Perceptual multistability predicted by search model for Bayesian decisions

Abstract

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 in finite 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. Figure 15. Assessing stationarity using survival probability curves. (A) No-context condition: Because the dark-blue (V A survival) has the same asymptote as the light-blue curve (1j V B survival), the survival of a percept at asymptote is independent of initial state-indicating a stationary process (B) Similar behavior is observed in the case of context. The two red survival curves correspond to V A context and the two green curves to V B context. 1 SE bars are shown.

Bayesian sampling in visual perception

“…With such stimuli, perception alternates stochastically between two possible interpretations, a behavior consistent with sampling as suggested by several works (16,19,20). However, the key question is what probability distribution is being sampled.…”

“…Our study is not the first study to investigate cue combination and perceptual bistability, but previous works did not test whether bistable perception is akin to what we defined as Bayesian sampling (19,20). The fact that bistable perception alternates between two interpretations is certainly suggestive of a sampling process but not necessarily of Bayesian sampling.…”

“…5B), as observed in our experimental data. The kind of analysis that has been used in previous studies to argue that bistable perception is a form of sampling (19,20) would also conclude that this network is sampling. However, this particular network does not perform Bayesian sampling; it does not follow the multiplicative rule (Fig.…”

Multistability and Perceptual Inference

“…Similarly, existing rational process models of multistability (Dayan, 1998;Schrater and Sundareswara, 2007;Sundareswara and Schrater, 2008) add specific adaptation-or memory-like constraints to produce this effect. In our model, these additional memory constraints are unnecessary because the spatial coupling of adjacent nodes in the latent scene creates a form of memory in the inference.…”

“…Moreover, when MCMC algorithms are applied to graphical models, they produce spatiotemporal dynamics in inference that correspond to experimentally observed phenomena of multistability, as we explore below. Prior work has argued that stochastic sampling produces perceptual multistability (Schrater and Sundareswara, 2007;Sundareswara and Schrater, 2008) in the case of the Necker cube. We go beyond this work by showing that a rational MCMC algorithm for approximate Bayesian inference in graphical models produces a broad range of perceptual multistability phenomena while approximating a rational solution to a real vision problem: inferring the correct image interpretation from noisy binocular inputs.…”