COGNITION AND EMOTION, 2014
Vol. 28, No. 6, 1110–1118, http://dx.doi.org/10.1080/02699931.2013.867832
BRIEF REPORT
Emotion recognition of static and dynamic faces
in autism spectrum disorder
Peter G. Enticott1,2, Hayley A. Kennedy1, Patrick J. Johnston3,
Nicole J. Rinehart2, Bruce J. Tonge2, John R. Taffe2, and Paul B. Fitzgerald1
1
Monash Alfred Psychiatry Research Centre, The Alfred and Central Clinical School,
Monash University, Melbourne, VIC, Australia
2
Centre for Developmental Psychiatry and Psychology, School of Psychology and Psychiatry,
Monash University, Clayton, VIC, Australia
3
Department of Psychology, University of York, York, UK
There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder
(ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that
clinical groups might benefit from the use of dynamic rather than static images. High-functioning
individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised
FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed
poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic
stimuli appeared to improve recognition of anger. This research provides further evidence of specific
impairment in the recognition of negative emotions in ASD, but argues against any broad advantages
associated with the use of dynamic displays.
Keywords: Autism; Asperger’s disorder; Facial emotion recognition; Dynamic faces.
A fundamental component of social cognition
involves the ability to recognise emotional states
in others from their facial expression. It is generally accepted that individuals with some form of
Correspondence should be addressed to: Peter G. Enticott, School of Psychology, Deakin University, 221 Burwood Hwy,
Burwood, VIC 3125, Australia. E-mail: peter.enticott@deakin.edu.au
Current address: Nicole J. Rinehart, School of Psychology, Deakin University, Burwood, VIC, Australia
The authors wish to thank all those who took part in the study and those who assisted with participant recruitment, including
Dr Tony Attwood, Ms. Tracel Devereux (Alpha Autism), Dr Richard Eisenmajer, Mr. Dennis Freeman (Wesley College
Melbourne), Ms. Pam Langford, Dr Kerryn Saunders, Ms. Linke Smedts-Kreskas (S.P.O.C.A.A.S.), Autism Victoria, A4, Autism
Spectrum Australia (ASPECT) and the Asperger Syndrome Support Network. The authors also thank Ms. Sara Arnold for
assistance with manuscript preparation.
© 2013 Taylor & Francis
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FACIAL EMOTION RECOGNITION IN ASD
autism spectrum disorder (ASD) display at least
some impairment in the recognition of emotion
from facial expressions (Harms, Martin, & Wallace, 2010), although there are a host of inconsistent results. From a clinical perspective,
impaired social interaction and understanding are
generally considered the hallmark of ASD, and an
inability to determine emotions from facial expressions is thought to contribute to these social
deficits. The Diagnostic and Statistical Manual
of Mental Disorders, 4th edition, text revision
(DSM-IV-TR) also notes impairments in the use
of non-verbal behaviour to regulate social interaction, including facial expression.
With respect to behavioural studies of facial
emotion recognition (FER) among “high-functioning” individuals with ASD (the focus of the current
study1), results are quite varied but have indicated
specific deficits in the recognition of negative
emotions such as disgust (e.g., Ashwin, BaronCohen, Wheelwright, O’Riordan, & Bullmore,
2007; Law Smith, Montagne, Perrett, Gill, &
Gallagher, 2010), sadness (e.g., Philip et al., 2010),
anger (e.g., Ashwin et al., 2007; Law Smith et al.,
2010; Philip et al., 2010) and fear (e.g., Philip et al.,
2010). Recently, Law Smith et al. (2010) found that
deficits for the recognition of surprise and anger
were only evident for lower intensity presentations
of these facial expressions, suggesting that perhaps it
is the subtlety of emotional facial expressions that
causes difficulties for individuals with ASD. Beyond
this, it has been suggested that variation in methodological factors (e.g., use of samples with different
diagnostic techniques, differing stimulus sets), and
the use of alternative strategies for emotion recognition in some individuals with ASD, underlies the
inconsistent results in this field (Harms et al., 2010).
