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)

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)

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...) is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...) models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

Finding the Mind Content Type Journal Article DOI 10.1007/s11098-010-9598-9 Authors Andy Clark, Philosophy, University of Edinburgh, Dugald Stewart Building, 3 Charles Street, Edinburgh, EH8 9AD Scotland, UK Journal Philosophical Studies Online ISSN 1573-0883 Print ISSN 0031-8116.

Tyler Burge presents an original study of the most primitive ways in which individuals represent the physical world. By reflecting on the science of perception and related psychological and biological sciences, he gives an account of constitutive conditions for perceiving the physical world, and thus aims to locate origins of representational mind.

The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). (...) The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between “Pearl blankets” to refer to the original epistemic use of Markov blankets and “Friston blankets” to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programmes: “inference with a model” and “inference within a model.” Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone. (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)

Much has been written about the free energy principle (FEP), and much misunderstood. The principle has traditionally been put forth as a theory of brain function or biological self-organisation. Critiques of the framework have focused on its lack of empirical support and a failure to generate concrete, falsifiable predictions. I take both positive and negative evaluations of the FEP thus far to have been largely in error, and appeal to a robust literature on scientific modelling to rectify the situation. A (...) prominent account of scientific modelling distinguishes between model structure and model construal. I propose that the FEP be reserved to designate a model structure, to which philosophers and scientists add various construals, leading to a plethora of models based on the formal structure of the FEP. An entailment of this position is that demands placed on the FEP that it be falsifiable or that it conform to some degree of biological realism rest on a category error. To this end, I deliver first an account of the phenomenon of model transfer and the breakdown between model structure and model construal. In the second section, I offer an overview of the formal elements of the framework, tracing their history of model transfer and illustrating how the formalism comes apart from any interpretation thereof. Next, I evaluate existing comprehensive critical assessments of the FEP, and hypothesise as to potential sources of existing confusions in the literature. In the final section, I distinguish between what I hold to be the FEP—taken to be a modelling language or modelling framework—and what I term “FEP models.”. (shrink)

Should cognitive scientists be any more embarrassed about their lack of a discipline-fixing definition of cognition than biologists are about their inability to define “life”? My answer is “no”. Philosophers seeking a unique “mark of the cognitive” or less onerous but nevertheless categorical characterizations of cognition are working at a level of analysis upon which hangs nothing that either cognitive scientists or philosophers of cognitive science should care about. In contrast, I advocate a pluralistic stance towards uses of the term (...) ‘cognition’ that eschews the urge to treat cognition as a metaphysically well-defined “natural” kind. (shrink)

According to F. Adams [this journal, vol. 68, 2018] cognition cannot be realized in plants or bacteria. In his view, plants and bacteria respond to the here-and-now in a hardwired, inflexible manner, and are therefore incapable of cognitive activity. This article takes issue with the pursuit of plant cognition from the perspective of an empirically informed philosophy of plant neurobiology. As we argue, empirical evidence shows, contra Adams, that plant behavior is in many ways analogous to animal behavior. This renders (...) plants suitable to be described as cognitive agents in a non-metaphorical way. Sections two to four review the arguments offered by Adams in light of scientific evidence on plant adaptive behavior, decision-making, anticipation, as well as learning and memory. Section five introduces the ‘phyto-nervous’ system of plants. To conclude, section six resituates the quest for plant cognition into a broader approach in cognitive science, as represented by enactive and ecological schools of thought. Overall, we aim to motivate the idea that plants may be considered genuine cognitive agents. Our hope is to help propel public awareness and discussion of plant intelligence once appropriately stripped of anthropocentric preconceptions of the sort that Adams' position appears to exemplify. (shrink)

