This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really that (...) way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

There has been much criticism of the idea that Friston's free-energy principle can unite the life and mind sciences. Here, we argue that perhaps the greatest problem for the totalizing ambitions of its proponents is a failure to recognize the importance of evolutionary dynamics and to provide a convincing adaptive story relating free-energy minimization to organismal fitness.

Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist (...) campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference. (shrink)

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...) of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

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)

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)

According to predictive processing, an increasingly influential paradigm in cognitive science, the function of the brain is to minimize the prediction error of its sensory input. Conceptual engineering is the practice of assessing and changing concepts or word meanings. We contribute to both strands of research by proposing the first cognitive account of conceptual engineering, using the predictive processing framework. Our model reveals a new kind of implementation problem as prediction errors are only minimized if enough agents embrace conceptual changes. (...) This problem can be overcome by emphasizing the importance of social norms and conceptual pluralism. (shrink)

The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may employ (...) various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (shrink)

I will take it that general intelligence is intelligence of the kind that a typical human being—Fred, say—manifests in his role as a cognitive agent, that is, as an acquirer, receiver and circulator of knowledge in his cognitive economy. Framed in these terms, the word “general” underserves our ends. Hereafter our questions will bear upon the all-purpose intelligence of beings like Fred. Frederika appears as Fred’s AI-counterpart, not as a fully programmed and engineered being, but as a presently unrealized theoretical (...) construct. Our basic question is whether it is in principle possible to equip Frederika to do what Fred does as an all-purpose participant in his own cognitive economy. Can she achieve a sufficiency of relevant similarity to him to allow us to say that she herself can do what Fred can do, perhaps even better? One of the things that Fred can do—or at least could learn from experience to do—is discharge the duties of an Appeals Court judge. As set down in the ancient doctrine of lex non scripta, Fred must be able to detect, understand and correctly apply certain tacit and implicit rules of law which defy express propositional formulation and linguistic articulation. Fred has an even more widespread capacity for the epistemically tacit and implicit, clearly one of his most cost-saving kinds of intelligence. Indeed, most by far of what Fred will ever know he will know tacitly and implicitly. So we must ask: how tightly bound to the peculiarities of Fred’s cognitive enablement conditions is the character of the intelligence that he manifests? And how far down Fred’s causal make-up does intelligence actually go? (shrink)

A weak version of the life-mind continuity thesis entails that every living system also has a basic mind (with a non-representational form of intentionality). The strong version entails that the same concepts that are sufficient to explain basic minds (with non-representational states) are also central to understanding non-basic minds (with representational states). We argue that recent work on the free energy principle supports the following claims with respect to the life-mind continuity thesis: (i) there is a strong continuity between life (...) and mind; (ii) all living systems can be described as if they had representational states; (iii) the ’as-if representationality’ entailed by the free energy principle is central to understanding both basic forms of intentionality and intentionality in non-basic minds. In addition to this, we argue that the free energy principle also renders realism about computation and representation compatible with a strong life-mind continuity thesis (although the free energy principle does not entail computational and representational realism). In particular, we show how representationality proper can be grounded in ’as-if representationality’. (shrink)

Whether perception involves the manipulation of representations is currently heavily debated. The embedded view advanced by Nico Orlandi seeks a middle passage between representationalism and radical enactivism. In this paper I argue for a non-representational take on EV. I argue that this is the best way to resolve the objections EV has received from both representationalists and non-representationalists. I analyze this debate, and distinguish four sorts of objections: the objection of the wrongfully cut middleman, the argument against explanatory exclusionism, the (...) case for scientific benefits of representations, and the charge of inconsistent ascription of representational status in EV. I argue that the middleman was never cut in EV, and is controversial to boot, otherwise equal, non-representational explanations have primacy over representational explanations, due to the lack of naturalistic grounds for representations and the unnecessarily ascribed cognitive load to the system. Further, I show that puts the cart before the horse, and the arguments on offer are viciously circular. However, the final objection, lays bare a deeper issue for EV. At the cost of giving up the middle position, however, the explanatory tools already available to EV can be shown to cover the work initially thought to require representation. I conclude that EV is best altered to be a non-representational theory of perception. (shrink)

