Perceptual multistability predicted by search model for Bayesian decisions

Perceptual multistability refers to the phenomenon of spontaneous perceptual switching between two or more likely interpretations of an image. Although frequently explained by processes of adaptation or hysteresis, we show that perceptual switching can arise as a natural byproduct of perceptual decision making based on probabilistic (Bayesian) inference, which interprets images by combining probabilistic models of image formation with knowledge of scene regularities. Empirically, we investigated the effect of introducing scene regularities on Necker cube bistability by flanking the Necker cube with fields of unambiguous cubes that are oriented to coincide with one of the Necker cube percepts. We show that background cubes increase the time spent in percepts most similar to the background. To characterize changes in the temporal dynamics of the perceptual alternations beyond percept durations, we introduce Markov Renewal Processes (MRPs). MRPs provide a general mathematical framework for describing probabilistic switching behavior infinite state processes. Additionally, we introduce a simple theoretical model consistent with Bayesian models of vision that involves searching for good interpretations of an image by sampling a posterior distribution coupled with a decay process that favors recent to old interpretations. The model has the same quantitative characteristics as our human data and variation in model parameters can capture between-subject variation. Because the model produces the same kind of stochastic process found in human perceptual behavior, we conclude that multistability may represent an unavoidable by-product of normal perceptual (Bayesian) decision making with ambiguous images.