framework
active
framework:free-energy-principleFree Energy Principle
A foundational variational principle from statistical physics that formalizes how self-organizing systems maintain structural integrity and adapt to their environment by minimizing free energy—a mathematical bound on surprise or prediction error. Originally developed by Karl Friston, the framework unifies action, perception, and learning as processes of active inference, where systems both update internal models of the world and act upon it to reduce the divergence between predictions and observations.
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Papers (6)
paper
- A Free energy principle for the brain (lecture summary)aboutintroduces
- Life as we know itextends
Thinkers (2)
thinker
- Karl Fristonassociated_withintroducesAuthor of the free energy principle framework; central thinker in the paper.
- Karl J. Fristonstudies
Methods (5)
method
- dynamic expectation maximisation (DEM)implementsA variational approach for dynamic Bayesian inversion of nonlinear causal models, named in this paper.
- Variational BayesimplementsMathematical framework for approximating posterior beliefs; converts exact Bayesian inference into optimization.
- Bayesian Inferenceassociated_with
- Helmholtz DecompositionimplementsMathematical technique decomposing flow into curl and divergence-free components; enables derivation of free energy principle.
- Markov Blanketsassociated_with
Concepts (18)
concept
- Cognitive Light Coneassociated_withConcept defining self by the spatiotemporal scale and nature of goals a system can pursue; limits of concern demarcate identity.
- free energyassociated_withThermodynamic potential ΔF = ΔE − TΔS; domain walls form if ΔF < 0
- Central concept: the dynamic ability to rewrite and remap information (memories) onto new media and contexts across multiple scales (behavioral, genetic, physiological).
- Homeostasisassociated_withBiological principle whereby agents maintain sensations within hospitable range; basis for active inference motivation.
- Perceptionassociated_withEquated with inference of past, present and future hidden states via minimization of variational free energy.
- Coherence Maximizationanalogous_toThe functional role consciousness plays: minimizing constraint violations between simultaneously active partial models of reality
- Markov Blanketassociated_withA statistical partition of states that separates internal states from external hidden states; fundamental to self-organization in the paper.
- Perceptual Learningassociated_withProcess of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.
- valueassociated_withProbability of sensory input expected by an agent, aligning value maximization with surprise minimization.
- Surpriseassociated_withThe negative log probability of sensory samples; minimized by free energy.
- actionassociated_withChanging configuration to sample environment differently; minimizes free energy.
- Value Learningassociated_withField of research integrating reward learning and optimization; shown to be unified with perceptual learning via free energy principle.
- Occam's Principleassociated_withPrinciple that simpler models generalizing evidence are preferred; implemented via complexity minimization in free energy
- Rock Problemassociated_withThe difficulty that broad FEP formulations technically apply to rocks maintaining thermodynamic equilibrium; avoided by the present thesis
- Variational Free Energy (VFE)associated_withUpper bound on surprisal minimised by any persisting agent; decomposes into noise and insufficient learning in the qFEP
- Fluctuation Theoremassociated_withStatistical mechanics principle governing entropy increase in open systems; free energy minimization resists this dispersal.
- Gibbs Energyassociated_withScalar potential in Helmholtz decomposition whose exponential form gives the ergodic density of dynamical systems.
- Lyapunov Functionassociated_withVariational free energy acts as Lyapunov function for neuronal dynamics, ensuring convergence.
Communities (1)
community
- Multiscale Cognitionmembers_of
Claims (6)
claim
- Acting to optimize value and perception are two aspects of exactly the same principle: minimization of free energy.associated_withsupportsFoundational claim unifying action and perception within single optimization framework.
- A system's state and structure encode an implicit and probabilistic model of the environment.supportsFoundational claim about internal representation emerging from free energy optimization.
- Sets the theoretical grounding in Section 2.
- Differentiation of the thesis from Friston's FEP to avoid the rock problem
- CIMC's position on the relationship between its coherence hypothesis and Friston's FEP
- Broad theoretical claim connecting the model's success to the FEP as a unifying framework
Frameworks (8)
framework
- Active Inferenceassociated_withextendsFoundational framework by Karl Friston; the paper extends it to three hierarchical levels for modeling meta-awareness.
- Autopoiesisassociated_withMaturana-Varela principle of self-maintaining systems that organize themselves through internal feedback; extended here to biological, technological, and hybrid systems.
- Stress Care Intelligence Loopassociated_with
- Integrated Information Theoryassociated_withTononi et al. framework quantifying consciousness via integration; provides mathematical tools for measuring agent complexity.
- Tame Technological Approach To Mind Everywhereassociated_withA conceptual framework for understanding cognition and intelligence across diverse substrates—including evolved biological systems, artificial systems, and bioengineered systems—using empirically-grounded, gradualist approaches. TTAME enables comparative analysis of mind-like phenomena regardless of the physical or biological substrate in which it emerges, facilitating cross-disciplinary study of unconventional intelligences.
- Enactivismassociated_withTheoretical approach treating cognition and self as emergent from embodied interaction; foundational to the paper's 'selfless self' model.
- Learning-Feeling IdentityextendsThe paper's own framework identifying signed evaluative computation with phenomenal valence in learning systems
- Bayesian Brain HypothesisextendsNormative theory proposing biological systems perform approximate Bayesian inference through free energy minimization.
Artifacts (3)
artifact
- Simulators (LessWrong post)mentionsThe paper being extracted.
- The commentary paper by Michael Levin.
- Primordial Soup SimulationimplementsComputational model of 128 coupled subsystems with Newtonian and electrochemical dynamics demonstrating emergence of life-like self-organization.
Events (1)
event
- First lecture of the series, introducing the free-energy principle and unification of action and perception.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Reformulation of FEP in quantum information theory terms; the primary formal apparatus of the paper
- Physical quantity sharing same minimum as variational free energy (via Jarzynski equality); proxy for computational cost
- Principles like least action that produce efficient forms in nature, contributing to simplicity and good shape.
- Free energy expected under future outcomes; guides policy selection via epistemic and extrinsic value.
- Friston 2010: The free-energy principle: a unified brain theory? (Nature Reviews Neuroscience)concept0.798Foundational free energy principle reference
- Proposed bridge to cognitive science for future empirical testing.
- KL divergence between predicted and preferred final states or outcomes.