Self-Report and Brain Indicators of Impaired Emotion Regulation in the Broad Autism Spectrum
Self-Report and Brain Indicators of Impaired Emotion Regulation in the Broad Autism Spectrum
0 Faculty of Social Sciences, Erasmus University Rotterdam , Burgemeester Oudlaan 50, 3062 PA Rotterdam , The Netherlands
Although not used as a diagnostic criterion, impaired emotion regulation is frequently observed in autism. The present study examined self-reported use of emotion regulation strategies in individuals scoring low or high on autistic traits. In addition, the late positive potential, which is sensitive to emotional arousal, was used to examine the effect of one strategy, reappraisal. Reporting more autistic traits was related to using more maladaptive and fewer adaptive emotion regulation strategies. Across both groups, no attenuation of the late positive potential during downregulation of unpleasant pictures was found, possibly because of the used valence-changing reappraisal operationalisation. Hence, although self-report indicated impaired emotion regulation in individuals high on autistic traits, electrophysiological findings could not confirm this.
Autism spectrum disorder (ASD); Emotion regulation; Late positive potential (LPP); Reappraisal; Autism spectrum hypothesis
© The Author(s) 2017. This article is an open access publication
Emotion regulation (ER) encompasses the capacity to
modulate or control the duration, latency, magnitude, and
valence of one’s emotional reactions in order to behave
adaptively and thereby meet situational demands (for a
review, see Gross 2002). ER does therefore not only cover
the ability to decrease negative emotions, but pertains to
increasing, maintaining, or decreasing positive or negative
emotions according to the situation one is in (Koole 2009).
Although many ER strategies exist, research has mostly
focussed on two: reappraisal and suppression. Reappraisal
is used early on in the emotional process, and encompasses
changing the way one thinks about emotional stimuli or
emotional events. Suppression comes later in the emotional
process, and covers changing one’s behavioural response
to these stimuli or events (Cutuli 2014; Gross 2002). Thus,
reappraisal focusses on reinterpretation, while suppression
is more focussed on hiding or inhibition. The consequences
of using reappraisal are judged as healthier than the
consequences of suppression. While reappraisal changes both
the emotional experience and the behavioural expression
of that experience, suppression fails to impact the
former (Cutuli 2014; John and Gross 2004). This has several
consequences. First, while reappraisal decreases negative
emotion and increases positive emotion, suppression fails
to reduce negative emotion, and even leads to increased
physiological activation (Cutuli 2014; Gross 1998, 2002;
John and Gross 2004). In addition, because suppression
comes relatively late in the emotional process, it takes up
more cognitive resources than reappraisal, thereby
impairing memory (Cutuli 2014; Gross 2002; John and Gross
2004). These depleted cognitive resources can, in turn,
impair social functioning, since individuals who suppress
emotions fail to absorb information needed for an
adequate social response (Cutuli 2014; John and Gross 2004).
Finally, reappraisal and suppression have different effects
on psychological well-being. While the use of reappraisal
is related to higher life-satisfaction, increased optimism,
and better self-esteem, the use of suppression is linked to
higher levels of depression (John and Gross 2004). In
general, difficulty in controlling negative emotional responses
is linked to problems in mood and anxiety (Campbell-Sills
et al. 2006; Hofmann 2014). On the other hand, increased
control over positive and negative emotions has been
associated with enhanced positive (up-regulated) and reduced
negative (down-regulated) feelings, respectively (Gross
et al. 1997).
Focussing on more chronic ER impairments, studies
show that persistent inability to successfully regulate
emotions is implicated in the development and maintenance
of many psychiatric problems (Aldao et al. 2010;
Campbell-Sills et al. 2006; Koole 2009). Moreover, ER related
characteristics are part of the diagnostic criteria of several
mental disorders, most notably mood disorders, anxiety
disorders, and some cluster B personality disorders such as
borderline personality disorder and histrionic personality
disorder (American Psychiatric Association 2013).
However, they have mostly been ignored in disorders that do not
include ER as a formal diagnostic criterion, but that often
show ER disturbances nonetheless (Mazefsky 2015). One
such disorder is autism spectrum disorder (ASD), a
pervasive neurodevelopmental syndrome that includes social
communicational deficits, restricted and repetitive
behaviour, preference for sameness and routines, and sensory
abnormalities (American Psychiatric Association 2013).
While someone does not need to show impaired ER to be
diagnosed with ASD, both clinicians and family members
have noticed emotional deficits in individuals afflicted with
the disorder, including irritability, amplified emotional
responses, poor emotional control (‘meltdowns’ or
‘outbursts’), and poor stress management (Bauminger et al.
2010; Mazefsky 2015; Mazefsky et al. 2013).
These observations are supported by several studies
showing that individuals with ASD indeed have poor ER
capacity. Children and adolescents with ASD rely,
compared to typically developing (TD) individuals, more on
maladaptive than adaptive ER strategies (Samson et al.
2014a). Although the level of reactivity to negative real
world scenarios did not differ, individuals with ASD used
less cognitive reappraisal and made more use of
suppression. The difficulty that individuals with ASD exhibited
in generating cognitive reappraisal even persisted when
the technique was explained to them and when they were
encouraged to use the strategy. The other examined ER
strategies, avoidance, distraction, problem solving,
relaxation, and venting, did not differ between groups. These
findings were partially supported by parent-reports and
daily diaries from children and adolescents with ASD, who
showed less use of adaptive and more use of maladaptive
ER strategies (Samson et al. 2015b). Averaging across
multiple emotions, especially the frequency and efficacy
of using acceptance, cognitive reappraisal, distraction, and
problem-solving differed between the ASD and TD group.
Focussing only on reappraisal and suppression, Samson
et al. (2012) found that adults with ASD showed a
deviant ER pattern as well: individuals with ASD had difficulty
in consciously down-reinterpreting the meaning of
negative and up-regulating the meaning of positive emotional
responses (together called cognitive reappraisal), and made
more use of suppression.
