Technical Program

Paper Detail

Paper: PS-1A.53
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: A causal inference model for the perception of complex motion in the presence of self-motion
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Sabyasachi Shivkumar, Gregory DeAngelis, Ralf Haefner, University of Rochester, United States
Abstract: Our subjective percept of motion differs from the actual velocity on the retina in striking ways that have traditionally been studied by disparate fields. Here, we present a Bayesian model, as well as new data to support it, that unifies our understanding of motion perception of complex objects (``grouping'') with our understanding of the influence of self-motion on our perception (``flow-parsing''). The central (recurring) motif in our hierarchical model is a prior over velocity consisting of a mixture of both a delta and a Gaussian centered on zero. This simple modification of the classic slow speed prior implies a ``causal inference'' process over whether the object is stationary or moving. Applied to multiple visual elements it leads to a ``chunking'' of these elements into groups, and groups of groups, with the goal to make the relative speed of as many of the elements zero with respect to the group they are inferred to belong to. As a result, our model infers individual motion relative to a group, and accounts for any inferred self-motion based on optic flow. Preliminary data from two experiments confirm new predictions of the model.