Stage-specific computational mechanisms of working memory deficits in first-episode and chronic schizophrenia

Cognitive dysfunction, particularly working memory (WM) impairment, constitutes a core feature of schizophrenia and is largely unresponsive to available antipsychotic treatments. The computational mechanisms underlying WM deficits at different illness stages and their associations with clinical symptom dimensions remain poorly understood.

We applied hierarchical drift diffusion modeling (HDDM) to dissect latent cognitive processes underlying WM performance in a two-back task among patients with first-episode schizophrenia (FES, N = 103, illness duration ≤2 years), chronic schizophrenia (ChSz, N = 108, illness duration ≥5 years), and healthy controls (HCs, N = 85). Multiple regression and mediation analyses were conducted to examine associations between HDDM parameters, clinical symptoms, and conventional metrics.

Both patient groups exhibited significant WM deficits compared to HCs, with ChSz patients demonstrating more pronounced impairments than FES patients. HDDM analysis revealed that patients showed significantly reduced drift rate and prolonged non-decision time compared to HCs. Notably, while non-decision time remained comparable between FES and ChSz groups, drift rate was significantly lower in ChSz patients, mediated the relationship between illness stage and WM performance, and negatively correlated with negative symptoms and general psychopathology.

This study reveals distinct computational profiles of WM deficits across different stages of schizophrenia. While non-decision time impairments emerge early and persist, reduced drift rate progressively deteriorates with illness duration and is closely linked to specific clinical symptoms. These findings enhance our understanding of WM dysfunction across illness stages and support the development of targeted cognitive interventions tailored to illness stage and symptom severity.