Taking the deviant ER pattern found in individuals with
ASD a step further, several studies have linked emotion
dysregulation to comorbid pathological outcomes.
Mazefsky et al. (2014) showed that the more frequent use of
maladaptive ER strategies such as rumination by adolescents
with ASD was associated with higher levels of anxiety and
depression. Focussing on both internalising and
externalising psychopathologies in children and adolescents with
ASD, stronger reliance on maladaptive (suppression) and
weaker reliance on adaptive (reappraisal) ER strategies has
shown to be related to higher levels of negative emotion
(Samson et al. 2015a). These increased levels of negative
emotion in turn resulted in maladjusted behaviour, such as
impulsivity, truancy, and temper tantrums. Thus, although
the core features of ASD are not affective in nature,
impaired ER may explain why certain psychopathologies
often accompany the disorder.
In addition to helping explain comorbid
psychopathology, it has been suggested that ER could be useful in
explaining the core symptoms of ASD as well (Mazefsky
et al. 2013; Richey et al. 2015; Weiss et al. 2014). Core
symptoms such as social communicational problems can
be understood in light of ER deficits because they require
adjustment of internal affective experiences to changing
situational demands. Given the problems many
individuals with ASD experience in social-affective functioning, it
is not surprising that ER might play an important role in
characteristics that are central to the disorder. Backing up
this supposition, Samson et al. (2014b) have shown that the
emotion dysregulation children and adolescents with ASD
show compared to TD individuals is related to the severity
of several of the core symptoms of ASD: impaired social
responsiveness, sensory abnormalities, and especially
restricted and repetitive behaviour.
Besides self-report and behavioural research,
knowledge of the biological processes involved in ER could help
us advance our understanding of ER deficits in e.g. ASD.
A small number of studies have indeed used
physiological measures to illustrate the relationship between deviant
ER patterns and ASD symptomatology. In their review of
this literature, Weiss et al. (2014) mention only four
studies doing so. One, a study by South et al. (2012), uses skin
conductance but focusses more on behavioural inflexibility
than actual ER. Three other studies use respiratory sinus
arrhythmia (RSA), a measure of parasympathetically
mediated heart rate variability. They show that individuals with
ASD have lower RSA, which is related to slower emotion
recognition (Bal et al. 2010), more social and
internalising problems (Neuhaus et al. 2014), fewer social skills,
and more problem behaviours (Vaughan Van Hecke et al.
2009). A subsequent RSA study (Guy et al. 2014) reports
findings similar to those of Neuhaus et al. (2014): children
with ASD were shown to have lower RSA compared to TD
children, which was associated with increased anxiety and
lower socialisation. While these findings all indicate bad
ER, they only provide information about symptoms related
to ER, but do not directly measure the construct. An
additional point of criticism concerns the interpretation of RSA.
While high baseline RSA is suggested to be related to
flexible emotional responses, the interpretation of change in
RSA depends heavily on context (Mazefsky et al. 2013):
effective coping in stressful situations is indicated by an
RSA decrease, but in non-stressful situations effective ER
is indicated by an RSA increase instead.
In contrast to the RSA studies, two functional magnetic
resonance imaging (fMRI) studies did examine ER directly.
After instructing children and adolescents to use cognitive
reappraisal to modulate their emotional responses to
disgusting images, TD individuals showed decreased activity
of the insula and amygdala (Pitskel et al. 2014). Individuals
with ASD however showed no change in insular activation
and had increased activity in the amygdala, even though the
affect ratings did not differ between groups. Similar
findings were obtained with the use of facial stimuli in an adult
population (Richey et al. 2015). Adults with ASD were,
compared to TD controls, not able to increase activity in
brain regions involved in effortful ER (e.g. striatum,
dorsolateral prefrontal cortex) when instructed to think
negatively or positively about neutral faces. Together, these
neuroimaging studies indicate that the hemodynamic responses
that TD individuals show when regulating emotions are not
or to a lesser extent observed in individuals with ASD.
The discussed neuroimaging studies succeed in
approximating the neurobiological basis of ER deficits in ASD,
especially compared to the neurophysiological studies
using RSA. However, the technical characteristics of
neuroimaging may not be ideal for examining ER. Emotion
regulation can be considered a momentary response to stimuli,
and its time course can be best assessed by the study of
event-related potentials (ERPs). ERPs are calculated using
electroencephalography (EEG), which can be sampled
in the order of milliseconds. An ERP component that has
repeatedly been linked to one specific ER strategy,
reappraisal, and that has shown good psychometric properties
in studying the concept (Moran et al. 2013), is the late
positive potential (LPP).
The LPP starts approximately 400–500 ms after
stimulus onset, lasts several 100 ms, and is maximal at the
posterior scalp. Hajcak et al. (2006) demonstrated that
the LPP is enhanced for affectively arousing (pleasant
and unpleasant) compared to neutral pictures. In
addition, the potential is reduced when participants are asked
to make non-affective compared to affective judgements,
indicating that the LPP is not only related to the
processing of emotional stimuli but to regulation of these stimuli
(ER) as well. This was confirmed by
electrophysiological studies focussing on reappraisal. In a study by Hajcak
and Nieuwenhuis (2006), adult participants were shown
pleasant, unpleasant, and neutral pictures, and were asked
just to attend to the picture or to reappraise the content.
During passive viewing, the LPP was enhanced for
pleasant and unpleasant compared to neutral pictures.
Reappraisal of unpleasant stimuli resulted in reduction of the
LPP, and the degree of LPP modulation was related to
reduction in self-reported emotional intensity. Similar
results were obtained for children (Dennis and Hajcak
2009): when asked to provide certain interpretations of
unpleasant pictures, the LPP was reduced when giving a
neutral compared to a negative interpretation. Stronger
modulation was also associated with clinically relevant
measures, namely reduced anxious-depressed symptoms,
and better parent-reported ER.
Despite these promising results concerning the LPP as
a neurobiological marker of reappraisal, and despite the
importance of ER in ASD, no research to date has used the
LPP as a neurobiological derivative of ER deficits in ASD.
