The relationship between cognitive flexibility and restricted, repetitive behaviors in children with autism: Parents’ reports vs. cognitive task performance

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Pouretemad, Setareh Mokhtari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7814208/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Cognitive flexibility (CF), a key aspect of executive functioning (EF), is often impaired in children with autism and closely linked to restricted and repetitive behaviors and interests (RRBIs). While higher-order RRBIs are associated with behavioral rigidity, few studies have examined CF using both performance-based and parent-report methods in early childhood. This study explored the relationship between CF, RRBIs, and EF in 43 children with autism aged 3 to 7 years through multiple methods of assessment. Children completed a computerized CF task measuring switch costs via reaction time and accuracy, while caregivers provided standardized ratings of EF, RRBIs, and behavioral flexibility. Significant correlations emerged across CF indices, suggesting convergence between lab-based and caregiver-reported assessments. Accuracy-based switch cost was the strongest predictor of cognitive inflexibility, with higher-order RRBIs and working memory deficits explaining over 80% of the variance. Reaction time measures were less predictive, and parent-reported CF was associated with higher-order RRBIs, communication challenges, and EF impairments. Lower-order sensorimotor RRBIs showed no significant link to CF. These findings highlight the importance of accuracy-based CF measures and multi-method assessment, pointing to modifiable targets for early intervention during critical EF development in autism. Moreover, the results offer insight into distinct underlying mechanisms differentiating higher-order and lower-order restricted and repetitive behaviors and interests. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Autism Cognitive flexibility Restricted and repetitive behaviors and interests (RRBIs) Executive functioning Parent report Performance-based assessment Figures Figure 1 Introduction Cognitive flexibility (CF)-the capacity to adapt thoughts and behaviors in response to changing circumstances-is a central component of executive functioning (EF) 1–3 . Deficits in cognitive flexibility manifest as difficulties in shifting between tasks or mental sets, rigid adherence to routines, and resistance to change, all of which are frequently observed in individuals with autism, both in laboratory-based assessments and in real-world contexts as reported by parents (e.g., Faja & Dawson 4 ; Dajani & Uddin 5 ; Buttelmann & Karbach 6 ; Garon et al. 7 ; Lage et al. 8 ). Autism is a neurodevelopmental condition characterized by differences in social communication and interaction, as well as restricted and repetitive patterns of behavior and interests 9 . Variations in cognitive flexibility among autistic individuals are closely linked to the diverse social and cognitive characteristics of autism, particularly the presence of restricted and repetitive behaviors and interests (RRBIs) which is a hallmark feature of the condition 10–12 . RRBIs are typically categorized into four behavioral subgroups: stereotypies, preoccupation with objects, restricted interests, and nonfunctional routines 13, 14 .These behaviors span a continuum, from motor stereotypies (e.g., hand-flapping) to highly specific fascinations and rigid routines. Conceptually, RRBIs may be further grouped into Insistence on Sameness (IS), reflecting higher-order behaviors such as compulsive routines and ritualistic actions, and Repetitive Sensory-Motor (RSM) behaviors, which include stereotyped movements and sensory-focused interest 15, 16 . Given their impact on learning and social engagement,-even in very young children-, caregivers frequently identify these behaviors as among the most challenging aspects of autism 17–20 . A growing body of research has demonstrated a robust association between higher-order RRBIs and difficulties with cognitive flexibility and related EF domains such as inhibitory control 8,10,21–25 . For example, Miller et al. 12 identified a direct relationship between difficulties in maintaining new behavioral sets and the severity of restricted and repetitive behaviors in a sample of 60 individuals with autism aged 6 to 44 years. Notably, these difficulties were specifically associated with RRBIs severity and not with other core autism symptoms such as social or communication deficits. The study further highlighted that increased regressive errors-instances where participants reverted to previously learned responses after successfully shifting were particularly associated with elevated levels of repetitive behaviors, especially insistence on sameness, rather than with repetitive sensorimotor behaviors. Similarly, Faja and Nelson Darling 10 found that cognitive shifting was associated with higher-order RRBIs in a cohort of 102 school-aged children with autism, while inhibitory control was more closely related to sensorimotor RRBs. In their study, children were asked to press a button when an image appeared, but on certain trials, a stop signal (such as a sound or color change) was presented 500 milliseconds before the anticipated response, requiring the child to inhibit their initial reaction and instead press an alternative button. High accuracy on these trials was interpreted as an indicator of cognitive flexibility, accompanied by parent ratings on the BRIEF Shift scale. The findings indicated that increased severity of higher-order RRBIs was specifically associated with reduced inhibition and cognitive flexibility, independent of age and IQ. Notably, although parent ratings on the BRIEF Shift scale were correlated with higher-order RRBIs, neither the accuracy on the Change Task nor the number of perseverative errors following change trials demonstrated a significant association with higher-order RRBIs. These results suggest that real-world assessments of cognitive flexibility may more effectively capture the relationship with higher-order RRBIs than laboratory-based tasks, and this conclusion is supported by findings from previous studies (e.g., Albein-Urios et al. 21 ). A meta-analysis by Iversen and Lewis 23 provided a comprehensive examination of the relationship between cognitive flexibility and inhibitory control, taking into account the severity of restricted and repetitive behaviors. The study analyzed data from nearly 3,000 participants, including both children and adults with autism and typical development. Findings revealed moderate yet significant associations between higher levels of RRBIs and impairments in set-shifting and inhibitory control, as well as in parent-reported EF assessments. Importantly, these associations did not significantly vary with age 23 . Despite this compelling evidence, findings remain inconsistent, particularly regarding the specificity of CF deficits across RRBIs subtypes. Lab-based tasks such as the Wisconsin Card Sorting Test (WCST) or Intra/Extra-Dimensional Set Shift Task (ID/ED) often assess broad EF domains and may not isolate CF effectively 8, 23 . Additionally, concerns about their ecological validity further underscore the importance of multi-informant assessments that integrate both performance-based and caregiver-reported data 26–29 . A critical gap in the literature is also involves the early developmental period. The majority of CF studies focus on older children, adolescents, or adults, despite evidence that early childhood is characterized by rapid EF maturation and represents an optimal window for intervention 30–33 . Understanding CF deficits in younger children is essential for identifying early markers and informing strategies that support flexible behavior and learning. In this context, the present study examines cognitive flexibility in young children with autism using multiple methods of assessment, combining parent-reported and performance-based assessments. It aims to evaluate the relationship between CF and restricted and repetitive behaviors, and to identify key factors that contribute to CF impairments in early childhood. By exploring both lab-based tasks and questionnaires completed by parents, this study seeks to refine our understanding of cognitive flexibility during early childhood which is a crucial developmental window. Method Participant Sixty children with autism, aged 36 to 72 months (M = 57.83, SD = 12.44), were recruited through the Tehran Autism Center (formerly known as the Center for the Treatment of Autistic Disorders). All participants had a confirmed diagnosis of autism spectrum disorder (ASD), established independently by at least two clinical teams blinded to each other’s assessments. Our team administered the Structured Diagnostic Interview for Autism, along with clinical observation and the Persian edition of the Gilliam Autism Rating Scale (GARS-P), while the other diagnostic evaluations were conducted by child psychiatrists and/or pediatric neurologists in accordance with DSM-5 (APA, 2013). Exclusion criteria comprised a concurrent documented history of genetic or neurological disorders, as well as severe sensory or motor impairments that could interfere with the participant’s ability to complete the task. Measures Parent-report measures Gilliam Autism Rating Scale- Persian edition (GARS-P) Similar to original version 19, 34 , the GARS-P consists of 56 items categorized into four scales: The Social Interaction, Communication and Stereotyped Behaviors scales evaluate the child’s current and typical behaviors, while the Developmental Disturbances scale evaluate past severe maladaptive behaviors. Respondents are instructed to assess the frequency of each behavior using a four-point Likert scale. The GARS-P demonstrated both face and content validity, as confirmed by experts in the relevant field. The construct validity was high, as the coefficient between the CARS and GARS-P scores was 0.80. The diagnostic validity was assessed through a discriminant analysis, which involved a comparison with 100 typically developing children and adolescents. With a cut-off point "52," the sensitivity and specificity rates recorded at "99%" and "100%" respectively. The GARS-P also demonstrated high reliability, evaluated using Cronbach's alpha, yielding a coefficient of 0.89 35 . Behavior Rating Inventory of Executive Functions -Preschool Version (BRIEF-P) The BRIEF-P 36 is a 63-item informant is a report measure of a preschooler’s everyday EF behavior that measures multiple aspects of executive functioning include Inhibit, Shift, Emotional Control, Working Memory, and Planning/Organization. These clinical scales yield three composite indexes: the Inhibitory Self-Control Index (ISCI), Flexibility Index (FI), and Emergent Metacognition Index (EMI). The five scales are then collapsed into a Global Executive Composite (GEC). The Flexibility index (FI) which shows ability to move flexibly among actions/responses/emotions/ behavior is Composed of the Shift and Emotional Control scales. The reliability is available as internal consistency in all subscales of the BRIEF in the Iranian children population with Cronbach’s alpha of 0.80 to 0.97 37 . Behavioral Flexibility Rating Scale-Revised version (BFRS-R) The BFRS-R 38 , 16 items, 4-point Likert scale, is a reliable and valid tool for investigating flexibility in everyday functioning in individuals with autism. It assesses insistence on sameness and resistance to change toward objects, persons, and the environment, across a wide age range (age 2–17 years). The total score represents the outcome measure. Higher scores indicate more inflexible behaviors. It has good internal consistency in Iranian children with ASD 39, 40 and significantly correlated with the BRIEF Shift