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Andrew McCollough, Brittany Dungan, Edward Vogel; Proximity Grouping in Visual Working Memory. Journal of Vision 2010;10(7):735. doi: 10.1167/10.7.735.
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© ARVO (1962-2015); The Authors (2016-present)
The ability to group information into “chunks” is a well know phenomenon in verbal working memory paradigms. However, the effects of chunking in the visual memory domain is not as well understood. Here, we investigate the effects of visual chunking on working memory capacity by utilizing gestalt principles to bias subjects to group individual items into larger, virtual objects. Previously, we have examined the effects of grouping in Kanizsa figures and demonstrated a reduction in working memory load for elements comprising illusory triangles compared to individual “pac-men”. Here, we investigate the effect of proximity in generating virtual objects. In Experiment 1 Subjects were presented with randomly spaced groups of 6 dots: 1 group of 6 dots, 2 groups of 3 dots, or 3 groups of 2 dots. Subjects performed a location change detection task on a single item probe after a brief delay, indicating whether the probe was in the same or different location as the sample. ERPs were also recorded during the experiment. In particular, we examined the contralateral delay activity, which is an ERP component sensitive to the number of items held in memory during the delay activity of a visual working memory task. By examining the amplitude of this activity we were able to further determine whether these grouping principles facilitated efficient allocation of memory capacity towards the “chunked” objects or whether the number of maintained representations in memory was set by the number of elements within the figure.
Change detection performance was greater in grouped conditions compared to individual presentation conditions. In addition, ERP activity indexing online working memory load was greater for higher numbers of elements compared to grouped figures containing the same number of elements. The implications of working memory grouping mechanisms for category learning will also be discussed.
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