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

Abstract

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.

Awareness of the Relative Quality of Spatial Working Memory Representations

“…These previous studies are all consistent with a model of WM whereby each item is encoded with a random level of precision (e.g., 'variable precision' models;van den Berg et al, 2012), and so these results support a similar model for spatial WM representations. This is an important demonstration because spatial WM tasks are commonly used in the animal physiology and computational modeling literature for probing the neural mechanisms supporting WM (Compte et al, 2000;Funahashi et al, 1989;Schneegans & Bays, 2016, and because deficits in spatial WM are observed in patients with schizophrenia (Cannon et al, 2005;Matthews et al, 2014;Zhao et al, 2021).…”

Spatial and non-spatial feature binding impairments in visual working memory in schizophrenia

“…Although individuals with schizophrenia were found to have undiminished precision when recalling basic visual dimensions, such as colour ( Gold et al, 2010 ), more recent evidence has demonstrated diminished visual WM precision both in individuals with more schizotypal features and schizophrenia ( Xie et al, 2018 ). This finding could be accounted by greater within-subject variability in the allocation of a limited resource for patients than controls ( Zhao et al, 2021 ). Our study demonstrates that diminished precision and increased proportion of swap errors jointly affect recall performance of PSZ in the same task suggesting simultaneous, but distinct impairments of feature resolution and feature binding in WM.…”

“…Hence, we cannot determine the extent to which group difference in precision may reflect differences in precision variability instead. Work in healthy participants indicates that variability in precision is introduced by unequal additive noise during maintenance of item level information ( Fougnie et al, 2012 ) and unequal resource sharing between items competing for memory representation ( Zhao et al, 2021 ). However, the observation that delay duration does not further modify group differences in recall precision is inconsistent with the proposal that patients with schizophrenia experience greater variability in temporal decay of item memory.…”

“…However, the observation that delay duration does not further modify group differences in recall precision is inconsistent with the proposal that patients with schizophrenia experience greater variability in temporal decay of item memory. Furthermore, the finding that recall accuracy and precision is lower even when a single item is memorized ( Carter et al, 1996 ; Zhao et al, 2021 ) suggests that greater inhomogeneities in resource sharing among items cannot account for precision differences on its own.…”

A Hitchhiker’s Guide to Bayesian Hierarchical Drift-Diffusion Modeling with dockerHDDM

“…Another common scenario is to examine the difference between two participant groups (e.g., patients vs. healthy controls) with respect to a specific model parameter (Crawley et al, 2020;Zhao et al, 2021), especially in emerging interdisciplinary fields such as computational psychiatry (e.g., Geng et al, 2022;Huys, Maia, & Frank, 2016). For example, several studies have revealed the atypical drift rate in patients with psychotic illnesses (Mathias et al, 2017), unmedicated adults with Major Depressive Disorder (Cataldo, Scheuer, Maksimovskiy, Germine, & Dillon, 2022;Lawlor et al, 2020), and ADHD (Shapiro & Huang-Pollock, 2019), as compared to healthy control participants.…”