The sheer amount of variability in the presentation
of ASD, and its heterogeneous nature, is also likely
to contribute to the variety of results regarding
emotion recognition impairment.
A relatively recent development in the assessment of FER relates to the increasing adoption of
“dynamic” stimulus materials; that is, moving
images of faces as opposed to static depictions of
emotion. Where moving faces have been used,
there are indications that they may facilitate facial
affect processing among individuals with intellectual disability (Harwood, Hall, & Shinkfield,
1999), while there is also some suggestion that,
among typically developing (TD) individuals,
dynamic displays are better recognised and rated
as more intense and realistic (although this latter
study involved animated avatar faces and might
not generalise to “real” facial expressions; Weyers,
Muhlberger, Hefele, & Pauli, 2006). There is also
evidence that dynamic displays facilitate recognition of subtle facial emotion displays (Ambadar,
Schooler, & Cohn, 2005). In the neuroimaging
literature, a number of studies have demonstrated
differential patterns of brain activation in response
to dynamic versus static facial emotion stimuli
(e.g., Kilts, Egan, Gideon, Ely, & Hoffman,
2003), with the former involving enhanced activity
in visual and temporal cortices. Since naturally
occurring facial expressions are intrinsically
dynamic, such findings have led to the suggestion
that dynamic depictions of faces may have greater
ecological validity than static depictions, and may
evoke richer neuronal, autonomic and behavioural
consequences than their static counterparts (Johnston et al., 2010; Kilts et al., 2003).
With the exception of Katsyri, Saalasti, Tiippana, von Wendt, and Sams (2008) (who examined the impact of spatial filtering on FER among
high-functioning individuals), static and dynamic
images have not been directly compared in ASD.
There have been relatively few FER studies
utilising dynamic images in ASD, but there is
some suggestion that children with ASD, at least
those considered “low-functioning”, might benefit
from slow dynamic displays of facial emotion
(Gepner, Deruelle, & Grynfeltt, 2001). Conversely, recognition of rapid displays in ASD (akin to
that encountered in “real-life” settings) might be
particularly problematic not only because of core
1
A comprehensive review of the entire literature, including neuroimaging and electrophysiology, is beyond the scope of
this study, but see (Harms et al., 2010) for an excellent recent review.
COGNITION AND EMOTION, 2014, 28 (6)
1111
ENTICOTT ET AL.
social cognitive deficits but also impaired visualmotion integration (Gepner & Feron, 2009). Eyetracking studies suggest a qualitative difference in
gaze direction for dynamic (but not static) presentations in children with high-functioning ASD
(Speer, Cook, McMahon, & Clark, 2007), with
decreased fixation of eye regions during the viewing of scenes (film and photograph) from the film
Who’s Afraid of Virginia Woolf? Neuroimaging
evidence suggests that adults with high-functioning ASD show widespread reductions in neural
activity (e.g., amygdala, fusiform gyrus) when
viewing dynamic (relative to static) facial images
(Pelphrey, Morris, McCarthy, & LaBar, 2007). By
contrast, however, Katsyri et al. (2008) found no
FER deficits in adults with Asperger’s disorder for
both static and dynamic images (although impairments were evident when images were filtered to
encourage “global” processing of facial images).
The current study examined FER of matched
static and dynamic images among adolescents and
adults with ASD and TD individuals. If a core
deficit of ASD involves impairments in social
processing (with FER as one aspect of this impairment), dynamic stimuli could be considered a better
representation of an aspect of this process (and
therefore a more precise means of investigating this
process). There is relatively little data from which to
derive a hypothesis; however, based on the literature
to date, particularly imaging research suggesting
greater reductions in neural activity for dynamic
depictions of facial expressions among adults with
high-functioning ASD, it was expected that individuals with ASD would show impairment for both
static and dynamic displays of facial emotion.
