August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Travel distance estimation from biological motion and optic flow
Author Affiliations & Notes
  • Anna-Gesina Hülemeier
    University of Münster
  • Markus Lappe
    Tulane University
  • Footnotes
    Acknowledgements  This work was supported by the German Research Foundation (DFG La 952-7).
Journal of Vision August 2023, Vol.23, 4826. doi:
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      Anna-Gesina Hülemeier, Markus Lappe; Travel distance estimation from biological motion and optic flow. Journal of Vision 2023;23(9):4826.

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

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Humans use visual motion to estimate how far they walked. In static environments, this visual motion is the optic flow. In populated scenes, the biological motion of other people destroys the one-to-on correspondence between optic flow and travel distance. We investigated how observers estimate travel distance when walking in a crowded environment. In three conditions, participants saw self-motion through a crowd of standing, approaching, or leading point-light walkers. For a standing crowd, optic flow is a veridical signal for distance perception. For an approaching crowd, the visual motion is the sum of the self-motion-induced optic flow and the optic flow produced by the approaching walkers. In contrast, when the self-motion follows a leading crowd, in which walkers keep their distance from the observer, no optic flow is produced. All conditions also contained a ground plane consisting of stripes oriented parallel to the self-motion that contained no optic flow cues but perspective cues to static distance. After each trial, participants (n = 25) reported their traveled distance by adjusting a target on the ground plane to match the distance of their travel. We found that, overall, distance estimation was quite similar in all conditions showing that biological motion information can be used (a) to compensate for excessive optic flow in the approaching condition and (b) to generate distance information in the leading condition. All conditions were well fit by the leaky path integration model that describes a disproportional underestimation of long distances. Analysis of the fit parameters revealed slight but significant differences that illustrate how biological motion and optic flow are combined for travel distance estimation.


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