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Related Concept Videos

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How can we compare the energy that releases from one reaction to that of another reaction? We use a measurement of free energy to quantitate these energy transfers. Scientists call this free energy Gibbs free energy (abbreviated with the letter G) after Josiah Willard Gibbs, the scientist who developed the measurement. According to the second law of thermodynamics, all energy transfers involve losing some energy in an unusable form such as heat, resulting in entropy. Gibbs free energy...
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One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
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Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
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The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
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Free Energy and Equilibrium00:55

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The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
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Free Energy and Equilibrium02:56

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The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
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Generalised free energy and active inference.

Thomas Parr1, Karl J Friston2

  • 1Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, UK. thomas.parr.12@ucl.ac.uk.

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Summary
This summary is machine-generated.

Active inference explains behavior using generative models. This study compares two free energy functionals, revealing how the brain optimizes predictions through perception and action, with identical policy belief updating despite different formulations.

Keywords:
Active inferenceBayesianData selectionEpistemic valueFree energyIntrinsic motivation

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Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Active inference posits that the brain employs a generative model to predict sensory input.
  • Behavioral adaptation involves optimizing the fit between the generative model and sensory data.
  • This optimization occurs via perceptual inference or by actively changing the environment.

Purpose of the Study:

  • To compare two free energy functionals within the Markov decision processes framework for active inference.
  • To analyze how different free energy formulations impact the integration of prior preferences and future uncertainty.
  • To investigate the implications for policy selection and belief updating in active inference models.

Main Methods:

  • Comparison of two free energy functionals: expected free energy and generalized free energy.
  • Analysis within the framework of Markov decision processes (MDPs).
  • Mathematical formulation and comparison of belief updating mechanisms for policies under both functionals.

Main Results:

  • Both expected and generalized free energy formulations lead to policies that realize prior preferences.
  • Policies selected under both formulations minimize uncertainty about future outcomes.
  • Posterior beliefs about policies and their updating exhibit identical forms, despite differing computational quantities.

Conclusions:

  • The study highlights a common objective function (variational free energy) for action and perception in active inference.
  • Generalized free energy explicitly incorporates prior beliefs about outcomes into the generative model.
  • The choice of free energy functional influences the explicit representation of preferences but not the fundamental process of policy inference.