December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Eye-movements during active sensing suffer from a confirmation bias
Author Affiliations
  • Ralf M Haefner
    Brain & Cognitive Sciences, Center for Visual Science, University of Rochester
  • Sabyasachi Shivkumar
    Brain & Cognitive Sciences, Center for Visual Science, University of Rochester
  • Ankani Chattoraj
    Brain & Cognitive Sciences, Center for Visual Science, University of Rochester
  • Yong Soo Ra
    Seoul National University
Journal of Vision December 2022, Vol.22, 4395. doi:https://doi.org/10.1167/jov.22.14.4395
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      Ralf M Haefner, Sabyasachi Shivkumar, Ankani Chattoraj, Yong Soo Ra; Eye-movements during active sensing suffer from a confirmation bias. Journal of Vision 2022;22(14):4395. https://doi.org/10.1167/jov.22.14.4395.

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

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Abstract

Human decision-making suffers from a range of biases, with the confirmation bias being one of the most ubiquitous ones (Nickerson 1998). Studying it in the context of perceptual decision-making using psychophysical experiments allows for robust insights based on thousands of trials, free of many confounds present in higher level cognitive contexts. Recent work showed that one type of confirmation bias - a biased interpretation of new information underlies the overweighting of evidence presented early in a trial (Lange et al. 2021). Here, we asked whether another type of confirmation bias - a biased seeking of new evidence - occurs in the context of an active sensing task involving saccades. Prior perceptual studies found no such biases (Najemnik & Geisler, 2005; Yang et al. 2016). We designed a new gaze-contingent task that required the observer to collect new sensory information by making saccades to peripheral targets. Our task design gives us precise control over the stimulus present in fovea and periphery and allows us to compute the frequency with which saccades are made to peripheral targets that agree with the observer's belief. We found that 14/16 observers were more likely to saccade to peripheral locations that they expected would yield new information that agreed with their current belief (12/16 individually statistically significant). Interestingly, the ideal observer for our task also has a small confirmation bias. However, the empirical bias of most observers was substantially larger. We could quantitatively account for the data by assuming that observers employed an approximate Bayesian active sensing strategy. Model comparison revealed that human observers deviated from the ideal observer in terms of model mismatch, and in terms of approximate computations. Interestingly, the data implies that the 'sensory computations' required by the Bayesian observer are more precise than its 'cognitive computations', as suggested by prior work.

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