From Descartes to Popper, philosophers have criticized and tried to improve the strategies of reasoning invoked in science and in everyday life. In recent years leading cognitive psychologists have painted a detailed, controversial, and highly critical portrait of common sense reasoning. Stephen Stich begins with a spirited defense of this work and a critique of those writers who argue that widespread irrationality is a biological or conceptual impossibility.Stich then explores the nature of rationality and irrationality: What is it that distinguishes (...) good reasoning from bad? He rejects the most widely accepted approaches to this question approaches which unpack rationality by appeal to truth, to reflective equilibrium or conceptual analysis. The alternative he defends grows out of the pragmatic tradition in which reasoning is viewed as a cognitive tool. Stich's version of pragmatism leads to a radical epistemic relativism and he argues that the widespread abhorrence of relativism is ill founded. (shrink)

An extended argument that cognitive phenomena—perceiving, imagining, remembering—can be best explained in terms of an interface between contentless and content-involving forms of cognition. -/- Evolving Enactivism argues that cognitive phenomena—perceiving, imagining, remembering—can be best explained in terms of an interface between contentless and content-involving forms of cognition. Building on their earlier book Radicalizing Enactivism, which proposes that there can be forms of cognition without content, Daniel Hutto and Erik Myin demonstrate the unique explanatory advantages of recognizing that only some forms (...) of cognition have content while others—the most elementary ones—do not. They offer an account of the mind in duplex terms, proposing a complex vision of mentality in which these basic contentless forms of cognition interact with content-involving ones. -/- Hutto and Myin argue that the most basic forms of cognition do not, contrary to a currently popular account of cognition, involve picking up and processing information that is then used, reused, stored, and represented in the brain. Rather, basic cognition is contentless—fundamentally interactive, dynamic, and relational. In advancing the case for a radically enactive account of cognition, Hutto and Myin propose crucial adjustments to our concept of cognition and offer theoretical support for their revolutionary rethinking, emphasizing its capacity to explain basic minds in naturalistic terms. They demonstrate the explanatory power of the duplex vision of cognition, showing how it offers powerful means for understanding quintessential cognitive phenomena without introducing scientifically intractable mysteries into the mix. (shrink)

Plant predictive processing suggests that plants anticipatorily perceive their environment. This hypothesis runs up against a challenge which takes the form of two constraints on per- ception advanced by Tyler Burge: the veridicality constraint and the constancy constraint. This paper argues that the veridicality constraint can be satisfied by assuming a general account of predictive processing. To show how the constancy constraint may be fulfilled, an ecologically informed account of invariant pick-up is developed and given a place within plant predictive (...) processing. It is concluded that, against our anthro- pocentric folk-psychological notions of perception, there is reason to believe that plants engage in minimal perception. (shrink)

Forty years’ experience as a bacterial geneticist has taught me that bacteria possess many cognitive, computational and evolutionary capabilities unimaginable in the first six decades of the twentieth century. Analysis of cellular processes such as metabolism, regulation of protein synthesis, and DNA repair established that bacteria continually monitor their external and internal environments and compute functional outputs based on information provided by their sensory apparatus. Studies of genetic recombination, lysogeny, antibiotic resistance and my own work on transposable elements revealed multiple (...) widespread bacterial systems for mobilizing and engineering DNA molecules. Examination of colony development and organization led me to appreciate how extensive multicellular collaboration is among the majority of bacterial species. Contemporary research in many laboratories on cell–cell signaling, symbiosis and pathogenesis show that bacteria utilise sophisticated mechanisms for intercellular communication and even have the ability to commandeer the basic cell biology of ‘higher’ plants and animals to meet their own needs. This remarkable series of observations requires us to revise basic ideas about biological information processing and recognise that even the smallest cells are sentient beings. (shrink)

