INTRODUCTION
Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder characterized by deficits in social communication and social interaction, and restricted, repetitive patterns of behavior, interests, or activities [1]. While it was previously estimated that ASD had a prevalence rate of less than 1%, recent studies indicate that its prevalence has risen to affect more than 2% of the population [2]. The rising prevalence of ASD can increase caregiving burdens on families and heighten healthcare demands [3]. Additionally, it is frequently associated with mental and physical comorbidities, such as attention-deficit/hyperactivity disorder (ADHD), anxiety, depression disorder, motor problems, and gastrointestinal diseases [4,5]. These comorbidities further exacerbate the negative impact on the mental and physical health of individuals with ASD, leading to more severe functional impairments.
Up to 70% of children and adolescents with ASD can have neurocognitive impairment [6]. They typically show impairments in different cognitive aspects, including non-social and social cognitive functions, which significantly interfere their daily function and increase caring burden [7-9]. Social cognition refers to the ability to understand and respond to others’ thoughts, emotions, and intentions, whereas non-social cognition encompasses cognitive processes such as attention, executive function, memory, and language that are not directly related to social interaction [10,11].
The associations between non-social cognitive functions and autism symptoms have been inconsistent in previous studies. Some studies have revealed the prognostic significance of intelligence in children with ASD [12], but other studies have also indicated little significant association between intelligence and clinical symptoms, such as autism symptoms and behavior dysregulation in individuals with ASD [13,14]. Moreover, a study revealed no significant correlation between intelligence and social communication difficulties after dividing autistic individuals into the high and low intelligence groups [15]. While both social and non-social cognitive domains are important, most research has focused on social cognition, given its central role in the core diagnostic features of ASD and its direct impact on social adaptation. Non-social cognitive functions have been relatively understudied, although commonly assessed in clinical practice. Because these functions are less influenced by social symptomatology, they can provide a clear perspective on how cognitive abilities relate to overall clinical characteristics, highlighting the need for further research.
Low-functioning autism children often need extensive support and resources, but high-functioning children with ASD can also face difficulties and challenges, and potentially receive fewer interventions and resources. Studies of correlations between non-social cognitive function and clinical characteristics such as autism symptoms in high-functioning individuals with ASD are limited. Yet these studies may increase understanding of associations between non-social cognitive functions and autistic symptoms, and help to identify individual needs, develop interventions, and evaluate prognosis. Furthermore, although the diagnostic distinction between Asperger’s disorder (AS) and other forms of high-functioning autism (HFA) is no longer emphasized in current nosology, children historically labelled under these categories may still present variations in clinical characteristics and cognitive performance. To address the gaps, our study had two complementary objectives: 1) to examine the correlations between non-social cognitive functions and clinical characteristics of high-functioning individuals with ASD, and 2) to perform an exploratory comparison between individuals with AS and those with HFA, as a robustness check to determine whether historical diagnostic labels were associated with differences in non-social cognitive performance.
METHODS
Participants
This study is part of an autism cohort that investigates multiple clinical domains in children and adolescents with ASD, aiming to gain a deeper understanding of their difficulties and to identify factors associated with their clinical presentations. All participants were recruited from the outpatient clinics at the medical center in northern Taiwan. Inclusion criteria were as follows: 1) diagnosis of ASD by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V), 2) age between 6 to 18, 3) ability to communicate verbally with others, and 4) willingness and ability to understand the study protocol and cooperate during the assessments.
Exclusion criteria were as follows: 1) intellectual disability (FSIQ <70), 2) diagnosis of severe psychiatric disorders, such as schizophrenia or bipolar disorder, 3) presence of severe physical diseases that can impact neurocognitive functions, such as severe heart disease or liver cirrhosis, 4) presence of severe neurological diseases such as stroke, 5) lack of ability to communicate verbally with others, and 6) inability to cooperate in the study.
All individuals and their caregivers provided written informed consent before entering this study. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (201506482A3, 202300305B0, and 202400427B0).
Study protocol
Children and adolescents received diagnostic interviews by experienced certified paediatric psychiatrists. The Kiddie-Schedule for Affective Disorders and Schizophrenia (epidemiologic version) [16] was administered to all participants to evaluate their mental condition. Those with the diagnosis of ASD according to the DSM-V received cognitive function tests including the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV) and Conners’ Continuous Performance Test, Second Edition (CPT-II) to evaluate their intelligence and attention/impulse control. After evaluation, those with FSIQ >70 were screened for AS or autistic disorder (HFA) according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for subgroup comparison [17], and invited to participate in the study. The demographic data were collected, and their caregivers completed questionnaires including the Child Behaviour Checklist for Ages 6-18 (CBCL/ 6-18), Social Responsiveness Scale, Second Edition (SRS-2), and Aberrant Behaviour Checklist (ABC). The assessment of comorbidities was conducted by board-certified child and adolescent psychiatrists and neurologists based on comprehensive clinical evaluation.