METHODS
Participants
Participants were 36 individuals (27 males; mean
age = 25.00 years, SD = 8.83) diagnosed with an
ASD [autism (n = 10) or Asperger’s disorder (n =
26)], all of whom were within or above the average
range of cognitive function [as determined by the
Kaufman Brief Intelligence Test Second Edition
(KBIT-2)], and 36 TD (23 males; mean age:
1112
COGNITION AND EMOTION, 2014, 28 (6)
25.78 years, SD = 6.65) individuals with no selfreported history of psychiatric or neurological
illness. There were no between-group differences
in either gender, χ2(1) = 1.05, p = .306, or age,
t(70) = 0.42, p = .674. ASD participants were
recruited via the Monash Alfred Psychiatry
Research Centre participant database, which is
comprised of clinically diagnosed individuals who
have previously taken part in research and have
agreed to be contacted in relation to future
projects, and advertisements placed in newsletters,
websites and offices of ASD-related organisations
(e.g., advocacy groups, support groups, vocational
services) and clinicians (e.g., psychologists, psychiatrists, paediatricians). Participants were diagnosed by a psychiatrist, clinical psychologist or
paediatrician. A diagnosis of DSM-IV autistic
disorder or Asperger’s disorder was confirmed via
diagnostic report or, where a diagnostic report was
not available, by telephone with the diagnosing
clinician (psychologist, psychiatrist or paediatrician). TD participants were recruited via advertisements placed within The Alfred hospital,
Monash University, and the newsletter of a local
secondary school (Wesley College). This project
was approved by the human research ethics
committees of both Monash University and The
Alfred hospital (Bayside Health). All adult participants (aged 18 and over) provided signed
informed consent, while a parent provided signed
informed consent for child participants (aged 14–
17). Children provided signed assent to participate. Participants were reimbursed (AU$30) for
travel expenses.
Participant information is presented in Table 1.
One TD participant did not complete the KBIT2. There was no difference in age, t(70) = −0.42,
p = .674, or gender, χ2(1) = 1.05, p = .306,
between the ASD and TD groups. The TD group
had a greater verbal intelligence quotient (VIQ)
than the ASD group, t(64) = −2.07, p = .042, but
there was no significant difference in non-verbal
IQ, t(69) = −1.05, p = .296, or composite IQ,
t(61) = −1.74, p = .087.
Adult participants completed the Ritvo Autism-Aspergers Diagnostic Scale (RAADS) (Ritvo
et al., 2008). This is a self-report measure
FACIAL EMOTION RECOGNITION IN ASD
Table 1. Participant demographics
N
Age in years (SD)
Gender (m:f)
KBIT-2 verbal IQ (SD)*
KBIT-2 non-verbal IQ (SD)
KBIT-2 composite IQ (SD)
AQ (SD)**
EQ (SD)**
RAADSasocial relating (SD)**
RAADSa language/communication (SD)**
RAADSa sensorimotor (SD)**
RAADSa total (SD)**
DBC autism screening algorithm (SD)b*
ASD
TD
36
25.00 (8.83)
27:9
99.17 (17.62)
107.19 (19.12)
103.89 (19.51)
30.21 (9.33)
25.82 (13.39)
40.89 (15.05)
33.12 (13.25)
30.64 (13.25)
104.64 (39.68)
18.12 (9.45)
36
25.78 (6.65)
23:13
106.80 (12.95)
111.29 (12.93)
110.71 (13.05)
12.11 (5.93)
49.57 (12.86)
14.58 (10.22)
8.24 (5.51)
8.15 (6.13)
30.97 (18.60)
1.00 (0.00)
*p < .05, **p < .001.
a
Completed by adult participants only.
b
Completed by parents of child participants only.
(comprising 78 items) that probes various aspects
of ASD, both in relation to childhood and
adulthood. An increased score is indicative of
greater ASD severity. All participants (child and
adult) completed the empathy quotient (EQ)
(Baron-Cohen & Wheelwright, 2004), while all
participants aged 16 and older completed the
Autism Spectrum Quotient (AQ) (Baron-Cohen,
Wheelwright, Skinner, Martin, & Clubley, 2001).