scale 41 . Repetitive Behavior Scale-Revised (RBS-R) The RBS-R (Bodfish et al. 42 ) consists of 43 items categorized into six subscales: self-injurious behavior, stereotyped behavior, ritualistic behavior, compulsions, sameness, and restricted behavior. These subscales are further grouped into two primary factors: the lower-level RRB factor, encompassing items 1 through 14, and the higher-level RRB factor, comprising items 15 through 43 43 . Items are rated by parents on a four-point Likert scale ranging from 0 (“never”) to 3 (“always”), with higher scores indicating greater severity of symptoms. Performance-based measure Dimensional Change Card Sort (DCCS- standard version) The DCCS is a widely used tool for assessing cognitive flexibility in young children, requiring them to match items according to changing task rules 44 . In the standard computerized version, each trial presents a test stimulus (for example, a red boat or a blue rabbit) at the top of the screen, while two target stimuli (a blue boat and a red rabbit) are displayed at the bottom. Children are instructed to match the test stimulus to one of the target stimuli based on the current rule, which may be to sort by shape or by color. Responses are made using the left and right arrow keys on the keyboard, each labeled with images corresponding to the target stimuli (the blue boat on the left key and the red rabbit on the right), with all other keys disabled. The task begins with six practice trials presented with performance feedback (i.e., “correct” or “incorrect”) and then 15 pre-switch trials where children sort according to shape, followed by a rule change that requires them to sort by color for 30 post-switch trials. During the pre-switch and post-switch trials, participants did not receive feedback. For each participant, the number of correct responses, errors, and reaction times (RT) for correct answers are recorded and analyzed. The RT used was the time between when the test card presented and when the child touched the target key (Fig. 1 ). Ethical clearance and participation consent The study protocol was approved by the Shahid Beheshti University Bio-Ethics Committee (IR.SBU.REC.1402.133). Written informed consent was obtained from the parents or legal guardians of all participating children, who themselves provided verbal assent prior to participation. All methods were performed in accordance with the relevant guidelines and regulations. Procedure Each child completed the DCCS task, administered by a licensed psychologist at the Tehran Autism Center. Concurrently, parents completed the Behavior Rating Inventory of BRIEF, RBS-R and BFRS questionnaires in an adjacent room. The DCCS task was delivered via a laptop computer equipped with a 15-inch screen. Children were seated approximately 45 cm from the monitor, positioned beside the experimenter. The testing environment was illuminated by a 100-watt lamp situated almost 200 cm from the screen to ensure consistent lighting conditions. Participants responded by pressing one of two designated keys-corresponding to the left and right arrow keys-each covered with task-specific stimuli; all other keys on the keyboard were masked to prevent inadvertent responses. Analysis Plan This study incorporates two indices of cognitive flexibility: a performance-based measure derived from DCCS reaction time and accuracy, and the parent-report measure based on the Behavioral Flexibility Rating Scale (BFRS-R). In DCCS task, mean reaction times were calculated exclusively for correct trials. Trials with RTs shorter than 150 ms or longer than 10 seconds were excluded as outliers 45, 46 , Only children who successfully passed the pre-switch phase-defined as achieving at least 11 correct responses out of 15 trials (p < 0.05)-were included in subsequent post-switch analyses. Out of the initial sample of 60 children, 17 were excluded from further analysis due to: lack of cooperation (n = 2), failure in the DCCS training phase (n = 5), failure to pass the pre-switch phase (n = 7), and RT outliers (n = 3). This resulted in a final analytic sample of 43 autistic children (11 girls and 32 boys). Two indices of performance-based cognitive flexibility were computed following Van Bers 47 : the RT-based switch cost; calculated as the mean RT of all post-switch trials minus the mean RT of all pre-switch trials. And the accuracy- based switch cost; calculated as the mean accuracy of all post-switch trials minus the mean accuracy of all pre-switch trials. A lower accuracy-based switch cost indicates greater cognitive flexibility, whereas a higher RT-based switch cost suggests reduced cognitive flexibility. Data were initially screened for outliers and assessed for normality using the Shapiro-Wilk test. all cognitive flexibility indices (accuracy-based, RT-based and BFRS-R) violated normality assumptions (p 0.05). Given the non-normal distribution of the dependent variables, Spearman’s rank-order correlations were employed to examine bivariate associations between cognitive flexibility indices (performance based and parent report) and predictor variables. Subsequently, separate stepwise linear regression models were conducted for each cognitive flexibility index to identify the unique contribution of predictors. Although the dependent variables were non-normally distributed, the assumption of normality of residuals was tested and met (p > 0.05), thereby validating the use of linear regression for these analyses. Results Table 1 presents the demographic and clinical characteristics of the participants. There were no significant gender differences across all variables (P > 0.05). Further analyses explored the relationship between cognitive flexibility indices and behavioral measures. Table 1 Demographic Characteristics and Descriptive Statistics of Children with Autism Autism (n = 43) Gender Girls N = 11 (25.60%) Boys N = 32 (74.40%) Mean Std. Deviation Age (months) 57.83 12.45 Cognitive flexibility indices ACC index -4.73 8.19 RT index -0.69 1.25 BFRS-R (16 items) 23.19 12.14 GARS-P Repetitive Behaviors subscale 7.02 4.26 Communication subscale 13.23 7.03 Social Interaction subscale 10.74 5.59 BRIEF-P Shift subscale 17.49 6.75 Working Memory subscale 29.74 9.07 Inhibitory control subscale 26.91 6.8 Emotional control subscale 16.91 3.94 Planning subscale 15.88 3.72 RBS_R Low level RRBs 7.05 4.02 High level RRBs 40.49 14.46 Note: ACC = Accuracy index; RT = Reaction Time index; GARS-P = Gilliam Autism Rating Scale-Persian edition; BRIEF-P = Behavior Rating Inventory of Executive Functions–Preschool Version; RBS-R = Repetitive Behavior Scale–Revised; RRB = Restricted and Repetitive Behaviors. Gender frequencies are presented as counts and percentages. All other values represent means and standard deviations. Spearman’s correlation analyses were performed to investigate the associations between cognitive flexibility indices- accuracy-based switch cost, reaction time-based switch cost and the parent report measure (BFRS-R) -and a range of behavioral measures (Table 2 ). These included the higher-level and lower-level subscales of the RBS-R, subscales of the BRIEF; Inhibit, Shift, Emotional Control, Working Memory, and Planning/Organization, and the three subscales of the GARS-P; Stereotyped Behaviors, Communication, and Social Interaction. Table 2 Spearman correlation coefficients between cognitive flexibility indices (accuracy-based and reaction time-based switch costs and BFRS-R) and behavioral/executive function variables in autistic children Variable Accuracy-Based Switch Cost Reaction Time-Based Switch Cost BFRS-R Age (months) -0.540** 0.218 − .487** GARS-P Repetitive Behaviors 0.040 (ns) -0.168 0.24 Communication 0.542** -0.296 .648** Social Interaction 0.175 -0.085 0.199 BFRS-R .855** − .516** 1 BRIEF-P Shift Subscale 0.933** -0.564** .884** Working Memory 0.580** -0.303* .574** Inhibition 0.669** -0.490** .720** Emotional Control 0.680** -0.364* .658** Planning 0.034 -0.084 0.22 RBS-R Lower-Level RRBs -0.020 0.042 0.139 Higher-Level RRBs 0.880** -0.495** .853** Significance levels: p < .05 (*), p < .01 (**), The three indices of cognitive flexibility—two performance-based measures (RT-based and ACC-based) and a parent-report measure (BFRS-R)—were highly correlated, indicating strong convergence between objective assessments and subjective parental evaluations. Specifically, BFRS-R scores showed significant positive correlations with accuracy-based switch cost (r = 0.855, p < 0.001) and significant negative correlations with reaction time-based switch cost (r = − 0.516, p < 0.001). RT-based and ACC-based measures were strongly inversely correlated (r = − 0.628, p < 0.001). Additionally, the BRIEF-P Shift subscale demonstrated significant correlations with all three cognitive flexibility indices (BFRS-R: r = 0.884, p < 0.001; ACC-based: r = 0.933, p < 0.001; RT-based: r = − 0.564, p < 0.001), indicating a strong association between set-shifting ability and cognitive flexibility across both performance-based and parent-reported measures. The accuracy-based switch cost demonstrated significant positive correlations with the GARS-P communication subscale (r = 0.542, p < 0.001), several BRIEF subscales including, Working Memory (r = 0.580, p < 0.001), Inhibit (r = 0.669, p < 0.001), and Emotional Control (r = 0.680, p < 0.001), as well as with the higher-level RBS-R subscale scores (r = 0.880, p < 0.001). Conversely, it was negatively correlated with age (r = -0.540, p < 0.001). These findings indicate that greater accuracy-based switch cost-which reflects reduced cognitive flexibility-is associated with poorer executive functioning, increased repetitive behaviors, and younger age. Regarding the reaction time-based switch cost, analyses revealed significant negative correlations with, Working Memory (r = -0.303, p = 0.048), Inhibition (r = -0.490, p = 0.001), Emotional Control (r = -0.364, p = 0.016), and higher-level repetitive behaviors on the RBS-R (r = -0.495, p = 0.001). It exhibited a nonsignificant correlation with age (r = 0.218, p = 0.161). These results suggest that higher reaction time-based switch cost-indicative of diminished cognitive flexibility-is related to greater executive dysfunction and repetitive behaviors. Analyses of the parent report measure (BFRS-R) revealed significant positive correlations with the Communication subscale of GARS-P (r = 0.648, p < 0.001), Working Memory (r = 0.574, p < 0.001), Inhibition (r = 0.720, p < 0.001), Emotional Control (r = 0.658, p < 0.001), and higher-level repetitive behaviors on the RBS-R (r = 0.853). These findings indicate that greater behavioral inflexibility is associated with more severe executive dysfunction and autism-related symptoms. Additionally, BFRS-R scores exhibited a significant negative correlation with age (r = − 0.487, p < 0.001), suggesting that behavioral inflexibility tends to decline as children grow older. Notably, the BRIEF Planning subscale, GARS-P subscales for repetitive behaviors and social interaction, and the lower-level RBS-R restricted and repetitive behavior scores showed weak or no significant associations with the cognitive flexibility indices. A stepwise multiple linear regression analysis was conducted to identify the strongest predictors of cognitive flexibility. Predictor variables were selected based on their significant correlations with cognitive flexibility in preliminary analyses. These included the BRIEF subscales (Working Memory, Inhibit, Emotional Control, Planning), the RBS-R higher-level repetitive behavior subscale, communication skills from the GARS-P, and age. The Shift subscale of BRIEF was excluded, as it directly defines cognitive flexibility and aligns with the three cognitive flexibility indices, making it redundant as an independent predictor. Additionally, communication skills and age were removed