2013. Vol.4, No.6, 515-519
Published Online June 2013 in SciRes (
Copyright © 2013 SciRes. 515
An ERP Study of Autistic Traits and Emotional Recognition in
Non-Clinical Adolescence
Ken’ichi Nixima1,2,3, Maiko Fujimori2,4, Kazuo Okanoya1,2,3
1Department of Life Sciences, Graduate School of Arts and Sciences, the University of Tokyo, Tokyo, Japan
2ERATO Okanoya Emotional Information Project, Japan Science and Technology Agency, Saitama, Japan
3Emotional Information Joint Research Laboratory, Riken Brain Science Institute, Saitama, Japan
4Psycho-Oncology Division, National Cancer Center Hospital East, Chiba, Japan
Received March 16th, 2013; revised April 19th, 2013; accepted May 17th, 2013
Copyright © 2013 Ken’ichi Nixima et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
Autistic-like traits are characterized by impaired emotional recognition and proposed to be continuously
distributed in the entire population. In the non-clinical population, however, the relationship between the
autistic-like traits and the behavioral/physiological patterns of emotion processing ability has not been
examined. In this study, we investigated the autistic traits (even including moderate-AQ participants) of
typically developing adolescents, measured with the Autism Spectrum Quotient (AQ), and the spectral
property of their behavioral and physiological responses during emotion-discrimination tasks. Participants
were screened and divided into three groups (high-, moderate-, and low-AQ groups) according to AQ
scores. Each group participated in the subsequent emotion-discrimination (using angry, happy and neutral
facial expressions) experiment involving event-related potentials (ERPs). The results indicated that
high-AQ group displayed significant different patterns (lower late positive potentials) of the emotional
processing involved in behavioral and physiological tasks compared with the moderate-AQ group. In
contrast, their behavioral and physiological patterns were comparable to those shown in the low-AQ
group. Thus, in the non-clinical adolescents, the spectrum of emotion recognition patterns might show a
non-linear relationship with AQ scores, even suggesting that AQ could not be considered as a simple in-
dex for emotional processing.
Keywords: Autistic Traits; AQ; Emotions; Facial Expression; Event-Related Potentials
Autism spectrum disorders (ASD) are neurodevelopmental
disorders that interrupt social functioning, language, or com-
munication and involve stereotyped repetitive behaviors and
highly restricted interests (DSM-IV; American Psychiatric As-
sociation, 1994, ICD-10; World Health Organization, 1992).
Previous researches have shown that recognition of emotional
facial expressions was interrupted by autism (Dawson, Webb,
Carver, Panagiotides, & McPartland, 2004; Dawson, Webb, &
McPartland, 2005; Golan, Baron-Cohen, Hill, & Golan, 2006;
Golan, Baron-Cohen, & Golan, 2008; Wong, Fung, Chua, &
McAlonan, 2008; Wong, Fung, McAlonan, & Chua, 2009), and
deficits in emotional processing are one of the central manifes-
tation of autism.
Autistic characteristics are proposed to be continuously dis-
tributed in the entire population and can therefore be measured
in a quantitative way by using an appropriate screening tool.
Baron-Cohen et al. (2001) indicated that autistic-like traits in
typically developing (non-clinical) individuals are measured
with the Autism Spectrum Quotient (AQ). The AQ enables
quantitative investigation of autistic traits among normally
developing individuals. Previous articles have used AQ to
compare between individuals with high AQ score and those
with low AQ score in studies of visual processing (Almeida,
Dickinson, Maybery, Badcock, & Badcock, 2010; Grinter,
Maybery, Van Beek, Pellicano, Badcock, & Badcock, 2009),
mirror-neuron activity (Puzzo, Cooper, Vetter, & Russo, 2010),
or emotional processing (Poljac, Poljac, & Wagemans, 2012).
These previous studies found that the high-AQ group displayed
abnormal patterns compared to the low-AQ group, which vali-
dates the assumption that autism is a continuum even in the
non-clinical population.
