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Stochastic sampling provides a unifying account of visual working memory limits.

Published version
Peer-reviewed

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Abstract

Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a framework to understand working memory limits. Reconceptualizing existing models in these terms reveals strong commonalities between seemingly opposing accounts, but also allows us to identify specific points of difference. We show that the discrete versus continuous nature of sampling is not critical to model fits, but that, instead, random variability in sample counts is the key to reproducing human performance in both single- and whole-report tasks. A probabilistic limit on the number of items successfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to enforce it. These findings resolve discrepancies between previous accounts and establish a unified computational framework for working memory that is compatible with neural principles.

Description

Keywords

capacity limits, population coding, resource model, visual working memory, Humans, Memory, Short-Term, Mental Recall, Models, Neurological, Models, Theoretical, Visual Perception

Journal Title

Proc Natl Acad Sci U S A

Conference Name

Journal ISSN

0027-8424
1091-6490

Volume Title

117

Publisher

Proceedings of the National Academy of Sciences
Sponsorship
Wellcome Trust (106926/Z/15/Z)