from the reaction time-based switch cost model, as they did not show significant correlations with this outcome measure. Accuracy-Based Switch Cost Model : A stepwise multiple regression analysis was conducted to identify predictors of accuracy-based cognitive flexibility impairments in autistic individuals. The initial model, which included only higher-level repetitive behaviors, explained a substantial 75.3% of the variance (R² = 0.753, Adjusted R² = 0.746, p < 0.001), indicating a strong positive association (β = 0.868, t = 10.752, p < 0.001). Subsequent models incorporated additional predictors: emotional control, inhibition, working memory, Communication Skills and Age. Model 2 demonstrated a significant improvement in explanatory power (Adjusted R² = 0.780, p < 0.001), with higher-level repetitive behaviors (β = 0.725, p 0.4). The fourth model provided the optimal balance between explanatory power and parsimony, accounting for 83.1% of the variance (R² = 0.831, Adjusted R² = 0.811, p < 0.001). In this model, higher-level repetitive behaviors (β = 0.712, p < 0.001) and working memory (β = 0.249, p = 0.007) remained significant, while emotional control approached significance (p = 0.060). The addition of Communication Skills and Age did not enhance predictive value (p > 0.4). Collinearity diagnostics confirmed the absence of multicollinearity (VIF < 3), ensuring model stability. The final regression equation is expressed as: Accuracy-based switch cost = − 30.497 + 0.389 (higher-level repetitive behaviors) + 0.222 (working memory). This model indicates that greater impairment in working memory along with higher levels of higher-order repetitive behaviors, is associated with increased Accuracy-Based Switch Cost, reflecting reduced cognitive flexibility. Reaction Time-Based Switch Cost Model Similarly, a stepwise multiple regression revealed key predictors of reaction time-based switch costs. The initial model, which included only higher-level repetitive behaviors, accounted for 25.9% of the variance (R² = 0.259, Adjusted R² = 0.241, p < 0.001), revealing a significant negative association (β = -0.509, t = -3.790, p 0.05). Consequently, the final parsimonious model retained only higher-level repetitive behaviors, confirming its dominant role in explaining reaction time-based cognitive flexibility. Collinearity diagnostics validated the absence of multicollinearity (tolerance > 0.39, VIF < 2.6), thereby ensuring reliable coefficient estimates. The final regression equation is formulated as Reaction time-based switch costs = 1.086 − 0.044 (higher-level repetitive behaviors) This suggests that higher levels of higher-order repetitive behaviors are associated with decreased reaction time-based switch costs, indicating reduced cognitive flexibility. Parent-Report Measure (BFRS-R) Model : The final stepwise regression analysis assessed predictors of BFRS-R scores in autistic individuals. This model demonstrated strong predictive validity, explaining 77.5% of the variance (R² = 0.775, Adjusted R² = 0.737, p < 0.001). The final model included six predictors: higher-level repetitive behaviors, inhibition, working memory, emotional control, Communication Skills, and Age. However, only higher-level repetitive behaviors (β = 0.381, p = 0.001), Communication Skills (β = 0.419, p = 0.031), and working memory (β = 0.299, p = 0.031) emerged as significant predictors. Inhibition, emotional control, and Age did not contribute meaningfully and were excluded from the final assessment. Collinearity diagnostics confirmed the absence of multicollinearity (VIF < 3), ensuring stability in coefficient estimates. The final regression equation is articulated as: BFRS-R= -16.878 + 0.381 (higher-level repetitive behaviors) + 0.419 (Communication Skills) + 0.299 (working memory) Regression analyses revealed that the accuracy-based switch cost is the most robust predictor of cognitive flexibility impairments in autistic individuals. This analysis explained the largest proportion of variance (Adjusted R² = 0.811), with higher-level repetitive behaviors and working memory deficits emerging as significant predictors. These findings emphasize the central role of these factors in accuracy-based cognitive flexibility deficits. In contrast, the reaction time-based switch cost model accounted for substantially less variance (Adjusted R² = 0.241) and included only higher-level repetitive behaviors as a significant predictor, indicating its lower sensitivity and predictive power. The parent-report model (BFRS-R) explained a moderate amount of variance (Adjusted R² = 0.737), but being based on caregiver reports, it is inherently more subjective and indirect compared to performance-based indices. Discussion This study investigated the relationship between cognitive flexibility, restricted and repetitive behaviors, and executive functioning in young children with autism using multiple methods of assessment. that combined performance-based assessments and parent-report measures. The findings provide robust evidence that reduced cognitive flexibility is closely associated with higher-order RRBIs and broader EF difficulties, particularly in working memory, inhibition, and emotional control. Across both performance-based and informant-reported indices, lower cognitive flexibility was significantly linked to increased behavioral rigidity and executive dysfunction. Notably, the accuracy-based switch cost emerged as the most sensitive and predictive index, with higher-order RRBIs and working memory deficits jointly explaining over 80% of the variance. This finding aligns with prior research emphasizing the developmental utility of accuracy-based measures in early childhood, (e.g., Qui & Zelazo 48 ; Garon et al. 49 ; Cragg & Chevalier 50 ; Espinet et al. 51 ; Williams 52 ), and supports the notion that working memory plays a foundational role in cognitive flexibility 1, 53, 54 . The BFRS-R also demonstrated strong associations with higher-order restricted and repetitive behaviors, communication difficulties, and working memory impairments, underscoring the ecological validity of caregiver-reported assessments. These findings are consistent with those of Lin et al. 55 who reported that RRBIs are closely linked to adaptive functioning outcomes in children with autism. While previous research has reported mixed findings regarding the relationship between communication abilities and cognitive flexibility (e.g., Costescu et al. 56 ), our results offer a more nuanced perspective. The strong intercorrelations among the three cognitive flexibility indices highlight meaningful convergence between laboratory-based and real-world assessments. This finding aligns with prior research on agreement across multiple methods of assessment in executive functioning studies 59–61 , despite broader literature reporting inconsistent associations across modalities 21, 41, 62, 63 . We interpret this convergence within the context of early childhood, a developmental period during which executive processes are still emerging. During this stage, performance-based and parent-reported measures may reflect overlapping constructs. Taken together, these findings highlight the utility of multi-method approaches for capturing the nuanced complexity of executive functioning in neurodevelopmental populations. Future studies should further investigate the developmental trajectory of cross-method concordance, examining how alignment between assessment modalities evolves with maturation and relates to real-world functional outcomes. Communication difficulties were significantly correlated with both performance-based (accuracy-based switch cost) and parent-reported (BFRS-R) measures of cognitive flexibility, with a stronger association observed for the BFRS-R. However, in the regression analyses, communication skills emerged as a significant predictor of parent-reported behavioral flexibility, but not of accuracy-based cognitive flexibility. This pattern suggests that while communication challenges are broadly related to flexibility, their predictive value may be more pronounced in everyday behavioral contexts as perceived by caregivers than in structured task performance. Although the reaction time-based switch cost showed comparatively lower predictive strength, it still revealed meaningful associations with RRBIs, suggesting that temporal efficiency may reflect a distinct but related facet of cognitive flexibility 57,58 . Higher-order RRBIs, including ritualistic routines, compulsive interests, and insistence on sameness, were robustly associated with both performance-based and parent-reported indices of cognitive inflexibility. In contrast, lower-order sensorimotor behaviors, such as stereotypies and sensory-focused preoccupations, showed weak or nonsignificant associations. These findings align with prior studies linking insistence on sameness and ritualistic behaviors to deficits in set-shifting and cognitive control. 10, 12, 23, 55 Importantly, these associations were independent of age, suggesting that behavioral rigidity may be a stable and early-emerging feature of autism that warrants targeted intervention. This behavioral dissociation invites further interpretation in light of neurocognitive models that distinguish between cognitively mediated and sensory-driven repetitive behaviors. Higher-order RRBIs were consistently linked to deficits in cognitive flexibility and executive functioning, supporting the view that these behaviors reflect impairments in cognitive control and set-shifting 64,65 In contrast, lower-order sensorimotor behaviors, including stereotypies and sensory-focused preoccupations, showed weak or nonsignificant associations with cognitive measures, suggesting that these behaviors may be more closely tied to atypical sensory processing and arousal regulation 65, 66 . This distinction aligns with emerging neurobiological models that propose separate developmental pathways for cognitively mediated versus sensory-driven repetitive behaviors. Recognizing this divergence has important implications for intervention design, as strategies targeting executive function may be more effective for reducing higher-order RRBIs, whereas sensory integration approaches may be better suited for addressing lower-order behaviors. These findings carry important clinical implications for designing interventions tailored to the distinct profiles of restricted and repetitive behaviors. For children exhibiting higher-order RRBIs interventions should prioritize executive function training, with a focus on enhancing cognitive flexibility, working memory, and inhibitory control. Cognitive remediation programs and behavioral flexibility protocols may help reduce rigidity and improve adaptive functioning. Conversely, for children presenting with lower-order sensorimotor behaviors, such as stereotypies and sensory-seeking actions, sensory integration therapies and arousal regulation strategies may be more appropriate. Techniques such as sensory modulation, occupational therapy and environmental management and adaptations can support sensory regulation and reduce repetitive motor behaviors. Importantly, a multi-modal approach that integrates both cognitive and sensory-focused strategies may offer the most comprehensive support, especially for children with mixed RRB profiles. These results underscore the need for individualized treatment planning that aligns with the neurodevelopmental mechanisms underlying each RRB subtype. The observed negative correlations between age and both accuracy-based and parent-reported cognitive flexibility indices suggest modest age-related improvements in cognitive flexibility during early childhood. 