The potential has sparsely been used in studying impaired
ER in obsessive compulsive disorder (Paul et al. 2016)
and schizophrenia (Horan et al. 2013; Strauss et al. 2015),
disorders that both show similarities with ASD in
symptomatology and aetiology (Couture et al. 2010; Jacob et al.
2009). These studies therefore demonstrate the possibility
to examine the LPP not only in people with
well-functioning ER but also in clinical populations of which the
individuals show ER impairments—at least as determined with
the use of self-report and behavioural measures. Because
the LPP is sensitive to ER, the LPP change when using ER
is possibly lower in individuals high on the autistic
spectrum. This would signify an electrophysiological indicator
of impaired ER in ASD.
The present article aims to provide an initial
examination of the electrophysiological basis of ER deficits in
ASD. This examination will be executed building on the
autism spectrum hypothesis, which postulates that ASD is
not a distinct disorder but a continuum that extends from
TD individuals in the general population to diagnosed
individuals belonging to the clinical population (Baron-Cohen
et al. 2001; Wing 1988). Across this continuum,
individuals show different levels of symptom severity. Responses
of both non-diagnosed individuals and people belonging
to the clinical population offer valuable insights into ASD,
since some of the difficulties experienced by diagnosed
people are (to a lesser extent) also experienced by TD
individuals scoring high on autistic traits (see e.g. De Groot
and Van Strien 2016).
The present research comprises two parts: replicating
the deviant ER patterns found in self-report and
behavioural research on ASD, and extending these findings to
electrophysiological measures. With regard to the first part,
we will examine whether self-reported ER scores differ
between TD individuals scoring low on ASD traits
(lowAQs) and TD individuals scoring high (high-AQs), with
the group names representing a median split based on the
Autism-Spectrum Quotient (AQ) score. Based on the
studies by Samson and colleagues, we expect that low-AQs
show more adaptive and less maladaptive ER compared to
high-AQs. The second set of analyses focusses on the link
between ASD and the LPP for one specific ER strategy:
reappraisal. The main question is whether low-AQs and
high-AQs differ in LPP-amplitude change when instructed
to use reappraisal compared to passive viewing. Based on
Hajcak and Nieuwenhuis (2006), and on the self-report and
behavioural studies showing ER deficits in individuals with
ASD, we expect that high-AQs show a less pronounced
LPP decrease when asked to down-regulate elicited
negative feelings (decrease effect), and that they show a less
pronounced LPP increase when asked to up-regulate elicited
positive feelings (increase effect), compared to low-AQs.
Besides the influence of reappraisal, we will also examine
the possible differences between low and high-AQs in their
initial LPP response to processing arousing (pleasant and
unpleasant) stimuli compared to neutral stimuli. This latter
analysis focusses on emotional reactivity rather than on ER,
but was included since most examinations of the LPP look
at both emotional reactivity and ER (Hajcak and
Nieuwenhuis 2006; Hajcak et al. 2006; Paul et al. 2016). However,
hypothesising on the outcome of emotional reactivity is
difficult. A self-report study found increased emotional
reactivity in individuals scoring high on the spectrum (Pisula
et al. 2015). A behavioural study found equally strong
reactivity (Samson et al. 2014a). Contrary to both, an fMRI
study showed reduced responses to happy versus neutral
faces in individuals with ASD compared to TD controls,
with unaffected siblings demonstrating intermediate levels
of reactivity (Spencer et al. 2011). Focussing on the nature
of the stimulus presentation, a study using pupillometry
showed reduced reactivity to backward-masked but equal
reactivity to consciously presented stimuli in ASD (Nuske
et al. 2014). Finally, a review on emotion impairments in
ASD states that the conflicting findings on emotional
reactivity may be the result of the impairment being
domainspecific: whereas the ‘hardware’ for emotional reactivity
seems to be functional, individuals with ASD may show
abnormal reactivity only for social stimuli (Nuske et al.
2013). Because of the lack of univocal findings on
reactivity, no hypotheses were formulated for this construct.
Participants were 60 TD students from the Institute of
Psychology who participated in reward for course credit. The
sample consisted of 19 male and 41 female participants
with a mean age of M = 20.25 (SD = 1.85), range 18–26
years. Participants were informed about the nature of the
measurements (EEG) beforehand, and informed consent
was obtained from all participants included in the study.
All procedures performed were in accordance with the
ethics standards of the institutional review board and with the
1964 Helsinki declaration and its later amendments.
Materials and Stimuli
The Autism-Spectrum Quotient (AQ, Baron-Cohen et al.
2001) is a continuous and quantitative self-report
measure of autistic traits in adults of normal intelligence. The
questionnaire consists of 50 questions, divided into five
subscales of ten items each: social skill, attention
switching, attention to detail, communication, and imagination.
Items are answered on a 4-point Likert-scale: definitely
agree, slightly agree, slightly disagree, definitely disagree.
Completing all items takes approximately 15 min. Both the
original English version of the test and its Dutch
translation show satisfactory psychometric properties
(BaronCohen et al. 2001; Hoekstra et al. 2008). The binary
scoring scheme as originally proposed by Baron-Cohen et al.
(2001) ignores the degree of agreement or disagreement.
In line with Hoekstra et al. (2008), we used a 4-point
rating scale, which has been shown to improve the reliable
range of measurement significantly (Murray et al. 2016).
This resulted in a minimum total score of 50 (the individual
reports having no autistic traits) and a maximum score of
200 (the individual reports having the full range of
autistic traits). As could be expected based on score
variability, reliability was better when using the full-range
scoring scheme. Cronbach’s alfa was α = .82 for the composite
score (as opposed to α = .71 using binary scores), α = .75
(α = .55) for social skill, α = .73 (α = .65) for attention
switching, α = .63 (α = .53) for attention to detail, α = .62
(α = .54) for communication, and α = .46 (α = .28) for
imagination. These reliabilities were similar to those found by
Hoekstra et al. (2008).