Evaluation tools
Non-social cognitive function tests
Wechsler Intelligence Scale for Children, Fourth Edition
WISC-IV can assess the intelligence quotient (IQ) of children aged 6 years to 16 years and 11 months [18]. The Chinese version used in this study [19] contains 14 subtests. Full Scale Intelligence Quotient (FSIQ) and four index scores, including Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI), are calculated based on the subtests [20]. This test has substantial validity (in Taiwan, correlation with WISC-III: 0.58-0.89) and reliability (in Taiwan, split-half reliability: 0.85-0.96; test-retest reliability [time interval: 21-35 days]: 0.83-0.94) [19].
Conners’ Continuous Performance Test, Second Edition
CPT-II is a useful computerized screening tool which yields 12 dependent variables to evaluate attention deficits in several domains, including inattention, impulsivity, and vigilance [21]. The scores of Omissions, Commissions, Hit Reaction Time (Hit RT), Hit RT Standard Error, Variability, and Detectability can account for inattention. The scores of Commissions, Hit RT, and Perseverations indicate impulsivity. The scores of Hit RT Interstimulus Interval Change (Hit RT ISI Change) and Hit Standard Error Interstimulus Interval Change (Hit SE ISI Change) indicate vigilance. This test has adequate internal consistency (Cronbach’s alphas: 0.64-0.96) and reliability (split-half reliabilities across variables: 0.66-0.95) [22,23]. In this study, the results for each dependent variable are presented in terms of T-scores. The mean T-score for the comparison group is 50 (standard deviation [SD]=10), and higher T-scores (60 or above) indicate higher possibility of attention problems [23].
Subjective questionnaires
Social Responsiveness Scale, Second Edition
SRS-2 is a 65-item rating scale completed by parents, measuring children’s deficits in social behavior associated with ASD [24]. There are 5 intervention subscales in total, namely Social Awareness (SA), Social Cognition (SCog), Social Communication (SCom), Social Motivation (SM), Restricted Interests, and Repetitive Behaviour (Autistic Mannerism [AM]). The Chinese version used in this study has adequate test-retest reliability (intraclass correlations [time interval: 4 weeks]: 0.751-0.852), internal consistency (Cronbach’s alphas: 0.944-0.947), and validity (correlation with the Chinese version of the Social Communication Questionnaire: 0.609-0.865) [25].
Child Behaviour Checklist for Ages 6-18
CBCL/6-18 is a parent report measure which can be used to assess behavioral and emotional problems in children and adolescents. Its subscales measure symptoms of anxiety, depression, somatic complaints, social problems (social withdrawal), thought problems (obsession/compulsion), attention problems, rule-breaking behavior (conduct problems), and aggressive behavior. The results for each subscale are presented in terms of T-scores (M=50 and SD=10), with higher T-scores indicating more severe symptoms [26]. This measure has sufficient internal consistency (Cronbach’s alphas: 0.78-0.97) [27], reliability (average test-retest reliabilities of all subscales: 0.90) [27] and validity (correlation with Behaviour Assessment System for Children: 0.85-0.89) [26].
Aberrant Behaviour Checklist
ABC is a rating instrument that can be used to evaluate aberrant behavior of children. It consists of 58 items which are used to assess 5 domains of aberrant behavior, namely Irritability (15 items), Social Withdrawal (SW) (16 items), Stereotypic Behaviour (7 items), Hyperactivity/Noncompliance (HY/NO) (16 items), and Inappropriate Speech (IS) (4 items). Higher scores indicate greater severity of aberrant behavior. This checklist has adequate validity, internal consistency and reliability [28].
Data analysis
All data analyses were performed using SPSS 22.0 (IBM Corp.), with a significance level of 0.05 (two-tailed). Quantitative variables were presented as descriptive statistics. Continuous variables were presented with their mean and SD. Categorical variables were presented as counts and percentages. Data of the demographic variables and cognitive function test scores were analyzed by independent sample t-test and chi-square test to evaluate group differences between AS and HFA individuals. Correlations between data of cognitive function tests and questionnaires were analyzed by partial correlation. Age and sex were adjusted for as covariates. ADHD diagnosis was also included as a covariate for analyses involving IQ indices. A p-value less than 0.05 was considered as statistically significant.
RESULTS
Demographic and clinical characteristics
A total of 98 participants (mean age, 11.44±3.13 years; 75.5% male) were recruited, 55 (mean age, 11.13±2.87 years; 70.9% male) and 43 (mean age, 11.85±3.42 years; 81.4% male) fit the diagnostic criteria for AS and autistic disorder (HFA) in the DSM-IV-TR, respectively (Figure 1). Table 1 shows the demographic and clinical data of the participants. There was no significant group difference in terms of age, sex, BMI, comorbidities, CBCL, and SRS-2 total score.
