Task-Specific Optimal Encoding and Decoding

Task-Specific Optimal Encoding and Decoding W Geisler, J Burge, A D'Antona, J S Perry
Center for Perceptual Systems, University of Texas, Austin, TX, United States
The visual system of an organism is likely to be well-matched to the specific tasks that the organism performs. Thus, for any natural task of interest, it is often valuable to consider how to perform the task optimally, given the statistical properties of the natural signals and the relevant biological constraints. Such a 'natural systems analysis' can provide a deep computational understanding of the natural task, as well as principled hypotheses for perceptual mechanisms that can be tested in behavioral and/or neurophysiological experiments. To illustrate this approach, I will briefly summarize the key concepts of Bayesian ideal observer theory for estimation tasks, and then show how those concepts can be applied to the tasks of binocular-disparity (depth) estimation and occluded-point estimation in natural scenes. In the case of disparity estimation, the analysis shows that many properties of neurons in early visual cortex, as well as properties of human disparity discrimination performance, follow directly from first principles; i.e., from optimally exploiting the statistical properties of the natural signals, given the biological constraints imposed by the optics and geometry of the eyes. In the case of occluded-point estimation, the analysis shows that almost all the relevant image information is contained in the immediate neighborhood of the occluded point, and that optimal performance requires encoding and decoding absolute intensities; the pattern of relative intensities (the contrast image) is not sufficient for optimal performance. Psychophysical measurements show that human estimation accuracy is sub-optimal, but that humans closely match an ideal observer that uses only the relative intensities. I conclude that analysis of optimal encoding and decoding in specific natural tasks is a powerful approach for investigating the mechanisms of visual perception in humans and other organisms.

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