The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective

Abstract

In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal-environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper.

Keywords: Action-readiness; Active inference; Affordances; Enaction; Free-energy principle; Metastability; Predictive-coding; Skilled intentionality.

Fig. 1

Schematic depiction of how free-energy is composed of divergence and embodied surprisal (suprisal${}_{\mathrm{E}}$) and how these components change with perceptual and active inference respectively (see text). Perceptual inference can only reduce divergence, but not embodied surprisal. Only active inference can reduce embodied surprisal

Fig. 2

Left A domino chain reaction as an exemplificiation of a Markov process. Every domino is only dependent on its previous domino, just as, in the figure on the right, every node is only dependent on its neighboring nodes. Right A schematic depiction of a Markov blanket, a spatial generalization of a Markov process. The gray circle represents the Markov blanket of a node, consisting of internal state (int), its children (the action states, act), its parents (sen), and parents of children (sen), with parent/child being understood in terms of cause/effect

Fig. 3

Original drawing from Huygens’ C. Horoloqium Oscilatorium. Two oscillating clocks hanging on a suspension beam that is itself able to move