Forty years’ experience as a bacterial geneticist has taught me that bacteria possess many cognitive, computational and evolutionary capabilities unimaginable in the first six decades of the twentieth century. Analysis of cellular processes such as metabolism, regulation of protein synthesis, and DNA repair established that bacteria continually monitor their external and internal environments and compute functional outputs based on information provided by their sensory apparatus. Studies of genetic recombination, lysogeny, antibiotic resistance and my own work on transposable elements revealed multiple (...) widespread bacterial systems for mobilizing and engineering DNA molecules. Examination of colony development and organization led me to appreciate how extensive multicellular collaboration is among the majority of bacterial species. Contemporary research in many laboratories on cell–cell signaling, symbiosis and pathogenesis show that bacteria utilise sophisticated mechanisms for intercellular communication and even have the ability to commandeer the basic cell biology of ‘higher’ plants and animals to meet their own needs. This remarkable series of observations requires us to revise basic ideas about biological information processing and recognise that even the smallest cells are sentient beings. Previous article in issue. (shrink)

It remains at best controversial to claim, non-figuratively, that plants are cognitive agents. At the same time, it is taken as trivially true that many animals are cognitive agents, arguably through an implicit or explicit appeal to natural science. Yet, any given definition of cognition implicates at least some further processes, such as perception, action, memory, and learning, which must be observed either behaviorally, psychologically, neuronally, or otherwise physiologically. Crucially, however, for such observations to be intelligible, they must be counted (...) as evidence for some model. These models in turn point to homologies of physiology and behavior that facilitate the attribution of cognition to some non-human animals. But, if one is dealing with a model of animal cognition, it is tautological that only animals can provide evidence, and absurd to claim that plants can. The more substantive claim that, given a general model of cognition, only animals but not plants can provide evidence, must be evaluated on its merits. As evidence mounts that plants meet established criteria of cognition, from physiology to behavior, they continue to be denied entry into the cognitive club. We trace this exclusionary tendency back to Aristotle, and attempt to counter it by drawing on the philosophy of modelling and a range of findings from plant science. Our argument illustrates how a difference in degree between plant and animals is typically mistaken for a difference in kind. (shrink)

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...) is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. (shrink)

This paper criticizes the role of intuition-based ascriptions of cognition that are closely related to the ascription of mind. This practice hinders the explication of a clear and stable target domain for the cognitive sciences. To move forward, the proposal is to cut the notion of cognition free from such ascriptions and the intuition-based judgments that drive them. Instead, cognition is reinterpreted and developed as a scientific concept that is tied to a material domain of research. In this reading, cognition (...) becomes a changeable theoretical concept that can and must be adapted to the findings within this target domain. Taking humans as the best-established existing example of the relevant material target domain, this central case is extended to include all living systems. To clarify what it is about living systems that warrants their role as cognitive target domain, the new concept of cobolism is introduced as a complement to metabolism. Cobolism refers to the systematic ways in which each living system encompasses structures, processes and external events that maintain the fundamental metabolic processes that constitute the core of each living system. Cobolism is perfectly general, applies to bacterial and human cases alike, and provides a general format to describe wildly different cognitive organizations. It provides a clear target for the cognitive sciences to work on, turning them into what we can call the cognitive life sciences. (shrink)

The free energy principle says that any self-organising system that is at nonequilibrium steady-state with its environment must minimize its free energy. It is proposed as a grand unifying principle for cognitive science and biology. The principle can appear cryptic, esoteric, too ambitious, and unfalsifiable—suggesting it would be best to suspend any belief in the principle, and instead focus on individual, more concrete and falsifiable ‘process theories’ for particular biological processes and phenomena like perception, decision and action. Here, I explain (...) the free energy principle, and I argue that it is best understood as offering a conceptual and mathematical analysis of the concept of existence of self-organising systems. This analysis offers a new type of solution to long-standing problems in neurobiology, cognitive science, machine learning and philosophy concerning the possibility of normatively constrained, self-supervised learning and inference. The principle can therefore uniquely serve as a regulatory principle for process theories, to ensure that process theories conforming to it enable self-supervision. This is, at least for those who believe self-supervision is a foundational explanatory task, good reason to believe the free energy principle. (shrink)

