Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization.
I discuss top-down modulation of perception in terms of a variable Bayesian learning rate, revealing a wide range of prior hierarchical expectations that can modulate perception. I then switch to the prediction error minimization framework and seek to conceive cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate. This approach retains cognitive penetration as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability. It prevents rampant, relativistic cognitive penetration of perception and yet is consistent with the continuity of cognition and perception.
Philosophy & Cognition Lab, Philosophy Department, Monash University, Melbourne, VIC 3800, Australia. Electronic address: Jakob.Hohwy@monash.edu.
Jakob
J
Hohwy
D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D010465:Perception / Q000502:physiology*; D011582:Psychological Theory
Active inference; Attention; Bayesian inference; Cognitive penetrability; Hierarchical Gaussian filter; Illusion; Prediction error minimization; Variable learning rate