Binocular rivalry and cross-orientation suppression are well-studied forms of competition in visual cortex, but models of these two types of competition are in tension with one another. Binocular rivalry occurs during the presentation of dichoptic grating stimuli, where two orthogonal gratings presented separately to the two eyes evoke strong alternations in perceptual dominance. Cross-orientation suppression occurs during the presentation of plaid stimuli, where the responses to a component grating presented to both eyes is weakened by the presence of a superimposed orthogonal grating. Conventional models of rivalry that rely on strong competition between orientation-selective neurons incorrectly predict rivalry between the components of plaids. Lowering the inhibitory weights in such models reduces rivalry for plaids, but also reduces it for dichoptic gratings. Using an exhaustive grid search, we show that this problem cannot be solved simply by adjusting the parameters of the model. Instead, we propose a robust class of models that rely on ocular opponency neurons, previously proposed as a mechanism for efficient stereo coding, to yield rivalry only for dichoptic gratings, not for plaids. This class of models reconciles models of binocular rivalry with the divisive normalization framework that has been used to explain cross-orientation. Our model makes novel predictions that we confirmed with psychophysical tests.
Binocular rivalry is a visual illusion that occurs when the two eyes are presented with incompatible images. Instead of perceiving a mixture of the two images, most people tend to experiences alternations in which they only see one image at a time. Binocular rivalry is more than just an interesting illusion: it reflects actual competition between neurons in the brain, and therefore provides a rare window into neural dynamics. To help us understand these mechanisms, researchers have developed several computational models of binocular rivalry. Yet surprisingly, as we show in this paper, previous computational models of rivalry make an incorrect prediction. They predict that certain types of images (similar to checkerboards) will cause strong perceptual alternations even when viewed normally. Since this prediction doesn't hold up, the existing models must not be telling the whole story. In this paper, we develop a new model of binocular rivalry that doesn't make this prediction. The model also makes novel predictions – not made by conventional models – that stand up to experimental test. Our model thus provides a better account of how neurons in the visual system interact with one another.