The paper links discussions of two topics: biological individuality and the simplest forms of mentality. I discuss several attempts to locate the boundary between metabolic activity and ‘minimal cognition.’ I then look at differences between the kinds of individuality present in unicellular life, multicellular life in general, and animals of several kinds. Nervous systems, which are clearly relevant to cognition and subjectivity, also play an important role in the form of individuality seen in animals. The last part of the paper (...) links these biological transitions to the evolutionary history of subjective experience. (shrink)

ABSTRACT:I contrast an ecological account of natural agency with the traditional Cartesian conception using recent research in bacterial cognition and cellular decision making as a test case. I argue that the Cartesian conception—namely, the view that agency presupposes cognition—generates a dilemma between mechanism, the view that bacteria are mere automata, and intellectualism, the view that they exhibit full-blown cognition. Unicellular organisms, however, occupy a middle ground between these two extremes. On the one hand, their capacities and activities are too adaptive (...) to count as mere machines. On the other hand, they lack the open-ended responsiveness of cognitive agents to rational norms. An ecological conception of agency as the gross behavioral capacity to respond to affordances, I argue, does not presuppose cognition and allows for degrees of agency along a continuum, from the simplest adaptive agents, such as unicellular organisms, to the most sophisticated cognitive agents. Bacteria, I conclude, are adaptiveagents, hence not mere automata, but notcognitiveagents. (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)

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)

Appropriate as a textbook for graduate courses, _The Evolution and Function of Cognition_ provides a systematic and progressively inclusive integration of the facts and principles of cognitive psychology. It includes contributions of information processing and reaction, and emphasizes historical continuity. In addition, the book shows how evolutionary psychology fits in with the mainstream of thought in psychological theory. _The Evolution and Function of Cognition_ will benefit scholars and researchers interested in the general topics of evolutionary psychology and cognitive science.

How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to (...) be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation. (shrink)

The central insight of Darwin's Origin of Species is that evolution is an ecological phenomenon, arising from the activities of organisms in the 'struggle for life'. By contrast, the Modern Synthesis theory of evolution, which rose to prominence in the twentieth century, presents evolution as a fundamentally molecular phenomenon, occurring in populations of sub-organismal entities - genes. After nearly a century of success, the Modern Synthesis theory is now being challenged by empirical advances in the study of organismal development and (...) inheritance. In this important study, D. M. Walsh shows that the principal defect of the Modern Synthesis resides in its rejection of Darwin's organismal perspective, and argues for 'situated Darwinism': an alternative, organism-centred conception of evolution that prioritises organisms as adaptive agents. His book will be of interest to scholars and advanced students of evolutionary biology and the philosophy of biology. (shrink)

When a chimpanzee stockpiles rocks as weapons or when a frog sends out mating calls, we might easily assume these animals know their own motivations--that they use the same psychological mechanisms that we do. But as Beyond the Brain indicates, this is a dangerous assumption because animals have different evolutionary trajectories, ecological niches, and physical attributes. How do these differences influence animal thinking and behavior? Removing our human-centered spectacles, Louise Barrett investigates the mind and brain and offers an alternative approach (...) for understanding animal and human cognition. Drawing on examples from animal behavior, comparative psychology, robotics, artificial life, developmental psychology, and cognitive science, Barrett provides remarkable new insights into how animals and humans depend on their bodies and environment--not just their brains--to behave intelligently. Barrett begins with an overview of human cognitive adaptations and how these color our views of other species, brains, and minds. Considering when it is worth having a big brain--or indeed having a brain at all--she investigates exactly what brains are good at. Showing that the brain's evolutionary function guides action in the world, she looks at how physical structure contributes to cognitive processes, and she demonstrates how these processes employ materials and resources in specific environments. Arguing that thinking and behavior constitute a property of the whole organism, not just the brain, Beyond the Brain illustrates how the body, brain, and cognition are tied to the wider world. (shrink)

A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is (...) that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it. (shrink)

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

"Perception is not something that happens to us, or in us," writes Alva Noe. "It is something we do." In Action in Perception, Noe argues that perception and perceptual consciousness depend on capacities for action and thought — that ...

