Emotion Perception Mediates the Predictive Relationship Between Verbal Ability and Functional Outcome in High-Functioning Adults with Autism Spectrum Disorder
Emotion Perception Mediates the Predictive Relationship Between Verbal Ability and Functional Outcome in High-Functioning Adults with Autism Spectrum Disorder
Sadao Otsuka 0
Shota Uono 0
Sayaka Yoshimura 0
Shuo Zhao 0
Motomi Toichi 0
0 The Organization for Promoting Neurodevelopmental Disorder Research (OPNDR) , 40 Shogoin Sanno-cho, Sakyo-ku, Kyoto 606-8392 , Japan
The aim of this study was to identify specific cognitive abilities that predict functional outcome in highfunctioning adults with autism spectrum disorder (ASD), and to clarify the contribution of those abilities and their relationships. In total, 41 adults with ASD performed cognitive tasks in a broad range of neuro- and social cognitive domains, and information concerning functional outcomes was obtained. Regression analyses revealed that emotion perception and verbal generativity predicted adaptive functioning directly, and the former mediated between the other two. These findings provide the first evidence of a triadic relationship among neuro- and social cognition and functional outcome in this population. Our results suggest that psychosocial interventions targeting these cognitive abilities could benefit social adaptation in adults with ASD.
Autism spectrum disorder (ASD); Social cognition; Emotion recognition; Adaptive behavior; Social functioning; Predictor
Autism spectrum disorder (ASD) is a lifelong
neurodevelopmental disorder characterized by deficits in social
communication and social interaction, coupled with restricted,
repetitive patterns of behavior, interests, or activities
(American Psychiatric Association 2013). ASD is a
heterogeneous condition in terms of severity and the type of
symptoms. Some individuals with subtle or inconspicuous
symptoms are not identified until as late as adulthood, as
a result of failure to detect them early (Begeer et al. 2013;
Lai et al. 2014, 2015; Lehnhardt et al. 2016; National
Collaborating Centre for Mental Health 2012). Reflecting the
heterogeneity of characteristics in individuals with ASD,
there is also considerable variation in outcomes.
Nevertheless, their adaptive behavior skills are typically lower than
predicted by cognitive capacity, and social functioning in
adulthood, including independent living, employment,
friendships, and intimate relationships, is generally poor
(for a review, see Howlin and Moss 2012). Although the
symptoms may be inconspicuous, ASD can affect adaptive
and social functioning in individuals with the condition
over the course of their life. In terms of prognostic
predictions and developing effective interventions for individuals
with ASD, it is important to identify predictors of
functional outcome, including adaptive and social functioning,
both of which correlate closely in individuals with ASD
(Farley et al. 2009).
Previous studies have demonstrated that general
cognitive ability during childhood (e.g., intelligence quotient:
IQ; developmental quotient: DQ), is the strongest
predictor of both adaptive and social functioning in adults
with ASD (for a review, see Magiati et al. 2014).
Limited cognitive ability is likely to worsen functional
outcome. However, as Howlin et al. (2004) showed, social
functioning in over half of adults with both childhood
verbal and performance IQs ≥70 did not differ from that
in individuals with an intellectual disability, indicating
that a better outcome in adults with ASD was not
necessarily guaranteed by an average or higher IQ in
childhood. Adaptive and social functioning in individuals with
ASD, but without an intellectual disability, may be more
variable and less predictable than previously thought.
Although only a few studies have examined the
predictive value of specific cognitive abilities, they suggest that
atypical characteristics in various domains of neuro- and
social cognition may predict functional outcomes in the
case of high-functioning children with ASD. A
crosssectional study found that verbal abilities, including
verbal learning, vocabulary, and spelling, predicted
adaptive functioning in high-functioning children with ASD
more strongly than IQ (Liss et al. 2001). Thus,
investigations into childhood cognitive predictors of functional
outcome are warranted. Additionally, it appears to be
beneficial to focus on cognitive abilities in adulthood,
considering the later diagnosis of high-functioning
individuals, expanded deficits in adaptive functioning in
adults (Kanne et al. 2011; Klin et al. 2007; Matthews
et al. 2015; Perry et al. 2009), the largely unmet needs
of support for adults (Kogan et al. 2008; Shattuck et al.
2012), and limited efforts to develop psychosocial
interventions for adults (Bishop-Fitzpatrick et al. 2013; Spain
et al. 2015; Spain and Blainey 2015). To identify
possible targets for psychosocial interventions, in the present
study, we investigated predictive relationships between
atypical characteristics in cognition and functional
outcome in high-functioning adults with ASD.
First, it is necessary to begin by detecting specific
cognitive abilities predicting, or accounting for the
variability in, functional outcome. Some studies have reported
that specific cognitive abilities relate to adaptive or
social functioning in adults. Regarding neurocognition,
Berger et al. (2003) found that cognitive flexibility
(shifting), evaluated on card sorting tests and other tasks, was
related to longitudinal changes in adaptive functioning.
For social cognition, Wallace et al. (2011) found that
performance in facial emotion recognition (FER) correlated
positively with adaptive functioning. Montgomery et al.
(2013) reported that mentalizing (theory of mind)
evaluated on the “Reading the Mind in the Eyes” test, revised
version (Eyes Test; Baron-Cohen et al. 2001a), predicted
self-reported social stress. These pioneering studies used
only a narrow selection of cognitive domains, making it
difficult to compare the predictive powers of the various
abilities. To identify specific cognitive abilities as targets
for psychosocial interventions, it is essential to examine
simultaneously the predictive values of a broad range of
domains in both neuro- and social cognition in which
atypicality has been found in individuals with ASD.
Second, to determine how various cognitive abilities
contribute to functional outcome, it is important to consider
the intertwined links between abilities, especially between
neuro- and social cognition. Previous studies have reported
a link between mentalizing and executive functions in
individuals with ASD (Ozonoff et al. 1991; Pellicano 2007).
White (2013) suggested that poor performance in executive
function tasks may be secondary to mentalizing
difficulties. However, Pellicano (2010) found that executive
functions longitudinally predicted mentalizing performance in
children with ASD, suggesting that executive dysfunction
underlies social communication impairment. Consistent
with this, it has been suggested that explicit cognitive or
verbally mediated processing compensates for inefficiency
in FER in individuals with ASD (Harms et al. 2010).
