2021 Volume 30 Pages 85-98
Bayesian models of human object perception have been widely studied. Helmholtz’s idea of perception as unconscious inference is formalized by Bayes’ theorem. Human object perception is now widely called Bayesian inference or statistical inference, while obtaining a Bayesian quantitative model of human perceptual learning has become a primary goal for the consciousness scientists who utilize Helmholtz’s idea.
Helmholtz’s model uses Bayesian inference (updating of degree of belief) as a scientific model. In this study, I perform a philosophical analysis of Helmholtz’s scientific model from the viewpoint of philosophical Bayesianism. I apply potential problems of philosophical Bayesianism concerning the updating rule to the Bayesian perceptual learning model. There are at least two problems concerning the updating rule in the philosophy literature: the so-called catch-all hypothesis problem and the old-evidence problem (Glymour 1980). I especially discuss the application of the old-evidence problem to Helmholtz’s model, and thereby claim that the model has a potential deficiency. Additionally, I offer a clue to the solution to the old-evidence problem in the Bayesian model.
Despite the fact that Bayesian inference has been widely used in science, it seems that inadequate attempts have been made to link scientific and philosophical Bayesian inference. This study aims to provide this link.
