4.4 Article

Clear evidence for item limits in visual working memory

Journal

COGNITIVE PSYCHOLOGY
Volume 97, Issue -, Pages 79-97

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cogpsych.2017.07.001

Keywords

Visual working memory; Capacity limits; Precision; Metacognition

Funding

  1. National Institute of Mental Health grant [5R01 MH087214-08]
  2. Office of Naval Research grant [N00014-12-1-0972]

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There is a consensus that visual working memory (WM) resources are sharply limited, but debate persists regarding the simple question of whether there is a limit to the total number of items that can be stored concurrently. Zhang and Luck (2008) advanced this debate with an analytic procedure that provided strong evidence for random guessing responses, but their findings can also be described by models that deny guessing while asserting a high prevalence of low precision memories. Here, we used a whole report memory procedure in which subjects reported all items in each trial and indicated whether they were guessing with each response. Critically, this procedure allowed us to measure memory performance for all items in each trial. When subjects were asked to remember 6 items, the response error distributions for about 3 out of the 6 items were best fit by a parameter free guessing model (i.e. a uniform distribution). In addition, subjects' self-reports of guessing precisely tracked the guessing rate estimated with a mixture model. Control experiments determined that guessing behavior was not due to output interference, and that there was still a high prevalence of guessing when subjects were instructed not to guess. Our novel approach yielded evidence that guesses, not low-precision representations, best explain limitations in working memory. These guesses also corroborate a capacity-limited working memory system - we found evidence that subjects are able to report non-zero information for only 3-4 items. Thus, WM capacity is constrained by an item limit that precludes the storage of more than 3-4 individuated feature values. (C) 2017 Elsevier Inc. All rights reserved.

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