framework
active
framework:free-energy-principle

Free 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.

Neighborhood — ranked by edge-count

Thinkers (2)

thinker
  • Karl Friston
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    Author of the free energy principle framework; central thinker in the paper.

Methods (5)

method

Concepts (18)

concept
  • Cognitive Light Cone
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    Concept defining self by the spatiotemporal scale and nature of goals a system can pursue; limits of concern demarcate identity.
  • free energy
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    Thermodynamic 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).
  • Homeostasis
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    Biological principle whereby agents maintain sensations within hospitable range; basis for active inference motivation.
  • Perception
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    Equated with inference of past, present and future hidden states via minimization of variational free energy.
  • The functional role consciousness plays: minimizing constraint violations between simultaneously active partial models of reality
  • Markov Blanket
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    A statistical partition of states that separates internal states from external hidden states; fundamental to self-organization in the paper.
  • Perceptual Learning
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    Process of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.
  • value
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    Probability of sensory input expected by an agent, aligning value maximization with surprise minimization.
  • Surprise
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    The negative log probability of sensory samples; minimized by free energy.
  • action
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    Changing configuration to sample environment differently; minimizes free energy.
  • Value Learning
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    Field of research integrating reward learning and optimization; shown to be unified with perceptual learning via free energy principle.
  • Occam's Principle
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    Principle that simpler models generalizing evidence are preferred; implemented via complexity minimization in free energy
  • Rock Problem
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    The difficulty that broad FEP formulations technically apply to rocks maintaining thermodynamic equilibrium; avoided by the present thesis
  • Upper bound on surprisal minimised by any persisting agent; decomposes into noise and insufficient learning in the qFEP
  • Fluctuation Theorem
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    Statistical mechanics principle governing entropy increase in open systems; free energy minimization resists this dispersal.
  • Gibbs Energy
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    Scalar potential in Helmholtz decomposition whose exponential form gives the ergodic density of dynamical systems.
  • Lyapunov Function
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    Variational free energy acts as Lyapunov function for neuronal dynamics, ensuring convergence.

Communities (1)

community

Frameworks (8)

framework
  • Active Inference
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    Foundational framework by Karl Friston; the paper extends it to three hierarchical levels for modeling meta-awareness.
  • Autopoiesis
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    Maturana-Varela principle of self-maintaining systems that organize themselves through internal feedback; extended here to biological, technological, and hybrid systems.
  • Tononi et al. framework quantifying consciousness via integration; provides mathematical tools for measuring agent complexity.
  • A 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.
  • Enactivism
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    Theoretical approach treating cognition and self as emergent from embodied interaction; foundational to the paper's 'selfless self' model.
  • The paper's own framework identifying signed evaluative computation with phenomenal valence in learning systems
  • Normative theory proposing biological systems perform approximate Bayesian inference through free energy minimization.

Artifacts (3)

artifact

Events (1)

event

Related by similarity (8)

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