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Bayesian perception models view the brain as a scientist. New predictive coding models show that stubborn predictions can be valuable in cognitive science.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Philosophy of Mind

Background:

  • Traditional Bayesian theories of perception model the brain as an ideal scientist.
  • This involves refining predictions about the external world using sensory evidence.

Purpose of the Study:

  • To explore the implications of predictive coding models in cognitive science.
  • To investigate the role of resistant predictions in perception.

Main Methods:

  • Review of existing Bayesian and predictive coding frameworks.
  • Conceptual analysis of how prediction resistance impacts cognitive modeling.

Main Results:

  • Predictive coding models incorporate predictions that are resistant to change.
  • These "stubborn" predictions offer a novel perspective on perceptual processes.

Conclusions:

  • The concept of resistant predictions challenges traditional views of the brain as a purely adaptive scientist.
  • Incorporating these stubborn predictions can enhance the utility of cognitive models.