Categorical bias as a crucial parameter in visual working memory: The effect of memory load and retention interval

Cherie Zhou*, Monicque M Lorist, Sebastiaan Mathôt

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
88 Downloads (Pure)

Abstract

Visual information can be stored as continuous as well as categorical representations in visual working memory (VWM) to guide subsequent behavior. Yet it is still unclear what determines whether VWM is represented as continuous or categorical information, or as a mix of both. Recent studies have shown that color VWM representations adjust flexibly depending on the number of memory items as well as the duration that these items need to be maintained for. The current study aims to extend and replicate these crucial effects. In a delayed estimation task, participants memorized one to four colored objects presented at different spatial locations, followed by a delay of 100, 500, 1000, or 2000 msec. Next, a probe indicated the location of the color that participants needed to report. We measured the extent to which responses were biased in the direction of prototypical colors. Crucially, we implemented this categorical bias in an extension to the classic mixture model (Zhang & Luck, 2008) in which the center of the error distribution is a crucial parameter that characterizes the extent to which VWM is biased by color categories. We found that VWM shows a strong categorical bias in all cases, and that this bias increases with increasing memory load; strikingly, this effect of memory load on categorical bias is stronger at longer intervals (1000 msec or longer), as compared to shorter intervals, yet it peaks for intermediate memory loads as opposed to the highest memory load. Overall, our results suggest that when visual information needs to be maintained for one second or longer, VWM becomes more reliant on categorical representations as memory load increases.

Original languageEnglish
Pages (from-to)311-321
Number of pages11
JournalCortex
Volume154
Early online date2022
DOIs
Publication statusPublished - Sept-2022

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