Abstract
BackgroundComputational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis.MethodsSpeech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects.ResultsCoherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder.ConclusionsAutomated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.
Highlights
Speech impairments in non-affective psychosis (NAP) can impede communication up to “discourse failure” [1]
The linguistic definition of coherence regards the deeper semantic meaning of speech – in contrast to cohesion, which is an aspect of the text surface [(20), p. 25]
We address that clinical ratings of incoherent discourse may be informed by concept similarity and by comprehensibility of discourse
Summary
Speech impairments in non-affective psychosis (NAP) can impede communication up to “discourse failure” [1]. Impairments comprise difficulties with structural aspects [2,3,4], the pragmatic use of language [5, 6] as well as cohesion [7,8,9,10] and semantic coherence [11,12,13,14,15]. Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis
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