Reliability of computational versus noncomputational metrics of working memory and episodic memory in serious mental illness.
Abstract
= 76). These participants were tested in working memory and episodic memory paradigms. We applied a computational model that yielded parameters corresponding to memory storage capacity, memory precision, and the rate of attention lapses. We also examined a noncomputational metric of performance, the mean error. Each participant was tested twice, separated by at least 28 days. In most cases, the computational parameters exhibited good-to-excellent split-half and test-retest reliability (>0.80) that was comparable with the reliability of the noncomputational performance metric. In one case, reliability was substantially better for the memory precision parameter than for the noncomputational metric. In another case, reliability for the memory precision parameter was substantially lower but was improved by the use of hierarchical Bayesian estimation. Thus, when applied thoughtfully, parameters derived from computational models can have excellent psychometric properties, providing a reliable means of isolating the factors that underlie individual and group differences in studies of mental illness. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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Institutions: University of Chicago, University of Maryland, Baltimore, Brown University, Washington University in St. Louis, University of Minnesota, University of California, Irvine, University of Rochester Medicine, University of California, Santa Cruz, University of California, Davis