Tracking the dynamic breakdown of contextual coherence in schizophrenia using language models

Disorganized speech is a core clinical feature of schizophrenia, reflecting disruptions in contextual integration. To objectively quantify the unfolding nature of natural speech, this study investigated the dynamic temporal trajectory of contextual coherence breakdown using sequential surprisal derived from autoregressive language models. We analyzed transcripts from 249 patients with schizophrenia and 159 matched healthy controls across eight diverse speech tasks. Using two Korean small language models (Polyglot-ko-5.8b and Kanana-1.5-8b-base), we calculated token-by-token surprisal, a measure of lexical predictability, from 100-token utterances. To account for the inherent volatility of autoregressive models at speech onset, temporal divergence of surprisal trajectories between groups was analyzed from tokens 11 to 100 using generalized additive mixed models. Patients exhibited progressively divergent surprisal trajectories as discourse unfolded, reflecting a breakdown in contextual predictability. Emerging early in the discourse, these deviations consistently intensified by tokens 50 to 70, indicating a rapid deterioration in the capacity to sustain global contextual constraints even within a short utterance. Both models also detected significantly higher overall mean surprisal in patients. Furthermore, this temporal divergence was most pronounced during unstructured narrative tasks, which lack the external visual cues of projective tasks. These findings provide quantitative evidence that language anomalies in schizophrenia are dynamic phenomena rapidly unfolding within a short discourse. By capturing precise temporal intervals of contextual disruption and paralleling clinical descriptions of disorganized discourse, this research highlights the utility of temporal natural language processing metrics in elucidating the psychopathology of schizophrenia.