The free energy principle (FEP) is an information-theoretic approach to living systems. FEP characterizes life by living systems’ resistance to the second law of thermodynamics: living systems do not randomly visit the possible states, but actively work to remain within a set of viable states. In FEP, this is modelled mathematically. Yet, the status of these models is typically unclear: are these models employed by organisms or strictly scientific tools of understanding? In this article, I argue for an instrumentalist take on models in FEP. I shall argue that models used as instruments for knowledge by scientists and models as implemented by organisms to navigate the world are being conflated, which leads to erroneous conclusions. I further argue that a realist position is unwarranted. First, it overgenerates models and thus trivializes the notion of modelling. Second, even when the mathematical mechanisms described by FEP are implemented in an organism, they do not constitute a model. They are covariational, not representational in nature, and precede the social practices that have shaped our scientific modelling practice. I finally argue that the above arguments do not affect the instrumentalist position. An instrumentalist approach can further add to conceptual clarity in the FEP literature.
The free energy principle (FEP) purports to provide a single principle for the organizational dynamics of living systems, including their cognitive profiles. It states that for a system to maintain non-equilibrium steady-state with its environment it must minimise its free energy. It is said to be entirely scale-free, applying to anything from particles to organisms, and interactive machines, spanning from the abiotic to the biotic. Because the FEP is so general in its application, it is for this reason that one might wonder in what sense this framework captures anything specific to biological characteristics, if details at all. We take steps to correct for this here. We do so by taking up a distinct challenge that the FEP must overcome if it is to be of interest to those working in the biological sciences. We call this the pebble challenge : it states that the FEP cannot capture the organisational principles specific to biology, for its formalisms apply equally well to pebbles. We progress in solving the pebble challenge by articulating how the notion of 'autonomy as precarious operational closure' from the enactive literature can be unpacked within the FEP. This enables the FEP to delineate between the abiotic and the biotic; avoiding the pebble challenge that keeps it out of touch with the living systems we encounter in the world and is of interest to the sciences of life and mind. This is a pre-print. This paper is currently under review.
This aim of this paper is two-fold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic understanding of social cognition that is compatible with active inference. While some social cognition theories seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, by assuming representational structures, in line with the classic Theory of Mind (ToM). Holding both enactivism and ToM, we argue, entails contradiction and confusion due to two ToM assumptions widely known to be rejected by enactivism: that (1) social cognition reduces to mental representation and (2) social cognition is a hardwired contentful ‘toolkit’ or ‘starter pack’ that fuels the model-like theorising supposed in (1). The paper offers a positive alternative, one that avoids contradictions or confusion. After rejecting ToM-inspired theories of social cognition and clarifying the profile of social cognition under enactivism, that is without assumptions (1) and (2), the last section advances an enactivist-dynamic model of cognition as dynamic, real-time, fluid, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of socio-cognitive novelty in developmental change and active inference as a tool to demonstrate social understanding as generalised synchronisation.
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