What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology (...) and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference. (shrink)

Predictive processing (PP) is now a prominent theoretical framework in the philosophy of mind and cognitive science. This review focuses on PP research with a relatively philosophical focus, taking stock of the framework and discussing new directions. The review contains an introduction that describes the full PP toolbox; an exploration of areas where PP has advanced understanding of perceptual and cognitive phenomena; a discussion of PP's impact on foundational issues in cognitive science; and a consideration of the philosophy of science (...) of PP. The overall picture is that PP is a fruitful framework, with exciting new directions awaiting exploration. (shrink)

We develop a truism of commonsense psychology that perception and action constitute the boundaries of the mind. We do so however not on the basis of commonsense psychology, but by using the notion of a Markov blanket originally employed to describe the topological properties of causal networks. We employ the Markov blanket formalism to propose precise criteria for demarcating the boundaries of the mind that unlike other rival candidates for “marks of the cognitive” avoids begging the question in the extended (...) mind debate. Our criteria imply that the boundary of the mind is nested and multiscale sometimes extending beyond the individual agent to incorporate items located in the environment. Chalmers has used commonsense psychology to develop what he sees as the most serious challenge to the view that minds sometimes extend into the world. He has argued that perception and action should be thought of as interfaces that separate minds from their surrounding environment. In a series of recent papers Hohwy has defended a similar claim using the Markov blanket formalism. We use the Markov blanket formalism to show how both of their objections to the extended mind fail. (shrink)

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a (...) naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness. (shrink)

We present a multiscale integrationist interpretation of the boundaries of cognitive systems, using the Markov blanket formalism of the variational free energy principle. This interpretation is intended as a corrective for the philosophical debate over internalist and externalist interpretations of cognitive boundaries; we stake out a compromise position. We first survey key principles of new radical views of cognition. We then describe an internalist interpretation premised on the Markov blanket formalism. Having reviewed these accounts, we develop our positive multiscale account. (...) We argue that the statistical seclusion of internal from external states of the system—entailed by the existence of a Markov boundary—can coexist happily with the multiscale integration of the system through its dynamics. Our approach does not privilege any given boundary, nor does it argue that all boundaries are equally prescient. We argue that the relevant boundaries of cognition depend on the level being characterised and the explanatory interests that guide investigation. We approach the issue of how and where to draw the boundaries of cognitive systems through a multiscale ontology of cognitive systems, which offers a multidisciplinary research heuristic for cognitive science. (shrink)

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also (...) unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. (shrink)

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. (shrink)

This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an (...) illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment. (shrink)

A proposal for the biological grounding of intrinsic teleology and sense-making through the theory of autopoiesis is critically evaluated. Autopoiesis provides a systemic lan- guage for speaking about intrinsic teleology but its original formulation needs to be elaborated further in order to explain sense-making. This is done by introducing adaptivity, a many-layered property that allows organisms to regulate themselves with respect to their conditions of via- bility. Adaptivity leads to more articulated concepts of behaviour, agency, sense-construction, health, and temporality than (...) those given so far by autopoiesis and enaction. These and other implications for understanding the organismic generation of values are explored. (shrink)

If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

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. (shrink)

An exciting theory in neuroscience is that the brain is an organ for prediction error minimization. This theory is rapidly gaining influence and is set to dominate the science of mind and brain in the years to come. PEM has extreme explanatory ambition, and profound philosophical implications. Here, I assume the theory, briefly explain it, and then I argue that PEM implies that the brain is essentially self-evidencing. This means it is imperative to identify an evidentiary boundary between the brain (...) and its environment. This boundary defines the mind-world relation, opens the door to skepticism, and makes the mind transpire as more inferentially secluded and neurocentrically skull-bound than many would nowadays think. Therefore, PEM somewhat deflates contemporary hypotheses that cognition is extended, embodied and enactive; however, it can nevertheless accommodate the kinds of cases that fuel these hypotheses. (shrink)

The mathematical theory of communication.

A proposal for the biological grounding of intrinsic teleology and sense-making through the theory of autopoiesis is critically evaluated. Autopoiesis provides a systemic language for speaking about intrinsic teleology but its original formulation needs to be elaborated further in order to explain sense-making. This is done by introducing adaptivity, a many-layered property that allows organisms to regulate themselves with respect to their conditions of viability. Adaptivity leads to more articulated concepts of behaviour, agency, sense-construction, health, and temporality than those given (...) so far by autopoiesis and enaction. These and other implications for understanding the organismic generation of values are explored. (shrink)

The question has long confounded philosophers and scientists, and it is this so-called explanatory gap between biological life and consciousness that Evan ...

The Embodied Mind provides a unique, sophisticated treatment of the spontaneous and reflective dimension of human experience.

Predictive processing is an increasingly popular approach to cognition, perception and action. It says that the brain is essentially a hierarchical prediction machine. It is typically construed in a representationalist and inferentialist fashion so that the brain makes contentful inferences on the basis of representational models. In this paper, I argue that the predictive processing framework is inconsistent with this epistemic position. In particular, I argue that the combination of hierarchical modeling, contentful inferentialism and representationalism entail an internal inconsistency. Specifically, (...) for a particular set of states, there will be both a representation requirement and not. Yet a system cannot both be required to represent a certain set of states and not be required to represent those states. Due to this contradiction, I propose to reject the standard view. I suggest that predictive processing is best interpreted in terms of reliable covariation instead, entailing an instrumentalist approach to the statistical machinery. (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for (...) avoiding these objections and I argue that both confront formidable challenges. (shrink)