Adaptive allocation of human visual working memory capacity during statistical and categorical learning

Journal of Vision
Christopher J BatesRobert A Jacobs

Abstract

Human brains are finite, and thus have bounded capacity. An efficient strategy for a capacity-limited agent is to continuously adapt by dynamically reallocating capacity in a task-dependent manner. Here we study this strategy in the context of visual working memory (VWM). People use their VWM stores to remember visual information over seconds or minutes. However, their memory performances are often error-prone, presumably due to VWM capacity limits. We hypothesize that people attempt to be flexible and robust by strategically reallocating their limited VWM capacity based on two factors: (a) the statistical regularities (e.g., stimulus feature means and variances) of the to-be-remembered items, and (b) the requirements of the task that they are attempting to perform. The latter specifies, for example, which types of errors are costly versus irrelevant for task performance. These hypotheses are formalized within a normative computational modeling framework based on rate-distortion theory, an extension of conventional Bayesian approaches that uses information theory to study rate-limited (or capacity-limited) processes. Using images of plants that are naturalistic and precisely controlled, we carried out two sets of experiments. E...Continue Reading

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Citations

Mar 12, 2020·The Behavioral and Brain Sciences·Christopher J BatesRobert A Jacobs
Jun 20, 2020·Experimental Psychology·Gaën Plancher, Robert L Goldstone
Aug 8, 2020·Proceedings of the National Academy of Sciences of the United States of America·Rubén Moreno-BoteBenjamin Y Hayden
Sep 4, 2019·Journal of Vision·Robert A Jacobs, Chenliang Xu
Dec 19, 2020·Scientific Reports·Allison L Bruning, Jarrod A Lewis-Peacock
May 22, 2021·Cognitive Science·Marina Dubova, Robert L Goldstone
Jul 21, 2021·Psychological Science·Jacob Feldman

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