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

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.

Anticipatory or predictive dynamics have been at the heart of a large number of influential and very diverse theories of mind, brain and skilled behavior more generally (Helmholtz 1860/1962; Gregory 1980; Grush 2004; Turvey 2010; Noë 2004; Stepp and Turvey 2010; Port and Van Gelder 1995; Thompson 2007; Chemero 2009). These approaches all share a focus on future-oriented activities, but they span a wide range of possible positions in the philosophy of mind. The apparent overlap disguises some fundamental theoretical differences and disagreements. It is within this incompatible plethora of broadly future-oriented frameworks for understanding cognition that one needs to situate the new kid on the philosophical block: the free-energy principle.

The free-energy principle (Friston and Stephan 2007) is a potentially unifying theory in theoretical neuroscience and theoretical biology, stating that all an organism needs to do in order to maintain its organization as an adaptive living system is to minimize its information-theoretic free-energy in its interactions with the environment. This minimization can be done by predicting or anticipating sensory input or by changing the environment to match what is anticipated. Adequate anticipation requires the organism to be tuned to its ecological niche in such a way that the coupled dynamics of the organism-environment system remain within a relatively small subset of states that maintain the organism’s viability in its econiche (Friston 2011). The promise of the free-energy principle is that it provides a theoretical framework able to unify the biological and the cognitive sciences. The free energy principle accomplishes this unifying work by showing how the organization and dynamics of living systems prefigures the organization and dynamics of cognitive systems. Living systems that regulate their interactions with the environment so as to maintain their viability, and cognitive systems that are the basis for complex capacities such as social cognition, cognitive control and language use, have fundamental organizational principles in common.1

The free-energy principle has often been combined with Bayesian predictive-coding,2 most notably in the work of its architect, Karl Friston (Friston and Stephan 2007; Friston and Kiebel 2009). The main idea of predictive-coding is that the brain generates top-down predictions that are matched bottom-up with sensory information, which results in prediction-errors.3 According to predictive-coding theorists, the brain is fundamentally in the business of minimizing prediction-errors (Hohwy 2013; Clark 2016). One of the aims of this paper is to better understand the relation between the free-energy principle and Bayesian predictive-coding. For the moment, it is important not to conflate them. The free-energy principle is a unifying framework for self-organizing living systems and is therefore not tied to any particular account of biological or neural functioning. Predictive-coding is a theory of neural functioning. How the latter relates to free-energy minimization is a question to which we will return later.4

Friston claims that the principle goes naturally together with older ideas about perception and cognition that date back to the nineteenth century psychologist Helmholtz. These ideas have been influential in more recent years in the work of the perceptual psychologist Gregory (1980) and the cognitive neuroscientist Frith (2007). Helmholtzians take perception to be “unconscious inference”, whereby the task of perception is to infer, based on top-down knowledge structures, the current causes of sensation. The process of inference is taken by them to work in ways that are analogous with scientific hypothesis-testing. On the other hand, the free-energy principle is consistent with many other accounts of self-organizing (living) systems such as synergetics (Tschacher and Haken 2007), global brain dynamics (Freeman 1987, 2000), metastability (Kelso 2012; Rabinovich et al. 2008), autopoiesis (Maturana and Varela 1980; but see Kirchhoff (2016)) and ecological psychology (Gibson 1979).5 Not all of this work is theoretically consistent with a Helmholtzian theory of perception, some of it is even explicitly aimed against such an account. Our aim in this paper is to show that the free-energy principle and the Helmholtzian account of perception are conceptually independent. One can explain how the free-energy principle applies to a biological system in terms of the dynamic coupling of the organism with its environment, we suggest. Whether this dynamic coupling is best described using the notion of “inference” we think is an open question. Within the Helmholtzian theory, the notion of inference is standardly understood as a probabilistic relation between prior beliefs, current evidence and posterior beliefs. However, the free-energy principle applies not just to humans but to all living systems, including the simplest of life forms such as bacteria.

