Symmetry of generalized rivalry network models determines patterns of interocular grouping in four-location binocular rivalry

Effects of interocular grouping demands on binocular rivalry

Binocular rivalry (BR) is a visual phenomenon in which perception alternates between two non-fusible images presented to each eye. Transition periods between dominant and suppressed images are marked by mixed percepts, where participants report fragments of each image being dynamically perceived. Interestingly, BR remains robust even when typical images are subdivided and presented in complementary patches to each eye, a phenomenon termed interocular grouping (IOG). The objective of the present study was to determine if increasing grouping demand in the context of BR changes the perceptual experience of rivalry. In 48 subjects with normal vision, mean dominant and mixed percept durations were recorded for classic BR and IOG conditions with increasing grouping demands from two, four, and six patches. We found that, as grouping demands increased, the duration of mixed periods increased. Indeed, durations of dominant and mixed percepts, as well as percentage of time spent in dominant or mixed state, differed significantly across conditions. However, durations of global dominant percepts remained relatively stable and saturated at about 1.5 seconds, despite the exponential increase in possible mixed combinations. Evidence shows that this saturation followed a nonlinear trend. The data also indicate that grouping across the vertical meridian is slightly more stable than for the horizontal meridian. Finally, individual differences in speed of alternation identified during BR were maintained in all interocular grouping conditions. These results provide new information about binocular visual spatial integration and will be useful for future studies of the underlying neural substrates and models of binocular vision.

New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics

When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.

A hierarchical model of perceptual multistability involving interocular grouping

Ambiguous visual images can generate dynamic and stochastic switches in perceptual interpretation known as perceptual rivalry. Such dynamics have primarily been studied in the context of rivalry between two percepts, but there is growing interest in the neural mechanisms that drive rivalry between more than two percepts. In recent experiments, we showed that split images presented to each eye lead to subjects perceiving four stochastically alternating percepts (Jacot-Guillarmod et al. Vision research, 133, 37-46, 2017): two single eye images and two interocularly grouped images. Here we propose a hierarchical neural network model that exhibits dynamics consistent with our experimental observations. The model consists of two levels, with the first representing monocular activity, and the second representing activity in higher visual areas. The model produces stochastically switching solutions, whose dependence on task parameters is consistent with four generalized Levelt Propositions, and with experiments. Moreover, dynamics restricted to invariant subspaces of the model demonstrate simpler forms of bistable rivalry. Thus, our hierarchical model generalizes past, validated models of binocular rivalry. This neuromechanistic model also allows us to probe the roles of interactions between populations at the network level. Generalized Levelt's Propositions hold as long as feedback from the higher to lower visual areas is weak, and the adaptation and mutual inhibition at the higher level is not too strong. Our results suggest constraints on the architecture of the visual system and show that complex visual stimuli can be used in perceptual rivalry experiments to develop more detailed mechanistic models of perceptual processing.