Basic Empathy Scale (Jolliffe and
bYSR is reported for N = 18 TD, due to missing data for one TD participant
report (YSR; Achenbach 1991) was used to assess general
psychopathology; data for one participant were missing but
none of the other TD boys scored in the clinical range on
the YSR externalizing or internalizing scales.
We examined participants’ fairness choices in the Dictator
Game (Kahneman et al. 1986) after receiving emotional
reactions from others, using a procedure previously used
in studies with adults and (conduct disordered) adolescents
(Klapwijk et al. 2013, 2016b; Lelieveld et al. 2013a).
Participants first took part in a preliminary study 1 week before
scanning (first phase of the experiment), where they read
a scenario after which they were instructed to divide ten
tokens between themselves and another person. They could
choose a 6–4 distribution in favor of themselves, an equal
distribution (5–5), or a distribution in favor of the other
(4–6). This negotiation scenario was intended to assure that
most participants chose the 6–4 option in this phase of the
study. Only participants that chose a 6–4 distribution took
part in the second phase of the experiment during scanning
(21 out of 23 ASD boys and 34 out of 37 TD boys chose a
6–4 distribution). Hereby we assured the credibility of the
second phase in which angry, disappointed, or happy
emotional reactions would be directed at the 6–4 offer chosen in
the first phase. Using these three emotions allows for
comparisons of the effects of negative and positive
communicated emotions and the effects of different types of negative
emotions (Lelieveld et al. 2013a; van Kleef et al. 2010).
Additionally, although it is not uncommon to find angry
and disappointed reactions in response to a 6–4 distribution
because of the relative unfairness of this distribution, happy
reactions should be considered acceptable since offers of
around 40% of the total are mostly accepted in economic
games (Falk and Fischbacher 2006).
In the second phase of the experiment, the boys were
told that their unfair offer (the 6–4 distribution chosen
in the first phase) was presented to 60 peers who were
given the opportunity to write out their reaction upon
receiving the offer. In reality, the reactions were
preprogrammed and we left at least one week between the
first and second phase to increase the credibility that
researchers actually collected reactions from others.
During scanning, participants were paired with a different
player on each trial, whose first name was provided and
whose reaction to the 6–4 distribution was angry,
disappointed, or happy. Participants read the reactions of their
peers and subsequently played a version of the
Dictator Game with the peer who provided the reaction (see
Fig. 1). In this Dictator Game the participants were the
allocator and had to divide ten tokens. They could choose
between different fair and unfair distributions while the
recipient had to accept any distribution they would make.
Each trial started with a jittered fixation (min = 0.55 s,
max = 4.95 s, M = 1.54 s), after which the participants
were presented with the emotional reaction for a period
of 3 s plus a jittered interval (min = 0.55 s, max = 4.95 s,
M = 1.54 s) and subsequently had 6 s to make a decision
between two distributions. The 60 trials were presented
in pseudo-random order divided over three blocks of
4 min each. Before the task started, participants learned
that at the end of the experiment the computer would
randomly select ten trials to determine their total earnings,
which would be added to the standard compensation for
their participation. At the end of the session, participant’s
pay-off was presented, which varied between 2.5 and 6
euros. Afterwards, participants completed a questionnaire
in which they were probed for suspicion. None of the
participants expressed doubt about the set-up of the task.
Fig. 1 Visual display and timing (in milliseconds; ms) of the task in
the scanner. The emotional reaction of the recipient (here “emotional
reaction”) was displayed after a jittered fixation cross. Subsequently,
the screen displayed two offers each containing red and blue tokens,
which indicated the share for the allocator and the recipient,
respectively (here 5–5 vs. 7–3). The name of the allocator was displayed
in red (here “allocator”) and the name of the recipient in blue (here
“recipient”). If participants did not respond within 6000 ms, a screen
displaying ‘Too late!’ was presented. After the response, the decision
screen remained on the screen until 6000 ms after the onset of the
decision screen. (Color figure online)
fMRI Data Acquisition
Imaging was carried out at the Leiden University
Medical Center on a 3 T Philips Achieva MRI scanner. Prior
to scanning, participants were familiarized with the
scanner environment using a mock scanner. For fMRI, T2*
weighted gradient echo, echo planar images (EPI) sensitive
to BOLD contrast were obtained with the following
acquisition parameters: repetition time (TR) = 2.2 s, echo time
(TE) = 30 ms, flip angle= 80°, 38 axial slices, field of view
(FOV) = 220 × 220 mm, 2.75 mm isotropic voxels, 0.25 mm
slice gap. Data from participants with excess motion
defined by relative mean displacement> 0.5 mm were
excluded from further analysis (ASD N = 2; TD N = 1). A
high-resolution anatomical image (T1-weighted ultra-fast
gradient-echo acquisition; TR = 9.75 ms, TE = 4.59 ms, flip
angle = 8°, 140 axial slices, FOV = 224 × 224 mm, in-plane
resolution 0.875 × 0.875 mm, slice thickness = 1.2 mm) was
acquired for registration purposes. All anatomical scans
were reviewed by a radiologist; no anomalies were found.
