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.

Speech 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.

Coherence 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.

Automated 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.

计算语言学方法允许量化非情感性精神病中的言语异常。对于这个患者群体，语无伦次的言语长期以来一直被描述为正式思维障碍的症状。我们的研究是一项跨学科尝试，旨在开发非情感性精神病的不连贯模型，通过计算语言学方法和精神病学研究提供信息，两者都将不连贯概念化为联想松动。这项试点研究的主要目的是方法论：根据临床数据验证模型并减少自动一致性分析中的偏差。

语音样本来自诊断为精神分裂症或分裂情感障碍的患者，根据积极的正式思维障碍的不同临床评级，将他们分为两组，每组 n = 20 名受试者，以及 n = 20 名健康对照受试者。

从访谈记录中自动得出的连贯性指标显着预测了思维障碍的临床评级。多项回归分析的显着结果表明，当考虑到偏差时，可以基于自动连贯性分析来预测组成员身份（对照组与有正式思维障碍的患者与没有正式思维障碍的患者）。通过纳入精神病学研究表明的变量来进一步改进回归模型，这些变量可以为积极的形式思维障碍的临床诊断提供信息。

与正式思维障碍的临床评级相比，自动连贯性分析可以捕捉到不连贯语音的不同特征。非情感性精神病的不连贯模型应包括自动导出的连贯性度量以及影响言语可理解性的词汇和句法特征。