Current Biology
Volume 27, Issue 10, 22 May 2017, Pages 1403-1412.e8
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Article
“What Not” Detectors Help the Brain See in Depth

https://doi.org/10.1016/j.cub.2017.03.074Get rights and content
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Highlights

  • The brain uses “what not” detectors to facilitate 3D vision

  • Binocular mismatches are used to drive suppression of incompatible depths

  • Proscription accounts for depth perception without binocular correspondence

  • A simple analytical model captures perceptual and neural responses

Summary

Binocular stereopsis is one of the primary cues for three-dimensional (3D) vision in species ranging from insects to primates. Understanding how the brain extracts depth from two different retinal images represents a tractable challenge in sensory neuroscience that has so far evaded full explanation. Central to current thinking is the idea that the brain needs to identify matching features in the two retinal images (i.e., solving the “stereoscopic correspondence problem”) so that the depth of objects in the world can be triangulated. Although intuitive, this approach fails to account for key physiological and perceptual observations. We show that formulating the problem to identify “correct matches” is suboptimal and propose an alternative, based on optimal information encoding, that mixes disparity detection with “proscription”: exploiting dissimilar features to provide evidence against unlikely interpretations. We demonstrate the role of these “what not” responses in a neural network optimized to extract depth in natural images. The network combines information for and against the likely depth structure of the viewed scene, naturally reproducing key characteristics of both neural responses and perceptual interpretations. We capture the encoding and readout computations of the network in simple analytical form and derive a binocular likelihood model that provides a unified account of long-standing puzzles in 3D vision at the physiological and perceptual levels. We suggest that marrying detection with proscription provides an effective coding strategy for sensory estimation that may be useful for diverse feature domains (e.g., motion) and multisensory integration.

Keywords

depth perception
convolutional neural network
da Vinci stereopsis
3D vision
wallpaper illusion
binocular disparity

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