Computational analyses of linguistic features with schizophrenic and autistic traits along with formal thought disorders

Formal Thought Disorder (FTD), which is a group of symptoms in cognition that affects language and thought, can be observed through language. FTD is seen across such developmental or psychiatric disorders as Autism Spectrum Disorder (ASD) or Schizophrenia, and its related Schizotypal Personality Disorder (SPD). Researchers have worked on computational analyses for the early detection of such symptoms and to develop better treatments more than 40 years. This paper collected a Japanese audio-report dataset with score labels related to ASD and SPD through a crowd-sourcing service from the general population. We measured language characteristics with the 2nd edition of the Social Responsiveness Scale (SRS2) and the Schizotypal Personality Questionnaire (SPQ), including an odd speech subscale from SPQ to quantize the FTD symptoms. We investigated the following four research questions through machine-learning-based score predictions: (RQ1) How are schizotypal and autistic measures correlated? (RQ2) What is the most suitable task to elicit FTD symptoms? (RQ3) Does the length of speech affect the elicitation of FTD symptoms? (RQ4) Which features are critical for capturing FTD symptoms? We confirmed that an FTD-related subscale, odd speech, was significantly correlated with both the total SPQ and SRS scores, although they themselves were not correlated significantly. In terms of the tasks, our result identified the effectiveness of FTD elicitation by the most negative memory. Furthermore, we confirmed that longer speech elicited more FTD symptoms as the increased score prediction performance of an FTD-related subscale odd speech from SPQ. Our ablation study confirmed the importance of function words and both the abstract and temporal features for FTD-related odd speech estimation. In contrast, embedding-based features were effective only in the SRS predictions, and content words were effective only in the SPQ predictions, a result that implies the differences of SPD-like and ASD-like symptoms. Data and programs used in this paper can be found here: https://sites.google.com/view/sagatake/resource.