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Sabyasachi Shivkumar, Gregory C. DeAngelis, Ralf M. Haefner; A causal inference model for the perception of complex motion in the presence of self-motion. Journal of Vision 2020;20(11):1631. https://doi.org/10.1167/jov.20.11.1631.
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© ARVO (1962-2015); The Authors (2016-present)
Our subjective percept of object motion has been shown to systematically differ from the observed velocity on the retina due to motion of other objects (“hierarchical grouping”: Gershman et al. 2016) or due to our self-motion (“flow-parsing”: Warren et al. 2009). We present a hierarchical Bayesian model (and new data from two psychophysics experiments to support it) to unify our understanding of how motion context influences object motion perception.
The recurring motif in our hierarchical model is the decomposition of object motion into group motion and motion relative to the group. The prior over velocities is a mixture of a delta function at zero velocity and a Gaussian centered at zero. This modification of the classic slow-speed prior (Stocker et al. 2006) effectively performs "causal inference" (Kording et al. 2007) over whether the object is stationary or moving and leads to a hierarchical "chunking" into groups and supergroups (of groups) when applied to multiple visual elements. Our model infers individual motion relative to a group, and accounts for inferred self-motion based on optic flow.
In the two experiments, fixating subjects report the perceived direction of a peripheral object (patch of dots) using a dial. The object is surrounded by moving groups of dots in one experiment, and optic flow dots simulating self motion in the other experiment. Subject responses are systematically biased depending on the difference between object and group velocity: (a) towards group velocity for small differences, (b) away from group velocity for large difference, and (c) either of the two for intermediate differences, varying from trial to trial and reflecting the subject’s uncertainty over the correct causal model.
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