Vision as Bayesian inference: analysis by synthesis?

Yuille, Alan; Kersten, Daniel

doi:10.1016/j.tics.2006.05.002

Cited by 745 publications

(615 citation statements)

References 41 publications

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“…The role of Bayesian inference in our analysis of children's ability to combine statistical evidence and constraints from naive theories was intended to be similar to that of ideal observer analysis in vision (e.g., Yuille & Kersten, 2006) and rational analysis in the study of adult cognition (e.g., Anderson, 1990;Marr, 1982;Shepard, 1987). Bayesian inference provides a rational solution to the problem of updating one's beliefs in the light of new evidence and can thus guide researchers in exploring how well children solve this problem.…”

Section: Discussionmentioning

confidence: 99%

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Schulz¹,

Bonawitz²,

Griffiths³

2007

Developmental Psychology

158

119

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Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB 3 E; CA 3 E; AD 3 E; and so forth. In 1 story, all variables came from the same domain; in the other, the recurring candidate cause, A, came from a different domain (A was a psychological cause of a biological effect). After receiving this statistical evidence, children were asked to identify the cause of the effect on a new trial. Consistent with the predictions of a Bayesian model, all children were more likely to identify A as the cause within domains than across domains. Whereas 3.5-year-olds learned only from the within-domain evidence, 4-and 5-year-olds learned from the cross-domain evidence and were able to transfer their new expectations about psychosomatic causality to a novel task.Keywords: domain-general and domain-specific causal learning, Bayesian models, naive theories, ambiguous evidence, psychosomatic causes Supplemental materials: http://dx.doi.org/10.1037/0012-1649. 43.5.1124.supp By the time children are 5 years old, they understand causal relationships in a variety of domains (Flavell, Green, & Flavell, 1995;Gelman & Wellman, 1991;Gopnik & Meltzoff, 1997;Inagaki & Hatano, 1993;Kalish, 1996;Perner, 1991;Shultz, 1982;Spelke, Breinlinger, Macomber, & Jacobson, 1992). Many researchers have suggested that children's causal knowledge can be best characterized as a set of naive theories: abstract, coherent representations of causal structure that support prediction, intervention, explanation, and counterfactual claims (Carey, 1985;Gopnik, 1988;Gopnik & Meltzoff, 1997;Harris, German, & Mills, 1996;Hickling & Wellman, 2001;Keil, 1989;Perner, 1991;Sobel, 2004;Wellman, 1990;Wellman, Hickling, & Schult, 1997). The view that children's causal representations resemble scientific theories (the theory theory) suggests both that patterns of evidence should affect children's causal commitments and that children's causal commitments should affect their interpretation of evidence. Indeed, this dynamic relationship between domain-appropriate causal beliefs and evidence has been taken as a defining feature of theories (e.g., Gopnik & Meltzoff, 1997).However, despite the expectation that theory and evidence should interact, developmental psychologists have been largely divided between accounts of causal reasoning emphasizing either domain-specific causal knowledge or domain-general learning from data. Thus, some researchers have suggested that children's naive theories might be generated by domain-specific modules (Leslie, 1994;Scholl & Leslie, 1999) or innate concepts in core domains (Carey & Spelke, 1994;Keil, 1995), whereas other researchers have focused on children's ability to learn causal rel...

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Section: Discussionmentioning

confidence: 99%

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Schulz¹,

Bonawitz²,

Griffiths³

2007

Developmental Psychology

158

119

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“…If the predictions are good, then the bottom-up signal will be explained away such that only the discrepancies between prediction and driving signalthe prediction error signal -remains as a bottom-up signal. As predictions get better, there will be less error signal associated with a given stimulus at relatively lower levels in the neural system (Friston, 2005;Yuille & Kersten, 2006). This suppression of best predicted input will be central for the explanation of rivalry.…”

Section: Explaining Away Of Bottom-up Signalmentioning

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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“…While models of object recognition that explicitly reconstruct the input are common in probabilistic modeling (conceptually discussed e.g. in Yuille and Kersten 2006), it is still unclear, how such reconstructions are realized by the brain. Hopefully this work can contribute to deepening our insight in this respect.…”

Section: Discussionmentioning

confidence: 99%

Information Routing, Correspondence Finding, and Object Recognition in the Brain

Wolfrum¹

2010

Studies in Computational Intelligence

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This work would not have been possible without the people I have had the honor of working with over the past years. First of all I want to thank Christoph von der Malsburg, whose scientific concepts have inspired large parts of this thesis, and who granted me enough freedom to follow my own research directions while at the same time providing motivation whenever this was necessary. I also thank Rudolf Mester for good discussions that helped me retain my engineering viewpoint on the interdisciplinary problems that I worked on. Discussions with Geoff Goodhill, Bruno Olshausen, Jochen Triesch, and Alan Yuille, among others, have shaped my thinking and had an important impact on this thesis.I thank Urs Bergmann, Jenia Jitsev, and Junmei Zhu for proof-reading parts of the thesis, and Jörg Lücke for good collaboration. The neurogroups at FIAS have provided a rich and stimulating environment, it has been fun working and thinking with you! I also thank all colleagues at FIAS for good company and the truly interdisciplinary interaction we had (e.g. at Hirschegg). Last but not least I owe this thesis to my parents, who planted curiosity and a desire for understanding in me that were stronger than the difficulties I met during this dissertation. iv

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Vision as Bayesian inference: analysis by synthesis?

Cited by 745 publications

References 41 publications

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Predictive coding explains binocular rivalry: An epistemological review

Information Routing, Correspondence Finding, and Object Recognition in the Brain

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