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 free energy principle (FEP) portends to provide a unifying principle for the biological and cognitive sciences. It states that for a system to maintain non-equilibrium steady-state with its environment it must minimise its (information-theoretic) free energy. Under the FEP, to minimise free energy is equivalent to engaging in approximate Bayesian inference. According to the FEP, therefore, inference is at the explanatory base of biology and cognition. In this paper, we discuss a specific challenge to this inferential formulation of adaptive (...) self-organisation. We call it the universal ethology challenge: it states that the FEP cannot unify biology and cognition, for life itself (or adaptive self-organisation) does not require inferential routines to select adaptive solutions to environmental pressures (as mandated by the FEP). We show that it is possible to overcome the universal ethology challenge by providing a cautious and exploratory treatment of inference under the FEP. We conclude that there are good reasons for thinking that the FEP can unify biology and cognition under the notion of approximate Bayesian inference, even if further challenges must be addressed to properly draw such a conclusion. (shrink)

Predictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle —a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies as representational. We deploy the Free Energy Principle instrumentally to distinguish four main notions of representation, which focus on organizational, (...) structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Rational beliefs and actions are typically evaluated against certain benchmarks, e.g., those of classical logic or probability theory. Rationality therefore is traditionally taken to involve some sort of reasoning, which in turn implies contentful cognition. Radically Enactive views of Cognition, on the other hand, claim that not all cognition is contentful. In order to show that rationality does not need to lie outside of REC’s scope of radicalizing cognition, I develop a Radically Enactive notion of Rationality, according to which rationality (...) is embodied, situated and contentless. For RER, an organism acts rationally insofar as it sustains a proficient interaction with its environment, which in turn requires the coordination of cognitive abilities in accordance with environmental constraints. Rationality is thus distinguished from reasoning, for reasoning is understood as a capacity to coordinate representational cognitive abilities. (shrink)

In the article “Why are we our brain: enactivism put into question” (this volume), Pereira and collaborators raise a battery of criticisms of enactivism, which is a family of approaches in the cognitive sciences that gives centrality to the body and to the autonomous action of organisms in explanations of their cognitive processes. The authors’ attacks target some central concepts of the enactivist proposal, such as practical knowledge, embodiment (or corporeity) and sensory-motor regularities. I argue that the criticisms by Pereira (...) et al. do not proceed for different reasons: some assume what they want to prove, others give excessive weight to intuitions about fictional scenarios and, finally, others attack scarecrows that do not represent the enactivist positions. None of the points I raise in defense of enactivism are new, but I consider it important to make them explicit in order to make the debate on the philosophy of cognitive sciences clearer. (shrink)

Our objective in this paper is twofold: first, we intend to address the tenability of the enactivist middle way between realism and idealism, as it is proposed in The Embodied Mind. We do so by taking the enactivist conception of bringing forth a world literally in three conceptual levels: enaction, niche construction and social construction. Based on this proposal, we claim that enactivism is compatible with the idea of an independent reality without committing to the claim that organisms have cognitive (...) access to a world composed of properties specified prior to any cognitive activity. Our second goal is to show that our literal interpretation of bringing forth a world not only sustains the legitimacy of the middle way, but it also allows us to revive the conception of evolution as natural drift—which is perhaps the least examined aspect of the original enactivist theory and is central to the understanding of cognition in an enactive way. Natural drift focuses on how structural couplings between organism and environment trigger viable pathways of maintenance and reproduction, instead of selecting the most adapted trait to a pregiven environment. Thus, although enactivists typically do not explore the consequences of their views regarding evolutionary dynamics, we show how natural drift provides a suitable starting point. (shrink)

Osiurak and Reynaud claim that research into the origin of cumulative technological culture has been too focused on social cognition and has consequently neglected the importance of uniquely human reasoning capacities. This commentary raises two interrelated theoretical concerns about O&R's notion of technical-reasoning capacities, and suggests how these concerns might be met.