6, 7 However, the absence of a significant age effect in the RT-based model may reflect the limited sensitivity of reaction time measures in this age group 48, 49, 51 These findings support the view that early childhood represents a critical window for identifying and addressing cognitive inflexibility, particularly through interventions that target higher-order RRBIs and working memory. Limitations and Future Directions Despite its strengths, this study has some limitations. First, the modest sample size (N = 43) and single-site recruitment limit the generalizability of findings. Future studies should aim to replicate these results in larger, more diverse samples. Second, although cognitive flexibility was assessed using both task-based and parent-report measures, other key constructs—such as RRBIs and executive function (EF) domains—were evaluated exclusively through caregiver reports. Although ecologically valid, such ratings may be influenced by informant bias. Incorporating teacher reports, clinician ratings, or direct behavioral observations would enhance construct validity. Third, the cross-sectional design precludes conclusions about developmental trajectories or causal relationships. Longitudinal studies are needed to examine how early EF impairments and behavioral rigidity evolve over time and whether they predict later adaptive outcomes or treatment responsiveness. Fourth, the absence of a direct measure of cognitive ability (e.g., IQ) limits interpretation of how general intellectual functioning may have influenced task performance. Additionally, the small number of female participants precluded analysis of sex differences—an important area for future research given emerging evidence of sex-specific EF profiles in autism. Conclusion This study provides compelling evidence that cognitive inflexibility in young autistic children is strongly associated with higher-order restricted and repetitive behaviors and executive dysfunction. Among the three indices of cognitive flexibility, accuracy-based switch cost emerged as the most robust and developmentally sensitive indicator of cognitive flexibility in children aged 3 to 7 years. The convergence between performance-based and parent-reported assessments underscores the value of multi-method approaches in capturing the complexity of cognitive flexibility in early childhood. Future research should prioritize longitudinal, ecologically grounded designs that incorporate diverse informants and measurement modalities to better support flexible thinking and adaptive development in autism. These findings also highlight the clinical relevance of distinguishing cognitively mediated from sensory-driven repetitive behaviors, as this dissociation informs tailored interventions—executive function training for higher-order RRBIs and sensory integration strategies for lower-order behaviors. Declarations Competing interests: The authors have no competing interests to declare that are relevant to the content of this article. Ethics declarations This study involving human participants was reviewed and approved by the Ethics Committee of Shahid Beheshti University (Approval Code: IR.SBU.REC.1402.133). Consent for Publication Written informed consent was obtained from the parents of all participating children prior to their involvement in the study. Parents were clearly informed of their right to withdraw their child from the research at any point without consequence. In addition to parental consent, verbal assent was obtained from the children before initiating any research procedures. Funding: No funding was received to assist with the preparation of this manuscript. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Sima Saniee, Hamid R. Pouretemad, and Setareh Mokhtari. The first draft of the manuscript was written by Sima Saniee, and all authors provided critical revisions and approved the final submitted version. Acknowledgement We extend our heartfelt appreciation to the children and families who generously participated in this research. We are also deeply grateful to the staff of the Tehran Autism Center (Center for Treatment of Autistic Disorders, CTAD) for their unwavering support, collaboration, and invaluable contributions to data collection and participant care throughout the study. Data Availability The datasets of this experiment are available from the corresponding author on reasonable request. References Diamond, A. Executive functions. Annu Rev Psychol . 64 (1), 135 − 68 (2013). Ionescu, T. Exploring the nature of cognitive flexibility. New Ideas Psychol . 30 , 190–200; 10.1016/j.newideapsych.2011.11.001 (2012). Vandierendonck, A, Liefooghe, B, Verbruggen, F. Task switching: interplay of reconfiguration and interference control. 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The study of executive function domains in children with high-functioning autism. Learn Motiv . 67 , 101578 (2019). Ten Eycke, KD, Dewey, D. Parent-report and performance-based measures of executive function assess different constructs. Child Neuropsychol . 22 (8), 889–906 (2016). Udhnani, MD, Kenworthy, L, Wallace, GL, Yerys, BE. Brief Report: Performance-Based Executive Functioning Abilities are Associated with Caregiver Report of Adaptive Functioning in Autism Spectrum Disorder. J Autism Dev Disord . 50 (12), 4541-7; 10.1007/s10803-020-04505-4 (2020). Dekker, MC, Ziermans, TB, Spruijt, AM, Swaab, H. Cognitive, Parent and Teacher Rating Measures of Executive Functioning: Shared and Unique Influences on School Achievement. Front Psychol . 8 , 48 (2017). Vriezen, ER, Pigott, SE. The relationship between parental report on the BRIEF and performance-based measures of executive function in children with moderate to severe traumatic brain injury. Child Neuropsycho . 8 (4), 296–303 (2002). Iversen, R. K. & Lewis, C. Executive function skills are linked to restricted and repetitive behaviors: three correlational meta-analyses. Autism Res. 14 , 1163–1185 (2021). Comparan-Meza, M. et al. Biopsychological correlates of repetitive and restricted behaviors in autism spectrum disorders. Brain Behav. 11 , e2341 (2021). Tian, J., Gao, X. & Yang, L. Repetitive restricted behaviors in autism spectrum disorder: from mechanism to development of therapeutics. Front. Neurosci. 16 , 780407 (2022). Additional Declarations No competing interests reported. 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Deficits in cognitive flexibility manifest as difficulties in shifting between tasks or mental sets, rigid adherence to routines, and resistance to change, all of which are frequently observed in individuals with autism, both in laboratory-based assessments and in real-world contexts as reported by parents (e.g., Faja \u0026amp; Dawson\u003csup\u003e4\u003c/sup\u003e; Dajani \u0026amp; Uddin\u003csup\u003e5\u003c/sup\u003e; Buttelmann \u0026amp; Karbach\u003csup\u003e6\u003c/sup\u003e; Garon et al.\u003csup\u003e7\u003c/sup\u003e; Lage et al.\u003csup\u003e8\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eAutism is a neurodevelopmental condition characterized by differences in social communication and interaction, as well as restricted and repetitive patterns of behavior and interests\u003csup\u003e9\u003c/sup\u003e. Variations in cognitive flexibility among autistic individuals are closely linked to the diverse social and cognitive characteristics of autism, particularly the presence of restricted and repetitive behaviors and interests (RRBIs) which is a hallmark feature of the condition\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRRBIs are typically categorized into four behavioral subgroups: stereotypies, preoccupation with objects, restricted interests, and nonfunctional routines\u003csup\u003e13, 14\u003c/sup\u003e.These behaviors span a continuum, from motor stereotypies (e.g., hand-flapping) to highly specific fascinations and rigid routines. Conceptually, RRBIs may be further grouped into Insistence on Sameness (IS), reflecting higher-order behaviors such as compulsive routines and ritualistic actions, and Repetitive Sensory-Motor (RSM) behaviors, which include stereotyped movements and sensory-focused interest\u003csup\u003e15, 16\u003c/sup\u003e. Given their impact on learning and social engagement,-even in very young children-, caregivers frequently identify these behaviors as among the most challenging aspects of autism\u003csup\u003e17\u0026ndash;20\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA growing body of research has demonstrated a robust association between higher-order RRBIs and difficulties with cognitive flexibility and related EF domains such as inhibitory control\u003csup\u003e8,10,21\u0026ndash;25\u003c/sup\u003e. For example, Miller et al.\u003csup\u003e12\u003c/sup\u003e identified a direct relationship between difficulties in maintaining new behavioral sets and the severity of restricted and repetitive behaviors in a sample of 60 individuals with autism aged 6 to 44 years. Notably, these difficulties were specifically associated with RRBIs severity and not with other core autism symptoms such as social or communication deficits. The study further highlighted that increased regressive errors-instances where participants reverted to previously learned responses after successfully shifting were particularly associated with elevated levels of repetitive behaviors, especially insistence on sameness, rather than with repetitive sensorimotor behaviors. Similarly, Faja and Nelson Darling\u003csup\u003e10\u003c/sup\u003e found that cognitive shifting was associated with higher-order RRBIs in a cohort of 102 school-aged children with autism, while inhibitory control was more closely related to sensorimotor RRBs. In their study, children were asked to press a button when an image appeared, but on certain trials, a stop signal (such as a sound or color change) was presented 500 milliseconds before the anticipated response, requiring the child to inhibit their initial reaction and instead press an alternative button. High accuracy on these trials was interpreted as an indicator of cognitive flexibility, accompanied by parent ratings on the BRIEF Shift scale. The findings indicated that increased severity of higher-order RRBIs was specifically associated with reduced inhibition and cognitive flexibility, independent of age and IQ. Notably, although parent ratings on the BRIEF Shift scale were correlated with higher-order RRBIs, neither the accuracy on the Change Task nor the number of perseverative errors following change trials demonstrated a significant association with higher-order RRBIs. These results suggest that real-world assessments of cognitive flexibility may more effectively capture the relationship with higher-order RRBIs than laboratory-based tasks, and this conclusion is supported by findings from previous studies (e.g., Albein-Urios et al.\u003csup\u003e21\u003c/sup\u003e). A meta-analysis by Iversen and Lewis\u003csup\u003e23\u003c/sup\u003e provided a comprehensive examination of the relationship between cognitive flexibility and inhibitory control, taking into account the severity of restricted and repetitive behaviors. The study analyzed data from nearly 3,000 participants, including both children and adults with autism and typical development. Findings revealed moderate yet significant associations between higher levels of RRBIs and impairments in set-shifting and inhibitory control, as well as in parent-reported EF assessments. Importantly, these associations did not significantly vary with age\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite this compelling evidence, findings remain inconsistent, particularly regarding the specificity of CF deficits across RRBIs subtypes. Lab-based tasks such as the Wisconsin Card Sorting Test (WCST) or Intra/Extra-Dimensional Set Shift Task (ID/ED) often assess broad EF domains and may not isolate CF effectively\u003csup\u003e8, 23\u003c/sup\u003e. Additionally, concerns about their ecological validity further underscore the importance of multi-informant assessments that integrate both performance-based and caregiver-reported data\u003csup\u003e26\u0026ndash;29\u003c/sup\u003e. A critical gap in the literature is also involves the early developmental period. The majority of CF studies focus on older children, adolescents, or adults, despite evidence that early childhood is characterized by rapid EF maturation and represents an optimal window for intervention\u003csup\u003e30\u0026ndash;33\u003c/sup\u003e. Understanding CF deficits in younger children is essential for identifying early markers and informing strategies that support flexible behavior and learning.