Some evidence also suggests that individuals with ASD
use local processing, or a feature-based strategy, in FER
tasks, in contrast to the global, configural-based
processing used by typically developing individuals (Behrmann
et al. 2006; Rutherford and McIntosh 2007; Walsh et al.
2014). Furthermore, previous studies have reported that
general cognitive or language ability predicts FER
performance in children with ASD, but not typically developing
children (Dyck et al. 2006; Hobson 1986). These findings
suggest the possibility that abilities in social cognition,
which predict adaptive functioning and are predicted by
neurocognitive abilities, may mediate a predictive
relationship between neurocognition and functional outcome
in adults with ASD. Additionally, atypical and
compensatory relationships between specific abilities in neuro- and
social cognition may contribute to social adaptation. To our
knowledge, no reported study had examined a triadic
relationship among neuro- and social cognition and functional
outcome in this population.
The primary purpose of the present study was to
identify specific cognitive abilities predicting, or accounting
for variance in, functional outcome in adults with ASD and
average or higher IQ, focusing on the heterogeneity of
characteristics and outcome in this population. In this
crosssectional study, we investigated the predictive relationship
among specific abilities in neuro- and social cognition and
adaptive and social functioning. Specifically, we examined
whether specific abilities in neuro- and social cognition
could predict functional outcome or whether social
cognition would act as a mediator, to clarify the contribution of
those abilities and their relationships. As a variable
representing functional outcome, we used adaptive functioning,
where substantial variance has been found to be explained
by specific cognitive abilities (38–64%; Liss et al. 2001).
Before examining cognitive predictors, we saw a
relationship between adaptive and social functioning, to validate
that adaptive functioning was an indicator of functional
outcome. We intended to take preliminary steps towards
developing evidence-based interventions that promote
adaptive and social functioning in adults with ASD. The
cognitive abilities predicting functional outcome could
be possible targets for psychosocial interventions in this
population. We hypothesized that (1) adaptive functioning
would predict social functioning, (2) a combination of
specific abilities in neuro- and social cognition could account
for variance in adaptive functioning, and (3) social
cognition would mediate the relationship between
neurocognition and functional outcome.
The ASD group consisted of 41 adults with no intellectual
(full-scale IQ ≥ 70) or language disability (verbal IQ ≥ 70)
aged 18–53 years (22 males, 19 females), who had been
referred to Kyoto University for consultation or cognitive
assessments by affiliated hospitals, public consultation
offices, or public organization for employment. The ASD
participants were diagnosed with autistic disorder (n = 1),
Asperger’s disorder (n = 21), or pervasive developmental
disorder, not otherwise specified (n= 19), according to the
DSM-IV-TR criteria (American Psychiatric Association
2000), by psychiatrists with expertise in developmental
disorders, based on an interview with the participants and
information from their parents, professionals who helped
them, and a clinical record of childhood, when available.
The symptom severity of the ASD participants was
assessed by the psychiatrists who made the diagnosis,
using the Childhood Autism Rating Scale-Tokyo version
(Kurita et al. 1989), which is the Japanese version of the
Childhood Autism Rating Scale (CARS; Schopler et al.
1986) and the Childhood Autism Rating Scale second
edition, High functioning version (CARS2-HF;
Schopler et al. 2010). The CARS and the CARS2-HF include
15 items that assess autism-related behaviors. Total
CARS and CARS2-HF scores are the sum of scores on
all items and range from 15.0 to 60.0, with higher scores
indicating more severe symptoms. The CARS has been
shown to be a useful tool for diagnosing autism in
children, adolescents, and adults (Mesibov et al. 1989). The
scores of participants in the present study were
comparable to those of individuals with high-functioning ASD
(mean scores ± SDs were 22.22 ± 3.57 in individuals with
Asperger’s syndrome and 23.61 ± 3.42 in individuals
with high-functioning autism) reported by Koyama et al.
(2007). Although our participants’ mean score on the
CARS, 24.7, were less than the clinical cut-off (27.0) for
a diagnosis of autistic disorder (see Mesibov et al. 1989),
participants’ mean score on the CARS2-HF, 30.0, was
higher than the cut-off for ASD (28.0). These data
indicated that the symptoms of the ASD participants were
severe enough to warrant a diagnosis of ASD. The CARS
and CARS2-HF scores of participants are presented in
The control (CON) group consisted of 21 typically
developing adults who were matched with 21 ASD
participants not taking any psychotropic medication (non-drug;
ASD-ND group) for age, gender, years of education, and
full-scale, verbal, and performance IQs (all p ≥ .12). The
ASD and CON groups did not differ in terms of these
variables (all p ≥ .06). The IQs of all participants were
measured using the Japanese version of the Wechsler Adult
Intelligence Scale, third edition (WAIS-III: Fujita et al.
2006; Wechsler 1997). Additionally, all participants
completed the Japanese version of the Autism-Spectrum
Quotient (AQ) questionnaire (Baron-Cohen et al. 2001b;
Wakabayashi et al. 2004), a 50-item self-rated scale measuring
autistic traits. The AQ scores of participants in the ASD
and ASD-ND groups were significantly higher than those
in CON participants (all p < .001) as expected. The
demographic characteristics, IQs and AQ of participants are also
provided in Table 1.
Exclusion criteria for all participants included a history
of or a current psychotic disorder, substance or alcohol
abuse, traumatic head injury, a genetic disorder associated
with autism (e.g., fragile X syndrome, tuberous sclerosis),
intellectual disability, or any other medical condition
significantly affecting brain function (e.g., epilepsy).
All procedures in this study were approved by the Ethics
Committee of the Graduate School and Faculty of
Medicine at Kyoto University and were performed in accordance
with the ethical standards in the 1964 Declaration of
Helsinki and its later amendments. All participants provided
written informed consent to participate in the study.
According to evidence of atypicality in individuals with
ASD, we selected measures in the following cognitive
domains of neuro- and social cognition. The social
cognitive domains were mentalizing, social perception, and
selfreferential cognition (Lai et al. 2014). The neurocognitive
domains were detail-focused processing (Happé and Frith
2006), executive function (Hill 2004; Kenworthy et al.