How do we ensure that future generally intelligent AI share our values? This is the value-alignment problem. It is a weighty matter. After all, if AI are neutral with respect to our wellbeing, or worse, actively hostile toward us, then they pose an existential threat to humanity. Some philosophers have argued that one important way in which we can mitigate this threat is to develop only AI that shares our values or that has values that ‘align with’ ours. However, there (...) is nothing to guarantee that this policy will be universally implemented—in particular, ‘bad actors’ are likely to flout it. In this paper, I show how the predictive processing model of the mind, currently ascendant in cognitive science, may ameliorate the value-alignment problem. In essence, I argue that there are a plurality of reasons why any future generally intelligent AI will possess a predictive processing cognitive architecture (e.g. because we decide to build them that way; because it is the only possible cognitive architecture that can underpin general intelligence; because it is the easiest way to create AI.). I also argue that if future generally intelligent AI possess a predictive processing cognitive architecture, then they will come to share our pro-moral motivations (of valuing humanity as an end; avoiding maleficent actions; etc.), regardless of their initial motivation set. Consequently, these AI will pose a minimal threat to humanity. In this way then, I conclude, the value-alignment problem is significantly ameliorated under the assumption that future generally intelligent AI will possess a predictive processing cognitive architecture. (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)

It is increasingly common in cognitive science and philosophy of perception to regard perceptual processing as a probabilistic engine, taking into account uncertainty in computing representations of the distal environment. Models of this kind often postulate probabilistic representations, or what we will call probabilistic states,. These are states that in some sense mark or represent information about the probabilities of distal conditions. It has also been argued that perceptual experience itself in some sense represents uncertainty (Morrison _Analytic Philosophy_ 57 (1): (...) 15 48, 2016 ). In this article, we will first consider three models of sensory activity from perceptual neuroscience, namely signal detection theory (SDT), probabilistic population codes (PPC), and sampling. We will then reflect on the sense in which the probabilistic states introduced in these models are probabilistic representations. To sharpen this discussion, we will compare and contrast these probabilistic states to credences as they are understood in epistemology. We will suggest that probabilistic representation, in an appropriately robust sense, can be understood as a form of analog representation. In the last part of the paper, we apply this to the issue of whether conscious experience represents uncertainty—we will interpret this as the claim that there are phenomenal features of experience that serve as analog probabilistic representations. (shrink)

The mechanistic model depicts scientific explanations as involving the discovery of multi-level, organized components that constitute a target phenomenon. Meanwhile, sensorimotor enactivism purports to offer a scientifically informed account of perceptual experience as a skill-laden interactive relationship, constitutively involving both perceiver and world, rather than as an agent-bound representation of the world. Insofar as sensorimotor enactivism identifies an empirically tractable phenomenon – skillful agent-world interaction – and mechanistic explanation establishes the subpersonal components of this phenomenon, the two approaches allow for (...) a fruitful division of labor in investigating perceptual experience. On closer inspection, however, two challenges arise. First, the “representation challenge” arises because promising attempts to set out implementational details of our sensorimotor interaction with the world implicate cognitive representations, creating tension with sensorimotor enactivism’s nonrepresentational commitments. Second, the “reconstitution challenge” arises when mechanistic explanation not only uncovers the components of some established phenomenon but plays a role in “reconstituting” this phenomenon. This means that, through investigating mechanisms, perceptual experience may be reconceived such that its constituents are wholly organism-bound. We explore both challenges to the compatibility of mechanism and sensorimotor enactivism and examine possible solutions. The result is a clearer understanding of the tensions and opportunities for learning between the frameworks. (shrink)

In this paper, I argue that enactivism and computationalism—two seemingly incompatible research traditions in modern cognitive science—can be fruitfully reconciled under the framework of the free energy principle. FEP holds that cognitive systems encode generative models of their niches and cognition can be understood in terms of minimizing the free energy of these models. There are two philosophical interpretations of this picture. A computationalist will argue that as FEP claims that Bayesian inference underpins both perception and action, it entails a (...) concept of cognition as a computational process. An enactivist, on the other hand, will point out that FEP explains cognitive systems as constantly self-organizing to non-equilibrium steady-state. My claim is that these two interpretations are both true at the same time and that they enlighten each other. (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)

