The precision of visual working memory is set by allocation of a shared resource

By Paul M. Bays, Raquel F G Catalao, Masud Husain. Journal of Vision, 2009

992 citations recorded by the source corpus.

The mechanisms underlying visual working memory have recently become controversial. One account proposes a small number of memory ‘slots’, each capable of storing a single visual object with fixed precision. A contrary view holds that working memory is a shared resource, with no upper limit on the number of items stored; instead, the more items that are held in memory, the less precisely each can be recalled. Recent findings from a color report task have been taken as crucial new evidence in favor of the slot model. However, while this task has previously been thought of as a simple test of memory for color, here we show that performance also critically depends on memory for location. When errors in memory are considered for both color and location, performance on this task is in fact well-explained by the resource model. These results demonstrate that visual working memory consists of a common resource distributed dynamically across the visual scene, with no need to invoke an upper limit on the number of objects represented.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b9733518929e3efafbdde39b0bc61ccc36f16c87

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In Cognitive Neuroscience

Citations in the corpus

An Interference Model of Visual Working Memory (2017, methodology): “Although binding errors are empirically well documented in visual WM tasks (Bays et al., 2009; Pertzov, Dong, Peich, & Husain, 2013; Rerko, Oberauer, & Lin, 2014; van den Berg et al., This article was published Online First November 21, 2016.”
The Distributed Nature of Working Memory. (2017, background): “Behavioral models assume either a slot-based [180,181] or resource-based [182,183] limitation of capacity.”
Alpha phase coding supports feature binding during working memory maintenance (2026, unknown): “Typically, in the presence of swap errors, responses would be clustered aroundnon-targetfeaturesandthehistogramofresponsedeviationsshould showacentralpeak,arounddeviationsofzero 31 .”
Prioritizing flexible working memory representations through retrospective attentional strengthening (2023, unknown): “Then, we fit the response error distribution with the mixture model (Bays et al., 2009) using the Analogue Report Toolbox in MATLAB.”
Tracking neural correlates of prioritizing working memory representations through retrospective attentional strengthening (2022, methodology): “Then, we fit the response error distribution with the mixture model (62) using the 13 Analogue Report Toolbox in MATLAB.”
How to refocus attention on working memory representations following interruptions—Evidence from frontal theta and posterior alpha oscillations (2021, methodology): “In order to provide a more detailed estimation of the effects of interruptions on working memory performance, we further fitted a mixture model to the angular response distributions (see Bays et al., 2009).”
Frontal theta and posterior alpha oscillations reflect the reactivation of working memory representations following interruptions (2020, methodology): “In order to provide a more detailed estimation of the effects of interruptions on working memory performance, we further fitted a mixture model to the angular error distributions (see Bays et al., 2009).”