Friston employs the notion of “inference” even in these minimal cases of simple life forms. This is clearly stretching the meaning of “inference” beyond its normal usage. We question whether the activities of simple life forms such as E. Coli can be taken to be inference-involving. In previous work, we have argued for an “ecological-enactive” understanding of the free-energy principle under the umbrella of the Skilled Intentionality Framework (SIF) (Bruineberg and Rietveld 2014, figure 1; Rietveld et al. forthcoming; see Ramstead et al. (2016); Kirchhoff (2015) and Gallagher and Bower (2014) for similar arguments). One aim of this paper is to build on our earlier work in part by situating our ideas in relation to those of Hohwy, Friston and Clark, in order to highlight some key differences. We think that Friston’s important theoretical work has been mistakenly aligned with Helmholtzian ideas about perception (Hohwy 2013; Clark 2013; Friston and Stephan 2007). The stress on Helmholtz leads to important aspects of the free-energy framework being missed in the philosophical discussion. More specifically, action and perception are not understood in the context of self-organization, and we will argue that this leads to philosophical errors.

Our argument proceeds as follows. In the first part of the paper, we introduce the free-energy principle in more detail. We emphasize the biological motivation for the free-energy principle (as found in Friston and Stephan 2007 and Friston 2011), because it shows that first and foremost the free-energy principle does not apply to brains or epistemic agents, but to embodied living systems as a whole. Second, the biological motivation for free-energy minimization highlights the continuity of mind and life (Thompson 2007), and hence the overlap with the enactive approach.

In Sect. 2, we introduce Bayesian predictive-coding as a theory of brain function and show how it has been taken to form a natural partner with the free-energy principle. We argue against the dominant Helmholtzian interpretation of Bayesian predictive coding in part on the grounds that it provides what we demonstrate to be a problematic conception of the brain as working in much the same way as a hypothesis-testing scientist. In Sect. 3 we then show how Friston’s concept of probabilistic inference, central in much of his work on the free-energy principle, can be given a deflationary interpretation using concepts from dynamical systems theory. We further argue that the free energy principle and Bayesian predictive-coding constrained by the free-energy principle, contrary to conventional wisdom, provides no support for a Helmholtzian inferential theory of perception.

One of the most fascinating questions in the biological sciences is how living systems can produce and maintain their organization in the face of a dynamic environment. A second, equally important and fascinating, question is how biological processes can give rise to minds. The continuity of life and mind thesis, as defended by enactivist philosophers of biology (Jonas 1966/2001; Di Paolo 2005; Thompson 2007), states that both these questions should be tackled at once. Life and mind share the same basic underlying principles. It is exactly this organisational continuity of life and mind that is the starting point of Friston’s free-energy principle, suggesting common ground between the free-energy principle and enactive philosophy of biology. Friston agrees that the defining feature of living systems is the way in which “biological systems [...] maintain their states and form in the face of a constantly changing environment” (Friston 2011, p. 92). Self-maintenance and self-production are the defining features of autopoietic (self-producing) systems, suggesting that there might be an intimate relation between free-energy minimization and autopoiesis as a defining characteristic of life [but see Kirchhoff (2016)].

From this conception of life, one can derive a theoretical framework for thinking about perception and action.6 Free energy is a function of the organism’s sensory states and the organism’s internal dynamics (called a generative model).7 Roughly, free energy is a measure for the ever present dis-attunement between environmental dynamics and internal dynamics (Bruineberg and Rietveld 2014). Free-energy can be minimized on short time scales by making the environment conform to the internal dynamics (“action”) or by making the internal dynamics conform to the environmental dynamics (“perception”). This proposal by Friston is, admittedly, a rather unorthodox view of perception. Sensory states here are the proximal stimulation of the organism which can only be changed by acting on the world. Rather than talking about perceptual states we (Bruineberg and Rietveld 2014; Kiverstein and Rietveld 2015) prefer to speak of states of action-readiness. States of action-readiness are the internal states of the individual that, given its sensory states and abilities, prepare the animal to achieve a grip on a particular situation.8

The free-energy principle describes how the very same properties that define life are also essential for cognition, understood as the capacity to regulate interactions with the environment. As Clark has suggested, the free-energy principle may “reveal the very deepest of links between life and mind, confirming and extending the perspective known as “enactivist” cognitive science” (Clark 2013, p. 24).

