Using Time Perception to Explore Implicit Sensitivity to Emotional Stimuli in Autism Spectrum Disorder
Using Time Perception to Explore Implicit Sensitivity to Emotional Stimuli in Autism Spectrum Disorder
Catherine R. G. Jones 0 1
Anna Lambrechts 0 1
Sebastian B. Gaigg 0 1
0 Department of Psychology, City University London , Northampton Square, London EC1V 0HB , UK
1 School of Psychology, Cardiff University , Tower Building, Cardiff CF10 3AT , UK
Establishing whether implicit responses to emotional cues are intact in autism spectrum disorder (ASD) is fundamental to ascertaining why their emotional understanding is compromised. We used a temporal bisection task to assess for responsiveness to face and wildlife images that varied in emotional salience. There were no significant differences between an adult ASD and comparison group, with both showing implicit overestimation of emotional stimuli. Further, there was no correlation between overestimation of emotional stimuli and autistic traits in undergraduate students. These data do not suggest a fundamental insensitivity to the arousing content of emotional images in ASD, or in individuals with a high degree of autistic traits. The findings have implications for understanding how emotional stimuli are processed in ASD.
Autism; Emotion; Face processing; Temporal bisection; Time perception; Arousal
Difficulties in understanding and responding appropriately
during social exchange are hallmarks of autism spectrum
disorder (ASD). These difficulties have led to close
scrutiny of the ability to process emotional cues, with a heavy
emphasis on recognising emotion in the face (Uljarevic and
Hamilton 2013). Investigation of facial emotion
recognition in ASD has typically involved labelling faces
expressing the six basic emotions (happiness, sadness, fear, anger,
surprise, disgust). Against a background of mixed
findings, recent meta-analyses have concluded that difficulties
in facial emotion recognition are characteristic of ASD,
although the severity of impairment varies according to
emotion (Lozier et al. 2014; Uljarevic and Hamilton 2013).
However, a challenge of any task that involves participants
explicitly engaging with the process being measured is that
they may use alternative strategies to ‘hack out’ the correct
response. For instance, similar behavioural emotion
recognition performance in participants with and without ASD is
found alongside different patterns of neural activation (e.g.
Rahko et al. 2012). A related issue is that uninterrupted
time to decide a person’s emotional state does not recreate
the demands of real-life social interactions. Therefore, it is
arguable that explicit and relatively straightforward
measures of emotion recognition provide only limited insight
into the more complex realities of processing emotion in
One way of circumventing these issues is to measure
emotion processing indirectly. For example, in
circumstances where implicit processing of the emotional content
of stimuli will influence the response, despite no explicit
instruction to pay attention to emotion. A similar approach
has been taken in characterising theory of mind in ASD,
with explicit mentalising being ostensibly unimpaired
while implicit and intuitive mentalising abilities are
compromised (Senju et al. 2009). For emotion processing, an
elegant paradigm that achieves this goal is an adapted
version of the temporal bisection task (Droit-Volet et al. 2004).
The temporal bisection task is a classic measure of interval
timing that was originally used in animals (Wearden 1991).
Within the last 25 years, the task has helped characterise
Fig. 1 Hypothetical psychophysical functions to illustrate the effect
of emotional stimuli on duration judgements in the temporal bisection
task. The mean proportion of long responses is plotted against
stimulus duration. The leftward shift of the emotional face data along the
x axis illustrates the response bias towards long responses. Dashed
lines illustrate the bisection point (x axis), which is the plotted
duration where 50% of responses are long (y axis) (see Method for further
the mechanistic structure and psychophysical hallmarks of
human perceptual timing in the millisecond- and
secondsrange (see Jones and Jahanshahi 2014 for a summary of
related tasks). It has been argued that a brain-based internal
clock (or clocks) govern this distinct type of timing process
(see Buhusi and Meck 2005). The temporal bisection task
requires participants to learn short and long standard
durations (e.g. 400 and 1600 ms), typically presented as
simple visual displays. During the testing phase, stimuli are
presented for both the standard and intermediate durations
and participants have to classify each as more similar to the
short or long standards. The proportion of ‘long’ responses
increases monotonically with stimulus duration and can be
plotted as a psychophysical function, or bisection curve,
with duration along the x axis. Various performance
measures can be obtained from the psychophysical function,
with the steepness of the slope indexing temporal
sensitivity and the lateral displacement along the x axis indexing
response bias (see Wearden 1991). In typical populations,
when face stimuli are used during the testing phase the
duration of emotional faces are consistently overestimated
compared to neutral faces, which is demonstrated in the
leftward displacement of the bisection curve (e.g.
DroitVolet et al. 2004; Effron et al. 2006; Fayolle and
DroitVolet 2014; Tipples 2008, 2011; Tipples et al. 2015). In
contrast to response bias, temporal sensitivity is typically
not affected (see Fig. 1 for a hypothetical illustration of
typical findings). The intuitive explanation is that implicit
recognition of the emotional content of the stimuli is
driving the effect.
