Computational linguistic investigation in schizophrenia and autism spectrum disorders.

Researchers analyzed speech patterns in people with autism, schizophrenia, and typical development using computer programs. They found that autism and schizophrenia groups had very similar speech characteristics, sharing more similarities than differences. Both groups used more repetitive language and expressed more negative emotions compared to the control group. The few differences included autism using fewer unique words when describing pictures and schizophrenia using fewer descriptive words overall.

This study used computational linguistic analysis to compare speech patterns in 35 individuals with schizophrenia spectrum disorder, 25 with autism spectrum disorder, and 25 healthy controls. Participants completed free speech and picture description tasks. Results revealed extensive similarities between the clinical groups, with only 5 of 45 linguistic features differing between autism and schizophrenia groups. Both clinical groups showed elevated local and global semantic similarity and more negative sentiment compared to controls.

Key differences included autism showing lower unique word frequency in picture descriptions and shorter pronouns/adverbs in free speech, while schizophrenia used shorter adjectives and lower adjective frequency. The findings suggest shared communication disturbances between these neurodevelopmental conditions.

Findings suggest transdiagnostic approaches to communication assessment and intervention may be beneficial. Computational linguistic analysis could supplement traditional diagnostic methods. Speech-language interventions might focus on shared features like semantic flexibility and emotional expression rather than disorder-specific approaches.

Study design not clearly specified. Sample sizes are relatively small (n=25-35 per group). Limited to two specific speech tasks which may not capture full range of communication abilities. Cross-sectional design prevents understanding of developmental trajectories or causality.

Computational linguistic analysis has been increasingly used to capture formal thought disorder in schizophrenia. Despite promising outcomes, investigations of the computational linguistic disturbances of schizophrenia in a transdiagnostic context are limited. Particularly, shared characteristics, neurodevelopmental origins, and the role of speech in the diagnosis of schizophrenia and autism indicate a need to explore both the commonalities and distinctions in the computational linguistic profiles of these groups. In this study, we investigated the semantic and structural properties of speech samples of 35 patients with schizophrenia spectrum disorder, 25 patients with autism spectrum disorder, and 25 healthy controls in free speech and picture description tasks.

Our findings showed that only 5 of 45 features differed between the clinical groups. All of these were from the structural domain, while semantic features did not differ between these neurodevelopmental disorders. The clinical groups demonstrated elevated local and global semantic similarity, and negative sentiment compared to controls. Moreover, the speech of autism spectrum disorder included lower unique word frequency in picture description, alongside shorter pronouns and adverbs in free speech relative to other groups.

Schizophrenia spectrum disorder used shorter adjectives than autism spectrum disorder and controls in free speech. Importantly, adjective frequency in schizophrenia spectrum disorder was lower than in autism spectrum disorder in free speech. Overall, our findings demonstrated an extensive dominance of similar computational linguistic traits between schizophrenia and autism spectrum disorders, indicating shared communication disturbances in these disorders. This outcome highlights the critical role of transdiagnostic and neurodevelopmental perspectives in computational linguistic investigations.