Group differences in the WISC-IV and CPT-II results
Table 2 presents the WISC-IV results of all participants. The AS group had significantly higher FSIQ than the HFA group (p=0.009) and demonstrated significantly better perceptual reasoning (p=0.020), working memory (p<0.001), and processing speed (p=0.016). In terms of the subtests, the AS group performed significantly better than the HFA group in Matrix Reasoning (MR) (p=0.002), Digit Span (p=0.003), Letter-Number Sequencing (LN) (p=0.017), Symbol Search (SS) (p=0.013), and Arithmetic (p=0.050). Table 3 shows the CPT-II results of all participants and the HFA group had significantly higher T-scores in Commissions (p=0.008) and Detectability (p=0.019) than the AS group.
Correlations between IQ, autistic symptoms, emotion and behavior of children and adolescents with ASD
Table 4 shows the correlations between the WISC-IV variables and SRS-2 after adjusting for age, sex, and ADHD diagnosis. Better performance on the WISCIV was generally associated with fewer social difficulties on the SRS-2. Lower scores in SA, SCog, and total score of the SRS-2 were consistently related to better performance across multiple indices, including FSIQ, WMI, and PSI. Among subtests, LN and SS showed the broadest associations, correlating with several SRS-2 domains such as SCom and SCog, for SS, even SM, SA, and total score. These findings suggest that specific cognitive processes, particularly working memory and processing speed, may be closely linked to the severity of social communication difficulties in ASD.
Supplementary Table 1 shows the correlations between the WISC-IV variables and CBCL/6-18, after adjusting age, sex, and ADHD diagnosis. Better performance on the WISCIV was generally associated with fewer emotion and behavior difficulties on the CBCL/6-18. Less Attentional Problems (AP) and Rule-Breaking Behavior were linked to better performance on FSIQ and all 4 intelligence indices. At the subtest level, MR, LN, and SS showed the strongest associations, each correlating with attentional difficulties and rule breaking behaviors. These results suggest that perceptual reasoning, working memory, and processing speed may be particularly relevant to behavioral regulation in children with ASD.
Correlations between attention/impulse control, autistic symptoms, emotion and behavior of children and adolescents with ASD
Table 5 presents the correlation analysis between CPT-II variables and SRS-2 after adjusting for age and sex. Correlations between attention performance on the CPT-II and social functioning on the SRS-2 showed a consistent pattern: poorer attention control was associated with greater social communication difficulties. Higher Confident Index, Commissions, Hit RT Standard Error, Variability, and Perseveration were each linked to elevated SRS-2 total scores and greater difficulties in SCog, SCom, SM, and AM. Detectability also showed similar positive associations, while a more conservative Response Style was related to fewer AM. These findings suggest that reduced response inhibition, inconsistent reaction times, and attentional fluctuation may be closely related to the severity of social difficulties in ASD.
Supplementary Table 2 presents the correlations between the CPT-II variables and CBCL/6-18 after adjusting age and sex. Attention performance on the CPT-II showed a consistent relationship with emotion and behavior dysregulation measured by the CBCL/6-18. Multiple CPT variables, including Confidence Index, Commissions, Hit RT Standard Error, and Variability, were associated positively with AP. Measures reflecting inconsistent reaction times, such as Hit RT Standard Error and Variability, showed associations with Social Problems (SP). A more conservative Response Style was linked to fewer Somatic Complaints, whereas greater Perseverations were associated with higher Internalizing and External Problems, SP, and AP. These findings indicate that impaired inhibition, reaction time inconsistency, and attentional lapses are closely related to emotion behavior dysregulation in children with ASD.
Correlations between objective non-social cognitive function tests and aberrant behavior of children and adolescents with ASD
Correlations between intellectuality (WISC-IV) and aberrant behavior (ABC) of children and adolescents with ASD are shown in Supplementary Table 3. Intellectual abilities measured by the WISC-IV were generally associated with fewer aberrant behaviors on the ABC. Higher FSIQ, PRI, and PSI were linked to lower HY/NO and IS. Measures of attention and impulse control from the CPT-II showed that poorer inhibitory control and higher perseveration were associated with greater SW and IS (Supplementary Table 4).
DISCUSSION
The assessments of different clinical aspects of ASD have found the correlations between non-social cognitive functions and clinical characteristics of high-functioning individuals with ASD, suggesting those with poorer intelligence, attention, and impulse control could have not only more social and communication difficulties but also more emotion and behavior dysregulation, even if their FSIQ falls within the normal range. Although the increased needs of individuals with low-functioning ASD cannot be ignored, many high-functioning individuals and their caregivers also face significant difficulties and challenges, emphasizing the need to develop appropriate interventions and management strategies [29,30].