\u003c/p\u003e\u003cp\u003eIn this context, the present study examines cognitive flexibility in young children with autism using multiple methods of assessment, combining parent-reported and performance-based assessments. It aims to evaluate the relationship between CF and restricted and repetitive behaviors, and to identify key factors that contribute to CF impairments in early childhood. By exploring both lab-based tasks and questionnaires completed by parents, this study seeks to refine our understanding of cognitive flexibility during early childhood which is a crucial developmental window.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipant\u003c/h2\u003e\u003cp\u003eSixty children with autism, aged 36 to 72 months (M\u0026thinsp;=\u0026thinsp;57.83, SD\u0026thinsp;=\u0026thinsp;12.44), were recruited through the Tehran Autism Center (formerly known as the Center for the Treatment of Autistic Disorders). All participants had a confirmed diagnosis of autism spectrum disorder (ASD), established independently by at least two clinical teams blinded to each other\u0026rsquo;s assessments. Our team administered the Structured Diagnostic Interview for Autism, along with clinical observation and the Persian edition of the Gilliam Autism Rating Scale (GARS-P), while the other diagnostic evaluations were conducted by child psychiatrists and/or pediatric neurologists in accordance with DSM-5 (APA, 2013). Exclusion criteria comprised a concurrent documented history of genetic or neurological disorders, as well as severe sensory or motor impairments that could interfere with the participant\u0026rsquo;s ability to complete the task.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eParent-report measures\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003eGilliam Autism Rating Scale- Persian edition (GARS-P)\u003c/h2\u003e\u003cp\u003eSimilar to original version\u003csup\u003e19, 34\u003c/sup\u003e, the GARS-P consists of 56 items categorized into four scales: The Social Interaction, Communication and Stereotyped Behaviors scales evaluate the child\u0026rsquo;s current and typical behaviors, while the Developmental Disturbances scale evaluate past severe maladaptive behaviors. Respondents are instructed to assess the frequency of each behavior using a four-point Likert scale.\u003c/p\u003e\u003cp\u003eThe GARS-P demonstrated both face and content validity, as confirmed by experts in the relevant field. The construct validity was high, as the coefficient between the CARS and GARS-P scores was 0.80. The diagnostic validity was assessed through a discriminant analysis, which involved a comparison with 100 typically developing children and adolescents. With a cut-off point \"52,\" the sensitivity and specificity rates recorded at \"99%\" and \"100%\" respectively. The GARS-P also demonstrated high reliability, evaluated using Cronbach's alpha, yielding a coefficient of 0.89 \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eBehavior Rating Inventory of Executive Functions -Preschool Version (BRIEF-P)\u003c/h3\u003e\n\u003cp\u003eThe BRIEF-P\u003csup\u003e36\u003c/sup\u003e is a 63-item informant is a report measure of a preschooler\u0026rsquo;s everyday EF behavior that measures multiple aspects of executive functioning include Inhibit, Shift, Emotional Control, Working Memory, and Planning/Organization. These clinical scales yield three composite indexes: the Inhibitory Self-Control Index (ISCI), Flexibility Index (FI), and Emergent Metacognition Index (EMI). The five scales are then collapsed into a Global Executive Composite (GEC). The Flexibility index (FI) which shows ability to move flexibly among actions/responses/emotions/ behavior is Composed of the Shift and Emotional Control scales.\u003c/p\u003e\u003cp\u003eThe reliability is available as internal consistency in all subscales of the BRIEF in the Iranian children population with Cronbach\u0026rsquo;s alpha of 0.80 to 0.97 \u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBehavioral Flexibility Rating Scale-Revised version (BFRS-R)\u003c/h2\u003e\u003cp\u003eThe BFRS-R\u003csup\u003e38\u003c/sup\u003e, 16 items, 4-point Likert scale, is a reliable and valid tool for investigating flexibility in everyday functioning in individuals with autism. It assesses insistence on sameness and resistance to change toward objects, persons, and the environment, across a wide age range (age 2\u0026ndash;17 years). The total score represents the outcome measure. Higher scores indicate more inflexible behaviors. It has good internal consistency in Iranian children with ASD\u003csup\u003e39, 40\u003c/sup\u003e and significantly correlated with the BRIEF Shift scale \u003csup\u003e41\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRepetitive Behavior Scale-Revised (RBS-R)\u003c/h3\u003e\n\u003cp\u003eThe RBS-R (Bodfish et al.\u003csup\u003e42\u003c/sup\u003e) consists of 43 items categorized into six subscales: self-injurious behavior, stereotyped behavior, ritualistic behavior, compulsions, sameness, and restricted behavior. These subscales are further grouped into two primary factors: the lower-level RRB factor, encompassing items 1 through 14, and the higher-level RRB factor, comprising items 15 through 43 \u003csup\u003e43\u003c/sup\u003e. Items are rated by parents on a four-point Likert scale ranging from 0 (\u0026ldquo;never\u0026rdquo;) to 3 (\u0026ldquo;always\u0026rdquo;), with higher scores indicating greater severity of symptoms.\u003c/p\u003e\n\u003ch3\u003ePerformance-based measure\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDimensional Change Card Sort (DCCS- standard version)\u003c/h2\u003e\u003cp\u003eThe DCCS is a widely used tool for assessing cognitive flexibility in young children, requiring them to match items according to changing task rules \u003csup\u003e44\u003c/sup\u003e. In the standard computerized version, each trial presents a test stimulus (for example, a red boat or a blue rabbit) at the top of the screen, while two target stimuli (a blue boat and a red rabbit) are displayed at the bottom. Children are instructed to match the test stimulus to one of the target stimuli based on the current rule, which may be to sort by shape or by color. Responses are made using the left and right arrow keys on the keyboard, each labeled with images corresponding to the target stimuli (the blue boat on the left key and the red rabbit on the right), with all other keys disabled. The task begins with six practice trials presented with performance feedback (i.e., \u0026ldquo;correct\u0026rdquo; or \u0026ldquo;incorrect\u0026rdquo;) and then 15 pre-switch trials where children sort according to shape, followed by a rule change that requires them to sort by color for 30 post-switch trials. During the pre-switch and post-switch trials, participants did not receive feedback. For each participant, the number of correct responses, errors, and reaction times (RT) for correct answers are recorded and analyzed. The RT used was the time between when the test card presented and when the child touched the target key (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEthical clearance and participation consent\u003c/h2\u003e\u003cp\u003e The study protocol was approved by the Shahid Beheshti University Bio-Ethics Committee (IR.SBU.REC.1402.133). Written informed consent was obtained from the parents or legal guardians of all participating children, who themselves provided verbal assent prior to participation. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eEach child completed the DCCS task, administered by a licensed psychologist at the Tehran Autism Center. Concurrently, parents completed the Behavior Rating Inventory of BRIEF, RBS-R and BFRS questionnaires in an adjacent room.\u003c/p\u003e\u003cp\u003eThe DCCS task was delivered via a laptop computer equipped with a 15-inch screen. Children were seated approximately 45 cm from the monitor, positioned beside the experimenter. The testing environment was illuminated by a 100-watt lamp situated almost 200 cm from the screen to ensure consistent lighting conditions. Participants responded by pressing one of two designated keys-corresponding to the left and right arrow keys-each covered with task-specific stimuli; all other keys on the keyboard were masked to prevent inadvertent responses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis Plan\u003c/h2\u003e\u003cp\u003eThis study incorporates two indices of cognitive flexibility: a performance-based measure derived from DCCS reaction time and accuracy, and the parent-report measure based on the Behavioral Flexibility Rating Scale (BFRS-R).\u003c/p\u003e\u003cp\u003eIn DCCS task, mean reaction times were calculated exclusively for correct trials. Trials with RTs shorter than 150 ms or longer than 10 seconds were excluded as outliers \u003csup\u003e45, 46\u003c/sup\u003e, Only children who successfully passed the pre-switch phase-defined as achieving at least 11 correct responses out of 15 trials (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)-were included in subsequent post-switch analyses. Out of the initial sample of 60 children, 17 were excluded from further analysis due to: lack of cooperation (n\u0026thinsp;=\u0026thinsp;2), failure in the DCCS training phase (n\u0026thinsp;=\u0026thinsp;5), failure to pass the pre-switch phase (n\u0026thinsp;=\u0026thinsp;7), and RT outliers (n\u0026thinsp;=\u0026thinsp;3). This resulted in a final analytic sample of 43 autistic children (11 girls and 32 boys).\u003c/p\u003e\u003cp\u003eTwo indices of performance-based cognitive flexibility were computed following Van Bers\u003csup\u003e47\u003c/sup\u003e: the RT-based switch cost; calculated as the mean RT of all post-switch trials minus the mean RT of all pre-switch trials. And the accuracy- based switch cost; calculated as the mean accuracy of all post-switch trials minus the mean accuracy of all pre-switch trials. A lower accuracy-based switch cost indicates greater cognitive flexibility, whereas a higher RT-based switch cost suggests reduced cognitive flexibility.\u003c/p\u003e\u003cp\u003eData were initially screened for outliers and assessed for normality using the Shapiro-Wilk test. all cognitive flexibility indices (accuracy-based, RT-based and BFRS-R) violated normality assumptions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas most predictor variables satisfied normality criteria (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eGiven the non-normal distribution of the dependent variables, Spearman\u0026rsquo;s rank-order correlations were employed to examine bivariate associations between cognitive flexibility indices (performance based and parent report) and predictor variables. Subsequently, separate stepwise linear regression models were conducted for each cognitive flexibility index to identify the unique contribution of predictors. Although the dependent variables were non-normally distributed, the assumption of normality of residuals was tested and met (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), thereby validating the use of linear regression for these analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and clinical characteristics of the participants. There were no significant gender differences across all variables (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Further analyses explored the relationship between cognitive flexibility indices and behavioral measures.