2008), working memory (Williams et al. 2005), long-term
memory (Williams et al. 2014; Minshew and Goldstein
2001; Toichi and Kamio 2002, 2003), verbal ability
(Rumsey and Hamburger 1988), and processing speed
(Nakahachi et al. 2006). Because approximately 4 h was required
to complete all cognitive measures, they were divided into
Table 1 Demographic, clinical, and cognitive characteristics of participants in each group
0.515 r = 0.10
0.038 r = 0.32
ASD-ND versus CON
t(60) = 0.36
0.249 d = 0.09
t(40) = −0.15
0.661 d = −0.04
t(60) = 6.79
<0.001 d = 1.80
t(40) = 5.73
<0.001 d = 1.75
113.57 (11.58) t(60) = −1.42
113.43 (12.35) t(60) = −0.34
110.81 (12.38) t(60) = −1.71
0.265 d = −0.34 t(40) = −0.47
0.737 d = −0.21 t(40) = 0.45
0.067 d = −0.49 t(40) = −1.42
0.001 d = −0.98 t(40) = −3.31
0.406 d = −0.23 t(40) = −1.53
0.023 d = −0.60 t(32.4) = −3.13
0.174 d = −0.56 t(26.3) = −2.03
Z = −1.42
0.157 r = −0.18 Z = −0.45
0.650 r = −0.07
Z = 1.32
Z = 2.37
0.187 r = 0.17
0.018 r = 0.30
Z = 0.65
Z = 2.07
0.073 d = −0.50 t(40) = −1.32
0.131 d = −0.50 t(40) = −1.62
t(60) = −1.45
0.243 d = −0.41 t(40) = −0.75
0.791 d = −0.23
Table 1 (continued)
ASD-ND versus CON
t(60) = −1.42
0.055 d = −0.57 t(40) = −1.34
0.186 d = −0.42
ASD autism spectrum disorder group, ASD-ND autism spectrum disorder participants not taking any psychotropic medication (non-drug), CON
control group, SD standard deviation, ES effect size, CARS Childhood Autism Rating Scale, CARS2-HF CARS, second edition, high-functioning
version, AQ Autism-Spectrum Quotient, IQ intelligence quotient, Eyes Test “Reading the Mind in the Eyes” test, revised version, FER Facial
Emotion Recognition task, FER-BP Facial Emotion Recognition from briefly presented expressions, SR Self-Reference task, EFT Embedded
Figures Test, BD Un/segmented Block Design task, WCST Wisconsin Card-Sorting Test, CPT Continuous Performance Test, LNS
Letter-Number Sequencing task, VS Visuospatial Span task, LM Logical Memory task, RCFT Rey Complex Figure Test, PM Prospective Memory task, VFT
Verbal Fluency task, DS Digit Symbol task
two parts and implemented over two continuous or
discontinuous days within 15 days in the same sequence,
considering that participant fatigue could influence task
performance. All cognitive tasks and neuropsychological tests
were administered individually by a clinical psychologist
trained in standardized testing procedures.
Social Cognitive Measures
Mentalizing We used the Eyes Test (Baron-Cohen et al.
2001a) to measure mentalizing ability. The Eyes Test,
comprising 36 items, required participants to infer mental state
from information in the eye region and to select the most
suitable adjective from four choices. The measure used in this
study was percentage of items answered correctly (accuracy).
Social perception The FER task, with which atypicality
in ASD is commonly reported (see Harms et al. 2010 for a
review), was used to assess ability to perceive emotions. This
task used a label-matching paradigm that was previously used
by Sato et al. (2002) and Uono et al. (2011, 2013). We used
pictures of 48 young adults with facial expressions depicting
six basic emotions (anger, disgust, fear, happiness, sadness,
and surprise) from a standard photograph set (Matsumoto
and Ekman 1988). Participants were shown each picture
presented on a computer monitor in a predetermined random
order for 2000 ms, which is the duration typically developing
adults require to recognize emotions from facial expressions
correctly (Wallace et al. 2015), and asked to choose the one
that best described the person’s emotion, of the six labels of
basic emotions presented next to each picture, within 10 s.
No feedback was provided about performance. Participants
viewed each emotional expression eight times, resulting in
a total of 48 trials for each participant. Half of the trials
presented pictures of 24 people with subtle (low-intensity, 60%)
facial expressions that have been reported to be sensitive
to FER deficits in high-functioning adults with ASD (Doi
et al. 2013; Smith et al. 2010). The pictures with subtle facial
expressions were rendered by smoothly blending neutral and
emotional expressions taken from the same individual at the
ratio of four to six, using commercial ‘morphing’ software
(FantaMorph 5, Abrosoft). In the other half of the trials, we
presented original pictures of other people (high-intensity,
100%). In each intensity condition, half of the pictures were
of females and half of males; also, half were of Japanese and
half were of Caucasians. Prior to testing, we established that
all participants understood the meaning of the emotional
labels and the task instructions, and participants engaged in
two training trials to become familiar with the procedure.
Accuracy was used as the measure.
Before this FER testing, participants performed a task
to recognize emotions from briefly presented facial
expressions (FER-BP; Clark et al. 2008). The FER-BP task has
been used to measure the ability to extract emotional
information rapidly, which relies more on automatic processing
and less on the use of verbal or other top-down strategies
that could compensate for inefficient performance on
emotional perception in high-functioning individuals with ASD
(see Harms et al. 2010). This task was identical to the FER
task except for the very short duration of picture
presentation, 50 ms, which is about the same duration used for
micro expressions (Ekman 2004). The pictures, which were
the same as those in the FER, were presented in a different
order from in the FER.
Following the two FER tasks, the face perception task
was performed. In this task, participants were shown
pictures of 12 people with neutral expressions for 50 ms in a
predetermined random order, and asked to choose one that
had been presented just before, of two pictures with neutral
expressions, within 10 s. The 24 people were selected from
the 48 whose pictures were used in the FER tasks,
keeping the same ratios of females to males and Caucasians to
Japanese. All participants in both groups, with the
exception of one ASD participant who made only one incorrect
answer, got all the answers right. Thus, the accuracy was
99.8% in the ASD group, indicating that ASD participants
could extract sufficient information about major facial
features to recognize individuals from pictures presented for
such a short duration.
