2011
DOI: 10.1073/pnas.1101430108
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Bayesian sampling in visual perception

Abstract: It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to samples from the probability distribution over image interpretations, a form of sampling that we refer to as Bayesian sam… Show more

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Cited by 149 publications
(141 citation statements)
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“…The imperfection arises in part from the fact that the global workspace reduces complex parallel sensory streams of probabilistic computation to a single conscious sample (27)(28)(29). Thus, probabilistic information is often lost on the way, and subjects feel over-confident in the accuracy of their perception.…”
Section: Dissociations Between C1 and C2mentioning
confidence: 99%
See 1 more Smart Citation
“…The imperfection arises in part from the fact that the global workspace reduces complex parallel sensory streams of probabilistic computation to a single conscious sample (27)(28)(29). Thus, probabilistic information is often lost on the way, and subjects feel over-confident in the accuracy of their perception.…”
Section: Dissociations Between C1 and C2mentioning
confidence: 99%
“…Such unconscious systems compute with probability distributions, but only a single sample, drawn from this probabilistic distribution, becomes conscious at a given time (27,28). We may become aware of several alternative interpretations, but only by sampling their unconscious distributions over time (29,30).…”
Section: Relation Between Consciousness and Attentionmentioning
confidence: 99%
“…MCMC algorithms have recently been proposed as models for the short-timescale dynamics of perceptual inferences in the brain (Gershman et al, 2009;Sundareswara & Schrater, 2008;Moreno-Bote et al, 2011), but they are also well-suited to understanding the much longer-term dynamics of learning.…”
Section: Probabilistically Accept Proposalmentioning
confidence: 99%
“…Second, MCMC can explain important aspects of cognitive phenomena ranging from category learning (Sanborn, Griffiths, & Navarro, 2010) to the temporal dynamics of multistable perception (Gershman, Vul, & Tenenbaum, 2012;Moreno-Bote, Knill, & Pouget, 2011), causal reasoning in children , and developmental changes in cognition . Third, MCMC is biologically plausible in that it can be efficiently implemented in recurrent networks of biologically plausible spiking neurons (Buesing, Bill, Nessler, & Maass, 2011).…”
Section: Resource-rational Analysis Of Numerical Estimationmentioning
confidence: 99%