One explanation for the findings is that the internal clock
that times the intervals is sensitive to the arousal induced
by viewing emotional faces (see Cheng et al. 2016;
DroitVolet and Meck 2007). In essence, the internal clock is
speeded by the increased levels of arousal, which means
that more clock ‘ticks’ (temporal units) are accrued and the
period of time is judge as longer. This explanation has been
interpreted within the most common internal clock model,
scalar expectancy theory (SET; Gibbon 1977; Gibbon et al.
1984). SET is an information processing model that
conceives that time is processed within a clock system
consisting of a pacemaker that emits pulses, which are passed via
a switch to an accumulator that represents current elapsed
time. Working memory and reference memory processes
are used to store time values and a comparator, or decision
making process, compares these values to enable a
temporal judgement to be made. Despite criticisms of SET (e.g.
Buhusi and Meck 2005; Droit-Volet and Meck 2007), the
explanation of overestimation being driven by a speeded
pacemaker and increased accumulation of temporal units
aligns with evidence demonstrating that stimulant drugs
lead to overestimation of duration (see Coull et al. 2011;
Droit-Volet et al. 2013). This arousal-based explanation
also fits the subjective phenomenon of time seeming to
slow when in a highly arousing situation such as an
accident. A range of evidence has indicated that emotional
stimuli trigger activation of the sympathetic autonomic
nervous system (e.g. Brouwer et al. 2013). Direct
physiological evidence for increased arousal during the
emotional temporal bisection task has remained unexplored,
partly as the multiple short trials do not lend themselves
to accommodating the refractory periods of physiological
responses. However, Gil and Droit-Volet (2012) found that
emotional images that were subjectively judged as highly
arousing produced greater overestimation than images
with lower ratings. Mella et al. (2011) directly measured
skin conductance response (SCR) during a duration and
emotion discrimination paradigm. High arousing sounds
were judged as longer and led to enhanced SCR when
participants attended to the emotional intensity of the stimuli,
although not all data were compatible with a simple
relationship between time, arousal and emotion. Regardless of
the physiological underpinnings, overestimation of
emotional stimuli is a reliable finding that can be used as an
indirect index that the emotional salience of the stimuli has
The emotional temporal bisection task can therefore
give insight into whether the implicit response to
emotion is intact in ASD, which is important for illuminating
the precise nature of the emotional processing difficulties
experienced in ASD. As performance requires a timing
Table 1 Summary of
participants in Study 1
Males (n = 38)
Total (n = 85)
aAutism Spectrum Quotient (Baron-Cohen et al. 2001)
judgement, the task also provides information on the
accuracy of perceptual timing in ASD. Previous research has
argued that there is a fundamental timing difficulty in
ASD (Allman et al. 2011; Brodeur et al. 2014; Falter et al.
2012; Karaminis et al. 2016; Kargas et al. 2015; Maister
and Plaisted-Grant 2011; Martin et al. 2010; Szelag et al.
2004). However, this is not a universal finding (Jones et al.
2009; Gil et al. 2012; Mostofsky et al. 2000; Wallace and
Happé 2008) and the debate remains open. An important
secondary aim of the study, therefore, is to add to the small
but growing body of literature that considers whether
interval timing poses difficulties for individuals with ASD.
As well as investigating the implicit responses to
emotional faces in ASD, the studies reported below also
examined responses to a set of wildlife images (e.g., spider)
that were chosen to vary in emotional salience to a similar
extent as the face stimuli. Emotion research in ASD often
focuses on the human face, making it difficult to
determine whether observed effects are face-specific or reflect a
more general difficulty with emotion processing (see Gaigg
2012). Our study was piloted in a large population of
typically developing (TD) young adults, reported in Study 1, in
which the Autism Quotient (AQ: Baron-Cohen et al. 2001)
was used to investigate if there was any meaningful
association between task performance and self-reported autistic
traits in the general population. Study 2 directly compared
adults with ASD to a comparison group without a
diagnosis. A reduced emotional temporal bisection effect in ASD
would suggest atypical implicit responsiveness to
emotional stimuli, whereas an intact emotional temporal
bisection effect would indicate that this response, thought to be
mediated by sub-cortical arousal mechanisms, is
Study 1: Temporal Bisection of Arousing Face
and Wildlife Images in a Typical Adult Population
Eighty-five undergraduate and postgraduate students (47
female; M = 22 years 7 months; SD = 4.94) from the
University of Essex participated. There was no significant
difference in age between male and female participants (see
Table 1). None of the participants had a history of
psychiatric or neurological disorder or illness. As one of the tasks
included pictures of spiders, all participants were screened
for arachnophobia. No participant had a diagnosis of ASD,
or a family member with ASD. An additional four
participants were tested but their data in both the face and wildlife
conditions were discarded because their responses across
the varying durations of the stimuli did not conform to a
sigmoid curve (see below), which suggests that they did not
follow the task instruction (i.e., the participants did not
discriminate between shorter and longer durations). All
participants gave informed consent and the study was approved
by the Ethics Committee of the University of Essex.