Self-referential cognition We used the Self-Reference task
(Toichi et al. 2002; Yoshimura and Toichi 2014) to assess
the processing of self-referential information. This memory
task consists of the learning phase, during which
participants encode 30 words (targets) on three levels of
processing, and an incidental test phase. Participants were initially
presented with each yes-or-no question on a computer
monitor for 8 s. These questions addressed each subsequently
presented target word (an adjective describing a personality
trait), which was presented for 2 s. Participants were then
required to answer within 5 s. Three types of questions that
made participants to encode each target on the different
levels were as follows: phonological (“Does the word rhyme
with xxx?”), semantic (“Is the meaning of the word
similar to xxx?”), and self-referential (“Does the word describe
you?”). Ten questions were arranged for each of three types.
In the incidental recognition test, which immediately
followed the learning-phase, participants were required to
identify the 30 targets from a list of 90 words, including 60
distractors, within 5 min. The percentage of correctly
recognized words in self-referenced target words was the measure
Detail-focused processing The Embedded Figures Test
(Witkin 1950) was used to measure attention to detail in
visuospatial cognition. This task, comprising 24 items,
required participants to locate a geometrically simple shape
(target) within a larger complex design. A time limit of 120 s
was set in accordance with Lai et al. (2012), and the mean of
the remaining time after finding each target was used as the
measure (in s; 0–120).
We also used the Un/segmented Block Design (BD)
task (Shah and Frith 1993) to assess superiority in
detailfocused processing style. In the BD task, participants were
asked to replicate designs using four blocks as quickly as
possible, as with the BD subtest of the WAIS-III. First,
participants were presented with eight unsegmented (whole)
designs in a fixed order and, in the second half, segmented
(separate) sets of the same designs were presented in the
same order. The percentage of response time to construct
all segmented designs, with time to construct unsegmented
designs as a baseline for comparison, was the measure
Executive function The Wisconsin Card Sorting Test
(Heaton et al. 1993) was used as a measure of cognitive
flexibility. This task requires participants to match response
cards, a maximum of 128 cards, to the four stimulus cards in
one of three categories (color, form, or number) on the basis
of only the examiner’s feedback as to whether each response
was right or wrong. The category changed without warning
when ten consecutive cards were sorted correctly, until six
categories were completed. The measure used was the
percentage of conceptual-level responses, which were
consecutive correct responses occurring in runs of three or more.
The Tower Test of the Delis-Kaplan Executive Function
System (Delis et al. 2001) was used to measure planning
ability. In the Tower Test, participants were asked to build a
tower using variously sized disks, a maximum of five disks,
stacked on three pegs in the fewest number of moves
possible according to the following rules: (1) move only one
disk at a time and (2) never place a big disk on top of a
little one. The total achievement score (sum of the raw scores;
0–30) was used as the measure.
We also used the Conners Continuous Performance
Test, third edition (Conners 2014), to assess inhibition.
This is a Go/No-Go task, in which participants are required
to left-click when any letter except the letter “X” (target)
appeared on a computer monitor (Go trial), and to give no
response to “X” (No-go trial). The measure used was the
age-adjusted T-score of detectability, which was reversed,
so that higher scores indicated better performance.
Working memory The Letter-Number Sequencing subtest
of WAIS-III was used as a measure of auditory working
memory. In this task, following the auditory presentation of
serial numbers and letters, participants were required to first
give the numbers in ascending order and then the letters in
alphabetical order. The age-adjusted scaled score was used
as the measure.
We used the Visuospatial Span subtest of the Japanese
version of the Wechsler Memory Scale, Revised (WMS-R;
Sugishita 2001, Wechsler and Stone 1987) to assess
visuospatial working memory. In the forward condition, after
the examiner had tapped the cubes in a predetermined
sequence, participants were asked to repeat the sequence.
In the backward condition, the sequence had to be repeated
backwards. The proportion of items correctly repeated was
used as the measure.
Long-term memory The Logical Memory subtest of the
WMS-R was used to measure verbal memory. In this task,
participants were asked to recall a story heard as accurately
as possible, immediately following auditory presentation of
the story (immediate recall) and at least 30 min after the
first recall (delayed recall). The study used story A from the
WMS-R. The measure used was the proportion of words
correctly recalled at the time of delayed recall.
The Rey Complex Figure Test (Meyers and Meyers
1995) was used to assess visuospatial memory. This task
requires participants to draw a design relying on memory,
3 min after completing the copy trial (immediate recall)
and at least 30 min after the first recall (delayed recall).
Each unit of the figure reproduced was scored according
to the criteria of Meyers and Meyers (1995), which were
originally developed by Rey (1941). The proportion of the
maximum score was used as the measure.
We used the Prospective Memory tasks of the Memory
for Intentions Screening Test (MIST; Raskin et al. 2010).
In this task, participants were required to do or say certain
things at assigned times (e.g. “In 15 min, tell me that it is
time to take a break”) during a word search task that lasts
about 25 min and serves as a distractor to prevent rehearsal.
This task includes eight items, counterbalanced for length
of delay (2 or 15 min), response type (verbal or action),
and cue type (time-based or event-based). The MIST also
contains the 24-h delayed task. This time-based task had
only one item that required participants to call and tell the
tester how many hours they slept last night. Each item was
assigned a score from 0 to 2, adding up to a maximum of
18. The proportion of the maximum score was the measure
Verbal ability The Verbal Fluency Task (VFT; Ito et al.
2004) was used to measure verbal generativity. In the VFT,
participants were asked to generate as many words that
began with a given letter or fell into a given category as
possible within 60 s in each trial. The Japanese syllables
“a,” “ka,” and “shi” and the categories of “animal,” “sport,”
and “occupation” were used in six separate trials. The total
number of words generated was scored.
Processing speed The Digit Symbol subtest of the
WAISIII was used as a measure of processing speed. This task
requires participants to copy symbols paired with digits
as quickly as possible in the empty boxes below a random
sequence of digits within 120 s. The measure used was the
age-adjusted scaled score.
Functional Outcome Measures
Adaptive functioning The Japanese version of the
Vineland Adaptive Behavior Scales, second edition
(VinelandII; Sparrow et al. 2005; Tsujii et al. 2014), was used to
assess adaptive functioning in ASD participants.
VinelandII provides standard scores, which have a mean of 100 and
a standard deviation of 15, in an overall adaptive
behavior composite and subdomains including Communication,
Daily Living Skills, and Socialization in adults. The scores
in 30 participants whose parents or spouses cooperated and
gave written informed consent were available. Vineland-II
was administered on or within 2 months after the first day of
the cognitive testing.
