September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Integrating feedback to improve reaching estimates in virtual reality
Author Affiliations
  • Taren Rohovit
    University of Utah
  • Holly Gagnon
    University of Utah
  • Hunter Finney
    University of Utah
  • Yu Zhao
    Vanderbilt University
  • John Franchak
    University of California, Riverside
  • Jeanine Stefanucci
    University of Utah
  • Sarah Creem-Regehr
    University of Utah
  • Bobby Bodenheimer
    Vanderbilt University
Journal of Vision September 2021, Vol.21, 2162. doi:https://doi.org/10.1167/jov.21.9.2162
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      Taren Rohovit, Holly Gagnon, Hunter Finney, Yu Zhao, John Franchak, Jeanine Stefanucci, Sarah Creem-Regehr, Bobby Bodenheimer; Integrating feedback to improve reaching estimates in virtual reality. Journal of Vision 2021;21(9):2162. https://doi.org/10.1167/jov.21.9.2162.

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

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Abstract

Immersive virtual environments (IVEs) now allow for visual representations of a virtual body that have precise motion tracking for mimicking action. IVEs thus offer a powerful tool for studying visual perception and its relation to action capabilities and action calibration. Judgments of action capabilities in IVEs represent an objective measure for determining how closely a viewer’s perception of space may match that of the real world. The current study evaluated judgments of reaching capabilities (both reaching up and reaching out) within an IVE. Our goal was to determine to what extent feedback from actual reaching improved participants’ abilities to accurately make reachability judgments and if recalibration due to feedback differed across reaching behaviors. Participants completed alternating blocks of adjustment trials (perceptual estimations) and feedback trials. In adjustment trials, participants viewed an object starting close or far from reach and adjusted the location to where they believed they could just reach it. In feedback trials, participants viewed targets that were farther or closer than their actual reaching ability (blocks consisted of +-30%, +-20%, +-10%, and +-5% of the participants actual ability). They then decided whether the target was reachable and reached out to the target to receive visual feedback from a hand-held virtual controller. We found that for both reaching behaviors, reach was initially overestimated, and then perceptual estimations became more accurate as the feedback blocks progressed. Accuracy in the feedback trials was lower for targets just beyond reach (5%), suggesting that these targets were more difficult to judge correctly. This study establishes a straightforward methodology that can be used for calibration of actions in IVEs and has implications for applications that depend on accurate reaching within IVEs.

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