Questionnaire of Emotion Regulation for Adults
The Questionnaire of Emotion Regulation for Adults
(FEEL-E, Grob and Horowitz 2014) is a self-report
questionnaire in which participants indicate in
approximately 15 min what they do or think when being angry
(24 items), scared (24 items) or sad (24 items). The
instrument differentiates between six adaptive (problem-oriented
action, acceptance, cognitive problem-solving, reappraisal,
evoking positive feelings, forgetting) and six maladaptive
(withdrawal, self-blame, resignation, rumination,
negative thinking, other-blame) ER strategies. Every item is
answered and scored on a 5-point scale: almost never,
seldom, sometimes, often, almost always. Scores can be
calculated for both adaptive and maladaptive strategies and
for each emotion separately. The psychometric properties
of the Dutch adaptation of the scales were satisfactory, and
examination of the criterion validity showed that people
with ASD scored lower on adaptive strategies than both
the normative group and individuals with other
psychiatric disorders (Punt 2015). The data from the present study
yielded a Cronbach’s alfa of α = .92 for the adaptive scale,
and α = .88 for the maladaptive scale.
The stimulus set consisted of 120 International Affective
Picture System (IAPS) pictures and was identical to the set
used by Hajcak and Nieuwenhuis (2006): 40 pleasant
pictures, 40 unpleasant pictures, and 40 neutral pictures.1
Because Hajcak and Nieuwenhuis (2006) forgot to report
one neutral item, an extra item was added. The reported
neutral items consisted of people (5 items), objects (25
items), and nature (9 items). We added one nature item
(5120, ‘pine needles’), which changed the mean neutral
valence and arousal rating with 0.01. Normative valence
ratings significantly differed between all picture categories,
with unpleasant pictures scoring lowest (M = 2.52), neutral
pictures scoring intermediate (M = 5.04), and pleasant
pictures scoring highest (M = 7.01), all p-values <.001.
Normative arousal ratings also significantly differed between
all picture categories: neutral pictures had the lowest
arousal ratings (M = 2.75), and differed significantly from
pleasant and unpleasant pictures, both p-values <.001. The
difference between pleasant (M = 5.49) and unpleasant
1 Numbers of the IAPS pictures used. Pleasant 1601, 2000, 2070,
2080, 2091, 2092, 2165, 2311, 2340, 4002, 4180, 4220, 4290, 4532,
4572, 4608, 4658, 4659, 4660, 4664, 4800, 4810, 5470, 5621, 5626,
5628, 7325, 8021, 8032, 8080, 8200, 8210, 8280, 8320, 8330, 8370,
8400, 8465, 8490, 8540. Neutral 2190, 2480, 2570, 2840, 2880,
5120, 5390, 5500, 5510, 5532, 5534, 5731, 5740, 5800, 5900, 7000,
7002, 7004, 7006, 7009, 7010, 7025, 7030, 7034, 7035, 7040, 7060,
7080, 7090, 7100, 7140, 7150, 7175, 7190, 7217, 7224, 7233, 7235,
7491, 7950. Unpleasant 1300, 1301, 2053, 2120, 2710, 2800, 2900,
3160, 3220, 3230, 3300, 3350, 3500, 3530, 6200, 6210, 6212, 6230,
6244, 6250, 6260, 6312, 6313, 6370, 6540, 6550, 6560, 6570, 6571,
6821, 9040, 9050, 9421, 9490, 9520, 9600, 9620, 9911, 9920, 9921.
(M = 6.03) pictures was small but significant (p = .036).
Multiple comparisons were Games-Howell corrected.
The measures were part of a larger study examining both
self-report and brain correlates of impairments often seen
in ASD. The total experimental session took approximately
90 min, including breaks. First, participants filled out three
questionnaires, including the AQ and the FEEL-E.
Thereafter, the participant was seated in a comfortable chair in
a light and sound-attenuated EEG room. After a brief
description of the experimental task, the electrodes were
placed. Then detailed task instructions were given.
Participants were presented with both a passive viewing block and
an ER block. The total viewing time was approximately
15 min, including a short break between the view and the
ER block. The order in which the blocks were presented,
The passive viewing block consisted of all 120 pictures
(neutral, pleasant, unpleasant). The instruction screen told
the participant to only pay attention to the stimuli. Then, all
120 pictures were presented in random order. Every picture
was presented for 1000 ms, and was preceded by a blank
interval for 1400–1600 ms, and a fixation word (‘view’ in
Dutch) for 1000 ms to remind the participant of the task.
The ER reappraisal block consisted of the 40 pleasant
and the 40 unpleasant pictures. Stimuli were again
randomly presented for 1000 ms, and preceded by a variable
interval and a fixation word. In line with previous studies
linking ER to the LPP, the ER instructions given to the
participants were focussed on reappraisal. Reappraisal was
explained as reinterpreting a picture in such a way that you
feel differently about it. Two possible situations were
illustrated: when seeing an unpleasant picture, you can
reinterpret it so that it no longer elicits a negative feeling, or when
seeing a pleasant picture, you can reinterpret it so that
the positive feeling you have is increased. Examples were
given as well. Down-regulation of negative emotions was
explained with the example of a funeral: seeing a picture of
this makes you feel sad, but you can decrease that sad
feeling by imagining that the deceased was very old and lived
a beautiful life, or that the shown event was staged.
Up-regulation of positive emotions was explained with the
example of a graduation: seeing a picture of this makes you feel
good, and you can increase that good feeling by imagining
that it was your own graduation or that it was followed by
a nice party. Only up-regulation of pleasant pictures and
down-regulation of unpleasant pictures was used, since
the treatment-relevant outcomes of ER are usually making
good feelings even better and turning bad feelings around.
The fixation word used in the ER block was the Dutch word
for ‘nicer’, meant to remind participants to interpret the
situation in such a way that the resulting feelings improved
compared to the initial feelings. The appropriateness of the
reminder word ‘nicer’ was supported by the fact that
several participants interrupted the ER instruction by stating
that ‘they had to make everything nicer’. The instruction
phase ended with asking the participants whether they had
completely understood the instructions and whether they
had any questions pertaining to the task.
