Biologically Inspired Dynamic Textures for Probing Motion Perception
Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative model specifically crafted to probe motion perception. In that context, we first provide an axiomatic, biologically-driven derivation of the model. This model synthesizes random dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns. Importantly, we show that this model can equivalently be described as a stochastic partial differential equation. Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework. Finally, we apply these textures in order to psychophysically probe speed perception in humans. In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.
The paper describes a novel way to parameterize stimuli for generating space-time texture fields modeled after real-world deformations. The intent is to provide a more rigid framework by which scientists may psychophysically test motion perception. The model allows one to generate causal time-structured "Motion Clouds" in real-time. Motion Clouds are textured stimuli, which could be gabor-like or described by other functions. The model includes independent variables to describe motion, position, and warping. Warping describes texture zooming and rotations. They further suggest parameter distributions and stimulus properties that match well with existing psychophysical data. They use their model to generate stimuli for a speed discrimination psychophysical task and demonstrate their ability to assess how the stimulus spatial frequency affects speed discrimination. I interpret their contribution as a GPU ready method for generating real-time stimuli that is parameterized in a meaningful way for studying texture-motion related psychophysical phenomena. A further advantage of this formalism is that it allows for statements of the model within a Bayesian framework. This sets the stage for psychophysical experiments. The main result of these (although the dataset is small) is that spatial frequency has a positive effect on perceived speed: stimuli of lower frequency with respect to the reference are perceived as going slower. The Bayesian model was able to explain these systematic biases for spatial frequency as shifts in priors on speed during the speed judgements. Overall I find the data part of this paper to be less satisfying because there is insufficient data for detailed analysis within the Bayesian frame work and the authors are unable to interpret some of their results.Other comments:The paper has some formatting problems (figure Legends not sufficiently different from text)that make it difficult to read.Note: The discussion section is cut off in the second sentence and doesn't continue. It seems as though they lost some text somewhere...