Friston formalizes the self-maintenance aspect of autopoiesis in terms of Shannon information: of all possible states the organism could find itself in, the organism must find the right subset of states that allow its organization to persist in its energetic exchanges with the environment. The claim is that there is a probability distribution over all possible states the organism can find itself in. At any point in time, this distribution is sharply peaked around certain values specifying conditions of the organism that are necessary for its viability and survival or—more exactly—that define the characteristic phenotypic state of the organism. For example, human body temperature has a high probability of being around 37 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘C and a low probability elsewhere. Mathematically, this means that the probability distribution of the variable body temperature has low Shannon entropy and that the event ‘measuring a body temperature of 37 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘C has low surprisal,9 while measuring a body temperature of 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘C has a very high surprisal (for homeothermic organisms like ourselves).

Importantly, whether a temperature has low or high surprisal is relative to different species of animal. Birds have a different average body temperature than humans, while ectothermic animals such as lizards have a different distribution altogether.

What holds for internal variables holds just as well for places in the environment. For whales, being in deep sea is an event with low surprisal, and being on shore has high surprisal, while this is reversed for humans. Hence, the particular embodiment or biological organization of an animal and the environmental conditions of the animal necessary for its viability constrain each other.10 This is an illustration of the mutualism of animal and environment, taken together they form a complementary pair:We commonly talk of the organism and the environment and of the adaptation of one to the other ...as if there were first an organism and an environment and then some adjustment of one to the other; but when we come to an analysis of the factors involved, it is quite necessary to start from the unity of function and see that the distinction of organism and environment arises because of adaptation in that process, not vice versa (Dewey 1976, p. 275, as quoted by Costall 2004, see also Maturana and Varela 1980).So far, all we have said is that the embodiment of the animal implies a range of environmental conditions in which it is able to prosper and that surprisal is a measure that captures this relationship. As such, the free energy principle is just an information-theoretic formalization of the observation that living systems are homeostatic (Bernard 1865/1927) or homeodynamic (Yates 2008) systems.

What is required is an account of how living systems are able to find the right subset of states so as to maintain themselves within viable bounds.11 The ideal temperature of a human is determined by its embodiment: at 37\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘ the enzymes regulating our metabolism perform optimally, while the metabolic cost of maintaining body temperature is still manageable within certain environmental conditions.

The challenge that the homeostatic system faces is that of regulating the animal’s interactions with the environment so as to keep the animal within this viable regime, without “knowing” about the animal’s own viability conditions. Surprisal is relative to this unknown probability distribution, thus an organism has no means of evaluating surprisal directly. Since surprisal is a more general term in information-theory, we will call this special case of surprisal, only related to the embodiment of the agent, embodied suprisal or surprisal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{E}$$\end{document}E. We will call the unknown distribution, which formally describes the conditions of viability of the animal, the bodily distribution. How then does the organism succeed in finding the right subset of states?

This is where the free-energy principle may hold a solution. The main insight of the free-energy principle is that information-theoretic free-energy is always greater than (or equal to) surprisal12 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E and is thus an upper bound on surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E. By minimizing free-energy, the organism will implicitly minimize surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E as well, and hence remain within viable bounds. Free-energy is accessible to the organism because it is a function of two quantities (i.e., probability distributions) that the organism embodies: a generative model and a variational density that is entailed by a system’s internal state. Mathematically, the generative model is thought of as the probability of the co-occurrence of a sensory state and a state of the environment. The variational density is a proxy for the real bodily (environmental) probability distribution (e.g. the distribution of temperature). Typically, one assumes that this distribution is Gaussian; making it fully specifiable by only its mean and standard deviation. Changing the variational density (i.e.

changing its mean or standard deviation) will give different values for the free-energy. What is needed then for free-energy minimization is for the organism to be able to change the variational density or to change the environment in a way that reduces free-energy. Friston appeals to the derivative of free-energy with respect to internal states (that encode the variational density) and to sensory states.13 This implies nothing more, we take it, than that the organism regulates its interactions with the environment and its own internal conditions based on what it takes to be the norm of maintaining its own viability or flourishing more generally.