FSIQ, WMI, and PSI were negatively correlated with SA, SCog, and total score in SRS-2, and IS in ABC. The associations between IQ, working memory, processing speed and social functions are consistent with previous studies [31-35] and supported by neuroimage studies [36,37]. Chien et al. [38] also assessed the correlations between CPT variables, ADHD and autistic symptoms, and their results were consistent with our findings. The correlations between Commissions, RT Standard Error, Variability and Perseverations with SP reflected in CBCL/6-18 and SRS-2 are also consistent with a recent study, stating that inattention and impulsivity contribute to reduced social responsiveness and social competency [39]. A study by Narganes-Pineda et al. [40] highlighted that social and non-social attention are both governed by the right parieto-temporo-occipital regions. Moreover, previous fMRI research proved that the frontoparietal network responsible for inhibition and impulse control functions differently in ASD individuals [41]. These structural and functional differences may explain the associations between attention, impulse control and social behavior in ASD individuals.
High-functioning individuals with ASD were stressed in not only social aspects as expected, but also non-social aspects such as academic stress [29,42], and both could contribute to parenting stress [30]. Current study further identifies the increased needs of high-functioning individuals with relatively poorer non-social cognitive functions, despite having normal FSIQ. Previous studies have revealed the prognostic significance of cognitive functions in children with ASD [12], and considering the associations between non-social cognitive functions and clinical characteristics, intervention targeting not only autistic symptom, emotion, and behavior but also nonsocial cognitive function, such as attention and executive function training, can be beneficial [43,44].
The correlations between non-social cognitive functions and clinical characteristics can have biological basis, but neuroimage findings cannot fully account for them [45,46]. The increase in social, emotional, and behavior difficulties of high-functioning individual with relatively poorer non-social cognitive performance may also be explained by their borderline situations. Compared with low-functioning peers, they may not receive as much attention and resources, but have to face more challenges and difficulties when dealing with the same level of requirements as typically developing peers or ASD peers with relatively better non-social cognitive performance. Interventions, such as individualized educational plan and parent counseling, can be helpful for these children. Proper evaluation and management should also be provided for their comorbid conditions such as depression, anxiety, and ADHD in order to reduce associated burdens. In case where pharmacological treatment is indicated, pharmacoeducation should be offered to enhance adherence.
Our findings also demonstrated that individuals with AS subtype exhibited significantly higher FSIQ compared to their HFA counterparts. Specifically, the AS group showed superior performance in PRI, WMI, and PSI, while the HFA group displayed greater commission errors and impaired detectability. Although a consensus on the cognitive profiles of AS and HFA has yet to be reached [47], our results align with previous reports [38,48], suggesting that the distinctions between these phenotypes may extend beyond early language milestones. From a clinical utility perspective, maintaining these distinctions helps clinicians, educators, and families better understand a child’s specific cognitive strengths and weaknesses. Such variations inform tailored intervention strategies. Individuals with the AS subtype and higher cognitive reserve may be better suited for cognitive-based therapies, whereas those with the HFA subtype characterized by more pronounced inhibitory control challenges may require more structured, environmental supports.
Limitations
This study has several limitations. First, our participants were predominantly male; many had psychiatric comorbidities. Although this demographic distribution is unavoidable when recruiting ASD individuals [49-51], it reflects real-world conditions but may have an impact on our results. Second, the large number of correlation analyses increased the risk of Type I error; several p-values were only slightly below 0.05, and only few robust correlations would remain statistically significant after false discovery rate adjustment (Supplementary Tables 5 and 6), such as those between SS, Commissions, Variability, Perseverations, and Attention Problem of CBCL/6-18. These exploratory findings should therefore be interpreted with caution, although they still provide useful preliminary insights for future research. Third, non-social cognitive functions were assessed using only the Wechsler intelligence test and the CPT. While both are widely used in clinical practice, they do not capture the full range of neurocognitive domains relevant to ASD, underscoring the need for future studies employing more comprehensive cognitive batteries. Fourth, because this study is part of an autism cohort, no a priori sample size calculation was performed; post hoc sensitivity analysis indicated adequate power for medium or larger effects, suggesting that smaller associations may have gone undetected. Finally, the comparison between AS and HFA was exploratory and secondary to the main study aims. Observed cognitive differences, particularly those associated with IQ, may reflect definitional factors rather than meaningful subtype distinctions and should be interpreted accordingly.
Conclusion
Significant correlations were found between non-social cognitive functions and autistic, emotional, and behavioral symptoms of high-functioning children and adolescents with ASD, highlighting the increased need of individuals with relatively poorer non-social cognitive abilities, despite having normal FSIQ. Individualized support and management strategies can be developed and provided based on our findings.
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