Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders.

Abstract

Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD. Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups. SSD-patients used less contingency (e.g., because) (p=.008) and multiclass connectives (e.g., as) (p<.001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p=.04) and temporality connectives (e.g., after) (adj-p<.001), narrower similarity-range of expansion (e.g., and) (adj-p=.002) and multiclass connectives (adj-p=.04), and lower maximum similarity of expansion connectives (adj-p=.005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85% accuracy. Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.