These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Behavior prescribed by active inference dynamics is approximately Bayes-optimal.claim0.840
  Process theory outcomes produce normatively sound decision-making.
• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.832
• Active inference describes the dynamics of systems that persist at non-equilibrium steady-state and that can be statistically segregated from their environment via a Markov blanket.claim0.829
  Sets the theoretical grounding in Section 2.
• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.829
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.826
  §3, after non-stationary results.
• Active inference offers a degree of plausibility as a process theory of brain function.claim0.818
  Overall assessment from Discussion.
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.816
  Abstract and §3, preference learning section.
• Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.814
  Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.