Social functioning Overall social functioning in all
participants was rated on an ascending scale of zero to four
based on four components: residential status, employment/
education, intimate relationship, and friendships,
according to previous studies (Farley et al. 2009; Howlin et al.
2004, 2013). Information needed for the rating was obtained
through a structured interview with participants on the first
day of the cognitive testing. Based on criteria in previous
studies (Farley et al. 2009; Howlin et al. 2013; Taylor and
Selzer 2012) and the proportion of ASD participants
meeting those, we established the following criteria for a ‘better’
outcome, to avoid a subjective judgment made by
participants and raters, on each component: s/he lives by
her/himself, or with his/her spouse and/or children (residential
status), s/he is employed full-time or part-time more than 10 h/
week, or in a graduate or postsecondary education program
(employment/education), s/he is married or has continued
an intimate relationship for more than 1 year (intimate
relationship), and s/he has met one or more friends in the past
3 months (friendships). A composite rating of social
outcome was scored by counting the number of items fulfilled
by each participant.
Data were analyzed in three steps. Statistical analyses were
conducted using the SPSS software (ver. 22). All analyses
were two-tailed, and α was set at 0.05.
Step 1: All variables of cognitive measures were tested
for a normal distribution with the Shapiro–Wilk test. Then,
for measures that were not normally distributed,
non-parametric Mann–Whitney U tests were used to investigate
between-group differences. Where appropriate, we used
independent t tests for group comparisons. Additionally,
paired t tests were used to compare the scores in the three
subdomains of Vineland-II.
Step 2: Pearson’s correlations were calculated to assess
associations between measures of cognition and adaptive
functioning, and between measures of neuro- and social
Step 3: To see whether measures of adaptive functioning
could predict social functioning in ASD participants, we
performed a step-wise multiple linear regression analysis
including Vineland-II composite score, age, gender, years
of education, medication, AQ, and CARS2-HF score as
independent variables (predictors), and overall social
functioning score as the dependent variable (outcome). Then,
to identify specific cognitive abilities predicting adaptive
functioning, step-wise multiple linear regression analyses
were conducted between all measures on neuro- or social
cognition as independent variables and Vineland-II
composite as the dependent variable. If the predictive
relationships between measures of both neuro- and social cognition
and adaptive functioning were significant, we conducted
regression analyses, which involved one measure each of
neuro- and social cognition, and a bootstrapping method
to test the mediation path (indirect effect of independent
variable on dependent variable through a mediator), using
the SPSS PROCESS macro (Hayes 2013). An estimate
of the indirect effect was the mean computed using 5000
bootstrap samples, and the 95% bias-corrected confidence
interval was constructed from the sampling distribution. If
zero was within the 95% confidence interval, the mediation
effect was considered to be significant at p < .05,
rejecting the null hypothesis that the mediation effect was zero
(Preacher and Hayes 2004, 2008).
Group Comparison on Cognition
Table 1 presents the results of t tests and Mann–Whitney
U tests. Regarding social cognition, performances on the
Eyes Test (p < .01) and FER-BP (p < .01) were impaired in
ASD-ND participants versus CON participants. Those
performances in the ASD group were also impaired in
comparison with CON (p < .01, p < .05, respectively). However,
performance in the CON and ASD groups did not differ on
the other measures in social cognition (all p ≥ .13).
Regarding neurocognition, performances on BD
(p < .05), the Wisconsin Card Sorting Test (p < .01), and
the Visuospatial Span task (p < .05) were impaired in
ASD-ND versus CON participants. In addition to those
measures (p < .01, p < .01, p < .05, respectively),
performances on the Rey Complex Figure Test (p < .01) and the
Prospective Memory task (p < .05) in the ASD group were
also impaired in comparison with those in the CON group.
However, performance in terms of these two measures in
ASD-ND participants did not differ from that in CON
participants (all p ≥ .05). Performance in the CON and ASD
groups did not differ on the other measures in
neurocognition (all p ≥ .05).
Functional outcome characteristics in ASD participants are
presented in Table 2. Regarding adaptive functioning,
Vineland-II scores were obtained for 30 participants with ASD.
The mean composite score was 71.33 (range = 20–109).
The mean subdomain scores were 74.70 (range = 31–103)
for communication, 84.60 (range = 31–110) for daily
living skills, and 71.20 (range = 38–101) for
socialization. Paired t tests comparing subdomain scores
demonstrated that the daily living skills were significantly higher
than communication (t(29) = 4.38, p < .001, d = 0.49) or
n = 41, SD standard deviation; Vinland-II scores were obtained for 30
ASD participants; the item of employment gives information about
28 ASD participants, excluding students. ASD autism spectrum
disorder, IQ intelligence quotient, Vineland-II Vineland adaptive behavior
scale, second edition
Table 2 Functional outcome in ASD participants
socialization (t(29) = 4.65, p < .001, d = 0.64). The domain
scores on communication and socialization did not differ
(t(29) = 1.53, p = .14, d = 0.16).
For social functioning, based on residential status,
employment/education, intimate relationship, and
friendships (Farley et al. 2009; Howlin et al. 2004, 2013), we
rated the overall social outcome in each participant using
a five-point scale. The outcomes in 4 (9.8%) ASD
participants were classified as “very good” (4 points), 12 (29.3%)
were “good” (3 points), 11 (26.8%) were “fair” (2 points),
7 (17.1%) were “poor” (1 point), and 7 (17.1%) were “very
poor” (0 points). The median score for social outcome in
the ASD group was 2 points (fair). In the CON group, 8
(38.1%) participants were classified as “very good,” 13
(61.9%) as “good,” and no participant was rated fair, poor,
or very poor, resulting in a median of 3 points (good).
Vineland-II composite scores showed the strongest
correlation with performance on VFT (p < .01) and were
correlated significantly with full-scale (p< .05) and verbal IQs
(p < .05), as well as with performances on FER (p < .01),
the Prospective Memory task (p < .05), and the Digit
Symbol task (p < .05). No other correlation was statistically
significant (all p ≥ .05).
Associations Between Neuro- and Social Cognition
Correlations between measures of neuro- and social
cognition in ASD participants are presented in Table 3.
