David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Synthese 159 (3):417 - 458 (2007)
If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure
|Keywords||Variational Bayes Free-energy Inference Perception Action Value Learning Attention Selection Hierarchical|
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References found in this work BETA
John Locke (1690/1970). An Essay Concerning Human Understanding, 1690. Menston,Scolar Press.
William A. Phillips & Wolf Singer (1997). In Search of Common Foundations for Cortical Computation. Behavioral and Brain Sciences 20 (4):657-683.
Citations of this work BETA
Jakob Hohwy (2013). Delusions, Illusions and Inference Under Uncertainty. Mind and Language 28 (1):57-71.
Jakob Hohwy (2011). Phenomenal Variability and Introspective Reliability. Mind and Language 26 (3):261-286.
Matteo Colombo (2014). Explaining Social Norm Compliance. A Plea for Neural Representations. Phenomenology and the Cognitive Sciences 13 (2):217-238.
K. Friston (2009). The Free-Energy Principle: A Rough Guide to the Brain? Trends in Cognitive Sciences 13 (7):293-301.
Michael Moutoussis, Pasco Fearon, Wael El-Deredy, Raymond J. Dolan & Karl J. Friston (2014). Bayesian Inferences About the Self (and Others): A Review. Consciousness and Cognition 25 (1):67-76.
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