Abstract
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.
Original language | English |
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Pages (from-to) | 75-85 |
Number of pages | 11 |
Journal | Consciousness and Cognition |
Volume | 47 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Keywords
- Active inference
- Attention
- Bayesian inference
- Cognitive penetrability
- Hierarchical Gaussian filter
- Illusion
- Prediction error minimization
- Variable learning rate