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics and Descriptive Statistics of Children with Autism\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAutism (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGirls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;11 (25.60%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;32 (74.40%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Deviation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCognitive flexibility indices\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACC index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRT index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBFRS-R (16 items)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGARS-P\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepetitive Behaviors subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Interaction subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRIEF-P\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking Memory subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhibitory control subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional control subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanning subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBS_R\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level RRBs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level RRBs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: ACC\u0026thinsp;=\u0026thinsp;Accuracy index; RT\u0026thinsp;=\u0026thinsp;Reaction Time index; GARS-P\u0026thinsp;=\u0026thinsp;Gilliam Autism Rating Scale-Persian edition; BRIEF-P\u0026thinsp;=\u0026thinsp;Behavior Rating Inventory of Executive Functions\u0026ndash;Preschool Version; RBS-R\u0026thinsp;=\u0026thinsp;Repetitive Behavior Scale\u0026ndash;Revised; RRB\u0026thinsp;=\u0026thinsp;Restricted and Repetitive Behaviors. Gender frequencies are presented as counts and percentages. All other values represent means and standard deviations.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpearman\u0026rsquo;s correlation analyses were performed to investigate the associations between cognitive flexibility indices- accuracy-based switch cost, reaction time-based switch cost and the parent report measure (BFRS-R) -and a range of behavioral measures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These included the higher-level and lower-level subscales of the RBS-R, subscales of the BRIEF; Inhibit, Shift, Emotional Control, Working Memory, and Planning/Organization, and the three subscales of the GARS-P; Stereotyped Behaviors, Communication, and Social Interaction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlation coefficients between cognitive flexibility indices (accuracy-based and reaction time-based switch costs and BFRS-R) and behavioral/executive function variables in autistic children\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy-Based Switch Cost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReaction Time-Based Switch Cost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBFRS-R\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.540**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.487**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGARS-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepetitive Behaviors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.040 (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.542**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.296\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.648**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBFRS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.855**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.516**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRIEF-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShift Subscale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.933**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.564**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.884**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking Memory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.580**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.303*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.574**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhibition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.669**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.490**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.720**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmotional Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.680**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.364*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.658**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlanning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower-Level RRBs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher-Level RRBs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.880**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.495**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.853**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSignificance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;.05 (*), p\u0026thinsp;\u0026lt;\u0026thinsp;.01 (**),\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe three indices of cognitive flexibility\u0026mdash;two performance-based measures (RT-based and ACC-based) and a parent-report measure (BFRS-R)\u0026mdash;were highly correlated, indicating strong convergence between objective assessments and subjective parental evaluations. Specifically, BFRS-R scores showed significant positive correlations with accuracy-based switch cost (r\u0026thinsp;=\u0026thinsp;0.855, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significant negative correlations with reaction time-based switch cost (r = \u0026minus;\u0026thinsp;0.516, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RT-based and ACC-based measures were strongly inversely correlated (r = \u0026minus;\u0026thinsp;0.628, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the BRIEF-P Shift subscale demonstrated significant correlations with all three cognitive flexibility indices (BFRS-R: r\u0026thinsp;=\u0026thinsp;0.884, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ACC-based: r\u0026thinsp;=\u0026thinsp;0.933, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RT-based: r = \u0026minus;\u0026thinsp;0.564, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a strong association between set-shifting ability and cognitive flexibility across both performance-based and parent-reported measures.\u003c/p\u003e\u003cp\u003eThe accuracy-based switch cost demonstrated significant positive correlations with the GARS-P communication subscale (r\u0026thinsp;=\u0026thinsp;0.542, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), several BRIEF subscales including, Working Memory (r\u0026thinsp;=\u0026thinsp;0.580, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Inhibit (r\u0026thinsp;=\u0026thinsp;0.669, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Emotional Control (r\u0026thinsp;=\u0026thinsp;0.680, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as with the higher-level RBS-R subscale scores (r\u0026thinsp;=\u0026thinsp;0.880, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, it was negatively correlated with age (r = -0.540, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate that greater accuracy-based switch cost-which reflects reduced cognitive flexibility-is associated with poorer executive functioning, increased repetitive behaviors, and younger age.\u003c/p\u003e\u003cp\u003eRegarding the reaction time-based switch cost, analyses revealed significant negative correlations with, Working Memory (r = -0.303, p\u0026thinsp;=\u0026thinsp;0.048), Inhibition (r = -0.490, p\u0026thinsp;=\u0026thinsp;0.001), Emotional Control (r = -0.364, p\u0026thinsp;=\u0026thinsp;0.016), and higher-level repetitive behaviors on the RBS-R (r = -0.495, p\u0026thinsp;=\u0026thinsp;0.001). It exhibited a nonsignificant correlation with age (r\u0026thinsp;=\u0026thinsp;0.218, p\u0026thinsp;=\u0026thinsp;0.161). These results suggest that higher reaction time-based switch cost-indicative of diminished cognitive flexibility-is related to greater executive dysfunction and repetitive behaviors.\u003c/p\u003e\u003cp\u003eAnalyses of the parent report measure (BFRS-R) revealed significant positive correlations with the Communication subscale of GARS-P (r\u0026thinsp;=\u0026thinsp;0.648, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Working Memory (r\u0026thinsp;=\u0026thinsp;0.574, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Inhibition (r\u0026thinsp;=\u0026thinsp;0.720, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Emotional Control (r\u0026thinsp;=\u0026thinsp;0.658, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher-level repetitive behaviors on the RBS-R (r\u0026thinsp;=\u0026thinsp;0.853). These findings indicate that greater behavioral inflexibility is associated with more severe executive dysfunction and autism-related symptoms. Additionally, BFRS-R scores exhibited a significant negative correlation with age (r = \u0026minus;\u0026thinsp;0.487, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that behavioral inflexibility tends to decline as children grow older.\u003c/p\u003e\u003cp\u003eNotably, the BRIEF Planning subscale, GARS-P subscales for repetitive behaviors and social interaction, and the lower-level RBS-R restricted and repetitive behavior scores showed weak or no significant associations with the cognitive flexibility indices.\u003c/p\u003e\u003cp\u003eA stepwise multiple linear regression analysis was conducted to identify the strongest predictors of cognitive flexibility. Predictor variables were selected based on their significant correlations with cognitive flexibility in preliminary analyses. These included the BRIEF subscales (Working Memory, Inhibit, Emotional Control, Planning), the RBS-R higher-level repetitive behavior subscale, communication skills from the GARS-P, and age. The Shift subscale of BRIEF was excluded, as it directly defines cognitive flexibility and aligns with the three cognitive flexibility indices, making it redundant as an independent predictor. Additionally, communication skills and age were removed from the reaction time-based switch cost model, as they did not show significant correlations with this outcome measure.