December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Visual Working Memory Performance With Just 1 Item Predicts Nearly All of the Variance in Performance with 5 Items
Author Affiliations & Notes
  • Timothy Brady
    University of California, San Diego
  • Footnotes
    Acknowledgements  NSF BCS-1653457 to TFB
Journal of Vision December 2022, Vol.22, 4360. doi:https://doi.org/10.1167/jov.22.14.4360
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Timothy Brady; Visual Working Memory Performance With Just 1 Item Predicts Nearly All of the Variance in Performance with 5 Items. Journal of Vision 2022;22(14):4360. https://doi.org/10.1167/jov.22.14.4360.

      Download citation file:


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

      ×
  • Supplements
Abstract

What limits working memory performance when there are many items to be held in mind? Many models of working memory capacity focus on factors that are present primarily at high set sizes (e.g., interference between items; upper bounds on number of items that can be held in mind; etc). These models assume that performance is effectively ‘at ceiling’ when remembering just 1 item, and so little can be learned about working memory from such "sub-capacity" trials. Here we test this by taking an individual differences approach. We use the TCC model (Schurgin et al. 2020) and both continuous report and a specially designed 4-AFC task to measure visual working memory performance at set size 1 and set size 5, and ask how they are related. In 3 studies (all N>100) we find that performance at set size 1 explains 80-95% of the explainable variance in set size 5 performance. By contrast, a challenging mental rotation task explains <40% of the variance at set size 5, suggesting this is specific to working memory capacity. This raises important challenges for models who focus primarily on explaining high set sizes when considering the source of working memory limits (slots, interference, etc). It is most consistent with resource models, particularly those where the same fixed set of resources is given all to a single item at set size 1 or split among all 5 items at set size 5. This work also suggests that the focus on studying working memory 'capacity' only at high set sizes is likely counterproductive: the same capacity seems to be measurable at set size 1, and the largest variation between participants occurs at set size 1, and the largest drop in performance occurs in moving from set size 1 to set size 2.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×