Electrophysiological Recordings and Signal Processing
EEG was recorded using a 32-channel amplifier and
ActiveTwo data acquisition software (Biosemi,
Amsterdam, The Netherlands). Ag/AgCl active electrodes were
placed on the scalp by means of a head cap according to
the 10–20 placing system. The electro-oculogram (EOG)
was recorded by placing flat electrodes above and below
the left eye (vertical EOG) and at the outer canthi of both
eyes (horizontal EOG). Referencing was done via two
electrodes placed on the mastoids. An active (CMS—common
mode sense) and a passive (DRL—driven right leg)
electrode were used to comprise a feedback loop for amplifier
referencing. All signals were digitised with a sampling rate
of 512 Hz.
The data were analysed offline with BrainVision
Analyzer 2 (Brain Products, Gilching, Germany). All EEG
channels were referenced to the mathematically linked
mastoid electrodes. A low cut-off of 0.1 Hz and a high
cutoff of 30 Hz were applied, together with a notch filter of
50 Hz to filter out artefact caused by electrical power lines.
Data were segmented into epochs from 100 ms
pre-stimulus onset till 1000 ms post-stimulus onset. Ocular artefact
corrections were done using the Gratton and Coles
algorithm (Gratton et al. 1983). After this, baseline correction
was applied over the selected 100 ms pre-stimulus onset
period. Automatic artefact rejection allowed a minimal
amplitude of −100 µV and a maximal amplitude of 100 µV.
Epochs were classified according to picture type and
instruction, yielding five conditions (view-pleasant,
viewunpleasant, view-neutral, ER-pleasant, ER-unpleasant).
Data of participants with less than 30 (out of 40) segments
in at least one condition was scrutinised to identify the
electrodes responsible for this low number.
Analysis-relevant electrodes for which more than 5% of data was
removed, were interpolated by spherical spline. This
resulted in an average number of M = 38.90 (SD = 2.01)
valid segments used for averaging across participants,
which did not differ between conditions, F(4, 295) = 0.20,
p = .939, ηp2 < 0.01.
The time epoch and electrode cluster used for pooling
oscillatory activity were based upon both previous
findings and visual inspection of the data. The LPP has been
Fig. 1 Head view image of averaged activity in the 400–800 ms time
window across all conditions (view-pleasant, view-unpleasant,
viewneutral, ER-pleasant, ER-unpleasant) and all participants (N = 60)
shown to be maximal at posterior sites, and a head view
image averaging all conditions indeed showed that for the
present data, activity was strongest at the Pz, P3, P4, P7,
P8, PO3, and PO4 electrodes (see Fig. 1). Therefore, the
amplitudes of these electrodes were averaged. A time epoch
of 400–800 ms was used across all analyses.
First, a set of preliminary analyses consisting of t-tests
examined the characteristics of the dependent variable,
AQ score. Then, independent samples t-tests were used
to determine whether low-AQs differed from high-AQs
in their use of adaptive and maladaptive ER strategies.
The low-AQ and high-AQ group were based on a median
split (median = 95), with tied scores assigned to the
lowAQ group. To provide a more comprehensive view of the
relationship between autistic traits and ER, a correlation
analysis was performed in which the Pearson’s correlations
between all AQ sub-scores and all FEEL-E sub-scores were
With regard to the electrophysiological data, group
differences in passive viewing (emotional reactivity) and
reappraisal (emotion regulation) were examined. First,
we examined the possible difference between low-AQs
and high-AQs in LPP-change in response to processing
Table 1 Mean (SD) AQ scores and ranges per gender and group
arousing (pleasant and unpleasant) stimuli compared to
neutral stimuli. This was done with the use of a mixed
two-way ANOVA: 2 (group, between: low-AQ,
highAQ) × 3 (picture type, within: view-neutral, view-pleasant,
view-unpleasant). Second, we examined the possible
difference between low-AQs and high-AQs in LPP-change
when participants were instructed to use reappraisal
compared to passive viewing. To this end, the mean
amplitudes were subjected to a mixed three-way ANOVA: 2
(group, between: low-AQ, high-AQ) × 2 (valence, within:
pleasant, unpleasant) × 2 (action, within: passive viewing,
reappraisal). In addition, we examined whether the
counterbalancing order impacted the results. To extend the
localization properties of the 2D solution to a 3D solution,
an explorative low-resolution electromagnetic tomography
(LORETA) algorithm was performed to examine which
areas were most activated during the tasks.
Across all analyses, an alpha level of α = .05 was used.
In case of multiple comparisons, alpha was corrected using
the Sidak procedure. If possible, effect sizes were reported
as ηp2 for easy comparison.
The Autism-Spectrum Quotient
The AQ scores ranged from 65 to 127 (low-AQ group
range 65–95, high-AQ group range 96–127). The
distribution was approximately normal as determined by visual
inspection of the normal Q–Q-plot. Table 1 shows the
mean AQ scores and standard deviations per sex and
group. The mean AQ score was comparable to findings
from other social sciences students samples (Hoekstra
et al. 2008). The median-split based low-AQ group and
the high-AQ group did not significantly differ in age,
t(58) = 1.16, p = .250, 95% CI [−1.51, 0.40], η2p = 0.02.
The difference in AQ score between men and women was
borderline significant, t(58) = 1.86, p = .068, 95% CI
[−0.46, 12.82], ηp2 = 0.06, with men having higher scores
than women. None of the participants scored above the
Au s c Traits
Maladap ve ER
Fig. 2 The relationship between autistic traits (x-axis) and the use of
adaptive and maladaptive ER strategies (y-axis)
clinical cut-off score of 32 (Baron-Cohen et al. 2001) as
determined by the binary scoring scheme (present range
is 3–26 out of 0–50).
