Variational Free Energy Minimization

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• Variational Free Energy (VFE)concept0.825
  Upper bound on surprisal minimised by any persisting agent; decomposes into noise and insufficient learning in the qFEP
• Gradient Descent on Variational Free Energymethod0.814
  Process by which neuronal dynamics minimize free energy; produces empirically observable neural phenomena.
• Free energy minimisation is asymptotically equivalent to the Principle of Unitarity, driving agent and environment toward dissolution of separabilityclaim0.801
  Remarkable convergence result showing optimal modelling erodes the distinction the modeller imposed
• By minimising variational free energy, agents perform approximate Bayesian inference.claim0.794
  Describes the epistemic function of variational free energy.
• Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.claim0.780
  Definitional claim from Section 2.
• Thermodynamic Free Energyconcept0.777
  Physical quantity sharing same minimum as variational free energy (via Jarzynski equality); proxy for computational cost
• Agents perceive by minimizing variational free energy to ensure model consistency with past observations and act by minimizing expected free energy to make future sensations consistent with preferences.claim0.777
  Formalization of perception-action cycle integrating inference and decision-making.
• "A system can minimize free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively."concept0.766
  Load-bearing definition of how action and perception implement free energy minimization.