Journal
PSYCHOLOGICAL REVIEW
Volume 125, Issue 4, Pages 486-511Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/rev0000101
Keywords
visual working memory; data compression; information theory; reinforcement learning; computational modeling
Categories
Funding
- NIMH [F32 MH102009, R01 MH080066-01]
- NIA [K99AG054732]
- NSF [1460604]
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH080066, F32MH102009] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [K99AG054732] Funding Source: NIH RePORTER
- OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTH [S10OD016366] Funding Source: NIH RePORTER
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The nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a chunking process to optimize performance on visual working memory tasks. We show that such chunking can: (a) facilitate performance improvements for abstract capacity-limited systems, (b) be optimized through reinforcement, (c) be implemented by center-surround dynamics, and (d) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, interitem dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for reevaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects.
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