Background: Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been
hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some
mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental
Methods: Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding
and recall. For this study, DISCERN learned sets of autobiographical and impersonal crime stories with associated emotion coding. In
addition, 20 healthy control subjects and 37 patients with schizophrenia or schizoaffective disorder matched for age, gender, and parental
education were studied using a delayed story recall task. A goodness-of-fit analysis was performed to determine the mechanism best
reproducing narrative breakdown profiles generated by healthy control subjects and patients with schizophrenia. Evidence of delusion-like
narratives was sought in simulations best matching the narrative breakdown profile of patients.
Results: All mechanisms were equivalent in matching the narrative breakdown profile of healthy control subjects. However, exaggerated
prediction-error signaling during consolidation of episodic memories, termed hyperlearning, was statistically superior to other mechanisms
in matching the narrative breakdown profile of patients. These simulations also systematically confused autobiographical agents with
impersonal crime story agents to model fixed, self-referential delusions.
Conclusions: Findings suggest that exaggerated prediction-error signaling in schizophrenia intermingles and corrupts narrative memories
when incorporated into long-term storage, thereby disrupting narrative language and producing fixed delusional narratives. If further
validated by clinical studies, these computational patients could provide a platform for developing and testing novel treatments.