Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.

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• 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.claim0.837
  Formalization of perception-action cycle integrating inference and decision-making.
• The variational free energy is an upper bound on surprise.claim0.821
  Key mathematical property linking free energy to surprise minimization.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.802
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• Variational Free Energy (VFE)concept0.801
  Upper bound on surprisal minimised by any persisting agent; decomposes into noise and insufficient learning in the qFEP
• Neuronal responses can be described as gradient descent on variational free energy.claim0.798
  Central claim: gradient descent on free energy is a valid process-level description of neural activity.
• By minimising variational free energy, agents perform approximate Bayesian inference.claim0.785
  Describes the epistemic function of variational free energy.
• The flow of internal and active states can be described as a gradient descent on variational free energy.claim0.780
  A formal result from the proof.
• Variational Free Energy Minimizationmethod0.780
  Minimizing variational free energy for perceptual inference and learning of model parameters.