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. (PsycINFO Database Record

1 2The nature of capacity limits for visual working memory has been the 3 subject of an intense debate that has relied on models that assume items are 4 encoded independently. Here we propose that instead, similar features are jointly 5 encoded through a "chunking" process to optimize performance on visual working 6 memory tasks. We show that such chunking can: 1) facilitate performance 7 improvements for abstract capacity-limited systems, 2) be optimized through 8 reinforcement, 3) be implemented by center-surround dynamics, and 4) increase 9effective storage capacity at the expense of recall precision. Human performance 10 on a variant of a canonical working memory task demonstrated performance 11 advantages, precision detriments, inter-item dependencies, and trial-to-trial 12 behavioral adjustments diagnostic of performance optimization through center-13 surround chunking. Models incorporating center-surround chunking provided a 14 better quantitative description of human performance in our study as well as in a 15 meta-analytic dataset, and apparent differences in working memory capacity 16 across individuals were attributable to individual differences in the 17 implementation of chunking. Our results reveal a normative rationale for center-18 surround connectivity in working memory circuitry, call for re-evaluation of 19 memory performance differences that have previously been attributed to 20 differences in capacity, and support a more nuanced view of visual working 21 memory capacity limitations: strategic tradeoff between storage capacity and 22 memory precision through chunking contribute to flexible capacity limitations that 23 include both discrete and continuous aspects.