Regarding the Eyes test, the correlations with full-scale (p < .05)
and verbal IQs (p < .05), and the Prospective Memory task
(p < .05) were significant. However, FER, FER-BP, and the
Self-Reference task showed no correlation with full-scale
or verbal IQs (all p ≥ .06). For FER, the correlation with
VFT was significant (p< .05). The correlations between
FER-BP and BD (p < .05) and between the Self-Reference
task and the Continuous Performance Test (p < .01) were
also significant. No other correlation was significant (all
p ≥ .06).
Regression of Functional Outcome
Table 4 presents the results of a step-wise multiple linear
regression analysis, which included social functioning
score as a dependent variable, and Vineland-II
composite score, age, gender, years of education, medication,
AQ, and CARS-HF score as independent variables. In
the final model, Vineland-II composite score was the
Eyes test FER
*Correlation was significant at .01< p < .05 level, **correlation was
significant at p < .01 level
n = 41. Eyes Test “Reading the Mind in the Eyes” test, revised
version, FER Facial Emotion Recognition task, FER-BP Facial Emotion
Recognition from briefly presented expressions, IQ intelligence
only significant predictor (p < .001), which accounted
for 64% of the variance in social functioning (p < .001).
No other variable was significant in combination with
Table 5 presents step-wise multiple linear regression
analyses that included the Vineland-II composite score as
a dependent variable and all measures of neuro- or social
cognition as independent variables. Regarding social
cognition, FER was the only significant predictor (p < .01),
accounting for 35% of the variance in adaptive
functioning (p < .01). For neurocognition, the final model
demonstrated that performances on both VFT (p < .001) and
BD (p < .05) were significant predictors of the
VinelandII score, accounting for 47% of the variance in adaptive
functioning (p < .001). No other cognitive measure was
significant in combination with FER, or VFT and BD.
Table 4 Step-wise multiple linear regression analysis, including
social functioning score as the dependent variable, Vineland-II
composite score, age, gender, years of education, medication, AQ, and
CARS2-HF as independent variables
n = 30. Vineland-II Vineland adaptive behavior scale, second edition,
AQ autism spectrum quotient, CARS2-HF childhood autism rating
scale, second edition, high-functioning version, CI represents
confidence interval, R2 represents variance explained by the independent
variable in the model
Analyses of Mediation
Because we could identify significant predictors of
adaptive functioning in neuro- (verbal ability and detail-focused
processing) and social cognition (emotion perception),
regression analyses were conducted with the bootstrapping
method to examine the validity of the mediation models
involving FER and VFT or BD. The results of regressions
are presented in Table 5. The combination of VFT and FER
accounted for 49% of the variance in adaptive functioning
(p < .001), and the bootstrapping method showed that zero
was not within the 95% CI of the indirect effect of VFT
on Vineland-II through FER, demonstrating a significant
effect of mediation (p < .05; Fig. 1). The combination of
BD and FER accounted for 41% of the variance in adaptive
functioning (p < .001), but the bootstrapping method
demonstrated that zero was within the 95% CI of the indirect
effect of BD on Vineland-II through FER, indicating that
the mediation effect was not significant (p ≥ .05).
This cross-sectional study tested three hypotheses
concerning predictive relationships among neuro- and social
cognition and adaptive and social functioning in adults with
ASD and average or higher IQ. Our results support all three
hypotheses that (1) adaptive functioning is an outcome
indicator related very closely to social functioning, (2) the
combination of verbal ability in neurocognition and
emotion perception in social cognition accounts for substantial
variance in adaptive functioning, and (3) emotion
perception partially mediates the predictive relationship between
verbal ability and functional outcome. To our knowledge,
these exploratory findings represent the first reported
evidence of a triadic relationship among neuro- and social
cognition and adaptive functioning in high-functioning
adults with ASD. In what follows, we begin by establishing
the generalizability of our results in this population.
Characteristics of Cognition in ASD Participants
ASD participants in the current study showed atypicalities
in several abilities in social cognition, including
mentalizing and emotion perception, and in neurocognition,
including detail-focused processing, cognitive flexibility, and
visuospatial working memory. Atypical or inefficient
performance on other measures was not seen in this ASD
sample, which does not contradict the accumulated knowledge
Table 5 Step-wise multiple
linear regression analysis,
including Vineland-II composite
score as the dependent variable
and measures of social
cognition or neurocognition
as independent variables, and
testing for mediation of social
cognition in the relationship
between neurocognition and
n = 30. Vineland-II Vineland adaptive behavior scale, second edition, FER Facial Emotion Recognition
task, VFT Verbal Fluency Task, BD Un/segmented Block Design task, CI represents confidence interval,
R2 represents variance explained by the independent variable in the model
Fig. 1 Illustration of the mediation model including adaptive
functioning as the dependent variable, verbal ability as the independent
variable, and emotion perception as the mediator. Arrows indicate
the direction of prediction. Numbers on arrows indicate standardized
on this issue. Given the heterogeneity in ASD, large
variations in cognitive performance between and within studies
of this condition should be expected.
Regarding social cognition, although many studies have
shown deficits on FER in individuals with ASD, some
high-functioning adults with ASD can recognize
prototypical facial expressions as well as typically developing
adults, presumably capitalizing on their cognitive resources
(see Harms et al. 2010). The results that ASD participants
could perform well on FER, but poorly on FER-BP,
support the notion that effortful processing can compensate
for inefficiency in emotion perception in high-functioning
adults with ASD. The correlation between performances on
VFT and FER, but not FER-BP, only in ASD participants
(correlation in CON: r = −0.12, p > .10) appears to indicate
that verbal ability may contribute to the effort after initial
processing in facial emotion perception. Additionally, the
correlation between BD and FER-BP, only in ASD
participants (correlation in CON: r = 0.09, p > .10), suggests that
local or feature-based processing is an advantage for ASD
individuals in extracting emotional information from facial
expressions rapidly. Performances on FER and on the
SelfReference task in this high-functioning ASD sample were
generally comparable to those in previous studies (Uono
et al. 2013; Yoshimura and Tocihi 2014).
Regarding neurocognition, similarly, performances
in planning (Losh et al. 2009), inhibition (Schmitz et al.
2006), verbal working memory (Williams et al. 2005),
verbal memory (Ambery et al. 2006), visuospatial memory
(Minshew and Goldstein 2001), verbal generativity
(Wilson et al. 2014), and processing speed (Lehnhardt et al.
2016) were comparable those in high-functioning adults
with ASD reported in previous studies. Prospective
memory performance on the MIST (Raskin et al. 2010) in
highfunctioning adults with ASD has never been investigated.
regression weights. Continuous arrows represent the direct effects.
