a variational approach to niche construction

In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields

Is the free-energy principle a formal theory of semantics? From variational density dynamics to neural and phenotypic representations

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.Comment: 35 pages, 4 figures, 1 tabl

A Multi-scale View of the Emergent Complexity of Life: A Free-energy Proposal

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales

A tale of two densities: Active inference is enactive inference

The aim of this paper is to clarify how best to interpret some of the central constructs that underwrite the free-energy principle (FEP) – and its corollary, active inference – in theoretical neuroscience and biology: namely, the role that generative models and variational densities play in this theory. We argue that these constructs have been systematically misrepresented in the literature; because of the conflation between the FEP and active inference, on the one hand, and distinct (albeit closely related) Bayesian formulations, centred on the brain – variously known as predictive processing, predictive coding, or the prediction error minimisation framework. More specifically, we examine two contrasting interpretations of these models: a structural representationalist interpretation and an enactive interpretation. We argue that the structural representationalist interpretation of generative and recognition models does not do justice to the role that these constructs play in active inference under the FEP. We propose an enactive interpretation of active inference – what might be called enactive inference. In active inference under the FEP, the generative and recognition models are best cast as realising inference and control – the self-organising, belief-guided selection of action policies – and do not have the properties ascribed by structural representationalists

Regimes of Expectations: An Active Inference Model of Social Conformity and Human Decision Making

How do humans come to acquire shared expectations about how they ought to behave in distinct normalized social settings? This paper offers a normative framework to answer this question. We introduce the computational construct of ‘deontic value’ – based on active inference and Markov decision processes – to formalize conceptions of social conformity and human decision-making. Deontic value is an attribute of choices, behaviors, or action sequences that inherit directly from deontic cues in our econiche (e.g., red traffic lights); namely, cues that denote an obligatory social rule. Crucially, the prosocial aspect of deontic value rests upon a particular form of circular causality: deontic cues exist in the environment in virtue of the environment being modified by repeated actions, while action itself is contingent upon the deontic value of environmental cues. We argue that this construction of deontic cues enables the epistemic (i.e., information-seeking) and pragmatic (i.e., goal- seeking) values of any behavior to be ‘cached’ or ‘outsourced’ to the environment, where the environment effectively ‘learns’ about the behavior of its denizens. We describe the process whereby this particular aspect of value enables learning of habitual behavior over neurodevelopmental and transgenerational timescales

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Mapping Husserlian phenomenology onto active inference

Phenomenology is the rigorous descriptive study of conscious experience. Recent attempts to formalize Husserlian phenomenology provide us with a mathematical model of perception as a function of prior knowledge and expectation. In this paper, we re-examine elements of Husserlian phenomenology through the lens of active inference. In doing so, we aim to advance the project of computational phenomenology, as recently outlined by proponents of active inference. We propose that key aspects of Husserl's descriptions of consciousness can be mapped onto aspects of the generative models associated with the active inference approach. We first briefly review active inference. We then discuss Husserl's phenomenology, with a focus on time consciousness. Finally, we present our mapping from Husserlian phenomenology to active inference.Comment: 10 page

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference