Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia.

Disturbances in self-experience—the sense of being the subject of one's own experiences and actions, and of being distinct from others—are central to schizophrenia. Traditionally assessed by manual interview rating, this study used natural language processing to analyze autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder and 90 healthy controls, totaling 490,000 words. Topics related to self-experience and agency were significantly more expressed in patients than controls and were decoupled from emotional tone, semantic coherence, and burden-related concepts. A classifier trained on these features discriminated patients from controls with an AUC of 0.80. These findings demonstrate that NLP can automatically detect higher-order metacognitive aspects of self-experience without explicit probing.

Study at a glance

Abstract

Disturbances in self-experience are a central feature of schizophrenia and its study can enhance phenomenological understanding and inform mechanisms underlying clinical symptoms. Self-experience involves the sense of self-presence, of being the subject of one's own experiences and agent of one's own actions, and of being distinct from others. Self-experience is traditionally assessed by manual rating of interviews; however, natural language processing (NLP) offers automated approach that can augment manual ratings by rapid and reliable analysis of text. We elicited autobiographical narratives from 167 patients with schizophrenia or schizoaffective disorder (SZ) and 90 healthy controls (HC), amounting to 490 000 words and 26 000 sentences. We used NLP techniques to examine transcripts for language related to self-experience, machine learning to validate group differences in language, and canonical correlation analysis to examine the relationship between language and symptoms. Topics related to self-experience and agency emerged as significantly more expressed in SZ than HC (P < 10-13) and were decoupled from similarly emerging features such as emotional tone, semantic coherence, and concepts related to burden. Further validation on hold-out data showed that a classifier trained on these features achieved patient-control discrimination with AUC = 0.80 (P < 10-5). Canonical correlation analysis revealed significant relationships between self-experience and agency language features and clinical symptoms. Notably, the self-experience and agency topics emerged without any explicit probing by the interviewer and can be algorithmically detected even though they involve higher-order metacognitive processes. These findings illustrate the utility of NLP methods to examine phenomenological aspects of schizophrenia.