Dotted arrows represent the indirect effect of verbal ability, which is
part of the direct effect of emotion perception on adaptive functioning
Our results showed that prospective memory performance
had a positive correlation with verbal IQ (r = 0.57, p < .001)
only in ASD participants. Performances on the Embedded
Figures Test (r = 0.43, p < .01), the Tower Test (r = 0.43,
p < .01), the Continuous Performance Test (r = 0.32,
p < .05), the Letter-Number Sequencing task (r = 0.53,
p < .001), and the Logical Memory task (r = 0.57, p < .001)
also correlated positively with verbal IQ only in the ASD
group (correlation in CON: all |r| ≤ 0.33, all p ≥ .14),
suggesting that atypical performance on those tasks in
individuals with superior verbal intelligence might be
inconspicuous or be compensated for. In the case of VFT, performance
on the task correlated positively with verbal IQ in ASD
participants (r = 0.50, p < .01) and those in the CON group
(r = 0.57, p < .01), indicating the validity of considering the
VFT score as a variable representing verbal ability.
Characteristics of Outcomes in ASD Participants
The fact that overall social functioning in more than 60%
of the adults in this ASD sample was poorer than in CON
participants demonstrates the difficulty in adjusting to the
community for high-functioning adults with ASD. The
distributions of the composite scores in ASD participants
generally corresponded to those in adults with verbal IQ
>70 (good or very good 42.9%, fair 28.6%, poor or very
poor 28.6%), as reported by Howlin et al. (2004). The
proportions of individuals living independently, involved
in regular full-time paid work, and married or continuing
an intimate relationship were comparable with those in a
Canadian sample (31.3, 42.9, and 25.0%, respectively;
Szatmari et al. 1989) of high-functioning adults with ASD.
The composite scores of adaptive functioning, assessed
by Vineland-II (Sparrow et al. 2005), in this ASD sample
were also comparable with those in high-functioning adults
with ASD in previous studies (Duncan and Bishop 2015;
Farley et al. 2009). The profile of adaptive functioning was
in accordance with the “autism profile” established
previously (e.g., Carter et al. 1998), characterized by lowest
scores in socialization, second lowest in communication,
and relatively high scores in daily living skills.
The characteristics of cognition and functional outcomes
in high-functioning adults with ASD reported in many
previous studies were generally replicated in this sample.
Thus, we consider that our results are generalizable in this
Predictive Relationship Among Neuro- and Social
Cognition and Functional Outcome
As expected, specific abilities in neuro- and social
cognition were identified as significant predictors of functional
outcome in high-functioning adults with ASD. Along with
verbal ability and emotion perception, which were reported
to relate to adaptive functioning in previous studies (Liss
et al. 2001; Wallace et al. 2011), detail-focused
processing style was found from a broad range of neurocognitive
domains in this study. Additionally, the large variance in
social functioning explained by the composite score of
Vineland-II supported the methodological validity of using
this measure as an indicator of functional outcome.
Regarding neurocognition, the substantial power of
specific abilities in verbal functions in predicting functional
outcomes, which had been found in high-functioning
children with ASD (Liss et al. 2001), was replicated in this
adult sample. Liss et al. (2001) demonstrated that IQs did
not contribute to the prediction of adaptive functioning
in high-functioning children with ASD when involved in
regression analyses along with verbal abilities.
Supplementary analyses (simple linear regressions) of data from
this adult sample also showed that general cognitive
ability accounted for only a moderate proportion of the
variance in adaptive functioning (full-scale IQ: F(1, 28) = 4.71,
R2 = 0.14, p < .05; verbal IQ: F(1, 28) = 4.95, R2 = 0.15,
p < .05; performance IQ: F(1, 28) = 2.16, R2 = 0.07,
p > .10). Although it is easy to imagine that social
adaptation is disrupted by language disability, even adaptive
functioning in adults with normal or higher verbal
intelligence appears to be affected by subtler linguistic problems,
including reduced verbal generativity, and possibly
stereotyped and repetitive use of language, speech
idiosyncrasies, and pragmatic deficits. For individuals with ASD who
also have deficits in non-verbal expression, such as facial
mimicry (Yoshimura et al. 2015), many verbal expressions
may be advantageous for building cooperative or friendly
relationships. Regarding detail-focused processing, the
minor power of prediction suggests that this cognitive style
does not independently influence adaptive functioning but
supplements the contribution of verbal abilities. This
finding appears to support the notion that a detail-focused style
in this population is due not so much to a deficit in global
processing but superiority in local processing (Happé and
Frith 2006). The talent that makes them focus on local
features is possibly advantageous for individuals with higher
verbal intelligence in their performance of the daily or
specialized tasks requiring sensitivity to details of information.
Regarding social cognition, the close relationship
between emotion perception and functional outcome in
adults with ASD, which was previously reported as a
significant correlation (Wallace et al. 2011), was confirmed
by regression analyses. Emotion perception is thought to
be bound to skills in communication and socialization in
adaptive functioning. The result that performance on the
FER, but not the FER-BP or Eyes test, was found to be a
significant predictor of functional outcome in this ASD
sample suggests that social cognitive skills to recognize
emotions carefully from prototypical facial expressions are
also advantageous for developing personal relationships,
although they have the underlying abnormality in the
ability to infer the mental state of others. Additional analyses
demonstrated that the Vineland-II composite score
correlated significantly only with accuracy in the recognition
of sad facial expressions (r = 0.42, p < .05) among the six
emotions (the correlations for the others: all r ≤ 0.36, all
p ≥ .05), which is in consistent with the previous finding
(see Wallace et al. 2011). The cognitive skills for
individuals with ASD to perceive others’ sadness accurately may
lead to an increase in kindness, which is important in
maintaining reciprocal relationships. Both emotion perception
and verbal ability are cognitive abilities related closely to
interpersonal communication and interaction. Thus, it may
be argued that both verbal and non-verbal, or expressive
and receptive, communication skills are crucial for
individuals with ASD to adapt to social needs.