Basic Emotion Theory, or BET, has dominated the affective sciences for decades (Ekman, 1972, 1992, 1999; Ekman and Davidson, 1994; Griffiths, 2013; Scarantino and Griffiths, 2011). It has been highly influential, driving a number of empirical lines of research (e.g., in the context of facial expression detection, neuroimaging studies and evolutionary psychology). Nevertheless, BET has been criticized by philosophers, leading to calls for it to be jettisoned entirely (Colombetti, 2014; Hufendiek, 2016). This paper defuses those criticisms. In addition, it shows (...) that we have good reason to retain BET. Finally, it reviews and puts to rest worries that BET’s commitment to affect programs renders it outmoded. We propose that, with minor adjustments, BET can avoid such criticisms when conceived under a radically enactive account of emotions. Thus, rather than leaving BET behind, we show how its basic ideas can be revised, refashioned and preserved. Hence, we conclude, our new BET is still a good bet. (shrink)

Predictive processing (PP) and embodied cognition (EC) have emerged as two influential approaches within cognitive science in recent years. Not only have PP and EC been heralded as “revolutions” and “paradigm shifts” but they have motivated a number of new and interesting areas of research. This has prompted some to wonder how compatible the two views might be. This paper looks to weigh in on the issue of PP-EC compatibility. After outlining two recent proposals, I argue that further clarity can (...) be achieved on the issue by considering a model of scientific progress. Specifically, I suggest that Larry Laudan’s “problem solving model” can provide important insights into a number of outstanding challenges that face existing accounts of PP-EC compatibility. I conclude by outlining additional implications of the problem solving model for PP and EC more generally. (shrink)

There is a longstanding debate between those who think that cognition extends into the external environment and those who think it is located squarely within the individual. Recently, a new actor has emerged on the scene, one that looks to play kingmaker. Predictive processing says that the mind/brain is fundamentally engaged in a process of minimising the difference between what is predicted about the world and how the world actually is, what is known as ‘prediction error minimisation’. The goal of (...) this paper is to articulate a novel approach to extended cognition using the resources of PP. After outlining two recent proposals from Constant et al. and Kirchhoff and Kiverstein, I argue that the case for extended cognition can be further developed by interpreting certain elements of the PP story as a “mark of the cognitive”. The suggestion is that when construed at an ‘algorithmic level’ PEM offers a direct route to thinking about extended systems as genuine cognitive systems. On route to articulating the proposal, I lay out the core argument, defend the proposal’s novelty, and point to several of the advantages of the formulation. Finally, I conclude by taking up two challenges raised by Hohwy about the prospects of using PEM to argue for extended cognition. (shrink)

Radical enactivists claim that cognition is split in two distinct kinds, which can be differentiated by how they relate to mental content. In their view, basic cognitive activities involve no mental content whatsoever, whereas linguistically scaffolded, non-basic, cognitive activities constitutively involve the manipulation of mental contents. Here, I evaluate how this dichotomy applies to imagination, arguing that the sensory images involved in basic acts of imaginations qualify as vehicles of content, contrary to what radical enactivists claim. To argue so, I (...) leverage what has appropriately been dubbed a “compare to prototype” argument. Hence, I will first identify, within the enactivist literature, the general functional profile of a vehicle of content complying with the austere standard of contentfulness radical enactivists adhere to. Provided such a profile, I will show, relying on a mixture of reasoning and empirical evidence, that basic sensory images satisfy it, and thus that they can rightfully be identified as vehicles of content. This, I claim, provides a sufficient reason to identify the sensory images involved in basic acts of imagination as vehicles of content, thereby denying that basic imagination does not involve mental content. (shrink)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only argument (...) offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

The papers in this special issue make important contributions to a longstanding debate about how we should conceive of and explain mental phenomena. In other words, they make a case about the best philosophical paradigm for cognitive science. The two main competing approaches, hotly debated for several decades, are representationalism and enactivism. However, recent developments in disciplines such as machine learning and computational neuroscience have fostered a proliferation of intermediate approaches, leading to the emergence of completely new positions, in particular (...) the Predictive Processing approach. Here, we will consider the different approaches discussed in this volume. (shrink)