August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Attentional differences predict ensemble coding and are moderated by probe effects
Author Affiliations
  • Laramie Starling
    University of South Florida
  • Chad Dubé
    University of South Florida
Journal of Vision August 2023, Vol.23, 5920. doi:https://doi.org/10.1167/jov.23.9.5920
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      Laramie Starling, Chad Dubé; Attentional differences predict ensemble coding and are moderated by probe effects. Journal of Vision 2023;23(9):5920. https://doi.org/10.1167/jov.23.9.5920.

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

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Abstract

Ensemble coding refers to an individual's ability to process groups of similar objects in a scene into summary statistical representations. This process is typically thought to be either preattentional or to occur during a distributed state of attention. To explore potential attentional selection in this process, we used a measure of individuals' selective attention to predict their precision on an ensemble averaging task. We also investigated the effect of the probe's initial orientation in the ensemble averaging task and it's potential interaction with our attentional measure. We measured selective attention through a classic visual search paradigm, comparing individuals' performance on a conjunction search task with their performance on a feature search task to create a single measure of selective attention. We then measured individuals' response precision when attempting to recreate the mean of an ensemble. Attentional performance scores and probe precision to the mean, along with an interaction between the two, were used to predict individuals' response precision. We found effects of both probe precision and selective attention ability, as well as an unexpected interaction effect. Different values of probe precision showed different effects of selective attention on ensemble averaging. We discuss our interpretation of these findings and how they relate to current and previous models of ensemble coding.

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