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Area of Science:

  • Computational linguistics
  • Psychiatry
  • Cognitive neuroscience

Background:

  • Large language models (LLMs) offer novel ways to analyze semantic space geometry.
  • Previous research indicated a 'shrinking' semantic space in psychosis.
  • Semantic space alterations may be relevant across different mental disorders.

Purpose of the Study:

  • To investigate semantic space dynamics in schizophrenia spectrum disorders (SSD) and major depressive disorder (MDD).
  • To compare semantic patterns in clinical groups with healthy controls.
  • To explore the 'shrinking' semantic space hypothesis in a transdiagnostic sample.

Main Methods:

  • Utilized LLM-generated word embeddings to analyze semantic space.
  • Examined picture descriptions from German patients with SSD (n=42), MDD (n=43), and healthy controls (n=44).
  • Analyzed time series of semantic distances, total distances, and trajectories.

Main Results:

  • Both SSD and MDD groups displayed more restricted dynamic navigational patterns compared to controls.
  • Clinical groups showed differential patterns in total distances and navigated trajectories.
  • Findings support a 'shrinking' semantic space in SSD and MDD.

Conclusions:

  • Alterations in the dynamics of meaning flow within the semantic space are evident in SSD and MDD.
  • The study reinforces the concept of a shrinking semantic space in these disorders.
  • LLMs provide valuable tools for understanding cognitive alterations in mental health.