Motion illusions as optimal percepts

By Yair Weiss, Eero P. Simoncelli, Edward H. Adelson. Nature Neuroscience, 2002

1,088 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4eda0e4608d35b62fb34a162d1de238caaed8faa

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Citations in the corpus

Optical Flow Estimation (2006, background): “Interestingly, many “illusions” in human motion perception can actually be explained with a prior favoring slow motions and a Bayesian model of inference [56].”
Probabilistic models of cognition: exploring representations and inductive biases. (2010, neutral): “representing motion components or depth gradients [37].”
Cognitive computational neuroscience (2018, background): “A third and critically important class of cognitive model is that of Bayesian models (Box 3).(33,35,36,34,143,144,145,32) Bayesian inference provides an essential normative perspective on cognition.”
Object perception as Bayesian inference. (2004, background): “In this case the visual system is forced to guess, but it can make intelligent guesses by biasing its guesses toward typical objects or interpretations (Sinha & Adelson 1993, Mamassian & Goutcher 2001,Weiss et al 2002) (see Figure 4A).”
Noise characteristics and prior expectations in human visual speed perception (2006, methodology): “5 and 6). Left and center panels: Gaussian models of [ 4 ] and [16], respectively.”
Bayesian integration in sensorimotor learning (2004, background): “and that some visual illusions can be understood within the bayesian framewor”
Bayesian Perceptual Psychology (2015, background): “Weiss, Simoncelli, and Adelson (2002) offer a Bayesian motion perception model with two features: The prior probability favours slow distal motions.”
Action understanding as inverse planning. (2009, background): “…have long played an important role in the study of core cognitive capacities, such as visual perception, memory retrieval, or language processing and acquisition (Anderson, 1990; Gold, 1967; Hale, 2001; Liu, Knill, & Ker-sten, 1995; Shiffrin & Steyvers, 1997; Weiss, Simoncelli, & Adelson, 2002).”
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017, background): “Our goal is a formal, computational account, analogous in scope and explanatory power to computational accounts of visual perception 1–3 that represent some of the greatest successes of model-building in cognitive science.”
Disambiguating Visual Motion Through Contextual Feedback Modulation (2004, methodology): “Weiss and Fleet (2001) and Weiss, Simoncelli, and Adelson (2002) solved the problem to determine the velocity of a moving object using a Bayesian estimation approach.”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “…environments indeed alters prior expectations, even when such expectations are the result of a lifetime of experience, such as the prior that light comes from above (Adams et al., 2004), or that slow motion speeds are more likely than fast ones (Weiss et al., 2002; Sotiropoulos et al., 2011).”