The latter yields the following subscales: social
skill, communication, imagination, attention to
detail and attention switching. Parents of child
participants completed the developmental behaviour checklist (DBC) (Einfeld & Tonge, 2002), a
parent-completed checklist of behavioural and
emotional problems that includes an autism screening algorithm, while parents of participants aged
14 or 15 also completed the AQ – Adolescent
Version (Baron-Cohen, Hoekstra, Knickmeyer, &
Wheelwright, 2006). For analysis purposes
(Table 1), child and adult AQ scores were
combined; there were five ASD and two TD
aged 14 or 15 for whom the child AQ was completed. These measures were not used to diagnose
participants, which was done by clinicians according to DSM-IV criteria, but rather to characterise
the sample. Independent samples t-test revealed a
significant difference between the ASD and TD
groups on all of these measures (see Table 1).
Procedure
The FER task was presented on a notebook
computer using E-prime software (v2.0; Psychology Software Tools Inc., Pittsburgh, PA, USA).
Images for the task were taken from the NimStim
set of facial expressions, a stimulus set that displays
strong reliability and validity (Tottenham et al.,
2009). There were eight actors in total (four males
and four females), each of which displayed the six
basic emotions (anger, disgust, fear, happy, sad
and surprise). These were selected to represent a
broad range of ethnicities. The images were
presented centrally over a black background, and
occupied the entire height of the screen. There
was a static and dynamic form of each expression,
making a total of 96 trials (which were presented
in a random order). The dynamic images were
achieved by creating a “morphed” display from a
neutral facial expression to the emotional facial
expression (duration = 1000 ms) (see Johnston
et al., 2010, for a description of dynamic stimulus
production identical to that used in the current
study). The task took approximately 8–10 minutes
COGNITION AND EMOTION, 2014, 28 (6)
1113
ENTICOTT ET AL.
to complete, depending on the participant’s
response time. The computer keyboard was covered so that only six keys were displayed (a, d, g, j,
l and “), and one of the six basic emotions was
written over each key (from left to right: anger,
disgust, fear, happy, sad and surprise). Participants
were instructed to press the key that best indicated
the emotion that the person was feeling based on
that person’s facial expression. Participants were
asked to respond as quickly as possible, but it was
emphasised that accuracy, not speed, was the main
aim of the task. The trials were not time limited.
There was a two second gap (black screen)
between a response and the onset of a new trial.
interaction was also investigated within this
model. Accuracy (i.e., number correct) served as
the dependent variable. VIQ was included due to
the language component of the task (i.e., identification of emotion words for identifying facial
expressions). Age and gender were included due to
the relatively broad age range and use of both male
and female participants (which could have an
impact on performance), and we wanted to ensure
that any possible influence of these factors was
accounted for. We also examined, for each of the
six emotions, which emotion was most commonly
selected for those responses that were incorrect (i.
e., index of confusion), and analysed via chisquare.
Data analysis
Data were analysed via separate random-effects
generalised least squares (GLS) regression models
(with participant as the random-effect) for each
emotion (using STATA 11.1, StataCorp, College
Station, TX). This involved the predictive factors
group (ASD, TD), motion (dynamic, static),
verbal IQ, age and gender. A group × motion
Figure 1.
1114
RESULTS
Mean group scores for each emotion, both static
and dynamic, are presented in Figure 1. Regression was not conducted for the recognition of
happy faces, as there were substantial ceiling
effects for both groups. Among ASD participants,
Mean FER performance (total correct, ±SE) by emotion for each group.
