Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia.
Chi C Chan, Raquel Norel, Carla Agurto, Paul H Lysaker, Evan J Myers, Erin A Hazlett, Cheryl M Corcoran, Kyle S Minor, Guillermo A Cecchi
Schizophrenia bulletin March 15, 2023 Peer reviewed DOI: 10.1093/schbul/sbac126 via PubMed
Summary
People with schizophrenia express more language related to self-experience and agency in their autobiographical narratives than healthy controls, and these linguistic features can be detected automatically by natural language processing. The analysis of 167 patients and 90 controls, totaling 490,000 words, showed that topics of self-experience and agency were significantly more prominent in the patient group and were distinct from emotional tone, semantic coherence, and burden-related concepts. A machine learning classifier distinguished patients from controls with 80% accuracy. These language features also correlated with clinical symptoms, demonstrating that automated text analysis can reveal phenomenological aspects of schizophrenia without explicit prompting.
Study at a glance
| Design | observational cohort |
|---|---|
| Sample size | 257 |
| Population | patients with schizophrenia or schizoaffective disorder and healthy controls |
| Key finding | Language topics related to self-experience and agency are significantly more expressed in patients with schizophrenia than healthy controls and can be automatically detected by natural language processing. |
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.