Object Perception as Bayesian Inference

Kersten, Daniel; Mamassian, Pascal; Yuille, Alan

doi:10.1146/annurev.psych.55.090902.142005

Cited by 1,152 publications

(904 citation statements)

References 142 publications

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“…Although judgments do sometimes adhere to Bayesian principles remarkably well (Ajzen, 1977;Griffiths & Tenenbaum, 2006;Kersten, Mamassian, & Yuille, 2004), human judgment certainly does not obey Bayes's Law perfectly (Edwards, 1968;Grether, 1990;McKelvey & Page, 1990).…”

Section: A Disclaimermentioning

confidence: 99%

The trouble with overconfidence.

Moore

Healy

2008

Psychological Review

1,902

1,414

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The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.

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Section: A Disclaimermentioning

confidence: 99%

The trouble with overconfidence.

Moore

Healy

2008

Psychological Review

1,902

1,414

View full text Add to dashboard Cite

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“…The posterior probability depends on the likelihood (i.e., how well the hypothesis predicts the input); and on the prior probability of the hypothesis (i.e., how probable the hypothesis was before the input) (Friston, 2002;Kersten et al, 2004;Murray, Kersten, Olshausen, Schrater, & Woods, 2002). These prior expectations are constructed hierarchically and are context-sensitive.…”

Section: Bayesian Perceptual Inferencementioning

confidence: 99%

“…More recently, it has been proposed that this intuitive idea can be captured in terms of hierarchical Bayesian inference, using generative models with predictive coding or free-energy minimisation; and that this is the main neurocomputational principle for the brain's perception of the environment as well as its learning of new contingencies (Ballard, Hinton, & Sejnowski, 1983;Dayan, Hinton, Neal, & Zemel, 1995;Friston, 2002;Friston, 2003;Friston, 2005;Kawato, Hayakawa, & Inui, 1993;Kersten, Mamassian, & Yuille, 2004;Knill & Pouget, 2004;Mumford, 1992;Murray, Schrater, & Kersten, 2004;Rao & Ballard, 1999).…”

Section: Introductionmentioning

confidence: 99%

Predictive coding explains binocular rivalry: An epistemological review

2008

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a b s t r a c tBinocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.

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“…The Bayesian framework for perceptual inference has its origins in Helmholtz's notion of perception as unconscious inference. Helmholtz realised that retinal images are ambiguous and that prior knowledge was required to account for perception (Kersten et al 2004). Kersten et al (2004) provide an excellent review of object perception as Bayesian inference and ask a fundamental question "Where do the priors come from?…”

Section: Perception and Sensationmentioning

confidence: 99%

“…Many people now regard the brain as an inference machine that conforms to the same principles that govern the interrogation of scientific data (MacKay 1956;Neisser 1967;Ballard et al 1983;Mumford 1992;Kawato et al 1993;Rao and Ballard 1998;Dayan et al 1995;Friston 2003;Körding and Wolpert 2004;Kersten et al 2004;Friston 2005). In everyday life, these rules are applied to information obtained by sampling the world with our senses.…”

Section: Introductionmentioning

confidence: 99%

Free-energy and the brain

2007

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If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.

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Object Perception as Bayesian Inference

Cited by 1,152 publications

References 142 publications

The trouble with overconfidence.

The trouble with overconfidence.

Predictive coding explains binocular rivalry: An epistemological review

Free-energy and the brain

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