The adaptive ER scores ranged from 55 to 153, with a
mean score of M = 122.82 (SD = 17.75). This mean value
was comparable to the mean score for a TD population
found in previous psychometric evaluation of the
questionnaire: M = 123.40 (Punt 2015). The total adaptive ER
scores violated both the normality assumption (negative
skew: D(60) = 0.13, p = .019) and the homoscedasticity
assumption, and therefore simple bootstrapping (1000
samples) was used to calculate corrected confidence
intervals. An independent samples t‑test showed that TD
individuals scoring high on the autistic spectrum
(M = 115.78, SD = 18.23) made significantly less use of
adaptive ER strategies than TD individuals scoring low
on the autistic spectrum (M = 128.58, SD = 15.32),
t(58) = 2.96, p = .005, 95% BCa CI [4.68, 20.74],
η2p = 0.13. The relationship between AQ score and the use
of adaptive ER is shown in Fig. 2 (solid line).
Table 2 Correlations between AQ sub-scores and adaptive FEEL-E scores for three separate emotions and six adaptive ER strategies
*Is significant at .05 level (2-tailed)
**Is significant at .01 level (2-tailed)
Do Low‑AQs and High‑AQs Differ in Terms of Their Use
of Maladaptive ER?
The maladaptive ER scores ranged from 65 to 132,
M = 98.88 (SD = 16.24). This mean value was comparable
to the mean score for a TD population found in previous
psychometric evaluation of the questionnaire: M = 98.40
(Punt 2015). The distribution was approximately normal as
determined by visual inspection of the normal Q–Q-plot.
An independent samples t‑test showed that TD individuals
scoring high on the autistic spectrum (M = 104.19,
SD = 16.02) made significantly more use of maladaptive
ER strategies than TD individuals scoring low on the
autistic spectrum (M = 94.55, SD = 15.31), t(58) = 2.38, p = .021,
95% CI [−17.76, −1.52], η2p = 0.09. The relationship
between AQ score and the use of maladaptive ER is also
shown in Fig. 2 (dashed line).
How are Autistic Traits Related to Adaptive ER Strategies?
Table 2 shows the correlations between the AQ scores and
the use of adaptive ER strategies. The FEEL-E sub-scores
are split up into the use of adaptive ER when
experiencing three separate emotions (covering all strategies) and
into the use of six separate strategies (covering all
emotions). Most correlations were negative and small (r = 0.10)
to medium (r = 0.30). The total AQ score was
significantly related to the total FEEL-E adaptive score: having
more autistic traits was related to less use of adaptive ER
strategies. Two AQ sub-scores were not related to the use
of adaptive ER: imagination and attention to detail. The
other AQ sub-scores were almost primarily related to the
use of adaptive ER when feeling angry, and not when
feeling scared or sad: the more social, attention switching and
communicational autistic traits individuals exhibited, the
less they used adaptive ER strategies when feeling angry.
With respect to the separate ER strategies, only evoking
positive feelings was significantly related to the total AQ
score, though problem-oriented action, acceptance, and
reappraisal were significantly related to one or more AQ
sub-scores. The use of cognitive problem-solving and
forgetting were not significantly related to any AQ score.
Table 3 shows the correlations between the AQ scores and
the use of maladaptive ER strategies. The FEEL-E
subscores are split up into the use of maladaptive ER when
experiencing three separate emotions (covering all
strategies) and into the use of six separate strategies (covering
all emotions). Most correlations were positive and medium
(r = 0.30) to large (r = 0.50). The total AQ score was
significantly related to the total FEEL-E maladaptive score,
meaning that having more autistic traits was accompanied
by using more maladaptive ER strategies. The AQ
imagination and attention to detail sub-scales were not related to
the use of maladaptive ER, just as they were not related to
the use of adaptive ER. However, the other AQ sub-scores
and the total AQ score were all significantly related to the
use of maladaptive ER when feeling angry, scared, and
sad: individuals who exhibited more autistic traits used
more maladaptive ER strategies when confronted with all
negative emotions. The separate maladaptive ER strategies
Table 3 Correlations between AQ sub-scores and maladaptive FEEL-E scores for three separate emotions and six maladaptive ER strategies
*Is significant at .05 level (2-tailed)
**Is significant at .01 level (2-tailed)
were also linked more strongly to AQ scores compared to
the link between AQ and adaptive ER; other-blame was
the only maladaptive ER strategy that was not significantly
linked to any of the AQ scores. Especially the use of
withdrawal, self-blame, and resignation was strongly related to
having more autistic traits.
Table 4 shows the mean ERP waveform values per
condition and group. Figure 3 shows how the effects of the
different conditions are distributed across the scalp, showing a
clear posterior pattern for all experimental conditions. The
ERP waveforms at the Pz electrode are shown in Fig. 4.
The 2 × 3 RM ANOVA showed a significant main effect
of condition, F(2, 116) = 53.17, p < .001, ηp2 = 0.48.
Pairwise comparisons indicated that viewing neutral images
Table 4 Mean (SD) LPP 400–800 ms area measures (in µV) for the
parietal cluster per condition and group
elicited significantly lower amplitudes than viewing both
pleasant (p < .001, 95% CI [2.40, 4.18]) and unpleasant
(p < .001, 95% CI [2.16, 3.95]) images. The
view-pleasant and view-unpleasant conditions did not differ
significantly from each other, p = .878, 95% CI [−0.61, 1.07].
The effect of group was not significant, F(1, 58) = 0.15,
p = .697, η2p < 0.01. Neither was the interaction between
116) = 1.48,
p = .231,
The 2 × 2 × 2 RM ANOVA showed a significant main effect
of action, F(1, 58) = 32.66, p < .001, ηp2 = 0.36, with higher
amplitudes for reappraisal (9.10 µV) compared to passive
viewing (7.21 µV). None of the other main effects or
interaction effects reached significance, meaning that there was
no difference across valences [F(1, 58) = 1.25, p = .268,
η2p = 0.02], groups [F(1, 58) = 0.48, p = .493, ηp2 < 0.01], or
any of the interactions between action, group, and valence
(all p-values > .05).
