Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.

Tiberiu Tesileanu,John J Briguglio,Vijay Balasubramanian,Jonathan D Victor,Mary M Conte,Ann M Hermundstad

doi:10.7554/elife.54347

Tiberiu Tesileanu, John J Briguglio + Show 4 more

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https://doi.org/10.7554/elife.54347

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Abstract

Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

Highlights

Neural circuits in the periphery of the visual [2, 3, 4, 5, 6, 7, 8, 9, 10], auditory [11, 12, 13, 14], and perhaps olfactory [15] systems use limited resources efficiently to represent sensory information by adapting to the statistical structure of the environment [16]
There is some evidence that this sort of efficient coding might occur more centrally, in the primary visual cortex [17, 18, 19, 20] and perhaps in the entorhinal cortex [21]
Because of translation invariance in natural images, the luminance histograms at each location must be the same, leaving only 2 independent dimensions of texture space from the single-check statistics

Summary

Introduction

Neural circuits in the periphery of the visual [2, 3, 4, 5, 6, 7, 8, 9, 10], auditory [11, 12, 13, 14], and perhaps olfactory [15] systems use limited resources efficiently to represent sensory information by adapting to the statistical structure of the environment [16]. Efficient coding implies that the threshold for perceiving a complex sensory cue, which depends on the collective behavior of many cells in a cortical circuit, should be set by its variance in the natural environment. The authors of [1] argued instead that texture perception occurs in a regime where sampling noise is the limiting factor. This leads to the opposite prediction [1, 22, 23], namely that high variance should lead to a low detection threshold, summarized as variance is salience [1]. Tests of this prediction in [1, 22] showed that it holds for the visual detection of simple black-and-white binary textures

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