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A computational model of the episodic memory impairment in schizophrenia

Poster Session E - Monday, March 9, 2026, 2:30 – 4:30 pm PDT, Fairview/Kitsilano Ballroom

Andrew Tornatore¹, Aslihan Imamoglu², Stephan Heckers², Sean Polyn¹; ¹Vanderbilt University, ²Vanderbilt University Medical Center

Individuals with schizophrenia show poorer performance than healthy comparisons with word-list free recall, remembering fewer words and reporting those words with impaired temporal organization. We evaluated which cognitive mechanisms may be critically involved in this deficit within the framework of the Context Maintenance and Retrieval (CMR) model of memory search. We used a behavioral dataset of 179 patients and 184 healthy comparison participants performing the Screen for Cognitive Impairment in Psychiatry (SCIP) free-recall task, focusing on trial 1 immediate free recall data. After demonstrating that CMR captures performance differences between the groups on several benchmark analyses, we constructed a family of models in which individual model parameters were allowed to vary between groups, to determine whether each parameter contributes meaningfully to explaining group-level behavioral differences. Two model parameters were primarily responsible for group differences, a recall termination parameter, and a parameter controlling the fidelity of retrieved contextual information. In a final set of analyses, we used CMR to create a classifier to predict disease status from task performance, and used a leave-one-subject-out design to demonstrate above-chance 61% correct classification. This work demonstrates that the CMR model provides a useful framework for understanding the cognitive mechanisms underlying memory search performance deficits in patients with schizophrenia, and shows promise for the development of computational models of memory in the identification and characterization of psychopathologies.

Topic Area: LONG-TERM MEMORY: Episodic