Atypically larger variability of resource allocation accounts for visual working memory deficits in schizophrenia

• Published in: PLoS computational biology Vol. 17; no. 11; p. e1009544
• Main Authors: Zhao, Yi-Jie, Ma, Tianye, Zhang, Li, Ran, Xuemei, Zhang, Ru-Yuan, Ku, Yixuan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2021; Public Library of Science (PLoS)
• Subjects: Adult; Bayes Theorem; Bayesian analysis; Behavior; Biology and Life Sciences; Case-Control Studies; Clinical trials; Cognition & reasoning; Color; Color Perception; Computational Biology; Computational neuroscience; Experiments; Female; Females; Health aspects; Humans; Male; Mathematical models; Medicine and Health Sciences; Memory; Memory Disorders - complications; Memory, Short-Term; Mental disorders; Mental task performance; Middle Aged; Models, Psychological; Nervous system; Psychiatric research; Resource Allocation; Schizophrenia; Schizophrenia - complications; Schizophrenia - physiopathology; Schizophrenic Psychology; Short term memory; Social Sciences; Spatial Processing; Standard deviation; Task Performance and Analysis; Variability; Visual perception; Visual tasks; Young Adult; China
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009544

Working memory (WM) deficits have been widely documented in schizophrenia (SZ), and almost all existing studies attributed the deficits to decreased capacity as compared to healthy control (HC) subjects. Recent developments in WM research suggest that other components, such as precision, also mediate behavioral performance. It remains unclear how different WM components jointly contribute to deficits in schizophrenia. We measured the performance of 60 SZ (31 females) and 61 HC (29 females) in a classical delay-estimation visual working memory (VWM) task and evaluated several influential computational models proposed in basic science of VWM to disentangle the effect of various memory components. We show that the model assuming variable precision (VP) across items and trials is the best model to explain the performance of both groups. According to the VP model, SZ exhibited abnormally larger variability of allocating memory resources rather than resources or capacity per se . Finally, individual differences in the resource allocation variability predicted variation of symptom severity in SZ, highlighting its functional relevance to schizophrenic pathology. This finding was further verified using distinct visual features and subject cohorts. These results provide an alternative view instead of the widely accepted decreased-capacity theory and highlight the key role of elevated resource allocation variability in generating atypical VWM behavior in schizophrenia. Our findings also shed new light on the utility of Bayesian observer models to characterize mechanisms of mental deficits in clinical neuroscience.