Bayesian perception is ecological perception∗

There is a certain excitement in vision science concerning the idea of applying the tools of Bayesian decision theory to explain our perceptual capacities. Bayesian models – or ‘predictive coding models’ – are thought to be needed to explain how the inverse problem of perception is solved, and to rescue a certain constructivist and Kantian way of understanding the perceptual process (Clark 2012, Gladzeijewski 2015, Hohwy 2013, Rescorla 2013, Rescorla forthcoming.) Anticlimactically, I argue both that Bayesian outlooks do not constitute good solutions to the inverse problem, and that they are not constructivist in nature. In explaining how visual systems derive a single percept from underdetermined stimulation, orthodox versions of predictive coding accounts encounter a problem. The problem shows that such accounts need to be grounded in Natural Scene Statistics (NSS), an approach that takes seriously the Gibsonian insight that studying perception involves studying the statistical regularities of the environment in which we are situated. Additionally, I argue that predictive coding frameworks postulate structures that hardly rescue a constructivist way of understanding perception. Safe for percepts, the posits of Bayesian theory are not representational in nature. Bayesian perceptual inferences are not genuine inferences. They are biased processes that operate over non-representational states. In sum, Bayesian perception is ecological perception. In section 1, I present what Bayesianism about perception amounts to. In section 2, I argue that Bayesian accounts, as standardly described, do not offer a satisfactory explanation of how the inverse problem is solved. Section 3 introduces Natural Scene Statistics and argues that predictive coding approaches should be combined with it. Section 4 argues that the posits of Bayesian theory are in line with an ecological way of understanding the