To attain an integrated view of the autistic-like trait continu-
ity in non-clinical population, however, we have limited knowl-
edge of the moderate-AQ group property, because previous AQ
researches just conducted the comparison between two groups
(high- and low-AQ groups). Also, these studies mainly ad-
dressed cognitive or behavioral patterns, and issues involving
the relationship between physiological aspects and autistic traits
remained almost unaddressed.
As a step toward elucidating the emotional spectral property
or linearity of autistic traits distributed in the entire population,
we investigated the behavioral (measured by accuracy and re-
action times), and physiological (measured by ERPs) aspects of
emotional face processing in non-clinical adolescents, even
including the high-, moderate-, and low-AQ groups.
A total of 533 typical developing students (380 males, 153
females; aged 19.5 ± 1.6) at the University of Tokyo were ini-
tially screened with the Autism Spectrum Quotient-Japanese
version (Wakabayashi, Tojo, Baron-Cohen, & Wheelwright,
2004). The Autism Spectrum Quotient (Baron-Cohen et al.,
2001) is a self-report instrument (total scores range from 0 to
50) consisting of five subscales (Social, Attention Switching,
Local Detail, Communication, and Imagination). The mean
score on the AQ was 22.4, and the standard deviation was 6.74.
We divided participants into three groups according to AQ
scores; participants with scores that were at least one standard
deviation higher (>29) or lower (<16) than the mean were
placed in the high- and low-AQ groups respectively. Partici-
pants with scores around the mean (21 - 24) were placed in the
moderate-AQ group. These three groups (high-AQ group, n =
97; moderate-AQ group, n = 135; low-AQ group, n = 111) were
contacted via e-mail and invited to participate in the subsequent
ERP experiment.
A total of 55 individuals (high-AQ group, n = 22; moder-
ate-AQ group, n = 17; low-AQ group, n = 16) responded our
invitation e-mail and agreed to participate in the ERP experi-
ment. In the whole ERP study, 13 (out of 55) participants were
excluded from the analysis due to excessive artifacts (n = 11;
see EEG Recording section), equipment failure (n = 1), or
sleepiness (n = 1). The high-AQ group comprised 14 adoles-
cents (nine males and five females) aged 18 - 25 (M = 20.3, SD
= 2.3) with AQ scores >29 (M = 31.7, SD = 2.4). The moder-
ate-AQ group comprised 14 adolescents (nine males and five
females) aged 18 - 22 (M = 20.1, SD = 1.4) with AQ scores 21
- 24 (M = 22.6, SD = .9). The low-AQ group comprised 14
adolescents (nine males and five females) aged 18 - 25 (M =
20.1, SD = 2.0) with AQ scores <16 (M = 11.8, SD = 2.7). All
participants were right handed and had no history of neurologi-
cal disease. All procedures were approved in advance by the
Ethics Committee of the University of Tokyo, and all partici-
pants provided written informed consent before each experi-
The stimuli for this study consisted of 144 photographs of 16
healthy professional actors (targets; eight males and eight fe-
males) displaying an emotional facial expression. Targets were
instructed to direct their gaze toward the lens of camera, and
instructed to express three emotions (anger, happiness, and
neutrality) at three levels (high, medium, and low) of intensity.
Each photograph was shown to a sample of 20 healthy partici-
pants (10 males and 10 females, age range: 22 - 58; randomly
sampled separate from the present ASD study) who were asked
to rate the emotions experienced by targets on a 7-point Likert
scale ranging from “not at all” (0) to “extremely” (6). The re-
sulting mean ratings ranged between 0 and 5.0 (high anger: M
= 3.1, SD = 1.6; medium anger: M = 2.1, SD = 1.4; low anger:
M = 1.7, SD = 1.4; high happiness: M = 4.0, SD = 1.4; medium
happiness: M = 2.9, SD = 1.6; low happiness: M = 2.1, SD =
1.3; neutrality: M = .2, SD = .4) for the 144 photographs.