Materials and Procedure
Temporal Bisection Tasks
The tasks were programmed in E-Prime 2.0 (Schneider
et al. 2002) and displayed on a PC. The experiment
consisted of two versions of a temporal bisection task in
which participants first learned to discriminate between a
short and a long reference duration and then tried to
categorise varying durations as more similar to the short or
long exemplars. The training phase of each version
consisted of 20 trials in which a monochrome grey rectangle
(15 cm × 19.3 cm) appeared for either 400 or 1600 ms on a
computer monitor. These durations served as the short and
long reference durations. During the first 10 training trials
the duration of the rectangle alternated, accompanied by a
visual display of the appropriate label (‘short’ or ‘long’).
For the final 10 training trials, the rectangle appeared
randomly for either 400 or 1600 ms and participants were
required to label the trial as either short or long by pressing
appropriate response keys following the on-screen
question, ‘Do you think this was SHORT or LONG?’.
Throughout the task, the ‘N’ key (re-labelled ‘S’) on the computer
keypad was used for a short response and the ‘M’ key
(relabelled ‘L’) was used for a long response. Participants
used their preferred hand/fingers to respond.
Two versions of the bisection task were administered in
counterbalanced order across participants. In the face
version, photographs of four Caucasian male models (#23,
26, 27 and 36) were selected from the NimStim database
(Tottenham et al. 2009), with each posing neutral, happy,
angry and fearful expressions (i.e. 16 face stimuli). In the
wildlife version, photographs of four different flowers,
puppies, snarling canine/felines (snarl) and spiders were
sourced from various web-sites (i.e. 16 wildlife stimuli).
The wildlife stimuli were chosen because they were
hedonically similar to the neutral, happy, angry and fearful facial
All experimental stimuli were converted to 24-bit
greyscale images and cropped to match the dimensions of the
grey rectangle used for training. In each of the two
versions of the task, the 16 stimuli (4 per hedonic category)
were presented once each at 7 different durations (400, 600,
800, 1000, 1200, 1400 and 1600 ms) for a total of 112
trials. The order of presentation was pseudo-randomised with
the constraint that no more than 2 successive trials could be
of the same duration or hedonic category. Each trial began
with a ‘READY’ screen that lasted randomly between 1800
and 2500 ms and was followed by the experimental
stimulus at one of the pre-set durations. A blank interval
lasting between 200 and 500 ms separated the stimulus from
the response prompt, ‘Do you think this was SHORT or
LONG’?’. The prompt terminated with the participants’
response and was followed by another 200–500ms blank
interval before the ‘READY’ signal reappeared to mark the
beginning of the next trial.
To establish that the images produced the predicted
subjective feelings of arousal, the participants were required
to rate each image for valence and arousal using the
SelfAssessment Manikin (SAM: Lang 1980). The SAM uses
cartoon images to represent 9-point scales of arousal and
valence. The images (16 face and 16 wildlife) were
presented in a random order and at a self-paced rate
immediately after completion of the temporal bisection tasks.
Fifty-four participants completed this stage of the study.
The AQ questionnaire (Baron-Cohen et al. 2001) was
used to measure self-reported autistic traits (range
available = 0–50). This was administered at the end of the testing
Analysis of the Temporal Bisection Data
The proportion of long responses (p(long)) for each
category and stimulus duration was calculated (i.e. proportion
of long responses out of 4). In addition, we fitted
participants’ response to a cumulative Gaussian sigmoid using
the psignifit MATLAB Toolbox (Wichmann and Hill
2001a, b) and extracted the bisection points of the
resulting response curves for each individual. The bisection
point is the point of subjective equality, i.e. the duration
at which short and long responses occur with equal
probability. It reflects accuracy in relation to the veridical
middle point, with lateral displacement indicating response
bias towards either short or long responses. It can be
measured as the x-axis value at which sigmoid functions
cross the 50% midpoint of the y-axis (p(long) = 0.5). For
the current experiment this would be expected to be close
to 1000 ms (i.e. half way between the shortest and longest
durations). Following similar principles the Weber ratio
can be calculated, which reflects the slope of the
sigmoid curve and serves as an index of temporal
sensitivity. It is half the difference between the upper difference
limen (p(long) = 0.75) and the lower difference limen
(p(long) = 0.25) divided by the bisection point. A lower
score indicates greater temporal sensitivity, reflected in
a steeper slope. As the p(long) and bisection point data
both enable inspection of response bias and to reduce the
amount of analysis reported, our main analysis focuses
on the p(long) and Weber ratio. The bisection data are
provided in tables for interest, presented alongside the
Weber ratio data, and are the index of temporal
overestimation that are correlated with the AQ.
To identify participants who may not have followed
the instructions and therefore performed no better than
chance, we applied a best-fit computation to compare the
quality of the response curve within two different models.