fMRI Data Analysis
FMRI data analysis was conducted using FEAT (fMRI
expert analysis tool) version 6.00, part of FSL (http://www.
fmrib.ox.ac.uk/fsl). The following prestatistics processing
was applied: motion correction using MCFLIRT, non-brain
removal using BET, spatial smoothing using a Gaussian
kernel of FWHM 5 mm, grand-mean intensity
normalization of the entire 4D dataset by a single multiplicative
factor, and high-pass temporal filtering (Gaussian-weighted
least-squares straight line fitting, with sigma= 50.0 s).
Functional scans were registered to the T1-weighted
anatomical images, and subsequently to the 2 mm MNI-152
standard space template. Time-series statistical analysis
was performed using FILM with local autocorrelation
correction. To investigate the effects of the communicated
emotions, we modeled the onset of the presentation of the
three different emotional reactions (i.e., anger,
disappointment, happiness) as an event with zero duration convolved
with a gamma hemodynamic response function. To account
for residual movement artifacts, the six realignment
parameters (three for translation in mm and three for rotation in
degrees) were included in the model as covariates of no
interest. Note that in the final sample used in the present
study there were no significant differences in the six
realignment parameters (all p > 0.05) between the ASD and
TD groups. At first-level for each run for each participant,
primary contrasts of interest were generated. Positive
versus negative emotions were contrasted [happiness > [(anger
and disappointment)] as well as happiness against the
separate negative emotions (happiness > anger; happiness >
disappointment) and the negative emotions against each other
(anger > disappointment). A second-level, fixed-effects
analysis combined data across the three runs for each
participant. Individual participant data were then entered into
a third-level group analysis using a mixed-effects design
(FLAME) whole-brain analysis. The general linear model
included the two groups (ASD and TD) and to account
for possible age effects, we included age (mean-centered)
as covariate of no interest. In addition, in the ASD group
we analyzed the effects of autistic traits on brain responses
during the different contrasts by using SRS scores as
regressors of interest, adding age (mean-centered) as
covariate of no interest. Resulting statistical maps were corrected
for multiple comparisons using cluster-based correction
(p < .0.05, initial cluster-forming threshold Z > 2.3). We
used Featquery and SPSS version 22 (IBM Corp., Armonk,
NY, USA) to conduct region of interest (ROI) analyses to
correlate task behavior and ASD symptom scores with
patterns of activity from regions that were identified in the
whole-brain analyses. Functional ROIs from these regions
were generated by masking the activation maps of the
contrasts of interest with binarized anatomical ROIs using the
Harvard-Oxford structural atlases distributed with FSL.
Finally, we explored whether additional clinical factors,
such as medication exposure or comorbidity, might have
influenced the results. Extracted z values from the ROIs
identified in the whole-brain analyses were entered into
SPSS to compare only those participants with ASD without
a comorbid disorder, those not using medication, or both to
TD controls. Additionally, we compared ASD participants
with a comorbid disorder to those without and ASD
participants who were on medication to those who were not.
Given the high rates of anxiety reported in ASD (White
et al. 2009) and the possible impact of anxiety on social
decision making (Luo et al. 2014; Wu et al. 2013), we
also repeated the fMRI analyses with YSR DSM-oriented
Anxiety problems as a covariate of no interest to account
for possible effects of anxiety. Mean group substitution was
used to replace missing YSR data for one TD participant.
Fairness decisions after the three different emotions were
compared between the groups with a 2 × 3 mixed ANOVA
(group × emotion). We found a main effect of emotion, F (1,
37) = 4.48, p = .015, caused by a higher percentage of unfair
offers in response to angry (M = 62.7%; SD = 29.9, p < .001)
and happy (M = 59.1%; SD = 31.0, p < .05) compared to
disappointed reactions (M = 47.5%; SD = 26.0). There was
no main effect of group, F (1, 37) = 0.18, p = .68,
showing that the groups did not differ on fairness levels across
Fig. 2 Percentage of unfair offers after communication of anger,
disappointment, and happiness, separate for ASD and TD groups.
