Object perception as Bayesian inference.

By Daniel J. Kersten, Pascal Mamassian, Alan L. Yuille. 2004

1,303 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

The limits and potentials of deep learning for robotics (2018, background): “Since then, psychologists have devised various experiments to investigate these unconscious mechanisms [33], modernized Helmholtz’ original ideas [94], and reformulated them in the framework of Bayesian inference [55].”
Bayesian learning of visual chunks by human observers (2008, result): “Discussion The present series of computational analyses and empirical findings extend previous Bayesian ideal observer analyses (29, 30) that have provided valuable insights into sensory and perceptual processing, to the domain of learning at the level of perception.”
Bayesian decision theory in sensorimotor control. (2006, positive): “Within this framework many illusions and other visual effects can be understood [18] by making general assumptions about priors over possible visual objects [19, 20 ] or the direction of illumination [21,22].”
Reconciling Predictive Coding and Biased Competition Models of Cortical Function (2008, background): “Similarly, the biased competition model has previously been considered to be compatible with the fMRI data due to the mechanism of response reﬁnement (Kersten et al., 2004; Murray et al., 2004; Olshausen and Field, 2005).”
Opposite modulation of high- and low-level visual aftereffects by perceptual grouping (2012, positive): “Predictive coding models [4–8], for example, are one class of models that suggest that feedback may operate to reduce activity in lower areas.”
Action and behavior: a free-energy formulation (2010, background): “signals and provides a principled account of perception; i.e., the Bayesian brain (Helmholtz 1860/1962; Barlow 1969; Ballard et al. 1983; Mumford 1992; Dayan et al. 1995; Rao and Ballard 1998; Lee and Mumford 2003; Knill and Pouget 2004; Kersten et al. 2004; Friston and Stephan 2007;Deneve 2008).”
A default mode of brain function: A brief history of an evolving idea (2007, background): “…to and even predicting environmental demands In this regard, a useful conceptual framework from theoretical neuroscience posits that the brain operates as a Bayesian inference engine designed to generate predictions about the future (Olshausen, 2003; Kersten et al., 2004; Knill and Pouget, 2004).”
The sense of self in the phenomenology of agency and perception (2007, background): “At rest (resting quietly with the eyes open or closed), there is roughly the same ratio of oxygen consumption to oxygen delivery throughout the brain (Gusnard and Raichle 2001; Raichle et al. 2001).”
Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus (2009, background): “Feedback could also act as a bayesian style prior [68–70], and adapt early level signals according to different sensory or behavioral conditions [71].”
Bayesian sampling in visual perception (2011, methodology): “This assumption is known to provide a good approximation to neural variability in vivo (11).”
The neural basis of metacognitive ability (2012, background): “Before closing our discussion on measures of metacognition, we note that a separate line of research has assessed the extent to which humans and other species use, or represent, uncertainty about the consequences of their actions to optimise decision-making (see [45, 46] for reviews).”
How Do Expectations Shape Perception? (2018, background): “These two mechanisms, which have been referred to as ‘shutting up’ and ‘stop gossiping’ [4,104] (see also previous paragraph on dampening vs sharpening) are both accommodated within predictive coding, and operate on distinct populations (error and prediction units, respectively).”
1 On what it means to see , and what we can do about it (2008, positive): “…the gradual realization that the prime candidate framework for managing uncertainty — graphical models, or Bayes networks — is ubiquitous in biological vision (Kersten et al., 2004; Kersten and Yuille, 2003; Knill and Richards, 1996), as it is, indeed, in cognition in general (Chater et al., 2006).”
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior (2015, background): “Systems trying to explicitly incorporate topdown information under the Bayesian formulation point to a promising direction [15,16,17,18] but in the absence of a clear solution.”
Perceptual multistability predicted by search model for Bayesian decisions. (2008, background): “1 Perceptual multistability predicted by search model for”
Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling (2010, positive): “Previous studies have shown that many human perceptual and motor processes approximate the behavior of an ideal Bayesian learner (Kersten et al., 2004; Körding and Wolpert, 2006).”
Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations (2010, positive): “grounded mostly in a Bayesian framework [1].”
Temporal context calibrates interval timing (2010, background): “Bayesian interval timing Bayesian models have had great success in describing a variety of phenomena in vision and sensorimotor contro”