Efficient coding in central visual processing: Variance predicts salience
Variance predicts salience in central sensory processing
Ann M. Hermundstad, John J. Briguglio, Mary M. Conte, Jonathan D. Victor, Vijay
Balasubramanian, and Gasper Tkacik
eLife 2014;10.7554/eLife.03722. (2014)
Abstract
Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime -- when sampling limitations constrain performance -- efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory
cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally,
where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability
Download preprint and SI
Download from eLife
Related paper: modeling discrimination of gray-level textures with spatial correlations
Related paper: efficient coding of image statistics -- extension to grayscale images
Related paper: perceptual space of local image statistics
Related paper: cortical locus of computation of multipoint correlations
Related paper: informative and uninformative high-order statistics in natural scenes
Related paper: generation of textures with specific low- and high-order statistics
Related paper: how local orientation signals combine
Related paper: simple mixtures of low- and high-order statistics
Review paper: textures as probes of visual processing
Publications related to texture and form processing
Return to publications list