Abstract
INTRODUCTION: This study examines whether specific lexicogrammatical features can reliably differentiate individuals with autism spectrum disorder (ASD) from non-ASD individuals. Classification models using logistic regression and deep neural networks (DNN) demonstrated high performance-80% accuracy, 82% precision, 73% sensitivity, and 87% specificity. To clarify which linguistic variables contribute to this differentiation, the analysis focused on identifying key syntactic features associated with ASD-specific patterns of lexicogrammatical choices. METHODS: This study used the Tag Linear Model, developed in prior work, which enables identification of specific lexicogrammatical discriminators. Although DNN models achieved higher predictive accuracy, their internal processes were not interpretable. To identify statistically significant features, we applied a logistic regression with 10,000 bootstrap iterations; p-values derived from this procedure indicated the statistical significance of each feature. The linear model thus provided transparent evidence of differences in lexicogrammatical features between ASD and non-ASD individuals. RESULTS: Of the 135 lexicogrammatical items analyzed, 46 were identified asstatistically significant discriminators (p < 0.05) between ASD and non-ASD speakers. From these 46 discriminators, 20 showing variation at the clause and phrase level were selected for detailed analysis. These were grouped into seven cognitive-functional domains implicated in ASD, including working memory, inferencing, joint attention, and mental space construction. DISCUSSION: These findings suggest that syntactic variation in ASD reflects underlying domain-specific cognitive constraints. Linking lexicogrammatical features to cognitive-functional domains provides a linguistically grounded perspective on the neurocognitive profiles of ASD and informs future diagnostic and intervention approaches.