Using MATLAB, the response curve produced for each
participant for each stimulus type was analysed to
establish if a sigmoid curve (two-parameter fit) or a
horizontal line (one-parameter fit, indicting no differentiation in
performance by image duration) was a significantly better
fit. If a sigmoid function did not best-fit a participant’s
data for any one of the images in a given condition (face
or wildlife) then the participant was excluded from that
Analysis of the data was conducted in SPSS (IBM
Corp, Version 20.0) using repeated measures analysis of
variance (ANOVA). Hypothesis significance testing was
supplemented by the calculation of effect size as well as
the 90% confidence intervals for the effect sizes (Lakens
2013; Steiger 2004). Effect sizes were calculated using
partial eta squared (ƞ2p), where a small effect is
considered 0.01, a medium effect 0.06 and a large effect 0.14.
One participant’s face data was lost for technical reasons
and another participant did not produce a best-fit
sigmoid-shaped curve (therefore, face n = 83). For the
wildlife images, one participant did not complete the task due
to self-reported arachnophobia and a further three
participants did not produce a best-fit sigmoid-shaped curve
(therefore, wildlife n = 81).
Table 2 Average valence and arousal ratings for the face and wildlife
stimuli. Score range was 1–9, with a high score indicating the image
produced more positive valence and greater arousal
Autism Spectrum Quotient
Scores ranged from 2 to 29, with no individuals scoring
above the cut-off (32) for clinical significance
(BaronCohen et al. 2001). The mean score for the group was
15.0 (SD = 5.5) with a median of 15.0. There was no
significant difference between the scores for males and
females (see Table 1).
Subjective Valence and Arousal Ratings for the Face
and Wildlife Data
Using two repeated measures ANOVAs to separately
examine the face and wildlife data (see Table 2), both
showed a main effect of emotion on the arousal ratings
(Face: F (3, 159) = 15.29; p < .001, ƞ2p=0.22, 90% CI
[.13,.30]; Wildlife: F (3, 159) = 17.86; p < .001, ƞ2p=0.25,
90% CI [.15, .33]). Planned simple contrasts indicated
emotional faces were rated as significantly more arousing
than the neutral face (all p < .001, ƞ2p range 0.24–0.38)
and emotional wildlife images were significantly more
arousing than the neutral flower (all p< .001, ƞ2p range
0.32–0.41). For the valence ratings (see Table 2),
repeated measures ANOVAs again showed main effects
of emotion (Face: F (3, 159) = 69.80; p < .001, ƞ2p=0.57,
90%CI [.48, .63]; Wildlife: F (3, 159) = 114.20; p < .001,
ƞ2p=0.68, 90% CI [.61,.73]). For the face stimuli, neutral
faces were rated as more positive than angry and
fearful faces and less positive than happy faces (all p < .01,
ƞ2p range 0.11–0.69). Similarly, for the wildlife images,
flower images were rated as more positive than spider
and snarl images but less positive than puppy images (all
p < .001, ƞ2p range 0.28–0.66).
Fig. 2 Proportion of long responses for the four facial expressions in
Study 1. For clarity, error bars are omitted
Temporal Bisection: Face
A 4(Emotion) × 7(Duration) ANOVA of p(long) showed
a main effect of Duration (F(3.04, 249.03)= 995.83,
p < .001, ƞ2p=0.92, 90% CI [.91, .93]), confirming that long
responses increased with stimulus duration, and Emotion
(F(3, 246) = 6.16, p < .001, ƞ2p=0.07, 90% CI [.02, .12])
(see Fig. 2). Planned simple contrasts indicated that
fearful (F(1, 82) = 13.70, p = .001, ƞ2p=0.14, 90% CI [.05, .26])
and happy (F(1, 82) = 13.05, p = .001, ƞ2p=0.14, 90% CI
[.04, .25]) faces elicited a significantly higher proportion of
long responses than neutral faces. There was no difference
between angry and neutral faces (p > .3, ƞ2p=0.01, 90% CI
[.00, .07]). The interaction of Emotion and Duration was
not significant (p> .2, ƞ2p=0.01, 90% CI [.00, .02]).
Parallel analyses of the Weber ratio (see Table 3) did
not produce significant findings (p> .7; ƞ2p=0.006, 90% CI
[.00, .02]) indicating that accuracy but not sensitivity was
affected by emotional faces.
Temporal Bisection: Wildlife
A 4(Emotion) × 7(Duration) ANOVA of p(long) showed
a main effect of Duration (F(2.86, 228.79)= 958.65,
p < .001, ƞ2p=0.92, 90% CI [.91, .93]) and Emotion
(F(3, 240) = 5.66, p = .001, ƞ2p=0.07, 90% CI [.02, .11])
(see Fig. 3). Planned simple contrasts showed that both
snarl (F(1, 80) = 17.92, p < .001, ƞ2p =0.18, 90% CI
[.07, .30]) and puppy images (F(1, 80) = 7.16, p = .009,
ƞ2p=0.08, 90% CI [.01, .19]) elicited significantly higher
Table 3 Average bisection points (ms) and Weber ratio for all
stimulus categories for the face and wildlife images in Study 1
Fig. 3 Proportion of long responses for the four wildlife images in
Study 1. For clarity, error bars are omitted
proportion of long responses than flower images. There
was no difference between spider and flower images
(p > .1, ƞ2p=0.02, 90% CI [.00, .10]). The interaction
of Emotion and Duration was also significant (F(10.16,
812.64 = 3.49, p < .001, ƞ2p=0.04, 90% CI [.90, .92]).