The 144 photographs were pseudo-randomized to generate a
sequence of 720 trials (each photograph was shown five times).
Each face was presented for 500 ms on a screen (15-inch
monitor), and inter-stimulus intervals varied between 1500 and
2000 ms to eliminate the effects of any anticipation experienced
by participants. Participants were asked to look at a fixation
point on a monitor situated 100 cm in front of them and not to
move their eyes. Before each block of 48 trials, participants
were instructed to press one of two buttons with their thumbs
that corresponded to “emotional” (angry/happy) and “neutral”
expressions in response to stimuli. Presentation version 14.1
(Neurobehavioral Systems, Inc., USA) was used to present the
stimuli and to record the behavioral data (reaction times and
button-press responses).
EEG Recording
A 32-channel NeuroScan scan system (Neuroscan, Inc., USA)
was used to record scalp EEGs ranging from nasion to inion
and from the right to the left ear. Electrode impedances were
kept below 10 k before recording. The continuous signal was
amplified (×1000) and sampled at 500 Hz using the nasal-apex
electrode as a reference. All electrodes were re-referenced to
linked electrodes placed on the left and right earlobes, and a
low-pass filter with a bandwidth of 30 Hz was applied. Vertical
and horizontal electrooculograms (EOG) were also recorded to
exclude trials with eye blinks and movements. ERPs and EOGs
were recorded with a 100-ms pre-stimulus baseline and an
800-ms post-stimulus interval. A semiautomatic artifact-reject-
tion procedure was applied to the continuous data. First, epochs
containing amplitude changes exceeding 75 μV for the EOG
and EEG channels were excluded. Next, all epochs and chan-
nels were scanned manually for additional disturbances. ERP
data including more than 480 (out of 720) noise-free trials were
used for the data analysis.
Data Analysis
We analyzed the behavioral data (accuracy and reaction
times) using repeated-measures ANOVA with emotion (angry/
happy/neutral) as the within-subject factor and group (high-,
moderate-, and low-AQ) as the between-subjects factor. One
participant in the high-AQ group was excluded from the analy-
sis of behavioral data due to equipment failure.
The ERP data was analyzed by averaging the mean scores for
each participant on the individual trials for each emotion and
intensity. Using the components commonly analyzed in other
ERP studies of emotional processing (Schupp, Ohman, Jung-
hofer, Weike, Stockburger, & Hamm, 2004; see details in dis-
cussion), the late positive potential (LPP) was identified based
on the direction of the peak and the grand-average latency of
each component across the groups. To identify the LPP, the
average PZ amplitudes between 500 and 600 ms post-stimulus
were analyzed using a one-way ANOVA for emotion (anger,
happiness, and neutrality) and using repeated-measures
ANOVA for emotion (anger, happiness) and intensity (high,
medium, and low). The mean LPP amplitude of each emotion
was examined with a one-way ANOVA for group (high-, mod-
erate-, and low-AQ). We applied the Greenhouse-Geisser ad-
justment for the violation of the sphericity assumption and per-
formed the least-significant-difference (LSD) post-hoc test to
assess specific differences. These calculations were performed
with SPSS Statistics 19 (IBM, USA).
Copyright © 2013 SciRes.
Copyright © 2013 SciRes. 517
Behavioral Data
Accuracy. The three groups did not differ significantly with
respect the mean accuracy of responses {F(2, 38) = .138, p =
n.s., power (1 β error prob) = .70, effect size (η2) = .0070;
Figure 1(a)}. Data on response accuracy reflected a significant
main effect of emotion {F(1.2, 47.1) = 13.98, p < .001, 1 β
= .98, η2 = .27}. Participants found it easier to discriminate
between happy and neutral expressions than between any other
pairs of expressions (anger vs neutral & anger vs happy); this
effect was beyond that for group membership (p < .001).
