August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Action consequences guide visual working memory use
Author Affiliations & Notes
  • Andre Sahakian
    Utrecht University, the Netherlands
  • Surya Gayet
    Utrecht University, the Netherlands
  • Chris Paffen
    Utrecht University, the Netherlands
  • Stefan Van der Stigchel
    Utrecht University, the Netherlands
  • Footnotes
    Acknowledgements  This project was supported by an ERC Consolidator Grant [grant number ERC-CoG-863732] to Stefan Van der Stigchel, and a Veni grant from Netherlands Organisation for Scientific Research (NWO) [grant number: Vl.Veni.191G.085] to Surya Gayet.
Journal of Vision August 2023, Vol.23, 4884. doi:https://doi.org/10.1167/jov.23.9.4884
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      Andre Sahakian, Surya Gayet, Chris Paffen, Stefan Van der Stigchel; Action consequences guide visual working memory use. Journal of Vision 2023;23(9):4884. https://doi.org/10.1167/jov.23.9.4884.

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

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Abstract

Visual working memory (VWM) is a store for temporary maintenance of visual information. It is often disregarded, though, that this information is typically stored to enable actions, and therefore, the context of such actions is of great importance for how VWM is used. The severity of the consequence of an action might, for example, affect the precision with which action-relevant information is stored. Here we set out to examine whether strategy changes in VWM-use occur when incorrect actions are penalized. We employed an (online) copying task, where participants recreated a model comprised of several items in a grid, using a (larger) pool of items. Crucially, we manipulated the severity of the penalty: in the high error cost condition participants had to wait 5 seconds after an erroneous item-placement (versus 0.5 seconds in the low error cost condition). Additionally, we manipulated the accessibility of task-relevant information, a well-studied manipulation in this paradigm (implemented here as a 0.5 versus 5 second wait to inspect the model). Manipulating the cost of sampling information provided a direct comparison for the effects of error cost. Our results showed that (1) the number of model inspections halved with higher sampling cost, but were unaffected by error cost; (2) inspection durations increased with higher sampling cost, but were again unaffected by error cost; and (3) the number of errors increased with higher sampling cost, but decreased with higher error costs. Thus, more severe action consequences (error costs) increase the reluctance to act on uncertain information in VWM; but (against our expectations) do not lead to longer, nor to more frequent attempts to store information in VWM. We conclude that, in contrast to the accessibility of information, action consequences do not affect how information is stored, but do affect the willingness to act based on the available information.

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