Article ID Journal Published Year Pages File Type
5039692 Cognitive Psychology 2017 19 Pages PDF
Abstract

•In a whole report task, participants report all items held in Working Memory (WM).•We directly observed variable precision in working memory responses.•For large arrays, many responses were best fit by a uniform guessing distribution.•Robust evidence for guessing confirms the presence of item limits in WM storage.

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.

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