Reaction time. We found a significant main effect of emotion
on the reaction times for accurate responses (F(1.3, 50.8) =
98.715, p < .001, 1 β = 1.00, η2 = .72; Figure 1(b)). The par-
ticipants responded more rapidly to emotional (angry p < .001;
happy p < .001) faces than to neutral faces. Additionally, we
found a significant interaction between group and emotion
(F(2.7, 50.8) = 3.843, p < .05, 1 β = .76, η2 = .17). The
post-hoc test has shown that the moderate-AQ group tended to
respond more rapidly to angry or neutral expressions than did
the high-AQ group, and to happy faces than did the low-AQ
group (p < .10).
The positivity of the average Pz amplitudes between 500 and
600 ms post-stimulus was significantly greater in response to
emotional faces than to neutral faces (F(1.7, 69.9) = 7.637, p
< .01, 1 β = .91, η2 = .16; Figure 2(a)). Additionally, we
found a significant main effect of intensity (F(1.4, 58.1) =
33.419, p < .001, 1 β = 1.00, η2 = .45; Figure 2(b)) in that
greater emotional intensity triggered greater positivity. Overall,
this positive potential was identified as the LPP that is corre-
lated with the emotional processing (see LPP details in discus-
We observed a significant main effect of group beyond that
of emotion (Figure 2(c); angry: F(2, 39) = 4.121, p < .05, 1 β
= .70, η2 = .17; happy: F(2, 39) = 3.322, p < .05, 1 β = .60,η2
= .15; neutral: F(2, 39) = 5.032, p < .05, 1 β = .79, η2 = .21)
on the mean LPP amplitudes. The moderate-AQ group showed
significantly higher LPP amplitudes than did the high-AQ
group in response to angry (p < .05), happy (p < .05), and neu-
tral faces (p < .01). The moderate-AQ group also showed sig-
nificantly higher amplitudes than did the low-AQ group in re-
sponse to all emotions (p < .05).
In the present study, we investigated the relationship between
autistic traits and the emotion recognition to verify the emo-
tional spectral property of autistic traits in the entire population.
Our data suggest that the high-AQ (and low-AQ) group might
experience greater difficulty (lower LPPs) with emotion recog-
nition than did the moderate-AQ group.
Behavioral Data
The response accuracy of individuals in the high-AQ group
was comparable to that shown by other groups on the behav-
ioral task. This result corresponds with the Interpersonal Reac-
tivity Index (Davis, 1980) data in our cognitive study (Nixima,
data not shown) and indicates that these individuals appropri-
ately discriminate among the emotions of others. In contrast,
the high-AQ group tended to require more time to detect facial
emotions, which supports the findings of a previous study
(Celani, Battacchi, & Arcidiacono, 1999). Consistent with a
previous study of autistic individuals (Hubl et al., 2003), the
behavioral data imply that the high-AQ group are able to cor-
rectly discriminate among emotional faces, although they some-
times require more time for processing.
It is important to note that the behavioral pattern of the
low-AQ group was similar to that of the high-AQ group. They
tended to require more time for emotional discrimination com-
pared to the moderate-AQ group, but no significant differences
in accuracy were observed among the three groups. The behav-
ioral patterns in the low-AQ group should be further investi-
gated in the future.
Figure 1.
(a) Accuracy results of the high-, moderate-, and low-AQ group for angry, happy, and neutral facial expressions; (b) Reaction time results of the high-,
moderate-, and low-AQ group for angry, happy, and neutral facial expressions. Error bars indicate standard error of the mean (s.e.m), respectively.
Figure 2.
(a) Grand-averaged ERPs elicited at Pz for angry, happy, and neutral facial expressions presented in a continuous sequence; (b) Grand-averaged ERPs
for the high, middle, and low intensities of angry and happy facial expressions; (c) Grand-averaged ERPs for each AQ group elicited at Pz for angry
and happy facial expressions.