The key finding from this study is that emotion
perception acts as a mediator of the predictive relationship
between verbal ability and adaptive functioning, whereas
the relationship between emotion perception and
detailfocused processing was not underpinned. Supplementary
analyses demonstrated that the mediation effect of
emotion perception in the relationship between verbal IQ and
Vineland-II composite score was also significant (β = 0.27,
95% CI 0.04–0.63, p < .05). This triadic relationship means
that the considerable predictive value of verbal ability on
functional outcome is actually an indirect effect,
reflecting the predictive power of emotion perception, the
performance of which depends partially on verbal ability. These
findings support the validity of the suggestion that verbal
ability contributes to the atypical, effortful, and less
automatic processing of emotion perception in high-functioning
adults with ASD. Higher verbal ability seems to make a
positive contribution to analytical thought process
involving judgements of others’ emotions based on currently
available information, accumulated experiences, and
linguistic knowledge acquired from books, the Internet, or
what someone says. This ability may also help to elicit
information related to emotions during conversation.
Atypical effort was also found in VFT in adults with ASD and
higher verbal intelligence in an fMRI study (Beacher et al.
2012). The current and previous findings suggest that
atypical abilities in recruiting cognitive resources or strategies to
compensate for inefficient performance in diminished
cognitive domains relate to functional outcomes in
high-functioning adults with ASD. This mechanism of compensation
is considered to stand on atypical, intertwined relationships
among specific abilities in neuro- and social cognition in
Implications for Treatment Interventions
We intended to provide evidence to open the door to
treatment interventions that target cognitive abilities in adults
with ASD. Our findings raise the hypothesis that
improvements in psychosocial intervention, or cognitive training,
on social perception and verbal ability may lead to
benefits in functional outcomes in high-functioning adults with
ASD, and warrant further investigations into the effects of
such interventions for this population. Regarding social
perception, some interventional studies had reported
improvement effects on cognitive measures in the domains targeted
(Bölte et al. 2015; Faja et al. 2012; Golan and Baron-Cohen
2006). Investigations into the effects on social and adaptive
functioning are a key challenge for future research.
Mediation of social perception between verbal ability and
functional outcome suggests that individuals with higher verbal
intelligence may receive substantial benefit from
interventions targeting this domain. Linguistic intelligence in or
above the average range seems to be required to understand
linguistic instructions in defining each emotion or using
cognitive strategies to read facial cues. As is the case with
social perception (Turner-Brown et al. 2008), cognitive
training programs that have demonstrated improvements in
verbal generativity in individuals with schizophrenia (e.g.,
Sánchez et al. 2014) may be applicable to this population.
Considering compensation by the effortful or strategic
processing, intervention programs focusing on compensatory
strategies (Twamley et al. 2012) are likely promising in this
population, in comparison with those depending on
repetitive drill practices. For children with ASD, several
intervention studies focusing on social perception and verbal
ability have been reported (see Wass and Porayska-Pomsta
2014 for a review). Future research is expected to identify
specific cognitive abilities in childhood that longitudinally
predict adult outcomes.
Our findings should be interpreted considering the
following limitations. First, the current study focused on adults
with ASD and average or higher IQ. These inclusion
criteria for ASD participants limit the generalizability of our
findings to this high-functioning population. Second, this
study used a cross-sectional design. Thus, our results do
not exactly reveal longitudinal predictions. Although we
have theorized that neurocognition affects social
cognition and functional outcome, our results cannot rule out the
possibility that social cognition and functional outcomes
affect neurocognition. The replicability of our findings
needs to be examined in longitudinal investigations. Third,
only a few measures were used in each cognitive domain
in this study, because we sought to identify specific
abilities relating to functional outcome among a broad range of
neuro- and social cognitive domains. When further
focusing on the intervention targets in verbal functions and in
social perception or emotion processing and expression,
multiple measures in both domains are needed.
Atypicalities on behaviors closely related to both domains, including
perception and production of prosody (see O’Connor 2012
for a review) and emotion processing on memory
(Beversdorf et al. 1998; Gaigg and Bowler 2008, 2009), should be
focused on. Future investigations clarifying
inconspicuous atypicalities or compensatory mechanisms in those
domains will be useful in developing effective
interventions for this population. Fourth, our statistical analyses
included only the composite score within scores in
Vineland-II because this study focused primarily on cognitive
predictors of adaptive functioning and their relationships.
Investigations into more complex relationship among social
functioning, subdomains of adaptive functioning, cognitive
domains, and specific abilities are expected in the future.
Finally, the relatively small sample size may limit both
the generalizability of our results and the statistical power,
mainly for group comparisons on cognition.
In the current study, we identified emotion perception,
verbal ability, and detail-focused processing from a broad
range of domains in neuro- and social cognition as
cognitive predictors of adaptive functioning in adults with ASD
and average or higher IQ. Furthermore, a direct test of
mediation revealed that emotion perception mediated the
predictive relationship between verbal ability and
adaptive functioning. This finding represents the first reported
evidence of a triadic relationship among neuro- and social
cognition and functional outcome in individuals with ASD.
In this triadic relationship, not only emotion perception
but also verbal ability acted as direct predictors of
adaptive functioning and their relationship also had a significant
effect, accounting for approximately half of the variance in
functional outcome. Our findings appear to provide new
insight that not only specific cognitive abilities in
neuroand social cognition but also atypical or compensatory
relationship among them contribute to social adaptation in
this heterogeneous population. The suggestion that
psychosocial interventions targeting social perception and verbal
ability will possibly provide benefits in functional outcome
should encourage further research concerning cognitive
training for adults with ASD.
Acknowledgments The authors are grateful to all participants and
their parents or spouses. We would like to thank Dr. Morimitsu
Sakihama, Dr. Yasutaka Kubota, and Dr. Teruhisa Uwatoko for helpful
advice, and Ms. Emi Yokoyama for technical support. This study was
supported by the Organization for Promoting Neurodevelopmental
Disorder Research (OPNDR).
Author Contributions SO conceived of and coordinated the study,
acquired data, performed data analyses, interpreted results, drafted
and revised the manuscript; SU made a substantial contribution to
writing and revising the manuscript; SY acquired data; SZ assisted
with the data analyses; MT directed and supervised the study. All
authors participated in the design of the study, contributed to the
manuscript development, read and approved the final manuscript.
Compliance with Ethical Standards
Ethical Approval All procedures performed in this study involving
human participants were in accordance with the ethical standards of
the Ethics Committee of the Graduate School and Faculty of Medicine
at Kyoto University, and with the 1964 Declaration of Helsinki and its
Informed Consent Written informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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