Asterisks indicate significant differences within groups (green ASD; blue
TD) and between groups (in black). (Color figure online)
Fig. 3 Group differences in the left precentral gyrus for the
happiness > anger contrast cluster-thresholded at z > 2.3, p < .05
the emotions combined. We found a significant interaction
effect, F (1, 37) = 8.52, p < .001, showing group differences
in the reactions after the different emotional expressions
(see Fig. 2). Post hoc tests revealed that within the ASD
group, participants more often chose the unfair than the fair
option when dealing with angry peers (71.8%, SD = 22.7)
than when dealing with disappointed (53.7%, SD = 23.1,
p = .001) and happy (47.9%, SD = 31.1, p < .05) peers. No
Fig. 4 a Higher autistic traits
in the ASD group were related
to higher activity in the left
postcentral gyrus in the
happiness > [anger and
cluster-thresholded at z > 2.3, p < .05 with b
graph showing mean z values
in the postcentral gyrus as a
function of SRS scores in the
differences in fairness decisions after disappointment and
happiness were found in the ASD group. Next, within the
TD group, we found that participants more often chose the
unfair than the fair option when dealing with angry (53.6%,
SD = 33.8, p < .01) and happy (70.3%, SD = 27.2, p < .005)
peers than when dealing with disappointed peers (41.3%,
SD = 27.8). No differences in fairness decisions after
anger and happiness were found in the TD group. Finally,
between-group comparisons showed that the ASD (versus
TD) group made significantly less unfair offers after happy
reactions (p < .05), and marginally significantly more unfair
offers after angry reactions (p= .058). No significant group
difference was found in unfairness after disappointed
reactions (p = .14).
The first set of whole-brain analyses aimed to identify
regions that differed between the ASD and TD groups
when receiving positive relative to negative emotional
reactions in general [i.e., happiness > (anger and
disappointment) contrast]. No group differences were found whilst
using this contrast. When analyzing the contrasts that
compared happiness to a specific negative emotion (i.e.,
happiness > anger, and the happiness > disappointment) we
found that the ASD (vs. TD) group showed less activation
in the left and right precentral gyrus and right middle
frontal gyrus in the happiness > anger contrast (see Table 2 and
Fig. 3). Finally, when comparing the two negative
emotions with each other, we found no significant group
differences between the ASD and TD groups when analyzing
the anger > disappointment and disappointment > anger
We also analyzed the effects of autistic traits as
measured by the SRS-A on brain responses during the
different contrasts in the ASD group separately. These
analyses revealed that higher activity in the left postcentral
gyrus and supramarginal gyrus in the happiness > [anger
and disappointment] contrast was related to higher
autistic traits in the ASD group (see Fig. 4). This
relation was also found between autistic traits and activity
in these regions in the separate happiness > anger and
Table 2 MNI coordinates, z values and cluster sizes for brain regions
revealed by the whole brain pairwise comparisons of the TD
control > ASD groups, z > 2.3, p < .05 cluster-corrected
happiness > disappointment contrasts. No other brain
regions showed an association between autistic traits
and activity in any of the contrasts. Additionally, control
analyses showed no relation between autistic traits and
brain activation in the TD group, suggesting the relation
between autistic traits and brain activation is specific
for the ASD group. We also repeated the fMRI analyses
with the lowest scoring participant removed, which also
showed a relation between autistic traits and activity in
the left postcentral gyrus (see Supplementary materials).
Next, we conducted exploratory analyses to investigate
the relation between fairness decisions and brain
activity in regions identified in our whole-brain analysis. We
investigated the relation between the percentage of unfair
offers in response to happy reactions and activity in the
right precentral gyrus for the happiness > anger contrast.
This analysis revealed a significant negative correlation
between the percentage unfair offers and left
precental gyrus activity for the TD control group (r = −0.56,
p < 0.5), but not for the ASD group (r = 0.08, p = 0.75).
However, Fisher z-values were calculated which
indicated that the difference between these correlations was
not significant (z = 1.56, p = 0.58).
Effects of Comorbidity and Medication
Post-hoc analyses revealed that all group differences
remained significant when excluding ASD participants
with comorbid disorders or those using medication (all
ps < .01). In addition, no significant group differences were
found between ASD participants with comorbid disorders
and those without (all ps > .2) or between ASD participants
using medication or not (all ps > .6). The analyses with the
YSR DSM-oriented Anxiety problems as a covariate did
not considerably alter results. Only minor changes in size
and peak coordinates of the clusters revealed in the main
analysis were observed (see Supplemental Table S1).
