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Incoherent discourse, with a disjointed flow of ideas, is a cardinal symptom in several psychiatric and neurological conditions. However, measuring incoherence has often been complex and subjective. We sought to validate an objective, intrinsically reliable, computational approach to quantifying speech incoherence. Patients with schizophrenia and healthy control volunteers were administered a variety of language tasks. The speech generated was transcribed and the coherence computed using Latent Semantic Analysis (LSA). The discourse was also analyzed with a standard clinical measure of thought disorder. In word association and generation tasks LSA derived coherence scores were sensitive to differences between patients and controls, and correlated with clinical measures of thought disorder. In speech samples LSA could be used to localize where in sentence production incoherence occurs, predict levels of incoherence as well as whether discourse "belonged" to a patient or control. In conclusion, LSA can be used to assay disordered language production so as to both complement human clinical ratings as well as experimentally parse this incoherence in a theory-driven manner.

Collaborative problem solving (CPS) is an important 21st century skill that is increasingly recognized as being critical to efficiency, effectiveness, and innovation in the modern global economy (Fiore, Graesser, & Greiff, 2018; Organisation for Economic Cooperation and Development [OECD], 2017a). CPS has attracted interest in international assessments; national assessments of middle and high school students; and training in colleges, industry, and the military (Care,

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