Free-energy minimization and the dark-room problem

Karl Friston, Christopher Thornton, Andy Clark*

*Corresponding author for this work

Research output: Contribution to journalArticle

109 Citations (Scopus)

Abstract

Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimiza- tion of surprise (or, equivalently, the maximization of expectation).The most comprehensive such treatment is the "free-energy minimization" formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b - see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there.This is the "Dark-Room Problem." Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington's Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark).

Original languageEnglish
Article numberArticle 130
Pages (from-to)1-7
Number of pages7
JournalFrontiers in Psychology
Volume3
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Bayesian brain
  • Free-energy principle
  • Optimality
  • Surprise

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