Free-Energy Minimization and the Dark-Room Problem

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 minimization 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).

[1]  Karl J. Friston,et al.  Free-energy and the brain , 2007, Synthese.

[2]  V. Pande,et al.  On the application of statistical physics to evolutionary biology. , 2009, Journal of theoretical biology.

[3]  W. Ashby,et al.  Every Good Regulator of a System Must Be a Model of That System , 1970 .

[4]  Tom Ziemke,et al.  Introduction to the special issue on situated and embodied cognition , 2002, Cognitive Systems Research.

[5]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[6]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[7]  Christopher D. Fiorillo,et al.  A neurocentric approach to Bayesian inference , 2010, Nature Reviews Neuroscience.

[8]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[9]  William T. Freeman,et al.  Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.

[10]  Karl J. Friston,et al.  Some free-energy puzzles resolved: response to Thornton , 2010, Trends in Cognitive Sciences.

[11]  David Mumford,et al.  On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[12]  Rick Grush,et al.  The emulation theory of representation: Motor control, imagery, and perception , 2004, Behavioral and Brain Sciences.

[13]  J. W. N.,et al.  Space, Time, and Gravitation: An Outline of the General Relativity Theory , 1921, Nature.

[14]  Wilfrid S. Sellars,et al.  PHILOSOPHY AND THE SCIENTIFIC IMAGE OF MAN , 2007 .

[15]  Chris Thornton,et al.  Some puzzles relating to the free-energy principle: comment on Friston , 2010, Trends in Cognitive Sciences.

[16]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[17]  Tom Ziemke,et al.  Brains, Bodies, and Beyond: Competitive Co-Evolution of Robot Controllers, Morphologies and Environments , 2005, Genetic Programming and Evolvable Machines.

[18]  David Kirsh,et al.  Adapting the Environment Instead of Oneself , 2022 .

[19]  R. Gregory,et al.  Perceptual illusions and brain models , 1968, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[21]  J. R. Holsinger,et al.  Speciation in Cave Faunas , 1985 .