There were no significant effects for a parallel analysis
of the Weber ratio (p > .1, ƞ2p=0.02, 90% CI [.00, .05]) (see
Table 3), again showing that response bias but not
sensitivity was moderated by the emotional salience of the stimuli.
For both the temporal bisection points and Weber ratio
scores, each emotional image was subtracted from the
neutral image to give an index of relative overestimation and
of temporal sensitivity, respectively, for each emotional
image. This was done for both the face and wildlife images,
resulting in six correlations (three temporal bisection point;
three Weber ratio) for each category of image. There was
no evidence of a substantive or significant correlation with
AQ for any measure (Pearson’s r range: −0.16 to 0.08).
Study 2: Temporal Bisection of Arousing Face
and Wildlife Images in Individuals with ASD
and a Matched Comparison Group
Twenty-four participants with a diagnosis of ASD (3
female) and 26 matched comparison participants (6 female)
without a history of psychiatric or neurological disorder
or illness took part. Four participants in the ASD group
were excluded from further analysis as their behavioural
responses in both versions of the task did not conform to
a sigmoid curve suggesting that they did not discriminate
between longer and shorter durations. The remaining 20
participants with ASD and all 26 matched comparison
participants produced valid experimental data in at least one
condition (see Results section for further details). They
were recruited from a database of participants at City
University, London. Participants in the clinical group were
diagnosed by local health authorities and a review of
medical records confirmed that all met the DSM-IV (American
Psychiatric Association, 2000) criteria for ASD that were
applicable at the time of their diagnosis. None of the
clinical group had a history of co-morbid psychiatric disorders
and none were taking prescribed medication. An
additional assessment using the Autism Diagnostic
Observation Schedule (ADOS: Lord et al. 1989) was possible for
18 of the participants. Fourteen met cut-off for ASD on
the Communication sub-score (M = 2.7, range 0–5), 19 on
the Reciprocal Social Interaction sub-score (M = 6.8, range
3–12), and 16 on the total diagnostic algorithm (M = 10.0,
range 5–17). As clinical records confirmed the validity of
the ASD diagnosis for all participants, and because
excluding the participants with sub-threshold ADOS scores did
not alter the pattern of significant results, all individuals
were retained in the study. Groups were closely matched
in terms of chronological age and IQ (Wechsler Adult
Intelligence Scale III; Wechsler 1999), and the distribution
of males and females was not significantly different (p= .5).
In contrast, AQ scores differed significantly (see Table 4).
AQ scores ranged from 22 to 45 in the ASD group, and
from 4 to 23 in the comparison group. Critically, no
participant in the comparison group had an AQ score over the
cutoff of 32. All participants gave informed consent to take
part in the study, which was approved by the Senate Ethical
Committee of City University, London.
Materials and Procedure
The materials and procedures were identical to Study 1
except that the valence and arousal ratings were not taken
as Study 1 had already confirmed that the stimuli varied, as
intended, on the valence and arousal dimensions. Another
minor difference in methodology was that responses in
Study 2 were given through the ‘1’ (re-labelled ‘S’) and ‘2’
(re-labelled ‘L’) keys (rather than ‘N’ and ‘M’) of the
number-pad of the keyboard. The IQ and ADOS assessments
were either already on file or taken specifically for the study
but at a different time point to the experimental assessment.
For the ASD group, one participant’s face data were not
collected and one participant did not produce a best-fit
sigmoidshaped curve (therefore, face n = 18). In the comparison
group, one participant did not produce a best-fit
sigmoidshaped curve in the face condition (therefore, face n = 25)1.
1 Due to the modest sample size in this Study, it is questionable
whether parametric or non-parametric inferential statistics are most
appropriate. Since both approaches yield the same pattern of results,
and for consistency with Study 1, parametric statistics are reported
throughout the results.
