Stochastic attractor models of visual working memory

Penny, Will D. ORCID: https://orcid.org/0000-0001-9064-1191 (2024) Stochastic attractor models of visual working memory. PLoS One, 19 (4). ISSN 1932-6203

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Abstract

This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.

Item Type:	Article
Additional Information:	Data Availability: The location report data are available - URL provided. The color report data were collected by the authors of reference [44] (Tim Bushman’s group at Princeton) and are available upon request. Funding: The author(s) received no specific funding for this work.
Uncontrolled Keywords:	3*,uoa4 ,/dk/atira/pure/researchoutput/REFrank/3_
Faculty \ School:	Faculty of Social Sciences > School of Psychology
Depositing User:	LivePure Connector
Date Deposited:	18 Oct 2024 09:30
Last Modified:	18 Oct 2024 19:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/97061
DOI:	10.1371/journal.pone.0301039

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