Abstract

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance—in relation to the ontological and epistemological status of representations—is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account—an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the ‘aboutness’ or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.

Highlights

  • Neural Representations and Their (Dis)ContentsRepresentations figure prominently in several human affairs

  • We argue that even pursuing a minimalist, deflationary account of the content of neural representations under the free-energy principles licenses an appeal to a robust kind of semantic content, the kind at stake in the ‘aboutness’

  • We expand upon the ecological component of deflated mathematical content, which we argue leads to a naturalistic theory of intentionality: a formal theory of semantic content based on the free-energy principle

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Summary

Introduction

Representations figure prominently in several human affairs. Human beings routinely use representational artifacts like maps to navigate their environments. There are several well-accepted constraints for the appropriateness of representational explanations: such an account should (1) cohere broadly with the actual practices that are used in computational cognitive science research; (2) allow for misrepresentation, i.e., the representation must be able to “get it wrong”; (3) provide the principled method for attributing of determinate contents to specific states or structures (typically internal to the system), and (4) be naturalistic, meaning that the account of semantic content does not itself appeal to semantic terms when defining how the representational capacity is realized by the physical system, on pain of circularity in reasoning [11,12]. We aim to determine whether or not the construct of neural representations plays an explanatory role in an increasingly popular framework for the study of action and cognition in living systems, namely, the variational free-energy principle and its corollary process theory, active inference. The ensuing position coheres broadly with, but rests on distinct assumptions from, the realist one

The Faces of Representationalism
Towards Anti-Realism
Representations Under the Free-Energy Principle?
The Free-Energy Principle and Active Inference
Markov Blankets and the Dynamics of Living Systems
Phenotypes: A Tale of Two Densities
Deflationary and Fictionalist Accounts of Neural Representation
A Deflationary
Fictionalism and Models in Scientific Practice
A Deflationary Account of Content Under the Free-Energy Principle
From a Computational Theory Proper to a Formal Semantics
Conclusions
The Langevin Formalism and Density Dynamics
Bayesian Mechanics
Information Geometry and Beliefs
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