Table 4 Summary of Age, IQ and Autism Spectrum Quotient
characteristics of the ASD and Comparison Groups in Study 2
Fig. 4 Proportion of long responses for the four facial expressions
for the a ASD group and the b comparison group in Study 2. For
clarity, error bars are omitted
Temporal Bisection: Faces
A 4(Emotion) × 7(Duration) × 2(Group) ANOVA of p(long)
showed a main effect of Duration (F(2.70, 110.66)= 421.95,
p < .001, ƞ2p=0.91, 90% CI [.89, .93]) and a non-significant
main effect of Emotion (F(3, 123)= 2.25, p = .09, ƞ2p=0.05,
90% CI [.00, .11]) (see Fig. 4). However, planned simple
contrasts indicated that angry (F(1, 41) = 5.45, p = .03,
Table 5 Average bisection
points and Weber ratio for all
stimulus categories for the face
and wildlife images for the
ASD and Comparison group in
ƞ2p=0.12, 90% CI [.01, .27]) and fearful (F(1, 41) = 4.25,
p = .046, ƞ2p=0.09, 90% CI [.001, .25]) faces produced a
significantly higher proportion of long responses than
neutral faces, with the difference for happy faces being on the
statistical threshold (F(1, 41) = 4.07, p = .050, ƞ2p=0.09,
90% CI [.00, .24]). Neither the main effect of group (p> .1,
ƞ2p=0.05, 90% CI [.00, .19]) nor any interactions (all
ps > 0.1, ƞ2p range 0.01–0.04) were significant.
Analysis of the Weber ratio produced no significant
differences (all p > .6, ƞ2p range 0.004–0.01) (see Table 5).
Temporal Bisection: Wildlife
A 4(Emotion) × 7(Duration) × 2(Group) ANOVA of p(long)
showed a main effect of Duration (F(2.60, 114.18)= 505.59,
p < .001, ƞ2p=0.92, 90% CI [.90, .93]) and of Emotion (F(3,
132) = 6.85, p = .001, ƞ2p=0.14, 90% CI [.05, .21]) (see
Fig. 4). Planned simple contrasts showed that both snarl
(F(1, 44) = 16.23, p < .001, ƞ2p=0.27, 90% CI [.10, .42])
and puppy (F(1, 44) = 10.61, p = .002, ƞ2p=0.19, 90% CI
[.05, .35]) images elicited significantly higher proportion
of long responses than flower images. However, there was
no significant difference between spider and flower images
(p > .09, ƞ2p=0.06, 90% CI [.00, .20]). Neither the main
effect of Group (p> .5, ƞ2p=0.006, 90% CI [.00, .09]) nor
any interactions (all ps > 0.1, ƞ2p range 0.003–0.04) were
There were no significant effects for the parallel analysis
of the Weber ratio (all ps > 0.1, ƞ2p range 0.000–0.04) (see
Table 5; Fig. 5).
As with Study 1, indices of relative overestimation and
temporal sensitivity for each emotional image were
calculated, with correlations for each group performed
separately. For the ASD group, a significant negative
correlation was found between the change in temporal
sensitivity in the fearful face condition (compared to neutral)
and the AQ (r=−0.55; p = .019). This would suggest
better temporal sensitivity for fearful faces is associated with
higher self-reported autistic traits. However, this finding
was not significant using a Bonferroni corrected p value
(three Weber ratio correlations for face stimuli) of 0.017.
Further, a post-hoc independent samples t-test established
a non-significant difference in Weber ratio between groups
in the fear condition, for both absolute Weber ratio scores
and for the Weber score relative to neutral (p > .8). No other
correlations, for either group, were significant (Pearson’s r
range −0.31 to 0.36).
Using a paradigm novel to ASD research, this study
investigated implicit responsiveness to emotionally-charged faces
and wildlife images in ASD. We found that adults with
ASD and without intellectual impairment did not differ
significantly from a matched comparison group and showed
a similar tendency to overestimate intervals in which an
emotional image was presented. Parallel measurement of
effect size indicated a negligible effect of group
membership on the estimation of wildlife images and a
small-tomedium effect for the face images. Consistent with this,
the performance of adults in the general population did not
correlate with self-reported autistic traits. Taken together,
these results do not suggest a meaningful difference
between ASD and comparison populations in the implicit
behavioural reaction to arousing stimuli, whether emotional
faces or hedonically matched wildlife images. Before we
consider the implications of this finding in relation to the
Fig. 5 Proportion of long responses for the four wildlife images for
the a ASD group and the b comparison group in Study 2. For clarity,
error bars are omitted
emotion-processing literature in ASD, we will briefly
consider the implications of our observations on temporal
processing in ASD.
Intact Temporal Perception in ASD
Although the primary focus of this study was on the
implicit processing of emotion, the temporal bisection task
is a direct and explicit measure of interval timing.
Therefore, the findings are relevant to the hypothesis that ASD
is underpinned by a fundamental disturbance to an
internal clock mechanism, which is an argument that has gained
ground in recent years. However, results in relation to this
hypothesis are equivocal, with evidence of intact (Jones
et al. 2009; Gil et al. 2012; Mostofsky et al. 2000;
Wallace and Happé 2008) and impaired (Allman et al. 2011;
Brenner et al. 2015; Brodeur et al. 2014; Falter et al. 2012;
Karaminis et al. 2016; Kargas et al. 2015; Maister and
Plaisted-Grant 2011; Martin et al. 2010; Szelag et al. 2004)
temporal processing in the millisecond and seconds range.
