September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Integration of visual confidence over time and across stimulus dimensions
Author Affiliations
  • Pascal Mamassian
    Ecole Normale Supérieure
Journal of Vision August 2017, Vol.17, 2. doi:https://doi.org/10.1167/17.10.2
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      Pascal Mamassian; Integration of visual confidence over time and across stimulus dimensions. Journal of Vision 2017;17(10):2. https://doi.org/10.1167/17.10.2.

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

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Abstract

Visual confidence refers to our ability to estimate our own performance in a visual decision task. Several studies have highlighted the relatively high efficiency of this meta-perceptual ability, at least for simple visual discrimination tasks. Are observers equally good when visual confidence spans more than one stimulus dimension or more than a single decision? To address these issues, we used the method of confidence forced-choice judgments where participants are prompted to choose between two alternatives the stimulus for which they expect their performance to be better (Barthelmé & Mamassian, 2009, PLoS CB). In one experiment, we asked observers to make confidence choice judgments between two different tasks (an orientation-discrimination task and a spatial-frequency-discrimination task). We found that participants were equally good at making these across-dimensions confidence judgments as when choices were restricted to a single dimension, suggesting that visual confidence judgments share a common currency. In another experiment, we asked observers to make confidence-choice judgments between two ensembles of 2, 4, or 8 stimuli. We found that participants were increasingly good at making ensemble confidence judgments, suggesting that visual confidence judgments can accumulate information across several trials. Overall, these results help us better understand how visual confidence is computed and used over time and across stimulus dimensions.

Meeting abstract presented at VSS 2017

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