الشرح
In Bayesian framework to perception (Kersten, Mamassian & Yuille, 2004; Knill & Richards, 1996), all fixation of perceptual belief is assumed to be connected to the computation of Bayesian posterior probability. Bayesian inference is a statistical procedure (see Cox, 1961; Jaynes, 1957/1988) that results in an optimal combination of the available evidence with prior beliefs. In perception, generally, this approach entails a rational estimate of the structure of the scene that combines fit to the available image data with the mental set of the perceiver (background knowledge, context, etc.).
Within this theoretical context, the main purpose of this project is to clearly articulate and investigate through novel visual illusions, based on limiting conditions, the claims that perception can be modeled by means of Bayesian inference. The ultimate purpose of the project is to test quantitatively the necessary and sufficient conditions (“If A, then B” and “A, only if B”) of the Bayesian framework for the occurrence of the new phenomena under consideration and, in the light of the results, to enrich and improve Bayesian framework.