To examine the impact of the counterbalancing, we
executed a second RM ANOVA with counterbalancing
condition included as well. The main effect of counterbalancing
condition was not significant [F(1, 56) = 0.54, p = .464,
η2p = 0.01]. Two interactions reached significance: the
interaction between action and counterbalancing [F(1,
56) = 10.72, p = .002, ηp2 = 0.16] and the higher-order
interaction between action, valence, and counterbalancing [F(1,
56) = 7.73, p = .007, ηp2 = 0.11]. Amplitudes were higher in
Fig. 3 Averaged topography
(400–800 ms) in response to
the view-neutral condition and
the experimental conditions:
Fig. 4 The ERP waveforms at the Pz electrode for the view-neutral condition (blue), the view-pleasant condition (black), the view-unpleasant
condition (red), the ER-pleasant condition (green), and the ER-unpleasant condition (purple). (Color figure online)
the ER compared to the view condition, but if participants
first performed the ER condition, the difference between
conditions disappeared for the unpleasant stimuli.
The result of the LORETA algorithm conversion is
shown in Fig. 5. Averaged across all conditions, the
lingual gyrus was the most active site since the best match
Fig. 5 Averaged tomography (400–800 ms) in response to the view-pleasant, view-unpleasant, view-neutral, ER-pleasant, and ER-unpleasant
was found at 2 mm in the lingual gyrus (x = −3, y = −81,
z = 1). The precuneus area was activated during the view
conditions, but not during reappraisal.
The present study employed both self-report and brain
measures to examine the capacity to regulate emotions in
individuals scoring low or high on the autistic trait
continuum. With regard to the self-report measures, it was
examined whether individuals scoring high on autistic traits
show a deviant ER pattern. In line with studies on
individuals from the clinical population (Samson et al. 2012, 2014a,
b, 2015a, b), people scoring high on autistic traits reported
using fewer adaptive and more maladaptive ER strategies
than individuals scoring low on autistic traits. Our results
thus demonstrate that similar associations between autistic
traits and ER strategies can be found in both a clinical and
a non-clinical sample, which is consistent with the broad
autism spectrum approach.
It should be noted that our results were partly discordant
from one study that showed that, although individuals with
ASD make more use of maladaptive ER strategies, they do
not differ in the amount of adaptive ER strategies that they
employ (Mazefsky et al. 2014). This discrepancy can be
explained by the fact that Mazefsky et al. (2014) employed
the Response to Stress Questionnaire (RSQ) to assess ER.
The RSQ does for the most part focus on sadness and fear,
and not on anger. Our self-report results (see Table 2)
indicate that the amount of autistic traits is negatively
associated with ER strategies in response to feeling angry rather
than feeling scared or sad. Therefore, it is not surprising
that the RSQ as employed by Mazefsky et al. (2014), which
mainly focusses on feelings other than anger, does not yield
a difference in the use of adaptive ER strategies between
individuals with and without ASD.
Many studies fail to look at a wider palate of ER
strategies, often exclusively focussing on the use of reappraisal
and suppression (e.g. Samson et al. 2012, 2015a). The
present self-report data show that this restricted focus is
not necessarily justifiable, since our findings indicate that
reappraisal is not the adaptive ER strategy most strongly
related to autistic traits: it only correlated significantly with
the attention switching sub-score, and it did not correlate
significantly with total AQ. Both acceptance and evoking
positive feelings were correlated more strongly with
autistic traits than reappraisal was. Although we cannot
comment on the link between suppression and autistic traits,
we found that several other maladaptive ER strategies
(withdrawal, self-blame, resignation, negative thinking)
were also linked to autistic traits. Besides emphasising the
need to examine a less limited range of ER strategies, these
findings are relevant for clinical practice as well.
Knowing that individuals with more autistic traits make less
use of acceptance and evoke less positive feelings when
dealing with a difficult emotional situation, and that they
make more use of withdrawal, self-blame, resignation, and
negative thinking, certainly has implications for therapy
With regard to the electrophysiological findings, the
ERP analyses examined the possible difference between
low-AQs and high-AQs in LPP-change in response to
viewing arousing (pleasant and unpleasant) stimuli compared
to neutral stimuli (emotional reactivity), and in response to
reappraising arousing stimuli compared to passively
viewing those stimuli (emotion regulation). The emotional
reactivity analyses showed that participants had a stronger
electrophysiological response to images with emotional content
compared to neutral images, as indicated by a larger LPP
amplitude. However, this effect did not differ between
individuals scoring low and high on autistic traits. These
findings fit with a behavioural study indicating equally
strong reactivity in individuals with ASD compared to TD
individuals (Samson et al. 2014a). However, it goes against
several other studies showing a difference in emotional
reactivity between those who score low and those who
score high on autistic traits (Nuske et al. 2014; Pisula et al.
2015; Spencer et al. 2011).
With regard to the ERP analyses focussing on ER, the
findings did not replicate previous research. In contrast to
Hajcak and Nieuwenhuis (2006), the LPP was not
attenuated when participants were asked to use reappraisal. In
fact, the electrophysiological response even increased. This
was to be expected for reappraising pleasant images for the
better, but was the opposite of what previous studies found
when reappraising unpleasant images. With regard to the
unpleasant images, we found an effect of
counterbalancing condition: amplitudes were generally higher in the ER
compared to the view condition, but if participants first
performed the ER condition, the difference between conditions
disappeared for the unpleasant stimuli. Finally, the
electrophysiological data did not show a difference between
individuals scoring low versus high on the autistic spectrum.
This could be because reappraisal was not strongly related
to autistic traits, at least according to the self-report data.
Therefore, future studies on the electrophysiological basis
of ER might benefit from looking at other ER strategies as
well. Another possibility is that the present sample
consisting of only TD individuals did not show enough variability
in autistic traits. However, the use of several self-reported
ER strategies was successfully correlated with AQ score,
indicating that the variability in autistic traits across the
sample was at least large enough to show an effect on
One possible explanation for the unexpected increase
instead of decrease in response to reappraising
unpleasant stimuli (when the participant did the passive viewing
condition first) is that the experimental manipulation failed,
meaning that participants did not successfully lower their
emotional response to the unpleasant stimuli as instructed.
