ORIGINAL RESEARCH article

Front. Hum. Neurosci.

Sec. Speech and Language

Volume 19 - 2025 | doi: 10.3389/fnhum.2025.1606701

This article is part of the Research TopicAutism Spectrum Disorder: Exploring the speech and language continuumView all articles

Cognitive constraints and lexicogrammatical variability in ASD: from diagnostic discriminators to intervention strategies

Provisionally accepted
Sumi  KatoSumi Kato1,2*Kazuaki  HanawaKazuaki Hanawa3
  • 1Department of Neuropsychiatry, Graduate School of Medicine and School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan
  • 2Aomori Chuo Gakuin University, Aomori, Aomori, Japan
  • 3Graduate School of Information Sciences, Tohoku University, Sendai, Japan

The final, formatted version of the article will be published soon.

Building upon the findings of Kato et al. (2024), this study investigates specific lexicogrammatical features that distinguish individuals with autism spectrum disorder (ASD) from non-ASD individuals. While previous research demonstrated that ASD could be identified through linguistic patterns using both logistic regression and deep neural network (DNN) models—achieving high accuracy (80%), precision (82%), sensitivity (73%), and specificity (87%)—the exact lexicogrammatical markers driving these distinctions were not clarified. This research seeks to clarify these distinctions by systematically identifying and analyzing key syntactic features that serve as reliable discriminators between ASD and non-ASD individuals. This study utilizes the same dataset as Kato et al. (2024), which consists of a spoken language corpus containing speech samples from individuals with and without ASD. The dataset includes 64 individuals diagnosed with ASD (M = 18, SD = 3.48) and 71 non-ASD individuals (M = 19, SD = 2.77), all aged 14 and above. Among the 135 lexicogrammatical items analyzed, 46 were identified as significant discriminators distinguishing individuals with ASD from those without ASD. This paper examines 25 of these, focusing on syntactic variations. The analysis revealed that ASD individuals exhibited a lower frequency of rankshifted clauses, suggesting potential working memory constraints influencing syntactic structuring. A reduced use of inferential structures was observed, indicating challenges in constructing and interpreting conditionals and implied causal relations within discourse. There was also a tendency toward reduced use of mental space builders, reflecting difficulties in managing mental spaces and decoupling immediate experience from imagined, hypothetical, or belief-based representations. Syntactic patterns indicated potential deficits in joint attention, affecting the ability to align focus with conversational partners. An increased use of existential processes suggested challenges in distinguishing self from others and in expressing agency. A preference for repetitive syntactic structures mirrored the restricted and repetitive behaviors observed in ASD. By elucidating these syntactic patterns, this study provides insights into the cognitive mechanisms underlying language use in ASD. The findings offer implications for refining diagnostic tools and developing targeted interventions that address the linguistic and cognitive profiles of individuals with ASD.

Keywords: Autism spectrum disorder (ASD), natural language processing (NLP), machine learning, Diagnostic assessment, corpus, lexicogrammatical discriminator, Systemic Functional Linguistics (SFL)

Received: 06 Apr 2025; Accepted: 10 Jun 2025.

Copyright: © 2025 Kato and Hanawa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Sumi Kato, Department of Neuropsychiatry, Graduate School of Medicine and School of Medicine, Hirosaki University, Hirosaki, 036-8562, Aomori, Japan

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