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

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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 languageEnglish
Pages (from-to)75-85
Number of pages11
JournalConsciousness and Cognition
Publication statusPublished - 1 Jan 2017


  • Active inference
  • Attention
  • Bayesian inference
  • Cognitive penetrability
  • Hierarchical Gaussian filter
  • Illusion
  • Prediction error minimization
  • Variable learning rate

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