title: Organizing probabilistic models of perception.
abstract: Probability has played a central role in models of perception for more than a century, but a look at probabilistic concepts in the literature raises many questions. Is being Bayesian the same as being optimal? Are recent Bayesian models fundamentally different from classic signal detection theory models? Do findings of near-optimal inference provide evidence that neurons compute with probability distributions? This review aims to disentangle these concepts and to classify empirical evidence accordingly.
authors.affiliation: Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. wjma@bcm.edu
authors.forename: Wei Ji
authors.initials: WJ
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003657:Decision Making; D006801:Humans; D015233:Models, Statistical; D010465:Perception / Q000502:physiology*; D017603:Signal Detection, Psychological