Free-Energy Minimization and the Dark-Room Problem

By Karl J. Friston, Chris J. Thornton, Andy Clark. 2012

331 citations recorded by the source corpus.

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).

Subfields: Cognitive Neuroscience

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Citations in the corpus

A nice surprise? Predictive processing and the active pursuit of novelty (2018, methodology): “Notice, however, that action also provides a potent means of testing the current hypothesis, for example disambiguating between equiprobable hypotheses (Seth (2015), Friston et al. (2012a)).”
Active inference and epistemic value (2015, methodology): “We have previously considered epistemic value and salience using continuous time predictive coding schemes and saccadic searches (Friston et al., 2012).”
Affective Value in the Predictive Mind (2017, background): “In complexer animals, “the gross bodily form, biomechanics, and gross initial neural architecture of the agent all form part of the (initial) ‘model’ and […] this model is further tuned by learning and experience” (Friston et al. 2012a), using the general-purpose PP mechanisms.”
An aberrant precision account of autism (2014, unknown): “May 2014 | Volume 8 | Article 302 | 5 mediate the precision of cues (with affordance) that induce behavior (Friston et al., 2012a).”
Perceptions as Hypotheses: Saccades as Experiments (2012, background): “Thissectionusesageneralbutratherabstract formulationofagents ,intermsofthestatestheycanoccupy,that enablesustoexplainaction,perception,andcontrolascorol-laries of a single principle.”
Precise minds in uncertain worlds: predictive coding in autism. (2014, background): “The prediction error minimization principle says that “we harvest sensory signals that we can predict” (Friston et al., 2012).”
The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs (2014, background): “…strictly, “winnerless” (Rabinovich et al., 2001) competition between states [“winnerless” because critical instability or metastability dictates that a state’s “vic-tory” is transient (Friston et al., 2012b)] the global system only ever entertains one winning state at any one time (Baars, 2005).”