Active inference & free energy minimization

Friston's framework unifying perception, action, and learning under variational free energy minimization.

14 members. Each node is clickable.

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The papers/notes whose extracted claims & findings make up this cluster.

Bridges (11)

Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.

Active inference & agent ecology11 shared
Active inference and free energy minimization3 shared
Active inference as Bayes-optimal exploration3 shared
Distributed biological agency and morphogenetic learning1 shared
Causal emergence in biological systems1 shared
Bioelectric memory & morphogenetic identity1 shared
Empirical operationalization of agency attribution1 shared
Free energy minimization in active inference1 shared
Bioelectric computation and morphological intelligence1 shared
Memory persistence through radical embodied transformation1 shared
Bioelectric morphogenesis & anatomical intelligence1 shared

Claims (12)

Acting to maximize value is the same as acting to minimize surprise; value is simply the probability of sensory input expected by an agent.Reinterprets classical reward/value concepts through free energy lens.
Acting to optimize value and perception are two aspects of exactly the same principle: minimization of free energy.Foundational claim unifying action and perception within single optimization framework.
Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.Abstract and §3, preference learning section.
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.Sets the theoretical grounding in Section 2.
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.Formalization of perception-action cycle integrating inference and decision-making.
All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
Behavior prescribed by active inference dynamics is approximately Bayes-optimal.Process theory outcomes produce normatively sound decision-making.
Biology commits to optimizing salience and meaning, not fidelity, via high-level, agent-based interpretation.Central claim that biological memory prioritizes gestalt over details.
Expected free energy decomposes into risk (exploitation) and ambiguity (exploration) terms, providing optimal balance between goal-seeking and novelty-seeking.Key insight into structure of decision-making; explains intrinsic motivation and curiosity.
Natural exploration-exploitation trade-offs emerge automatically from expected free energy minimization without hyperparameter tuning.Active inference achieves Bayes-optimal arbitration between exploration and exploitation without handcrafted mechanisms like ε-greedy.
Neuronal responses can be described as gradient descent on variational free energy.Central claim: gradient descent on free energy is a valid process-level description of neural activity.
Salience preservation over fidelity in memory

Findings (2)

Active inference agents engage in information-seeking behavior in reward-free FrozenLake environments, contrasting with Q-learning but similar to Bayesian RL.Empirical demonstration on FrozenLake; shows epistemic value drives exploration absent reward signal.
All three agent types (active inference, Q-learning, Bayesian RL) perform adequately in stationary FrozenLake; only active inference achieves Bayes-optimal behavior in non-stationary settings.Key empirical result validating online planning capability of active inference.