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Action and behavior: a free-energy formulation

Friston, K.J.; Daunizeau, J.; Kilner, J.; Kiebel, S.J.; (2010) Action and behavior: a free-energy formulation. Biological Cybernetics , 102 (3) pp. 227-260. 10.1007/s00422-010-0364-z. Green open access

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We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.

Type: Article
Title: Action and behavior: a free-energy formulation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s00422-010-0364-z
Publisher version: http://dx.doi.org/10.1007/s00422-010-0364-z
Language: English
Additional information: © The Author(s) 2010. This article is published with open access at Springerlink.com. The article is published under the Creative Commons Attribution Noncommercial License, which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited; please see http://creativecommons.org/licenses/by-nc/2.5
Keywords: Computational, motor, control, Bayesian, hierarchical, priors
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/20040
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