Perceptual multistability predicted by search model for Bayesian decisions.

By Rashmi Sundareswara, Paul R. Schrater. 2008

104 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

Unsupervised Learning of 3D Structure from Images (2016, methodology): “This is the simplest dataset we use and consists of 40⇥ 40⇥ 40 volumes with a 10⇥ 10⇥ 10 wire-frame cube drawn at a random orientation at the center of the volume [25].”
Theory learning as stochastic search in the language of thought (2012, background): “…have been proposed as models for the short-timescale dynamics of perceptual inferences in the brain (Gershman, Vul, & Tenenbaum, 2009; Moreno-Bote, Knill, & Pouget, 2011; Sundareswara & Schrater, 2008), but they are also well-suited to understanding the much longer-term dynamics of learning .”
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, methodology): “Sampling can naturally capture the effect of apparent stochasticity in neural responses and seems to be furthermore consistent with multiple experimental effects reported in cognitive science literature [17,18].”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, methodology): “MCMC algorithms have recently been proposed as models for the short-timescale dynamics of perceptual inferences in the brain [53, 173, 122], but they are also well-suited to understanding the much longer-term dynamics of learning.”
Bayesian sampling in visual perception (2011, background): “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.”
Probability Matching as a Computational Strategy Used in Perception (2010, background): “Sampling-based representational coding has been proposed to account for neurophysiological phenomena such as spontaneous neural activity [35] and variability in neural responses [36], as well as other stochastic perceptual phenomena such as bistability [37,38].”
Multistability and Perceptual Inference (2012, background): “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.”