A Computational Approach to Analyzing Disrupted Language in Schizophrenia: Integrating Surprisal and Coherence Measures

TL;DR

This paper introduces a computational method combining surprisal and coherence measures to analyze language disruptions in schizophrenia, aiming to improve understanding and diagnosis of the disorder.

Contribution

It integrates surprisal and semantic coherence metrics to quantify language disruptions in schizophrenia, providing new insights into their relation to symptom severity.

Findings

Schizophrenia patients show higher surprisal and lower coherence than controls.

Language measures correlate with symptom severity levels.

Computational metrics differentiate between patient groups and healthy controls.

Abstract

Language disruptions are one of the well-known effects of schizophrenia symptoms. They are often manifested as disorganized speech and impaired discourse coherence. These abnormalities in spontaneous language production reflect underlying cognitive disturbances and have the potential to serve as objective markers for symptom severity and diagnosis of schizophrenia. This study focuses on how these language disruptions can be characterized in terms of two computational linguistic measures: surprisal and semantic coherence. By computing surprisal and semantic coherence of language using computational models, this study investigates how they differ between subjects with schizophrenia and healthy controls. Furthermore, this study provides further insight into how language disruptions in terms of these linguistic measures change with varying degrees of schizophrenia symptom severity.