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Active inference and epistemic value.

Karl Friston1, Francesco Rigoli1, Dimitri Ognibene2

  • 1a The Wellcome Trust Centre for Neuroimaging , Institute of Neurology , London , UK.

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Summary
This summary is machine-generated.

This study presents a formal theory of choice behavior where agents minimize expected free energy. This approach balances maximizing expected utility (extrinsic value) with information gain (intrinsic value), resolving the exploration-exploitation dilemma.

Keywords:
Active inferenceAgencyBayesian inferenceBayesian surpriseBounded rationalityEpistemic valueExploitationExplorationFree energyInformation gainUtility theory

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Decision Theory

Background:

  • Choice behavior is often modeled using expected utility theory.
  • However, these models do not fully capture the exploration-exploitation trade-off inherent in decision-making.

Purpose of the Study:

  • To propose a formal framework for choice behavior based on minimizing expected free energy.
  • To demonstrate how this framework resolves the exploration-exploitation dilemma.

Main Methods:

  • Formal mathematical treatment of choice behavior.
  • Decomposition of free energy into extrinsic and epistemic value.
  • Simulations illustrating the theory and comparing results to neurobiological observations.

Main Results:

  • Minimizing expected free energy is equivalent to maximizing extrinsic (utility) and intrinsic (information gain) value.
  • The framework naturally resolves exploration (maximizing epistemic value) and exploitation (maximizing extrinsic value).
  • Simulations show precision updates align with dopaminergic activity in conditioning paradigms.

Conclusions:

  • The proposed free-energy minimization framework offers a unified account of choice, exploration, and exploitation.
  • This approach provides a normative account of policy selection and belief updating.
  • The theory has implications for understanding decision-making in both artificial and biological systems.