We argued so far that surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E (for instance related to temperature) cannot be evaluated directly because the ‘true’14 embodied probability distribution is unknown. Instead, based on the generative model the system itself generates a variational density about its own optimal temperature that can be used to evaluate “how far off” the current temperature is. We will use the general information-theoretic notion of surprisal to describe this discrepancy. Free energy is always greater than the average surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E.

This can be seen intuitively: if the internal mechanism regulates temperature to be 38\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘, while our embodiment requires 37\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘, there will be a big discrepancy (‘surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E’) over time. The closer the variational density approximates the bodily distribution, the lower the surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E will be. Hence minimizing the discrepancy between the ‘predicted’ and the ‘actual’ temperature requires that the following condition be met: the internally generated (variational) distribution and the actual distribution must be mutually compatible with respect to the sensory constraints (in the sense of being sufficiently similar enough in their dynamics).15

The organism changes the variational density by changing its internal dynamics with respect to the constraints of the environment. Friston calls this “perceptual inference”. The latter is not sufficient for minimizing surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E (and therefore survival, if the logic of the free-energy principle holds). In order to minimize surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E an organism needs to change its sensory states, which it does through action. Perception, understood in Friston’s terms as changing the internal dynamics of the organism, will only lower the upper-bound on surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E (i.e. free-energy) and not change surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E itself.

Crucially, to change surprisal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E itself, the organism needs to change its sensory states by acting on its environment. Suppose a human being finds itself with a temperature of 39.8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ $$\end{document}∘. If it would be accommodating enough to change its internal dynamics so that this was its expected temperature, it would not survive for long. What matters for the organism is avoiding finding itself in such a state by changing its sensory states through acting in the world (e.g. by going to the doctor).

Action is therefore necessary for minimizing surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E; this cannot be done on the basis of perception alone since perceptual inference cannot reduce surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E. Mathematically, this can be seen by the fact that one can write the free-energy term as a Kullbeck–Leibler16 divergence between the variational density and the posterior plus the term for surprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}E. Minimizing this term by only changing the internal states (perception) minimizes the KL-divergence, and not the surprisal itself (see Fig. 1, see also for instance Friston 2011, Figure 1). We will in the following refer to KL-divergence as “divergence”.Fig. 1Schematic depiction of how free-energy is composed of divergence and embodied surprisal (suprisal\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{\mathrm{E}}$$\end{document}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

So within the free-energy framework, it is action that does the work of actually minimizing surprisal. Actions change an organism’s relation to the environment, thereby changing the sensory states of the organism, a process that Friston calls active inference (2012). Free energy, as we understand it (Bruineberg and Rietveld 2014), is a measure of the dis-attunement of the internal dynamics and the environmental dynamics: it is low when the sensory states are anticipated, by the animal, and high when they are not. The free energy principle says that minimizing free-energy is a necessary17 condition for living systems to maintain their organization in their econiche. What is crucial for the organism, is that it anticipates the kind of interactions with the environment that contribute to its flourishing (such as for example eating food, staying warm, avoiding a passing car or learning to read). The function of the generative model is therefore not to provide the agent with a representation of the dynamical structure of the environment per se. It mediates the organism’s interactions with its environment in such a way that a robust brain-body-environment system is maintained. The generative model is not a model of the environment as it happens to be. It embodies in its organization and structure longer-term regularities between action, environment and the state of the organism. How can we understand the relationship between the niche and the generative model?

