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

Abstract

Background and hypothesis: 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.

Study design: 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.

Study results: 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.

Conclusions: 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.

Keywords: artificial intelligence; machine learning; natural language processing; phenomenology; psychosis; self-disturbance.

Fig. 1.

Weights of the 10 language features in the machine learning classifier using logistic regression (LR) and linear support vector machine (LSVM) algorithms. ^aEmotional tone; ^bFirst-person singular pronouns; ^cWords related to risk; ^dRobust maximum to “burden”; ^eRobust maximum to “I feel in control”; ^fMaximum coherence to 2 phrases later; ^gMedian to “What happened to them?”; ^hRobust maximum coherence to next phrase; ⁱRobust maximum to “What happened to them?”; ^jRobust maximum to “What happened to me?”.

Fig. 2.

Results of factor analysis. Panel (A) shows the factors’ weights, corresponding to the bolded linguistic features listed in Panel (C); panel (B) shows the histograms for the projection onto each factor for the schizophrenia and healthy control groups.

Fig. 3.

Canonical correlation between Positive and Negative Syndrome Scale (PANSS) scores and language factors in the training set (a) and hold-out set (b). Canonical weights of the language factors (c) and PANSS subscales (d). circles = schizophrenia group; crosses = control group.