COGNITION AND EMOTION, 2014, 28 (6)
(.908)
(.868)
(.370)
(.055)
(.384)
(.487)
[0.24]
[0.39]
[0.34]
[0.01]
[0.02]
[0.37]
0.07
0.03
0.06
−0.31
0.02
−0.02
0.26
(.651)
(.489)
(.025)
(.385)
(.839)
(.190)
[0.18]
[0.32]
[0.26]
[0.01]
[0.02]
[0.32]
0.10
0.08
−0.22
−0.58
0.01
0.01
0.41
(.826)
(.192)
(.485)
(.026)
(.725)
(.771)
[0.25]
[0.42]
[0.36]
[0.01]
[0.02]
[0.40]
0.12
0.06
−0.54
−0.25
0.03
−0.01
0.12
(.741)
(.021)
(.815)
(.072)
(.644)
(.504)
[0.25]
[0.46]
[0.36]
[0.01]
[0.03]
[0.45]
0.14
0.08
−1.05
0.08
0.02
0.01
0.30
(.026)
(.000)
(1.00)
(.003)
(.165)
(.572)
[0.18]
[0.38]
[0.25]
[0.01]
[0.02]
[0.39]
Sad β [SE] (p)
Fear β [SE] (p)
Disgust β [SE] (p)
Anger β [SE] (p)
0.34
0.38
−1.52
0.00
0.03
0.03
−0.22
R (overall)
Motion
Group
Motion × group
Verbal
Age
Gender
2
Individuals with ASD were impaired in the
recognition of facial expressions showing anger
and disgust. Although there were no overall group
differences for the recognition of sad facial
expressions, people with ASD were disadvantaged
by the presentation of dynamic (compared with
static) images of sad facial expressions. Lastly,
although there were group differences for
IV
DISCUSSION
Table 2. Results of regression analyses for each emotion (excluding happy; significant results in bold typeface)
27 participants scored 16/16, 6 participants scored
15/16 and 1 participant each score 14/16, 13/16
and 12/16. Among controls, 34 participants
scored 16/16, while the remaining 2 scored 15/16.
Results of regression analyses are presented in
Table 2. There was no significant overall effect
of the regression for fear (p = .07) or surprise
(p = .31).
For anger, there was a significant effect of
motion, with dynamic images associated with
increased accuracy, and an effect of group, with
TD outperforming ASD. There was also a significant effect of VIQ, with increased VIQ associated with marginally improved performance.
For disgust, there was a significant effect of
group, with TD again outperforming ASD.
For sad, there was a significant Group
× Motion interaction effect, with accuracy for
dynamic stimuli poorer than static stimuli among
the ASD group.
There were no group differences with respect
to the index of confusion (i.e., most commonly
selected incorrect response for each emotion). For
both groups, fear was most commonly mistaken
for surprise (ASD: 56% of participants; TD: 64%;
χ2(8) = 8.09, p = .425), while surprise was most
confused with fear (ASD: 76%; TD: 86%; χ2(7) =
8.31, p = .306). Similarly, for both groups anger
was most frequently confused with disgust (ASD:
56%; TD: 64%; χ2(9) = 11.17, p = .265), and
disgust was most commonly mistaken for anger
(ASD: 77%; TD: 89%; χ2(4) = 7.56, p = .109).
Lastly, for both groups sad was most commonly
mistaken for fear (ASD: 48%; TD: 69%; χ2(10) =
9.77, p = .461).
Surprise β [SE] (p)
FACIAL EMOTION RECOGNITION IN ASD
COGNITION AND EMOTION, 2014, 28 (6)
1115
ENTICOTT ET AL.
recognition of expressions of anger (with the ASD
group performing worse), here there was an overall
advantage for both groups when recognising
dynamic images showing facial expressions of
anger.
Impaired recognition of anger and disgust in
ASD is somewhat consistent with studies involving similar patient groups (i.e., high-functioning
ASD), where deficits have been found in the
recognition of basic negative emotions such as
fear, disgust, anger and sadness (Harms et al.,
2010). These data further highlight that FER
impairments in ASD do not extend to all emotions, but rather seem to be limited to negative
emotions. From a clinical perspective, a failure to
accurately recognise negative emotions in others
may be substantially more disadvantageous than a
failure to recognise positive emotions, and lead to
greater interpersonal difficulties across a range of
settings. For instance, a failure to recognise anger
or disgust in another person, and a consequent
failure to adjust behaviour accordingly, might
exacerbate interpersonal difficulties or produce
conflict across a range of settings. By contrast, if
there was a failure to accurately recognise, for
example, happiness, this would be unlikely to produce a similarly negative outcome. Interestingly,
and despite the observed differences, there did not
appear to be any differences in the pattern of
errors for specific emotional expressions between
the groups. There were no group differences for
happy or surprise; while happy expressions were
associated with a ceiling effect, that we did not
detect a difference for surprise seems consistent
with recent research. For instance, Law Smith
et al. (2010) reported deficits in recognising surprise in ASD but only at low intensities. The
surprise stimuli used in the present study may have
lacked the necessary subtlety to detect impairment
in ASD.
