Although language impairment is commonly associated with the autism spectrum disorder (ASD), the Diagnostic Statistical Manual no longer includes language impairment as a necessary component of an ASD diagnosis (American Psychiatric Association, 2013). However, children with ASD and no comorbid intellectual disability struggle with some aspects of language whose precise nature is still outstanding. Narratives have been extensively used as a tool to examine lexical and syntactic abilities, as well as pragmatic skills in children with ASD. This study contributes to this literature by investigating the narrative skills of 30 Greek-speaking children with ASD and normal non-verbal IQ, 16 with language skills in the upper end of the normal range (ASD-HL), and 14 in the lower end of the normal range (ASD-LL). The control group consisted of 15 age-matched typically-developing (TD) children. Narrative performance was measured in terms of both microstructural and macrostructural properties. Microstructural properties included lexical and syntactic measures of complexity such as subordinate vs. coordinate clauses and types of subordinate clauses. Macrostructure was measured in terms of the diversity in the use of internal state terms (ISTs) and story structure complexity, i.e., children's ability to produce important units of information that involve the setting, characters, events, and outcomes of the story, as well as the characters' thoughts and feelings. The findings demonstrate that high language ability and syntactic complexity pattern together in ASD children's narrative performance and that language ability compensates for autistic children's pragmatic deficit associated with the production of Theory of Mind-related ISTs. Nevertheless, both groups of children with ASD (high and low language ability) scored lower than the TD controls in the production of Theory of Mind-unrelated ISTs, modifier clauses and story structure complexity.

Impairments in Theory of Mind (ToM) are a core feature of Autism Spectrum Disorder (ASD). ToM may be enhanced by various factors, including bilingualism, executive functions (EF), and complex syntax. This work investigates the language-cognition interface in ASD by exploring whether ToM can be enhanced by bilingualism, whether such ToM boosts would be due to EF or syntax, and whether routes to mentalizing would differ between bilinguals and monolinguals on the spectrum. Twenty-seven monolingual Greek-speaking and twenty-nine bilingual Albanian-Greek children with ASD were tested on ToM reasoning in verbal and low-verbal ToM tasks, an executive function 2-back task, and a sentence repetition task. Results revealed that bilingual children with ASD performed better than monolinguals with ASD in the low-verbal ToM and the 2-back tasks. In the sentence repetition task, bilinguals scored higher than monolinguals in complex sentences, and specifically in adverbials and relatives. Regarding the relations between ToM, EF, and sentence repetition, the monolingual group’s performance in the verbal ToM tasks was associated with complement syntax, whereas, for the bilingual children with ASD, performance in both verbal and low-verbal ToM tasks was associated with EF and adverbial clause repetition. The overall pattern of results suggests that mentalizing may follow distinct pathways across the two groups.

Impairments in the broader domain of pragmatics are considered to be a defining feature of Autism Spectrum Disorders (ASD). A challenging aspect of pragmatic competence is the ability to process nonliteral language. Interestingly, previous studies in figurative language comprehension in ASD have demonstrated conflicting results regarding participants' performance. The main scientific debate focuses on the underlying skills which facilitate processing of nonliteral speech in ASD. Namely, Theory of Mind (ToM), language abilities and Executive functions (EFs) are regarded as factors affecting autistic individuals' performance. This review addresses figurative language comprehension in ASD in light of the above three interpretive accounts. We reviewed data from recent studies in this field concluding that autistic children indeed encounter systematic difficulties in the processing of non‐literal language. Moreover, only ToM and verbal skills were found to correlate the most with figurative language comprehension in ASD. Notably, we found that differences related to research methodology and tasks' properties may have led to discrepancies between studies' results. Finally, we argue that future studies should encompass in their experimental design figurative comprehension tasks with minimal linguistic demands and also measures of ToM, verbal ability and EFs in order to shed more light in the independent contribution of those skills to the processing of nonliteral language in ASD.

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.