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Neural Comput. 2018 Oct 12:1-38. doi: 10.1162/neco_a_01142. [Epub ahead of print]

Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures.

Author information

1
Département de Mathématique et Applications, École Normale Supérieure, Paris 75005, France; UNIC, Gif-sur-Yvette 91190, France; and CNRS, France jonathan.vacher@ens.fr.
2
Institut des Neurosciences de la Timone, Marseille 13005, France, and Faculty of Science and Technology, Bournemouth University, Poole BH12 5BB, U.K. ameso@bournemouth.ac.uk.
3
Institut de Neurosciences de la Timone, Marseille 13005, France, and CNRS, France Laurent.Perrinet@univ-amu.fr.
4
Département de Mathématique et Applications, École Normale Supérieure, Paris 75005, France, and CNRS, France gabriel.peyre@ens.fr.

Abstract

A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The study presented here details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as three-dimensional gaussian fields. Second, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real-time, on-the-fly texture synthesis using time-discretized autoregressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log likelihood of the probability density. The log likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicate previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a gaussian likelihood centered at the true speed and a spatial frequency dependent width with a "slow-speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width.

PMID:
30314424
DOI:
10.1162/neco_a_01142

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