Figure 1.

Pipeline for automated extraction of the semantic coherence features. Texts were initially split into sentences/phrases. Each word was represented as a vector in high-dimensional semantic space using Latent Semantic Analysis (LSA). Summary vectors were calculated as the mean of each vector in each phrase. Coherence was determined based on the semantic similarity between adjacent phrases, calculated as the cosine of their respective vectors. The semantic coherence feature that best discriminated those who transitioned to psychosis from those who did not was the minimum semantic coherence value (i.e., the coherence at the point of maximal discontinuity) within each transcribed text.