While we were primarily interested in the
influence of dynamic images, it appears that the
use of dynamic facial expressions had relatively
little effect. It is often suggested that dynamic
stimuli have greater ecological validity and should
be more easily recognised than static images, and
this was the case, for all participants, for anger.
1116
COGNITION AND EMOTION, 2014, 28 (6)
Somewhat paradoxically, however, the presentation of dynamic images for the sad facial expression resulted in a reduction in accuracy (relative to
static) among individuals with ASD (although
overall group differences were not significant),
suggesting that in some instances individuals
with ASD might actually be disadvantaged by
dynamic (relative to static) facial expressions. It is
not immediately clear why individuals with ASD
would be disadvantaged by a dynamic presentation
for just one emotion. Neuroimaging research
suggests that brain networks that process static
and dynamic faces do differ somewhat (Kilts et al.,
2003), and these different networks (or functions
underlying these networks) could be differentially
affected in ASD (at least in relation to the
recognition of sadness). For example, structures
thought to comprise the mirror neuron system,
which is seemingly responsive to goal-directed and
dynamic expressions of behaviour, have been
shown as underactive in ASD (Dapretto et al.,
2006). Recent research also implicates poor neural
connectivity in ASD, particularly to and within
prefrontal and parietal regions (Just, Cherkassky,
Keller, Kana, & Minshew, 2007; Wicker et al.,
2008). In this respect, functional magnetic resonance imaging (fMRI) studies of people with ASD
using the current stimuli will be of great interest,
as will similar studies with alternate stimulus sets.
Lastly, it is possible that the duration of the
dynamic images was too brief to benefit individuals with ASD (Gepner & Feron, 2009),
although the extent to which this would apply to
“high-functioning” individuals is not clear.
This study is limited by a relatively broad age
range, which may introduce developmental factors
related to visual processing. Groups, however,
were matched according to age, and regression
analyses revealed no significant effects of age.
These (and other) dynamic facial stimuli could
be criticised because they finish with a final frame
that remains displayed until a response is given.
Thus, a response is given while viewing a static
image, albeit one preceded by a dynamic display.
This, however, was considered necessary in order
to (1) maintain consistency with the static condition (in which the face is presented until a
FACIAL EMOTION RECOGNITION IN ASD
response is given) and (2) avoid the perceptual
oddness of either cutting off or looping the
dynamic display. Additional limitations include
the use of morphed stimuli, which might be
argued to have less ecological validity than a video
of an individual performing a facial expression,
and stimuli demonstrating only strong intensity
emotional displays. It would also have been useful
to have better diagnostic information on our
participants (e.g., autism diagnostic interview
[ADI]/autism diagnostic observation schedule
[ADOS]), while concurrent eye tracking in future
studies will allow a determination of possible
visual and attentional influences. For example,
children with autism have been found to favour
the mouth region and/or attend less to the eye
regions when attending to faces (e.g., Joseph &
Tanaka, 2003; Rutherford, Clements, & Sekuler,
2007), and this could place them at a significant
disadvantage when attempting to determine a
facial emotional display, be it static or dynamic.
In summary, these findings add to the considerable literature concerning the recognition of
emotion from facial expressions in ASD, and
again highlight specific impairments that seem
associated with negative emotions (in this instance,
anger and disgust). Dynamic facial expressions are
often considered an improvement over traditional
static images, but here were associated with
relatively little change that was linked to ASD.
Manuscript received 15 January
Revised manuscript received 29 August
Manuscript accepted 16 November
First published online 13 December
2013
2013
2013
2013
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