September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Visual Prediction of Bounce Trajectories
Author Affiliations & Notes
  • Vivian C. Paulun
    Department of Psychology, Giessen University
  • Filipp Schmidt
    Department of Psychology, Giessen University
  • Florian S. Bayer
    Department of Psychology, Giessen University
  • Julia Wangler
    Department of Psychology, Giessen University
  • Joshua B. Tenenbaum
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
    Center for Brains, Minds and Machines, Massachusetts Institute of Technology
  • Roland W. Fleming
    Department of Psychology, Giessen University
    Center for Mind, Brain and Behavior, Marburg University and Giessen University
  • Footnotes
    Acknowledgements  This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–project number 222641018–SFB/TRR 135 TP C1 and project number PA 3723/1-1 by the European Research Council (ERC) Consolidator Award ‘SHAPE’–project number ERC-CoG-2015-682859.
Journal of Vision September 2021, Vol.21, 2492. doi:https://doi.org/10.1167/jov.21.9.2492
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      Vivian C. Paulun, Filipp Schmidt, Florian S. Bayer, Julia Wangler, Joshua B. Tenenbaum, Roland W. Fleming; Visual Prediction of Bounce Trajectories. Journal of Vision 2021;21(9):2492. doi: https://doi.org/10.1167/jov.21.9.2492.

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

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Abstract

To catch or avoid an object it is crucial to predict its future trajectory. Here, we test how well observers can predict the landing position of a bouncing object and identify the strategies they employ to solve this task. A large database (N=100,000) of short simulations of a cube bouncing in a 3-dimensional room allows us to measure the cube’s typical behavior, while its elasticity, initial orientation, position and velocity are varied. We randomly selected and rendered 240 animations from this database as stimuli. Fourteen observers saw either the first 10, 20, 30, 40, or 50 frames of each animation and had to predict where the cube would eventually come to rest. The number of remaining frames in which the cube was still moving (but that were not presented) varied between 10-107 frames. Observers responded by moving a marker to the predicted position on the floor, indicated their certainty by adjusting its radius, and then saw the remaining frames for feedback. We found that observers make systematic predictions of the cube’s final position and are better than chance for predictions of up to about 60 frames. Unsurprisingly, observers were more accurate the fewer frames (and thus fewer bounces) they had to predict. For their predictions, observers took into account the final (i.e., the last visible) direction along which the cube was moving and often predicted the landing position to be in a similar direction. Thus, the closer the trajectory was to a straight line after disappearance, the more accurate they were. Furthermore, observers predicted longer paths for cubes that moved faster before they disappeared. Presumably, observers use a simplified mental simulation strategy, which—because it is physically inaccurate—accumulates errors over time and therefore produces a larger divergence from the computer-simulated landing position with a growing number of simulation steps.

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