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAccuracy-Based Switch Cost Model\u003c/em\u003e: A stepwise multiple regression analysis was conducted to identify predictors of accuracy-based cognitive flexibility impairments in autistic individuals. The initial model, which included only higher-level repetitive behaviors, explained a substantial 75.3% of the variance (R\u0026sup2; = 0.753, Adjusted R\u0026sup2; = 0.746, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a strong positive association (β\u0026thinsp;=\u0026thinsp;0.868, t\u0026thinsp;=\u0026thinsp;10.752, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequent models incorporated additional predictors: emotional control, inhibition, working memory, Communication Skills and Age. Model 2 demonstrated a significant improvement in explanatory power (Adjusted R\u0026sup2; = 0.780, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher-level repetitive behaviors (β\u0026thinsp;=\u0026thinsp;0.725, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and emotional control (β\u0026thinsp;=\u0026thinsp;0.243, p\u0026thinsp;=\u0026thinsp;0.013) identified as significant predictors. In contrast, inhibition did not contribute significantly in later models (p\u0026thinsp;\u0026gt;\u0026thinsp;0.4). The fourth model provided the optimal balance between explanatory power and parsimony, accounting for 83.1% of the variance (R\u0026sup2; = 0.831, Adjusted R\u0026sup2; = 0.811, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In this model, higher-level repetitive behaviors (β\u0026thinsp;=\u0026thinsp;0.712, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and working memory (β\u0026thinsp;=\u0026thinsp;0.249, p\u0026thinsp;=\u0026thinsp;0.007) remained significant, while emotional control approached significance (p\u0026thinsp;=\u0026thinsp;0.060). The addition of Communication Skills and Age did not enhance predictive value (p\u0026thinsp;\u0026gt;\u0026thinsp;0.4). Collinearity diagnostics confirmed the absence of multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;3), ensuring model stability. The final regression equation is expressed as:\u003c/p\u003e\u003cp\u003e\u003cem\u003eAccuracy-based switch cost\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;30.497\u0026thinsp;+\u0026thinsp;0.389 (higher-level repetitive behaviors)\u0026thinsp;+\u0026thinsp;0.222 (working memory).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis model indicates that greater impairment in working memory along with higher levels of higher-order repetitive behaviors, is associated with increased Accuracy-Based Switch Cost, reflecting reduced cognitive flexibility.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eReaction Time-Based Switch Cost Model\u003c/strong\u003e\u003cp\u003eSimilarly, a stepwise multiple regression revealed key predictors of reaction time-based switch costs. The initial model, which included only higher-level repetitive behaviors, accounted for 25.9% of the variance (R\u0026sup2; = 0.259, Adjusted R\u0026sup2; = 0.241, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing a significant negative association (β = -0.509, t = -3.790, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subsequent inclusion of inhibition, emotional control, and working memory did not yield significant improvements to the model (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Consequently, the final parsimonious model retained only higher-level repetitive behaviors, confirming its dominant role in explaining reaction time-based cognitive flexibility. Collinearity diagnostics validated the absence of multicollinearity (tolerance\u0026thinsp;\u0026gt;\u0026thinsp;0.39, VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.6), thereby ensuring reliable coefficient estimates. The final regression equation is formulated as\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eReaction time-based switch costs\u0026thinsp;=\u0026thinsp;1.086\u0026thinsp;\u0026minus;\u0026thinsp;0.044 (higher-level repetitive behaviors)\u003c/h2\u003e\u003cp\u003eThis suggests that higher levels of higher-order repetitive behaviors are associated with decreased reaction time-based switch costs, indicating reduced cognitive flexibility.\u003c/p\u003e\u003cp\u003e\u003cem\u003eParent-Report Measure (BFRS-R) Model\u003c/em\u003e: The final stepwise regression analysis assessed predictors of BFRS-R scores in autistic individuals. This model demonstrated strong predictive validity, explaining 77.5% of the variance (R\u0026sup2; = 0.775, Adjusted R\u0026sup2; = 0.737, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The final model included six predictors: higher-level repetitive behaviors, inhibition, working memory, emotional control, Communication Skills, and Age. However, only higher-level repetitive behaviors (β\u0026thinsp;=\u0026thinsp;0.381, p\u0026thinsp;=\u0026thinsp;0.001), Communication Skills (β\u0026thinsp;=\u0026thinsp;0.419, p\u0026thinsp;=\u0026thinsp;0.031), and working memory (β\u0026thinsp;=\u0026thinsp;0.299, p\u0026thinsp;=\u0026thinsp;0.031) emerged as significant predictors. Inhibition, emotional control, and Age did not contribute meaningfully and were excluded from the final assessment. Collinearity diagnostics confirmed the absence of multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;3), ensuring stability in coefficient estimates. The final regression equation is articulated as:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eBFRS-R= -16.878\u0026thinsp;+\u0026thinsp;0.381 (higher-level repetitive behaviors)\u0026thinsp;+\u0026thinsp;0.419 (Communication Skills)\u0026thinsp;+\u0026thinsp;0.299 (working memory)\u003c/h2\u003e\u003cp\u003eRegression analyses revealed that the accuracy-based switch cost is the most robust predictor of cognitive flexibility impairments in autistic individuals. This analysis explained the largest proportion of variance (Adjusted R\u0026sup2; = 0.811), with higher-level repetitive behaviors and working memory deficits emerging as significant predictors. These findings emphasize the central role of these factors in accuracy-based cognitive flexibility deficits.\u003c/p\u003e\u003cp\u003eIn contrast, the reaction time-based switch cost model accounted for substantially less variance (Adjusted R\u0026sup2; = 0.241) and included only higher-level repetitive behaviors as a significant predictor, indicating its lower sensitivity and predictive power. The parent-report model (BFRS-R) explained a moderate amount of variance (Adjusted R\u0026sup2; = 0.737), but being based on caregiver reports, it is inherently more subjective and indirect compared to performance-based indices.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the relationship between cognitive flexibility, restricted and repetitive behaviors, and executive functioning in young children with autism using multiple methods of assessment. that combined performance-based assessments and parent-report measures. The findings provide robust evidence that reduced cognitive flexibility is closely associated with higher-order RRBIs and broader EF difficulties, particularly in working memory, inhibition, and emotional control.\u003c/p\u003e\u003cp\u003eAcross both performance-based and informant-reported indices, lower cognitive flexibility was significantly linked to increased behavioral rigidity and executive dysfunction. Notably, the accuracy-based switch cost emerged as the most sensitive and predictive index, with higher-order RRBIs and working memory deficits jointly explaining over 80% of the variance. This finding aligns with prior research emphasizing the developmental utility of accuracy-based measures in early childhood, (e.g., Qui \u0026amp; Zelazo\u003csup\u003e48\u003c/sup\u003e; Garon et al.\u003csup\u003e49\u003c/sup\u003e; Cragg \u0026amp; Chevalier\u003csup\u003e50\u003c/sup\u003e; Espinet et al.\u003csup\u003e51\u003c/sup\u003e; Williams\u003csup\u003e52\u003c/sup\u003e), and supports the notion that working memory plays a foundational role in cognitive flexibility\u003csup\u003e1, 53, 54\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe BFRS-R also demonstrated strong associations with higher-order restricted and repetitive behaviors, communication difficulties, and working memory impairments, underscoring the ecological validity of caregiver-reported assessments. These findings are consistent with those of Lin et al.\u003csup\u003e55\u003c/sup\u003e who reported that RRBIs are closely linked to adaptive functioning outcomes in children with autism.\u003c/p\u003e\u003cp\u003eWhile previous research has reported mixed findings regarding the relationship between communication abilities and cognitive flexibility (e.g., Costescu et al. \u003csup\u003e56\u003c/sup\u003e), our results offer a more nuanced perspective. The strong intercorrelations among the three cognitive flexibility indices highlight meaningful convergence between laboratory-based and real-world assessments. This finding aligns with prior research on agreement across multiple methods of assessment in executive functioning studies\u003csup\u003e59\u0026ndash;61\u003c/sup\u003e, despite broader literature reporting inconsistent associations across modalities\u003csup\u003e21, 41, 62, 63\u003c/sup\u003e. We interpret this convergence within the context of early childhood, a developmental period during which executive processes are still emerging. During this stage, performance-based and parent-reported measures may reflect overlapping constructs. Taken together, these findings highlight the utility of multi-method approaches for capturing the nuanced complexity of executive functioning in neurodevelopmental populations. Future studies should further investigate the developmental trajectory of cross-method concordance, examining how alignment between assessment modalities evolves with maturation and relates to real-world functional outcomes.\u003c/p\u003e\u003cp\u003eCommunication difficulties were significantly correlated with both performance-based (accuracy-based switch cost) and parent-reported (BFRS-R) measures of cognitive flexibility, with a stronger association observed for the BFRS-R. However, in the regression analyses, communication skills emerged as a significant predictor of parent-reported behavioral flexibility, but not of accuracy-based cognitive flexibility. This pattern suggests that while communication challenges are broadly related to flexibility, their predictive value may be more pronounced in everyday behavioral contexts as perceived by caregivers than in structured task performance. Although the reaction time-based switch cost showed comparatively lower predictive strength, it still revealed meaningful associations with RRBIs, suggesting that temporal efficiency may reflect a distinct but related facet of cognitive flexibility\u003csup\u003e57,58\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHigher-order RRBIs, including ritualistic routines, compulsive interests, and insistence on sameness, were robustly associated with both performance-based and parent-reported indices of cognitive inflexibility. In contrast, lower-order sensorimotor behaviors, such as stereotypies and sensory-focused preoccupations, showed weak or nonsignificant associations. These findings align with prior studies linking insistence on sameness and ritualistic behaviors to deficits in set-shifting and cognitive control.\u003csup\u003e10, 12, 23, 55\u003c/sup\u003e Importantly, these associations were independent of age, suggesting that behavioral rigidity may be a stable and early-emerging feature of autism that warrants targeted intervention.\u003c/p\u003e\u003cp\u003eThis behavioral dissociation invites further interpretation in light of neurocognitive models that distinguish between cognitively mediated and sensory-driven repetitive behaviors. Higher-order RRBIs were consistently linked to deficits in cognitive flexibility and executive functioning, supporting the view that these behaviors reflect impairments in cognitive control and set-shifting \u003csup\u003e64,65\u003c/sup\u003e In contrast, lower-order sensorimotor behaviors, including stereotypies and sensory-focused preoccupations, showed weak or nonsignificant associations with cognitive measures, suggesting that these behaviors may be more closely tied to atypical sensory processing and arousal regulation \u003csup\u003e65, 66\u003c/sup\u003e. This distinction aligns with emerging neurobiological models that propose separate developmental pathways for cognitively mediated versus sensory-driven repetitive behaviors. Recognizing this divergence has important implications for intervention design, as strategies targeting executive function may be more effective for reducing higher-order RRBIs, whereas sensory integration approaches may be better suited for addressing lower-order behaviors.