The current study does not support a fundamental timing
disturbance in adults with ASD who have no intellectual
impairments. The varied results could relate to both the
heterogeneity of the tasks used and the population being
tested (see Jones and Jahanshahi 2014). A key
consideration is that interval timing cannot be dissociated from a
range of ancillary cognitive processes, including
attention, working memory, decision making and motor
execution, which are all additional sources of variance. Maister
and Plaisted-Grant (2011) argued that attentional
variability explained the poor performance of their ASD group on
a measure of time reproduction of 0.5 s intervals, while
insufficient episodic processes explained poor performance
on 45 s intervals. Further, Brenner et al. (2015) found
evidence that consistency of responding on a measure of time
reproduction was associated with auditory working
memory ability in children and adolescents with ASD. Stewart
et al. (2015) recently found that autistic traits in the
general population correlated positively with performance on
a duration discrimination task (comparing a standard and
comparison interval) when a fixed standard interval was
used but not when a variable standard was used. They
suggest high levels of autistic traits may be associated with an
enhanced ability to form a stable and accurate perceptual
representation of a repeated stimulus, a mechanism that is
not limited to temporal processing. More thorough
exploration of the temporal processing profile in ASD is needed,
including testing across a range of tasks and durations (see
Jones and Jahanshahi 2014 for discussion of this approach
in a different clinical population).
To our knowledge, three previous studies have used the
temporal bisection task in ASD. Two report impairment
in children with ASD with a mean age of 10 years
(Allman et al. 2011; Brodeur et al. 2014). However, Allman
et al., did not measure IQ in the majority of their
comparison group, which means that IQ matching between groups
could not be established. Brodeur et al. (2014) found
evidence of flatter temporal bisection slopes in children with
ASD and generally low mental age, indicating less
sensitivity to duration. They remark that the shallower slope
reflects the findings in younger TD children (McCormack
et al. 1999) and suggest a developmental delay that partly
relates to less well-developed attention mechanisms.
However, Gil et al. (2012) found no evidence of impairment
across four temporal bisection tasks in autistic children
and adolescents with no reported intellectual impairments.
The most obvious difference between studies is the level of
intellectual functioning of the ASD groups. However,
Brodeur et al. also used pure tone stimuli, compared to a simple
visual display in the Gil et al. study. Given known
differences in the way that auditory and visual stimuli are timed
(Wearden et al. 1998), it may be relevant to further
investigate modality effects. To summarise across the current
and previous studies, it can be concluded that both adults
and adolescents with ASD and without intellectual
impairment are relatively unimpaired on the temporal bisection
task when visual stimuli are used. However, further work
using children and testing across the range of intellectual
ability will be important for fully understanding the profile
Returning to the primary focus of this study, the data
suggest that adults with ASD demonstrate an implicit
responsiveness to emotional stimuli that is not meaningfully
distinguishable from those without ASD. This conclusion is
supported by the lack of group differences in Study 2 as
well as the absence of reliable correlations between
autistic traits and relevant indices of emotional responsiveness
across both experiments. It is notable that the pattern of
performance was observed for both emotional face stimuli
and for hedonically similar wildlife stimuli. The common
explanation for overestimation on the temporal bisection
task is that arousal has been elevated (e.g. Droit-Volet et al.
2004). The current data therefore suggest that the
psychophysiological response to simple emotional images in ASD
is relatively intact. However, caution must be exercised
in the absence of confirmatory physiological data. This is
particularly the case as the multiple cognitive mechanisms
inherent to the temporal judgement (e.g. working memory,
attention, decision making) are other sources of variance
that may be affected by emotional content (e.g. Droit-Volet
and Gil 2009; Gibbon et al. 1984). Indeed, it is assumed
that emotion also has an impact on attentional
mechanisms relevant to timing, which can enhance or diminish
the arousal effect (e.g.Droit-Volet et al. 2013). On the basis
of the current data, we cannot rule out the possibility that
superficially similar patterns of behavioural responses in
ASD are mediated by a different combination of underlying
processes compared to the comparison group. Systematic
investigation, which varies conditions to probe all relevant
processes, is necessary to shed light on this issue.
A pertinent question is how well the current data fit
with other studies that have sought to characterize implicit
emotional responses in ASD. Hubert et al., (2009)
measured SCR during an explicit labelling task, using video
clips of actors expressing happiness or anger. Supporting
the current study, there was no significant difference in
SCR between those with and without ASD when the
emotional states were implicitly processed. Another measure of
implicit response to emotion is the production of automatic
facial muscle movements that mimic the emotion being
portrayed. Mimicry is argued to induce an internal
simulation of the emotion, leading to a physiological response and
consequences for emotion recognition, empathy and
emotional reciprocity (e.g. Niedenthal 2007). It has been argued
that facial mimicry of emotions triggers the increased
arousal driving the overestimation of facial displays of
emotion (Effron et al. 2006). Evidence that
overestimation of emotional faces is extinguished when participants’
facial movements are inhibited (e.g. holding a pen in the
mouth) has provided support for this theory (Effron et al.
2006). The majority of studies on facial mimicry in ASD
report evidence of impairment (Beall et al. 2008; Mathersul
et al. 2013; McIntosh et al. 2006; Rozga et al. 2013).