ERP Data
We found significant group differences of late positive po-
tentials (LPPs), late ERP components commonly analyzed in
previous ERP studies of emotional processing (Schupp et al.,
2004). LPPs have been considered to correlate with emotional
processing and defined as the positive potentials of the ERPs
over the centro-parietal sites between 400 and 600 ms post-
stimulus (Schupp et al., 2004; Schupp, Flaisch, Stockburger, &
Junghofer, 2006) that might reflect activity within neocortical
brain circuits (Eimer & Holmes, 2007). Such LPP patterns for
angry and happy faces in this study were consistent with a pre-
vious ERP study (Eimer & Holmes, 2003).
In the present study, the high-AQ group displayed significant
lower LPP amplitudes than the moderate-AQ group. The neural
activity difference between these two groups can be supported
by the results of previous ERP studies of autistic individuals
(Dawson et al., 2004; Wong et al., 2008; as described below).
In contrast, the low-AQ group also demonstrated lower LPP
amplitudes than did the moderate-AQ group. Although these
results are consistent with the result of the reaction time pat-
terns in this study, the similar patterns between the high- and
low-AQ groups should be further investigated in the future.
The abnormal ERP patterns of autistic individuals compared
with the typical developing participants have been reported by
previous studies (Dawson et al., 2004; Wong et al., 2008). For
example, Dawson et al. (2004) utilized high-density ERPs to
investigate responses to fearful versus neutral faces in children
with autism, and found that children with autism demonstrated
impaired neural responses to both kinds of faces. These previ-
ous researches mainly addressed early ERP components (such
as P1 or N170), which might represent an early-low level stage
of holistic face perception or the structural encoding of a face
(Taylor, 2002; Itier & Taylor, 2002). In our own study, we also
analyzed these early ERP components at each channel, but
there were no significant difference among groups (data not
shown). The early ERP components (P1 or N170) are generally
smaller components than LPPs, and the difference among
groups in the non-clinical participants might be hardly detected.
Autistic Traits and Emotional Recognition in
Non-Clinical Adolescence
The relationship of behavioral and physiological patterns
between the high- and moderate-AQ group, taken together,
validate the conceptualization of autistic traits as a continuum
that includes normally developing individuals. We suggest that
the presence of high autistic traits interrupts the instantaneous
discrimination of emotions.
In contrast, the low-AQ group demonstrated comparable be-
havioral and physiological patterns to the high-AQ group. If
this is the case, the spectrum of emotional recognition possibly
shows a non-linear relationship with AQ scores. The emotional
continuum of autistic traits might be ambiguous in the non-
clinical population, rendering the AQ unable to identify autistic
traits in those with low scores. The difference between low and
moderate AQ score means little in terms of the presence of
autistic traits for emotional recognition, given that the AQ, as a
clinical screening tool, identifies only high autistic-like individu-
als. In other words, the AQ can be considered to be sensitive to
Copyright © 2013 SciRes.
the spectrum of emotional processing in high or moderate autis-
tic traits, and might not adequately capture the variability in the
normal range including low autistic traits. To apply the AQ for
comparison with high autistic traits in normal individuals,
moderate autistic traits should be more remarked than low au-
tistic traits, from the behavioral/physiological aspect of emo-
tional processing. Consequently, investigations of the useful-
ness of the AQ for quantifying autistic traits in non-clinical
individuals, especially those with low autistic-like traits, must
Our results suggest that the high-AQ group have different
patterns (longer reaction times and lower LPPs) of the emo-
tional processing underpinning behavioral and physiological
responses compared to the moderate-AQ group, which affirms
the autistic-like trait continuity underlying the AQ as well as
the results of previous studies. This study has also shown that,
similar to the high-AQ group, the low-AQ group shows a com-
parable pattern of emotion recognition, suggesting that the
spectrum of emotion recognition in autism would show a
non-linear relationship with AQ scores. The results in our study
possibly mean that AQ could not be considered as a simple
index for emotional processing and needs to be further investi-
gated in the future.
This work was supported by the Okanoya Emotional Infor-
mation Project of the Japan Science and Technology Agency
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