\u003c/p\u003e\u003cp\u003eThese findings carry important clinical implications for designing interventions tailored to the distinct profiles of restricted and repetitive behaviors. For children exhibiting higher-order RRBIs interventions should prioritize executive function training, with a focus on enhancing cognitive flexibility, working memory, and inhibitory control. Cognitive remediation programs and behavioral flexibility protocols may help reduce rigidity and improve adaptive functioning. Conversely, for children presenting with lower-order sensorimotor behaviors, such as stereotypies and sensory-seeking actions, sensory integration therapies and arousal regulation strategies may be more appropriate. Techniques such as sensory modulation, occupational therapy and environmental management and adaptations can support sensory regulation and reduce repetitive motor behaviors. Importantly, a multi-modal approach that integrates both cognitive and sensory-focused strategies may offer the most comprehensive support, especially for children with mixed RRB profiles. These results underscore the need for individualized treatment planning that aligns with the neurodevelopmental mechanisms underlying each RRB subtype.\u003c/p\u003e\u003cp\u003eThe observed negative correlations between age and both accuracy-based and parent-reported cognitive flexibility indices suggest modest age-related improvements in cognitive flexibility during early childhood.\u003csup\u003e6, 7\u003c/sup\u003e However, the absence of a significant age effect in the RT-based model may reflect the limited sensitivity of reaction time measures in this age group\u003csup\u003e48, 49, 51\u003c/sup\u003e These findings support the view that early childhood represents a critical window for identifying and addressing cognitive inflexibility, particularly through interventions that target higher-order RRBIs and working memory.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eDespite its strengths, this study has some limitations. First, the modest sample size (N\u0026thinsp;=\u0026thinsp;43) and single-site recruitment limit the generalizability of findings. Future studies should aim to replicate these results in larger, more diverse samples.\u003c/p\u003e\u003cp\u003eSecond, although cognitive flexibility was assessed using both task-based and parent-report measures, other key constructs\u0026mdash;such as RRBIs and executive function (EF) domains\u0026mdash;were evaluated exclusively through caregiver reports. Although ecologically valid, such ratings may be influenced by informant bias. Incorporating teacher reports, clinician ratings, or direct behavioral observations would enhance construct validity.\u003c/p\u003e\u003cp\u003eThird, the cross-sectional design precludes conclusions about developmental trajectories or causal relationships. Longitudinal studies are needed to examine how early EF impairments and behavioral rigidity evolve over time and whether they predict later adaptive outcomes or treatment responsiveness.\u003c/p\u003e\u003cp\u003eFourth, the absence of a direct measure of cognitive ability (e.g., IQ) limits interpretation of how general intellectual functioning may have influenced task performance. Additionally, the small number of female participants precluded analysis of sex differences\u0026mdash;an important area for future research given emerging evidence of sex-specific EF profiles in autism.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides compelling evidence that cognitive inflexibility in young autistic children is strongly associated with higher-order restricted and repetitive behaviors and executive dysfunction. Among the three indices of cognitive flexibility, accuracy-based switch cost emerged as the most robust and developmentally sensitive indicator of cognitive flexibility in children aged 3 to 7 years. The convergence between performance-based and parent-reported assessments underscores the value of multi-method approaches in capturing the complexity of cognitive flexibility in early childhood.\u003c/p\u003e\u003cp\u003eFuture research should prioritize longitudinal, ecologically grounded designs that incorporate diverse informants and measurement modalities to better support flexible thinking and adaptive development in autism. These findings also highlight the clinical relevance of distinguishing cognitively mediated from sensory-driven repetitive behaviors, as this dissociation informs tailored interventions\u0026mdash;executive function training for higher-order RRBIs and sensory integration strategies for lower-order behaviors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics declarations\u003c/h2\u003e\u003cp\u003eThis study involving human participants was reviewed and approved by the Ethics Committee of Shahid Beheshti University (Approval Code: IR.SBU.REC.1402.133).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for Publication\u003c/h2\u003e\u003cp\u003e Written informed consent was obtained from the parents of all participating children prior to their involvement in the study. Parents were clearly informed of their right to withdraw their child from the research at any point without consequence. In addition to parental consent, verbal assent was obtained from the children before initiating any research procedures.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Sima Saniee, Hamid R. Pouretemad, and Setareh Mokhtari. The first draft of the manuscript was written by Sima Saniee, and all authors provided critical revisions and approved the final submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our heartfelt appreciation to the children and families who generously participated in this research. We are also deeply grateful to the staff of the Tehran Autism Center (Center for Treatment of Autistic Disorders, CTAD) for their unwavering support, collaboration, and invaluable contributions to data collection and participant care throughout the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets of this experiment are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiamond, A. Executive functions. \u003cem\u003eAnnu Rev Psychol\u003c/em\u003e. \u003cb\u003e64\u003c/b\u003e(1), 135 − 68 (2013).\u003c/li\u003e\n\u003cli\u003eIonescu, T. Exploring the nature of cognitive flexibility. \u003cem\u003eNew Ideas Psychol\u003c/em\u003e. \u003cb\u003e30\u003c/b\u003e, 190–200; 10.1016/j.newideapsych.2011.11.001 (2012).\u003c/li\u003e\n\u003cli\u003eVandierendonck, A, Liefooghe, B, Verbruggen, F. 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The relationship between parental report on the BRIEF and performance-based measures of executive function in children with moderate to severe traumatic brain injury. \u003cem\u003eChild Neuropsycho\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e(4), 296–303 (2002).\u003c/li\u003e\n\u003cli\u003eIversen, R. K. \u0026amp; Lewis, C. Executive function skills are linked to restricted and repetitive behaviors: three correlational meta-analyses. \u003cem\u003eAutism Res.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1163–1185 (2021).\u003c/li\u003e\n\u003cli\u003eComparan-Meza, M. \u003cem\u003eet al.\u003c/em\u003e Biopsychological correlates of repetitive and restricted behaviors in autism spectrum disorders. \u003cem\u003eBrain Behav.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, e2341 (2021).\u003c/li\u003e\n\u003cli\u003eTian, J., Gao, X. \u0026amp; Yang, L. Repetitive restricted behaviors in autism spectrum disorder: from mechanism to development of therapeutics. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 780407 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Autism, Cognitive flexibility, Restricted and repetitive behaviors and interests (RRBIs), Executive functioning, Parent report, Performance-based assessment","lastPublishedDoi":"10.21203/rs.3.rs-7814208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7814208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive flexibility (CF), a key aspect of executive functioning (EF), is often impaired in children with autism and closely linked to restricted and repetitive behaviors and interests (RRBIs). While higher-order RRBIs are associated with behavioral rigidity, few studies have examined CF using both performance-based and parent-report methods in early childhood. This study explored the relationship between CF, RRBIs, and EF in 43 children with autism aged 3 to 7 years through multiple methods of assessment. Children completed a computerized CF task measuring switch costs via reaction time and accuracy, while caregivers provided standardized ratings of EF, RRBIs, and behavioral flexibility. Significant correlations emerged across CF indices, suggesting convergence between lab-based and caregiver-reported assessments. Accuracy-based switch cost was the strongest predictor of cognitive inflexibility, with higher-order RRBIs and working memory deficits explaining over 80% of the variance. Reaction time measures were less predictive, and parent-reported CF was associated with higher-order RRBIs, communication challenges, and EF impairments. Lower-order sensorimotor RRBIs showed no significant link to CF. These findings highlight the importance of accuracy-based CF measures and multi-method assessment, pointing to modifiable targets for early intervention during critical EF development in autism. Moreover, the results offer insight into distinct underlying mechanisms differentiating higher-order and lower-order restricted and repetitive behaviors and interests.\u003c/p\u003e","manuscriptTitle":"The relationship between cognitive flexibility and restricted, repetitive behaviors in children with autism: Parents’ reports vs. cognitive task performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:29:35","doi":"10.21203/rs.3.rs-7814208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-16T07:03:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T22:53:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T13:51:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T14:00:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66040203223386265826056726105507817852","date":"2025-11-10T14:42:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163896245010223378497607364448873781286","date":"2025-11-10T09:38:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261486134519721199322994558666831155184","date":"2025-11-08T20:52:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22251633584656125197053518186533105527","date":"2025-11-07T17:22:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149771043197903891913564996769166121582","date":"2025-11-07T13:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50827378273219853927111430682127564434","date":"2025-11-05T14:17:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T12:32:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T12:17:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T14:36:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-11T06:20:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-11T06:17:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b53a1aa-548e-4cf9-9f68-383c06a1d93d","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58043159,"name":"Biological sciences/Neuroscience"},{"id":58043160,"name":"Biological sciences/Psychology"},{"id":58043161,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-04T16:02:26+00:00","versionOfRecord":{"articleIdentity":"rs-7814208","link":"https://doi.org/10.1038/s41598-026-42853-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-30 15:57:21","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-11-18 08:29:35","video":"","vorDoi":"10.1038/s41598-026-42853-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42853-w","workflowStages":[]},"version":"v1","identity":"rs-7814208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7814208","identity":"rs-7814208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}