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Action and behavior: a free-energy formulation.

Karl J Friston1, Jean Daunizeau, James Kilner

  • 1The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, UK. k.friston@fil.ion.ucl.ac.uk

Biological Cybernetics
|February 12, 2010
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Summary
This summary is machine-generated.

This study shows that active sampling of sensory data, driven by minimizing free energy, explains adaptive motor behaviors like goal-seeking. This offers a new perspective on motor control, linking it closely with perception.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Perceptual inference and learning are explained by the free-energy principle, aiming to understand the brain via energy minimization.
  • Making inferences about sensory data causes can be framed as minimizing a free-energy bound on sensory input likelihood, using an internal generative model.

Purpose of the Study:

  • To investigate the consequences of actively sampling sensory data to minimize the free-energy bound.
  • To demonstrate how this active sampling explains motor control and adaptive behavior.

Main Methods:

  • Utilizing ergodic arguments to show that active sampling is mandated by the existence of adaptive agents.
  • Simulating oculomotor control to illustrate the principles.
  • Applying the principles to cued and goal-directed movements.

Main Results:

  • Active sampling, driven by free-energy minimization, accounts for various motor behaviors, including retinal stabilization and goal-seeking.
  • Motor control is understood as fulfilling prior expectations about proprioceptive sensations.
  • The formulation provides an alternative to optimal control theory.

Conclusions:

  • The free-energy formulation offers a unified perspective on perception and action, highlighting their intimate relationship.
  • It explains the emergence of adaptive behavior in biological agents.
  • This approach provides a simple, alternative framework to optimal control theory for understanding motor control.