The slime mould Physarum polycephalum has been used in developing unconventional computing devices for in which the slime mould played a role of a sensing, actuating, and computing device. These devices treated the slime mould as an active living substrate, yet it is a self-consistent living creature which evolved over millions of years and occupied most parts of the world, but in any case, that living entity did not own true cognition, just automated biochemical mechanisms. To “rehabilitate” slime mould from (...) the rank of a purely living electronics element to a “creature of thoughts” we are analyzing the cognitive potential of P. polycephalum. We base our theory of minimal cognition of the slime mould on a bottom-up approach, from the biological and biophysical nature of the slime mould and its regulatory systems using frameworks such as Lyon’s biogenic cognition, Muller, di Primio-Lengelerś modifiable pathways, Bateson's “patterns that connect” framework, Maturana's autopoietic network, or proto-consciousness and Morgan's Canon. (shrink)

Behaviour is often described as the computation of a response to a stimulus. This description is incomplete in an important way because it only examines what occurs between the reception of stimulus information and the generation of an action. Behaviour is more correctly described as a control process where actions are performed in order to affect perceptions. This closed-loop nature of behaviour is de-emphasized in modern discussions of brain function, leading to a number of artificial mysteries. A notable example is (...) the ‘symbol grounding problem'. When behaviour is viewed as a control process, it is natural to explain how internal representations, even symbols, can have meaning for an organism, and how actions can be motivated by organic needs. (shrink)

Externalism comes in varieties. While the landscape isn.

This paper considers questions about continuity and discontinuity between life and mind. It begins by examining such questions from the perspective of the free energy principle (FEP). The FEP is becoming increasingly influential in neuroscience and cognitive science. It says that organisms act to maintain themselves in their expected biological and cognitive states, and that they can do so only by minimizing their free energy given that the long-term average of free energy is entropy. The paper then argues that there (...) is no singular interpretation of the FEP for thinking about the relation between life and mind. Some FEP formulations express what we call an independence view of life and mind. One independence view is a cognitivist view of the FEP. It turns on information processing with semantic content, thus restricting the range of systems capable of exhibiting mentality. Other independence views exemplify what we call an overly generous non-cognitivist view of the FEP, and these appear to go in the opposite direction. That is, they imply that mentality is nearly everywhere. The paper proceeds to argue that non-cognitivist FEP, and its implications for thinking about the relation between life and mind, can be usefully constrained by key ideas in recent enactive approaches to cognitive science. We conclude that the most compelling account of the relationship between life and mind treats them as strongly continuous, and that this continuity is based on particular concepts of life (autopoiesis and adaptivity) and mind (basic and non-semantic). (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)

One problem faced in discussions of the evolution of intelligence is the need to get a precise fix on what is to be explained. Terms like "intelligence," "cognition" and "mind" do not have simple and agreed-upon meanings, and the differences between conceptions of intelligence have consequences for evolutionary explanation. I hope the papers in this volume will enable us to make progress on this problem. The present contribution is mostly focused on these basic and foundational issues, although the last section (...) of the paper will look at some specific models and programs of empirical work. (shrink)

The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features described here (...) are not shared in all PP accounts. Some may not be necessary for an individual model; others may be contested. Indeed, not even all authors of this collection will accept all of them. To make this transparent, we have encouraged contributors to indicate briefly which of the features are necessary to support the arguments they provide, and which (if any) are incompatible with their account. For the sake of clarity, we provide the complete list here, very roughly ordered by how central we take them to be for “Vanilla PP” (i.e., a formulation of predictive processing that will probably be accepted by most researchers working on this topic). More detailed explanations will be given below. Note that these features do not specify individually necessary and jointly sufficient conditions for the application of the concept of “predictive processing”. All we currently have is a semantic cluster, with perhaps some overlapping sets of jointly sufficient criteria. The framework is still developing, and it is difficult, maybe impossible, to provide theory-neutral explanations of all PP ideas without already introducing strong background assumptions. (shrink)

Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of (...) tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates. (shrink)

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. (shrink)