This is the first study focusing on the effects of emotions
on fairness decisions and brain responses in ASD.
Behavioral analyses showed that ASD participants were more
unfair when dealing with angry compared to disappointed
and happy peers, whereas TD participants more often were
unfair when dealing with angry but also with happy peers
compared to those that communicated disappointment.
These group differences were mainly driven by
differences in reactions to happy peers, as the TD group chose
significantly more unfair offers after happy reactions than
the ASD group. The imaging results showed reduced brain
responses in the precental gyrus and middle frontal gyrus
in the ASD versus TD group when receiving happy versus
angry reactions. Additionally, more autistic traits in the
ASD group were associated with more activity in the
postcentral gyrus in the happiness versus anger and
Although we hypothesized that the ASD group would
be less likely to differentiate between the three emotions
when making fairness decisions, this hypothesis was not
supported as the behavioral results suggest that individuals
with ASD did adjust their allocation behavior in response
to the emotions of others. However, participants with ASD
reacted less unfair than TD controls in response to
happiness (and more unfair in response to anger compared to TD
controls, although this difference failed to reach
significance). The increase in unfairness in response to happiness
of the TD participants is in line with findings from
previous studies (Klapwijk et al. 2016b, 2013; van Kleef et al.
2004). When receiving a happy reaction after a previous
unfair offer, one could infer that the other was already
satisfied and would therefore be content with another unfair
offer (Cacioppo and Gardner 1999; van Kleef et al. 2010).
Possibly, our participants with ASD used different
heuristics that require less such inferences about mental states
since they did not choose to be more unfair in response to
happiness compared to the TD participants. However, this
interpretation could not be supported by altered activation
in brain regions usually associated with mentalizing in the
ASD group in the current study.
We did not find group differences in the specifically
hypothesized brain regions that have been previously linked
to atypical social-affective functioning in ASD such as the
IFG and TPJ (Greimel et al. 2010; Lombardo et al. 2011).
The absence of group differences in these areas might result
from the specific task used in the current study, in which
written emotions were presented and participants made
fairness decisions subsequently. However, previous
studies did report differences between ASD and TD controls
in these regions in tasks using written stimuli (Lombardo
et al. 2011) and the TPJ specifically has been implicated
in previous studies using the same paradigm as in the
current study (Klapwijk et al. 2016b; Lelieveld et al. 2013a).
It might also be that individuals with ASD do not recruit
these hypothesized social-affective brain regions differently
from controls when making social decisions. The only
other study that used fMRI to study social decisions in an
economic game in ASD found group differences between
individuals with ASD and controls in the middle
cingulate gyrus (Chiu et al. 2008), and not in either IFG, mPFC,
TPJ or amygdala. Given the sparse number of
neuroimaging studies that employed economic games in ASD and the
posited potential for understanding mental disorders using
neuroeconomics (Hasler 2012; King-Casas and Chiu 2012;
Kishida et al. 2010; Sharp et al. 2012), future studies are
warranted to further test which brain regions are
differentially recruited when making social decisions in ASD.
Interestingly, however, the reduced responses observed
in the current study in the precentral gyrus and middle
frontal gyrus, and also the postcentral gyrus activation
related to autistic traits, align with results from recent
metaanalyses of fMRI studies in ASD (Di Martino et al. 2009;
Dickstein et al. 2013; Patriquin et al. 2016).
Hypoactivation during social tasks in ASD versus controls was found
in both the left and right precentral gyrus in the
meta-analysis by Di Martino et al. (2009) and in the left precentral
gyrus in the Patriquin et al. (2016) meta-analysis. Reduced
responses in this area in ASD versus controls have been
reported during imitation of emotional expressions and
finger movements (Dapretto et al. 2006; Williams et al.
