Fig. 1.

Item responses represented as vectors in semantic space. (A) In the category task participants named as many animals as they could in 5 min. Words were embedded within a common 300-dimensional semantic space using a pretrained word-embedding model (40). (B) Semantic proximity (association) between any two response items was defined as the cosine similarity between item vectors (color axis 0 to 0.8). (C) Initial word lists for 3 PScz visualized as trajectories through semantic space [3-dimensional projection derived from Uniform Manifold Approximation and Projection (UMAP) algorithm (41) applied to [item, 300] embedding matrix using cosine distance in ambient space. Item color from data-driven community assignment (see Fig. 4)].