However, there is some suggestion of delayed development of
the mimicry response, rather than absolute absence (Beall
et al. 2008), and studies have shown that typical mimicry
responses can occur but with a delayed onset (Mathersul
et al. 2013; Oberman et al. 2007). Further, Magneé et al.
(2007) found no evidence of impairment in EMG response
in adults with ASD. As with the current study, the
participants were engaged in a non-emotion task (sex judgement)
so were not explicitly oriented to the emotional cues. If, as
others suggest (e.g. Effron et al. 2006), mimicry is
necessary for the overestimation of emotional faces, then the
current results indicate that any differences in the production
of facial mimicry in ASD are not sufficient to significantly
disrupt performance on the task.
Considering some of the wider implications of preserved
implicit emotion processing in ASD, the data suggest that
individuals with and without ASD experience similar
distortions in the perception of the temporal unfolding of
emotional events. These distortions have been argued to play an
important role in social interaction (see Droit-Volet et al.
2013; Droit-Volet and Gil 2009). Indeed, social interaction
can be conceptualised as a series of complex,
self-organising temporal dynamics between interlocutors (Fusaroli
et al. 2014). For example, successful conversational
interaction involves synchronising of speech rate (e.g. Schultz
et al. 2016) and movement (e.g. Ramseyer and Tschacher
2011), the co-ordination of visual attention (e.g.
Richardson et al. 2007), and timely turn-taking (e.g. Levinson and
Torreira 2015). The extent to which this broad category
of ‘social timing’ may be impaired in ASD has thus far
received limited attention (see Wimpory et al. 2002). The
speeding of time when presented with emotional faces has
been theoretically related to the need for action readiness,
with timely preparation to act leading to effective
anticipation of a social partner’s subsequent behaviour (see
DroitVolet et al. 2013; Droit-Volet and Gil 2009). Knowledge
of ASD predicts that this preparation to act is not utilised
in a typical way, perhaps because intrinsic understanding
of how to act is not forthcoming. Indeed, Loveland (1991,
2005) suggested that individuals with ASD are not
necessarily insensitive to the emotions of others but that they
experience difficulties regulating their own behaviour
accordingly. Such difficulties early in life could lead to
abnormalities in the explicit understanding of the
expressions of others, which would be mediated by a complex set
of interacting processes (e.g., language, perception,
attention, memory etc.). Implicitly responding to the emotions
of others, however, could remain spared.
Before concluding, it is important to acknowledge some
possible limitations of the current study. First, the use of
static stimuli dictates that replication is required with more
ecologically valid stimuli, such as dynamic videos. This
is particularly the case given the modest effect size found
between groups for the face stimuli, and the suggestion in
the literature that ecologically valid stimuli can pose greater
difficulties for individuals with ASD (e.g., Gaigg 2012). It
could be argued that the temporal overestimation of
arousing events becomes heightened when stimuli become more
complex and the emotional response is triggered in
multiple channels (e.g. auditory, visual). Whether individuals
with ASD would show the same ramping response
predicted in a typical population would give further insight
into their processing of emotion in real life situations.
Similarly, the emotions used in the current study were all
‘basic’ (e.g. Ekman 1992). Using the same paradigm but
with more complex emotions would establish whether there
are limitations to this particular type of emotional
responsiveness in ASD.
It is also important to recognise that a null effect of
group in Study 2 could reflect issues relating to power or
sampling, and replication of these findings is therefore
necessary, particularly with more heterogeneous samples in
terms of chronological age and language/intellectual
ability. Similarly, although our typical population presented
with a range of AQ scores that would be expected in the
general population (Baron-Cohen et al. 2001), a wider
spread of scores would have provided more variance for
detecting an experimental effect. Despite these caveats,
there is strength in our replication of findings across two
studies that use two different categories of image, and in
our use of a paradigm that yields replicable effects across
numerous studies in the literature.
Across two separate experiments, we established that ASD
and autistic traits appear to be associated with a typical
implicit behavioural response to simple emotional images,
both face and wildlife. This is the first time the paradigm
has been used with an ASD population and demonstrates
the value of testing implicit responses, which circumvent
issues with strategy use or uncontrolled cognitive or
perceptual variables. The majority of research into ASD is
focused on highlighting and delineating differences from
individuals not on the spectrum. The current data
provide evidence of a circumstance in which individuals
with and without ASD appear to have a shared perceptual
Acknowledgments Open access funding provided by Cardiff
University. With thanks to Amy Jones, Sandhya Joseph, Laura Stevens
and Juan Williams for their help in data collection in Study 1.
Additional thanks to Dr Richard Morey for a useful discussion about
statistical analysis and interpretation of data.
Author Contributions CJ conceived and designed the study,
analysed data, and drafted the manuscript. CJ supervised data collection
while at the University of Essex, UK. AL collected and analysed data,
and helped draft the manuscript. SG designed the study, analysed
data, and helped draft the manuscript.
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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