Lookup NU author(s): Professor Jenny Read
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2019 The Authors.Early vision proceeds through distinct ON and OFF channels, which encode luminance increments and decrements respectively. It has been argued that these channels also contribute separately to stereoscopic vision. This is based on the fact that observers perform better on a noisy disparity discrimination task when the stimulus is a random-dot pattern consisting of equal numbers of black and white dots (a ''mixed-polarity stimulus,'' argued to activate both ON and OFF stereo channels), than when it consists of all-white or all-black dots (''same-polarity,'' argued to activate only one). However, it is not clear how this theory can be reconciled with our current understanding of disparity encoding. Recently, a binocular convolutional neural network was able to replicate the mixed-polarity advantage shown by human observers, even though it was based on linear filters and contained no mechanisms which would respond separately to black or white dots. Here, we show that a subtle feature of the way the stimuli were constructed in all these experiments can explain the results. The interocular correlation between left and right images is actually lower for the samepolarity stimuli than for mixed-polarity stimuli with the same amount of disparity noise applied to the dots. Because our current theories suggest stereopsis is based on a correlation-like computation in primary visual cortex, this postulate can explain why performance was better for the mixed-polarity stimuli. We conclude that there is currently no evidence supporting separate ON and OFF channels in stereopsis.
Author(s): Read JCA, Cumming BG
Publication type: Article
Publication status: Published
Journal: Journal of Vision
Online publication date: 07/06/2019
Acceptance date: 31/10/2018
Date deposited: 02/07/2019
ISSN (electronic): 1534-7362
Publisher: Association for Research in Vision and Ophthalmology
PubMed id: 31173632
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