September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Humans maintain probabilistic belief states when tracking occluded objects
Author Affiliations
  • Matjaz Jogan
    Department of Psychology, University of Pennsylvania
  • Alan He
    Department of Bioengineering, University of Pennsylvania
  • Alexander Tank
    Department of Statistics, University of Washington
  • Alan Stocker
    Department of Psychology, University of Pennsylvania
Journal of Vision September 2015, Vol.15, 188. doi:
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      Matjaz Jogan, Alan He, Alexander Tank, Alan Stocker; Humans maintain probabilistic belief states when tracking occluded objects. Journal of Vision 2015;15(12):188.

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

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Humans are quite accurate in tracking moving objects whose trajectories are occluded. An intuitive sense for the laws of physics (so-called "intuitive physics") allows us to predict the future position of a dynamic object based on initial information about its motion. Recent experimental results suggest that these predictions reflect the outcome of a probabilistic inference process based on noisy observations and an accurate physics model of the world. However, it remains unknown whether humans mentally track and update i) an estimate or ii) a full probabilistic description of the object position (belief state). We designed a set of psychophysical experiments to specifically distinguish the two hypotheses. Subjects were first asked to predict the collision location of a moving object with a hidden wall. The trajectory of the object was occluded and subjects were only given the object's initial motion and an acoustic signal at the precise time of collision. Subjects exhibited clear biases in their location estimates that indicated that they were performing probabilistic inference using prior expectations over speed and location. Subjects then repeated the experiment receiving, however, an additional spatial cue about the hidden wall location. By introducing different levels of uncertainty associated with this cue we expected subjects to assign different relative weights in combining the cues if they were maintaining full belief states while tracking. More specifically, by measuring subjects performance for each cue alone we were able to individually predict optimal behavior and verify whether it matched subjects' actual behavior. We found that subjects' behavior was indeed well predicted by a Bayesian belief state model that optimally combined cues across space, time, and object motion. Our results suggest that humans maintain and update full belief states when predicting object trajectories.

Meeting abstract presented at VSS 2015


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