September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
The capacity of visual working memory for scenes
Author Affiliations
  • Kazuhiko Yokosawa
    The University of Tokyo
  • Qi Li
    The University of Tokyo
Journal of Vision September 2018, Vol.18, 1297. doi:10.1167/18.10.1297
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      Kazuhiko Yokosawa, Qi Li; The capacity of visual working memory for scenes. Journal of Vision 2018;18(10):1297. doi: 10.1167/18.10.1297.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

How many scenes can we encode and maintain in visual working memory (VWM)? Previous research suggests that VWM stores three to four objects at a time, independently of the number of features possessed by each object (Luck & Vogel, 1997; Vogel, Woodman, & Luck, 2001). In the present study, we examined VWM capacity for scenes using a change detection task. In Experiment 1, the memory array consisted of 1-8 images of scenes and was presented for 300ms. After a 1700-ms retention interval, a test array consisting of one image was presented. This image was either an old image, presented at the same position as in the memory array, or it was a new image, presented at one of the positions in the memory array. The number of scenes maintained in VWM was estimated using Cowan's K formula (K = (hit rate − correct rejection rate) × set size, Cowan, 2001). Results revealed that VWM performance reached a stable plateau around two scenes. In Experiment 2, the duration of the memory array was extended to 600ms to test whether the length of encoding time affects the capacity of VWM for scenes. We found that the pattern of results was almost identical to that observed in Experiment 1. In Experiment 3, the retention interval was shortened to 700ms to test whether the capacity for scenes can be enlarged. However, the results still showed a similar capacity limit of about two scenes. Our three experiments consistently show that VWM can encode and maintain only about two scenes at a time.

Meeting abstract presented at VSS 2018

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