This possibility remains open since we did not ask the
participants whether they thought they had been successful in
applying the reappraisal strategy. So, future studies should
check whether participants implemented ER as instructed,
and whether participants implement the strategy
automatically when they are not instructed to do so (e.g. in the
passive viewing condition). However, for the present case,
both situations are not likely to have led to the observed
increased amplitude when reappraising unpleasant images
versus passively looking at them. After all, if participants
fail in reappraising unpleasant stimuli, the amplitude would
be equal to the amplitude observed when passively
looking at the stimuli, and not increased. Likewise, if people
automatically reappraise stimuli when they are instructed
to passively look at them, the expected outcome would
be an equal amplitude across the passive viewing and the
reappraisal condition, since reappraisal is then applied
in both conditions (which might have been the case for
unpleasant images in participants who first performed the
ER condition). Hence, the increased amplitude when
reappraising unpleasant stimuli compared to passively looking
at them is not what could be expected if the experimental
manipulation had failed. Anyway, the unexpected larger
LPP amplitudes that we have found in this condition might
indicate that the instruction to reappraise unpleasant images
induced more arousal in the participants, especially when
this condition followed the view condition.
An alternative explanation for the unexpected increase
instead of decrease in response to reappraising
unpleasant stimuli is the chosen design. The present reappraisal
instructions focused on improving the emotional outcome
of the presented stimuli, hence the reminder word ‘nicer’.
However, asking participants to reappraise unpleasant
stimuli by changing the meaning of the stimulus does not
necessarily mean that the emotion and thereby the LPP
attenuates. Reappraising a picture of a gun could e.g. take
place by not looking at it as a dangerous weapon but by
thinking of it as an action movie you are about to see. This
changes the valence from unpleasant to pleasant, but does
not necessarily change the arousal, and therefore does not
change the LPP. Because we are asking for change and not
attenuation of the experienced emotions, the LPP does not
decrease after reappraisal of unpleasant images, and can
even increase during this process.
In addition to explaining the present failure to
attenuate the electrophysiological response to unpleasant stimuli
with the use of reappraisal, the valence-changing account
also explains previous reappraisal studies that were
successful in modulating the LPP. This is e.g. the case for
studies explicitly instructing participants to decrease or
increase elicited emotions (Hajcak and Nieuwenhuis
2006; Krompinger et al. 2008; Moser et al. 2006;
Schönfelder et al. 2014), and for studies inducing a non-affective
instead of an affective context (Dennis and Hajcak 2009;
Hajcak et al. 2006). Another example is formed by a study
using a more ecologically valid design, showing that LPP
amplitudes of unpleasant images are attenuated when
participants think the images were depicting art as
compared to real scenes (Van Dongen et al. 2016). However,
using a non-valence-changing paradigm does not
guarantee successful LPP modulation. Even though Langeslag
and Van Strien (2010) explicitly instructed participants to
either increase or decrease the elicited emotions, only the
increase-instructions impacted the LPP. Another study
using a non-valence-changing paradigm found increased
LPP amplitudes in both the enhance and decrease
condition, similar to the present findings (Wu et al. 2013).
The issue of the operationalisation of reappraisal (in
our case: changing the valence) represents a fundamental
problem: what does reappraisal mean? Despite all
discussed LPP studies claiming to use the reappraisal
construct, they do not convey a univocal interpretation of
the concept. The prevailing opinion is that reappraisal
encompasses a decrease or increase of emotional
intensity, though this is in fact the end goal of ER strategies
in general. Operationalising reappraisal as changing the
way one thinks about emotional stimuli and thus
turning around the accompanying feelings does differentiate
reappraisal from other ER strategies. In addition, this
interpretation seems to fit with what participants think,
since several of them interrupted the present reappraisal
instructions by stating that ‘they had to make everything
nicer’. However, this interpretation was presently shown
to be unfit to be examined with the use of the LPP, since
successfully changing the valence does not necessarily
result in LPP modulation. The conflicting findings across
studies using different operationalisations of reappraisal
should be addressed in future studies. In short, the field
requires a more detailed examination of the meaning and
electrophysiological consequences of different
interpretations of the reappraisal construct.
Lastly, the localization properties of the 2D solution
were extended to a 3D solution using a LORETA
algorithm. These tomography findings show that the most
active site across all conditions was the lingual gyrus,
which is sensible considering its role in processing
emotional stimuli (Goldin et al. 2008). However, it does raise
the question why the lingual gyrus was also the most active
posterior site during the view neutral condition. The only
differentiation found across conditions was in the
precuneus, which was activated during view conditions, but not
during reappraisal. A possible explanation for this pattern
is a change in the default mode network between viewing
images and engaging in goal-directed actions when
reappraising, which is related to decreases in tonic activity
(Cavanna and Trimble 2006).
To conclude, the present findings show that studies
focused on ER in both behaviour and brain should expand
the range of ER strategies they focus on, and should
operationalise the examined ER strategies properly. In addition,
studies on ER should focus more on pathological
populations for which impaired ER is not a diagnostic criterion,
but for which ER difficulties are often observed
nonetheless. From the present findings, it is clear that individuals
high on the autistic spectrum show impaired ER. However,
one should keep in mind that the present findings apply to
a TD population. A logical next step would be to extend
these findings to a diagnosed population as well to
determine exactly which ER strategies are impaired and how
this shows in the brain. Knowing this could prevent both
research and clinical practice from putting time and effort
into the wrong domains.
Acknowledgments Open access funding provided by Erasmus
University. We thank two anonymous reviewers for their helpful
comments on an earlier draft of this manuscript.
Author Contributions KDG conceived of the study, participated in
its design, performed the measurements and analyses, and interpreted
the data. JWVS participated in the design of the study, assisted in
analysing the electrophysiological data, and revised the manuscript
critically for important intellectual content. All authors read and approved
the final manuscript.
Compliance with Ethical Standards
Conflict of interest Kristel De Groot and Jan W. Van Strien declare
that they have no conflict of interest.
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
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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