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Attention in the predictive mind.

Madeleine Ransom1, Sina Fazelpour1, Christopher Mole1

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Summary
This summary is machine-generated.

Bayesian prediction error minimization is a popular theory of cognition. However, this study argues it cannot fully explain all forms of voluntary attention, challenging the claim that it is

Keywords:
AttentionHohwyPhilosophy of perceptionPrediction-error codingVoluntary attention

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Philosophy of Mind

Background:

  • The theory of Bayesian prediction error minimization suggests cognition arises from minimizing prediction errors.
  • Attention is proposed to function by optimizing the precision of these prediction errors.

Purpose of the Study:

  • To evaluate the comprehensive explanatory power of Bayesian prediction error minimization for attention.
  • To identify limitations of the Bayesian framework in explaining all facets of voluntary attention.

Main Methods:

  • Conceptual analysis of existing theories on Bayesian prediction error minimization and attention.
  • Examination of specific phenomena of voluntary attention that may challenge the proposed framework.

Main Results:

  • The Bayesian prediction error minimization framework successfully explains several attention-related phenomena.
  • Certain forms of voluntary attention present a challenge to the current Bayesian framework, indicating it is not a complete explanation.

Conclusions:

  • While powerful, Bayesian prediction error minimization is not a sufficient explanation for all cognitive processes, specifically certain types of voluntary attention.
  • The claim that Bayesian prediction error minimization is 'all the brain ever does' is an overstatement.