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The Gestalt principle of similarity benefits visual working memory

Overview of attention for article published in Psychonomic Bulletin & Review, May 2013
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Title
The Gestalt principle of similarity benefits visual working memory
Published in
Psychonomic Bulletin & Review, May 2013
DOI 10.3758/s13423-013-0460-x
Pubmed ID
Authors

Dwight J. Peterson, Marian E. Berryhill

Abstract

Visual working memory (VWM) is essential for many cognitive processes, yet it is notably limited in capacity. Visual perception processing is facilitated by Gestalt principles of grouping, such as connectedness, similarity, and proximity. This introduces the question, do these perceptual benefits extend to VWM? If so, can this be an approach to enhance VWM function by optimizing the processing of information? Previous findings have demonstrated that several Gestalt principles (connectedness, common region, and spatial proximity) do facilitate VWM performance in change detection tasks (Jiang, Olson, & Chun, 2000; Woodman, Vecera, & Luck, 2003; Xu, 2002, 2006; Xu & Chun, 2007). However, one prevalent Gestalt principle, similarity, has not been examined with regard to facilitating VWM. Here, we investigated whether grouping by similarity benefits VWM. Experiment 1 established the basic finding that VWM performance could benefit from grouping. Experiment 2 replicated and extended this finding by showing that similarity was only effective when the similar stimuli were proximal. In short, the VWM performance benefit derived from similarity was constrained by spatial proximity, such that similar items need to be near each other. Thus, the Gestalt principle of similarity benefits visual perception, but it can provide benefits to VWM as well.

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Geographical breakdown

Country Count As %
United States 1 <1%
China 1 <1%
Germany 1 <1%
South Africa 1 <1%
Unknown 176 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 18%
Student > Master 31 17%
Student > Bachelor 18 10%
Researcher 16 9%
Professor > Associate Professor 8 4%
Other 33 18%
Unknown 41 23%
Readers by discipline Count As %
Psychology 58 32%
Neuroscience 18 10%
Arts and Humanities 9 5%
Design 9 5%
Computer Science 8 4%
Other 34 19%
Unknown 44 24%