September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Visual working memory of multiple preferred objects
Author Affiliations
  • Holly Lockhart
    Psychology Department, Brock University
  • Stephen Emrich
    Psychology Department, Brock University
Journal of Vision August 2017, Vol.17, 114. doi:
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      Holly Lockhart, Stephen Emrich; Visual working memory of multiple preferred objects. Journal of Vision 2017;17(10):114.

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

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A key debate regarding visual working memory (VWM) mechanisms focuses on the differences between discrete- versus continuous-resource models of VWM capacity limits. Recent findings have demonstrated that VWM resources can be allocated disproportionately according to the probability that an item would be probed, consistent with the continuous resource model. However, this finding was based on a single report on each trial, with the assumption that all items in the display would get the predicted quantity of VWM resources. The current study sought to address this methodological limitation, and determine whether multiple items are reported according to attentional prioritization during encoding. Using a two-item report task we tested the flexibility and quality of VWM when two items are cued during the encoding of a super-capacity display of six coloured items. To establish attentional priority, the cued items were probed on 50% of the trials, while in 25% of trials one cued item and one uncued item were reported, and two uncued items were reported on the remaining 25% of trials. Measures of precision, guess rate, and rate of non-target errors were taken from the three-component mixture model. Results show that participants reported the two cued items with approximately equal precision to each other, suggesting that in fact multiple items can be prioritized simultaneously. Uncued items were also half as likely to be correctly reported and three times as likely to be misremembered compared with cued items. The results are in line with the predictions of a flexible resource model in which VWM resources can be allocated across multiple items in accordance with the task demands.

Meeting abstract presented at VSS 2017


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