Friston claims that “an agent does not have a model of its world, [but rather] it is a model” (Friston 2013b). One thing this means is that the free-energy principle does not just apply to the brain, but rather to the organization of the biological agent as a whole in its econiche. Friston himself states, somewhat provocatively, that: “each [...] agent embodies an optimal model of its econiche” (Friston 2011). In previous work (Bruineberg and Rietveld 2014) we have provided a minimal yet still practically useful interpretation of “being a model of its econiche”. Minimally this implies: (1) that the agent embodies in its structure and organization skills that complement the econiche and (2) that the coupled dynamics of organism and niche18 together lead towards some dynamical equilibrium or grip on the situation. The free-energy principle provides an explanatory framework for understanding the coupling, or attunement, between internal and external dynamics. Following Friston, we are led to the conclusion that, because of the organism’s skills and affordance-related states of action readiness, the organism itself is its own best model of the causal structure of its niche, a paradoxical sounding twist on the familiar embodied theme that “the world is its own best model” (Brooks 1991).

The main motivation for the free-energy principle is the biological requirement for self-organization and self-maintenance. The causal processes that hold internal variables and environmental variables within bounds are coupled. For instance, a decrease in blood sugar level of a mammal might alter the anticipated sensory input such that it anticipates finding a food source, hence making the behaviour of the animal conform to its metabolic needs. Auletta (2013) provides a good example of how a coupled informational (sensorimotor) and metabolic system can provide a model for bacterial chemotaxis.19 The system that is minimizing free-energy is nothing less than the whole self-organising and self-regulating affordance responsive system.

The free-energy principle then reveals the deep continuity between mind and life that Clark (2013) has hinted at as being reminiscent of enactive approaches towards life and mind20 (Varela et al. 1991; Thompson 2007; Stewart et al. 2010; Colombetti 2013; Di Paolo et al. 2010). From our enactive point of view, it is the myriad of self-maintaining and self-producing processes that make up the animal, that give the animal a lived perspective on its environment. Metabolic and thermal disequilibria structure the way in which the world is perceived in the sense that it makes the individual organism selectively open and responsive to relevant affordances, to the relevant possibilities for action the environment offers. An apple might invite eating when I am hungry, and a blanket might solicit wrapping around me when I am cold. What counts as minimizing surprisal is thus relative to the current state and situation of the animal and its lived perspective on the many affordances offered by its environment. In acting in order to minimize surprisal, the individual is responsive to only the disequilibria with the environment that are relevant given its current state and situation.

In many kinds of animals, these disequilibria might be experienced as an affective tension or something-to-be-improved. The animal is drawn to act by the relevant affordances in its situation, so as to reduce this tension, which results in its improving its grip on its environment. We call this the tendency towards an optimal grip on the situation, which we take to be a basic concern of every living animal (Rietveld 2008a; Bruineberg and Rietveld 2014; Kiverstein and Rietveld 2015). This basic concern to improve grip is a precondition for and structures the animal’s lived perspective on the basis of which certain affordances offered by the environment stand out as significant or relevant. Minimizing surprisal then can be understood as an animal reducing its disattunement with the environment. In doing this, the animal improves its grip on the environment.

There are two points we therefore wish to stress that will be important for our later argument. First, surprisal is relative to an animal with an affective perspective on its ecological niche. The individual cares about something, namely about improving or at least maintaining grip on its changing situation. Friston emphasizes self-maintenance as a distinguishing feature of living systems from other self-organizing systems, where self-maintenance is understood as the ability to change their relationship with the environment and maintain thermodynamic homeostasis (Friston and Stephan 2007, p. 5). We have characterized this ability as an instance of what in philosophical phenomenology is described as being moved so as to tend towards an optimal grip. Thus on our view self-maintenance implies an affective perspective on its environment. Certain aspects of the animal’s econiche stand out as significant for the organism on the basis of its basic concern to improve its grip on the environment, and to reduce its disequilibria with the environment.