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Updated: May 9, 2026

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism
Published on: October 3, 2018
A multi-method phenotypic study of sex differences in pragmatic language in autism
Emily Landau1, Sarah E Brooks1, Janna Guilfoyle1
1Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, United States.
Introduction:
Autism spectrum disorder (ASD) is characterized in part by differences in pragmatic (i.e., social) language use. However, few studies on pragmatic language have included a meaningful number of autistic females, and even fewer have evaluated pragmatic language profiles for sex-specific differences. The existing literature on pragmatic language in autistic individuals without intellectual disability suggests that females may have stronger social communication skills compared to males, but findings are mixed, and there is not a clear profile of specific pragmatic skills that are liable to sex differences. It is also important to develop novel methodologies, such as computational methods, to characterize pragmatic language in ways less labor-intensive than gold-standard hand-coding methods, which are extremely time consuming and typically not feasible in clinical settings.
Methods:
The present study examined hand coding of pragmatic language data samples alongside multiple computational linguistic methodological approaches to characterize sex differences in pragmatic language in autistic males and females across narrative and semi-structured conversational tasks that might reveal context-specific patterns across sex and diagnostic groups.
Results:
Results indicated that most pragmatic domains differed between autistic and non-autistic groups, with autistic males showing the most obvious pragmatic differences, and that differences between diagnostic groups were more pronounced in the semi-structured conversational context. The alignment between computational and hand-coded findings was strongest in domains with clear theoretical overlap (e.g., frequency of emotional words) but was less consistent in areas that were less theoretically aligned (e.g., conversational dynamics and single word function).
Discussion:
These findings support the promise of computational methods for characterizing narrative abilities, though further study including validation against hand-coded approaches is warranted.
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