语言的实时几何复杂性与精神病和重度抑郁症的全局功能有关

Abstract Language disturbances are prominent across psychiatric disorders, yet their underlying organization and relationship to clinical functioning remain poorly understood. We conceptualized spontaneous speech as a temporally evolving sequence of contextual representations through semantic space, in which successive concepts define transitions between large-language-model embeddings. We characterized the local dynamics of these representations using Lyapunov-inspired measures of sensitivity to perturbation and anisotropy—the extent to which nearby neighboring contextual representations dive