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Keith May, Li Zhaoping, Paul Hibbard; Effects of image statistics on stereo coding in human vision. Journal of Vision 2010;10(7):359. doi: https://doi.org/10.1167/10.7.359.
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© ARVO (1962-2015); The Authors (2016-present)
Biological visual systems continuously optimize themselves to the prevailing image statistics, which gives rise to the phenomenon of adaptation. For example, post-adaptation color appearance can be explained by efficient coding which appropriately combines the input cone channels into various chromatic and achromatic channels with suitable gains that depend on the input statistics [Atick, J.J., Li, Z. ∓ Redlich, A.N. (1993). Vision Research, 33, 123-129]. In this study we focus on the ocular channels corresponding to the two eyes. We investigated how image statistics influence the way human vision combines information from the two eyes. Efficient coding in ocular space [Li, Z. ∓ Atick, J.J. (1994) Network, 5, 157-174] predicts that the binocularity of neurons should depend on the interocular correlations in the visual environment: As the interocular correlations increase in magnitude, the neurons should become more binocular. In natural viewing conditions, interocular correlations are higher for horizontal than vertical image components, because vertical binocular disparities are generally smaller than horizontal disparities. Thus, adaptation to natural stereo image pairs should lead to a greater level of binocularity for horizontally-tuned neurons than vertically-tuned neurons, whereas adaptation to pairs of identical natural images should not. We used interocular transfer of the tilt illusion as an index of binocularity of neurons with different characteristics. Subjects adapted either to natural stereo pairs or pairs of identical natural images. As predicted, interocular transfer was higher for near-horizontal than near-vertical stimuli after adaptation to natural stereo pairs, but not after adaptation to pairs of identical natural images.
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