Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization

- Top-down modulation of perception can be quantified in terms of a variable Bayesian learning rate, revealing a wide range of prior expectations that can modulate perception.
- The prediction error minimization framework can be used to define cognitive penetration specifically as prediction error minimization deviations from a variable Bayesian learning rate.
- Cognitive penetration is retained as a category somewhat distinct from other top-down effects, and carves a reasonable route between penetrability and impenetrability.
- Rampant, relativistic cognitive penetration of perception is prevented, and yet cognition and perception can be viewed as continuous.

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.