2006) and when observing fearful expressions (Deeley
et al. 2007). Although the precentral gyrus is considered
to be part of motor-related cortex, activity in this area has
previously been associated with social-emotional
functioning. Precentral gyrus activity has been found to increase
when receiving empathic responses from others (Seehausen
et al. 2014, 2016) and activity in this area is also related
to self-reported affective empathy in social versus
nonsocial emotional scenes (Hooker et al. 2010). Furthermore,
atypical functional connectivity within the precentral gyrus
has been associated not only with impaired motor skills
but also with social deficits in ASD (Nebel et al. 2014). In
the current study, reduced activation in the precental gyrus
was found in the ASD versus TD group specifically when
contrasting happy versus angry reactions. This might
suggest that the ASD participants process the happy emotional
information differently than the TD controls in this area and
therefore also responded less unfair in response to
happiness than the TD group. However, future studies are needed
to further clarify the role of the precentral gyrus in
socialemotional functioning. For example, the current paradigm
does not allow inferring whether the different response to
happiness in the ASD group is the result of less
responsiveness to happy emotions in general or to a different
cognitive appraisal of happiness that leads to increased fairness
and decreased precentral gyrus activation. Experiments in
which the emotional intensity of happiness is varied could
resolve whether responsiveness to happiness is related to
precentral gyrus activation or not. The current findings as
well as the precentral gyrus hypoactivation in ASD during
social tasks in two meta-analyses (Di Martino et al. 2009;
Patriquin et al. 2016) might point to a relation between
precentral gyrus dysfunctions and social deficits in ASD.
The current results additionally showed a positive
association between autistic traits and activity in the postcentral
gyrus in the ASD group in the happiness versus anger and
disappointment contrasts. The postcentral gyrus is a
somatosensory region that is also not usually discussed in the
context of ASD social deficits, although it has consistently
been revealed as a hyperactivated region in ASD
meta-analyses of social tasks (Di Martino et al. 2009; Dickstein et al.
2013; Patriquin et al. 2016) and it has also been reported
as a region being structurally altered in ASD (Hyde
et al. 2010). Previous studies in healthy populations have
reported the involvement of primary somatosensory cortex
in affective touch (Gazzola et al. 2012), in processing facial
and vocal emotions (Adolphs et al. 2000; Heberlein and
Atkinson 2009) and in affective language use (Saxbe et al.
2013). The relation between autistic traits and postcentral
gyrus activation in response to happy versus angry and
disappointed emotions in the current paradigm might suggest
a specific relation between somatosensory processing of
positive emotions and ASD symptoms.
Several limitations to this study should be noted. First,
although our sample size (N = 19 per group) is comparable
with other task-related fMRI studies in ASD, this sample
size is relatively small and may have limited the power to
detect group differences in brain regions usually linked to
social cognition and emotion processing. Second, since our
sample contained adolescent boys only, we do not know
whether our results are generalizable to girls and to
children and adults with ASD. Third, the task design employed
in the current study contained written preset emotions only.
Future studies could further increase the amount of
interaction by studying face-to-face interactions, for example by
using virtual reality. Finally, it remains unclear why
differences in ASD versus controls were found in the precentral
gyrus, whilst a correlation with autistic traits was found
in the postcentral gyrus but not in the precentral gyrus. It
can be speculated that the relatively small sample size has
limited the power to find a correlation between precentral
gyrus activation and autistic traits. It is also possible that a
correlation between autistic traits and brain activity within
the ASD group does not necessarily imply group
differences in the same region between the ASD and TD groups.
In conclusion, the current study provides an initial step
in examining how explicit emotional feedback influences
interactive decisions and associated brain responses in
ASD. The results suggest that individuals with ASD do
employ explicitly expressed emotional information when
making social decisions, although responses towards
happiness seemed atypical and were fairer than controls. The
neuroimaging results might point to a possible role of
precentral and postcentral gyrus in social-affective difficulties
in ASD, although more research is needed to specify the
neurocognitive mechanisms that are associated with these
brain regions during social cognition. Future research in
which the role of others’ expressed emotions is further
investigated could help to refine models for social
interactions in ASD.
Acknowledgments Open access funding provided by Leiden
University Medical Center (LUMC). The authors are grateful to all
participants and their parents, to the participating centers (Centrum
Autisme Rivierduinen, Curium-LUMC), and to Romy Emmerig
and Simone van Montfort for their help with data collection. This
study was supported by the Netherlands Organization for Scientific
Research (NWO) Grant No. 056-23-011.
Author Contributions ETK participated in the design of the study,
collected and analyzed data, interpreted the results and drafted the
manuscript; MA participated in the design of the study, collected
data and contributed to the interpretation of the results; GJL
developed stimuli, participated in the design of the study and contributed
to the interpretation of the results; NDJvL, AP, NJAvdW, OFC,
RRJMV participated in the design of the study and interpretation of
the results. All authors critically reviewed and edited the manuscript
for important intellectual content, and all authors approved the final
Compliance with Ethical Standards
Ethical Approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
Informed Consent Informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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