Bilingualism effects on cognition in autistic children are not all-or-nothing: The role of socioeconomic status in intellectual skills in bilingual autistic children

The Acquisition of Relative Clauses in Autism: The Role of Executive Functions and Language

PURPOSE

Relative clauses present a well-known processing asymmetry between object-extracted and subject-extracted dependencies across both typical and atypical populations. The present study aimed at exploring the comprehension of object and subject relative clauses as conceptualized by the Relativized Minimality framework in autistic children and in a group of age- and IQ-matched typically-developing children. The study also explored the way performance in relative clauses would be affected by the children's language and executive function skills.

METHOD

Relative clause comprehension was tested through a sentence-picture matching task and language was tested with a receptive vocabulary task. Executive functions were assessed through backward digit recall and a Flanker test.

RESULTS

Object relative clauses were harder to parse for both groups than subject relatives, while number mismatch between the moved object Noun Phrase and the intervening subject Noun Phrase in object relatives boosted both groups' performances. Typically-developing children's performance in object relatives was predicted by both language and executive functions, while autistic children failed to use language and did not systematically draw on their executive functions in object relative clause comprehension.

CONCLUSION

The findings suggest that relative clause processing in autism follows a normal developmental trajectory, and that difficulty with parsing object relative clauses stems from reduced language and executive functions rather than deficits in the children's morphosyntactic skills.

Bilingualism and second-order theory of mind development in autistic children over time: Longitudinal relations with language, executive functions, and intelligence

Theory of Mind has long been studied as a core weakness in autism spectrum disorder due to its relationship with social reciprocity, while bilingualism has been shown to compensate for autistic individuals' mentalizing weaknesses. However, our knowledge of the Theory of Mind developmental trajectories of bilingual and monolingual autistic children, as well as of the factors related to Theory of Mind development in autism spectrum disorder is still limited. The current study has examined first- and second-order Theory of Mind skills in 21 monolingual and 21 bilingual autistic children longitudinally across three time points, specifically at ages 6, 9, and 12, and also investigated associations between Theory of Mind trajectories and trajectories of the children's language, intelligence and executive function skills. The results reveal that bilingual autistic children outperformed their monolingual peers in second-order Theory of Mind at ages 9 and 12, and that intelligence and, especially, expressive vocabulary skills played a pivotal role in advancing bilingual autistic children's second-order Theory of Mind development. On the other hand, monolingual autistic children only managed to capitalize on their language and intelligence resources at age 12. The findings highlight the importance of investigating bilingualism effects on autistic children's advanced cognitive abilities longitudinally.

Bilingualism Predicts Affective Theory of Mind in Autistic Adults

PURPOSE

This study examined the impact of bilingualism on affective theory of mind (ToM) and social prioritization (SP) among autistic adults compared to neurotypical comparison participants.

METHOD

Fifty-two (25 autistic, 27 neurotypical) adult participants (ages 21-35 years) with varying second language (L2) experience, ranging from monolingual to bilingual, completed an affective ToM task. A subset of this sample also completed a dynamic eye-tracking task designed to capture differences in time spent looking at social aspects of a scene (SP). Four language groups were compared on task performance (monolingual autism and neurotypical, bilingual autism and neurotypical), followed by analyses examining the contribution of L2 experience, autism characteristics, and social face prioritization on affective ToM, controlling for verbal IQ. Finally, we conducted an analysis to identify the contribution of SP on affective ToM when moderated by autism status and L2 experience, controlling for verbal IQ.

RESULTS

The monolingual autism group performed significantly worse than the other three groups (bilingual autism, monolingual neurotypical, and bilingual neurotypical) on the affective ToM task; however, there were no significant differences between the bilingual autism group compared to the monolingual and bilingual neurotypical groups. For autistic individuals, affective ToM capabilities were positively associated with both verbal IQ and L2 experience but did not relate to autism characteristics or SP during eye tracking. Neurotypical participants showed greater SP during the eye-tracking task, and SP did not relate to L2 or autism characteristics for autistic individuals. SP and verbal IQ predicted affective ToM performance across autism and neurotypical groups, but this relationship was moderated by L2 experience; SP more strongly predicted affective ToM performance among participants with lower L2 experience (e.g., monolingual) and had less of an impact for those with higher L2 experience.

CONCLUSION

This study provides support for a bilingual advantage in affective ToM for autistic individuals.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25696083.

Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis

Despite the consensus that early identification leads to better outcomes for individuals with autism spectrum disorder (ASD), recent research reveals that the average age of diagnosis in the Greek population is approximately six years. However, this age of diagnosis is delayed by an additional two years for families from lower-income or minority backgrounds. These disparities result in adverse impacts on intervention outcomes, which are further burdened by the often time-consuming and labor-intensive language assessments for children with ASD. There is a crucial need for tools that increase access to early assessment and diagnosis that will be rigorous and objective. The current study leverages the capabilities of artificial intelligence to develop a reliable and practical model for distinguishing children with ASD from typically-developing peers based on their narrative and vocabulary skills. We applied natural language processing-based extraction techniques to automatically acquire language features (narrative and vocabulary skills) from storytelling in 68 children with ASD and 52 typically-developing children, and then trained machine learning models on the children's combined narrative and expressive vocabulary data to generate behavioral targets that effectively differentiate ASD from typically-developing children. According to the findings, the model could distinguish ASD from typically-developing children, achieving an accuracy of 96%. Specifically, out of the models used, hist gradient boosting and XGBoost showed slightly superior performance compared to the decision trees and gradient boosting models, particularly regarding accuracy and F1 score. These results bode well for the deployment of machine learning technology for children with ASD, especially those with limited access to early identification services.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Background

Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.

Method

From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).

Result

Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.

Conclusion

Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Despite the fact that the urge to investigate bilingualism and neurodevelopmental disorders as continuous indices rather than categorical ones has been well-voiced among researchers with respect to research methodological approaches, in the recent literature, when it comes to examining language, cognitive skills and neurodivergent characteristics, it is still the case that the most prevalent view is the categorisation of adults or children into groups. In other words, there is a categorisation of individuals, e.g., monolingual vs. bilingual children or children with typical and atypical/non-typical/non-neurotypical development. We believe that this labelling is responsible for the conflicting results that we often come across in studies. The aim of this review is to bring to the surface the importance of individual differences through the study of relevant articles conducted in bilingual children and children with autism, who are ideal for this study. We concur with researchers who already do so, and we further suggest moving away from labels and instead shift towards the view that not everything is either white or black. We provide suggestions as to how this shift could be implemented in research, while mostly aiming at starting a discourse rather than offering a definite path.