Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...) representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them. (shrink)

This paper engages critically with anti-representationalist arguments pressed by prominent enactivists and their allies. The arguments in question are meant to show that the “as-such” and “job-description” problems constitute insurmountable challenges to causal-informational theories of mental content. In response to these challenges, a positive account of what makes a physical or computational structure a mental representation is proposed; the positive account is inspired partly by Dretske’s views about content and partly by the role of mental representations in contemporary cognitive scientific (...) modeling. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

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)

This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can (...) be shown to be the same as covariance (through a set of additional steps). It is well known that facts about covariance do not entail facts about representational content. So there is no reason to think that the KL-divergence is a measure of (mis-)representational content. This paper thus provides an enactive, non-representational account of Bayesian belief optimisation in hierarchical PP. (shrink)

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the representation). We (...) show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This the final submitted version of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

Debunking arguments aim to undermine common sense beliefs by showing that they are not explanatorily or causally linked to the entities they are purportedly about. Rarely are facts about the aetiology of common sense beliefs invoked for the opposite aim, that is, to support the reality of entities that furnish our manifest image of the world. Here I undertake this sort of un-debunking project. My focus is on the metaphysics of ordinary physical objects. I use the view of perception as (...) approximate Bayesian inference to show how representations of ordinary objects can be extracted from sensory input in a rational and truth-tracking manner. Drawing an analogy between perception construed as Bayesian hypothesis testing and scientific inquiry, I sketch out how some of the intuitions that traditionally inspired arguments for scientific realism also find application with regards to proverbial tables and chairs. (shrink)

The received view holds that there is a significant divide between full-blown representational states and so called ‘detectors’, which are mechanisms set off by specific stimuli that trigger a particular effect. The main goal of this paper is to defend the idea that many detectors are genuine representations, a view that I call ‘Strong Liberal Representationalism’. More precisely, I argue that ascribing semantic properties to them contributes to an explanation of behavior, guides research in useful ways and can accommodate misrepresentation.

In recent years, the ‘mechanistic view’ has developed as a popular alternative to the ‘semantic view’ concerning the identity of physical computation. However, semanticists have provided powerful arguments that suggest the mechanistic view fails to deliver essential distinctions between paradigmatic computational operations. This article reviews responses on behalf of the mechanist and uses this opportunity to propose a type of pluralism about computational identity. This pluralism contends that there are multiple ‘levels’ of properties and relations pertaining to computation that can (...) inform different kinds of individuation. As such, for the pluralist, there are multiple legitimate ways of classifying computation, depending on the nature of the system in question, and one’s own epistemic priorities. (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)

A promising strategy for defending the role that representation plays in explanations of cognition frames the concept in terms of internal models or map‐like mechanisms. “Structural representation” offers an account of representation that is grounded in well‐specified, empirical criteria. However, anti‐representationalists continue to press the issue of how to account for the paradigmatic semantic properties of representation at the subpersonal level. In this paper, I offer an account of how the proponent of structural representation should think about content. There are (...) really two problems of content any account of representation must overcome: the “hard problem of content” and the “content determination problem.” I argue that the structural representation account has the resources to provide a naturalistically respectable notion of content that overcomes both problems. (shrink)

Some philosophers have offered structural representations as an alternative to indicator-based representations. Motivating these philosophers is the belief that an indication-based analysis of representation exhibits two fatal inadequacies from which structural representations are spared: such an analysis cannot account for the causal role of representational content and cannot explain how representational content can be made determinate. In fact, we argue, indicator and structural representations are on a par with respect to these two problems. This should not be surprising, we contend, (...) given that the distinction between indicator and structural representations is better conceived as one involving degree rather than kind. (shrink)

One big challenge faced by cognitive science is the development of a unified theory that integrates disparate scales of analysis of cognitive phenomena. In this paper, I offer a unified framework that provides a way to integrate neural and behavioral scales of analysis of cognitive phenomena—typically addressed by neuroscience and experimental psychology, respectively. The framework is based on the concept of resonance originated in ecological psychology and aims to be the foundation for a unified theory for radical embodiment; that is, (...) a unified theory for that dissident part of cognitive science that shares a methodological commitment to dynamic systems theory and remains skeptical about the adequacy of mechanism and representationalism as the guiding ideas in the field. In the course of my presentation, I analyze different issues regarding the requirements and constraints unification poses to radical embodiment. (shrink)

For the past 40 years, philosophers have generally assumed that a key to understanding mental representation is to develop a naturalistic theory of representational content. This has led to an outlook where the importance of content has been heavily inflated, while the significance of the representational vehicles has been somewhat downplayed. However, the success of this enterprise has been thwarted by a number of mysterious and allegedly non-naturalizable, irreducible dimensions of representational content. The challenge of addressing these difficulties has come (...) to be known as the “hard problem of content” (Hutto & Myin, 2012), and many think it makes an account of representation in the brain impossible. In this essay, I argue that much of this is misguided and based upon the wrong set of priorities. If we focus on the functionality of representational vehicles (as recommended by teleosemanticists) and remind ourselves of the quirks associated with many functional entities, we can see that the allegedly mysterious and intractable aspects of content are really just mundane features associated with many everyday functional kinds. We can also see they have little to do with content and more to do with representation function. Moreover, we can begin to see that our explanatory priorities are backwards: instead of expecting a theory of content to be the key to understanding how a brain state can function as a representation, we should instead expect a theory of neural representation function to serve as the key to understanding how content occurs naturally. (shrink)

The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: systems that minimise free energy are systems with a proper function; and Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope (...) this will aid further philosophical evaluation of the free energy principle. (shrink)

Enactivism has influentially argued that the traditional intellectualist ‘act-content’ model of intentionality is insufficient both phenomenologically and naturalistically, and minds are built from world-involving bodily habits – thus, knowledge should be regarded as more of a skilled performance than an informational encoding. Radical enactivists have assumed that this insight must entail non-representationalism concerning at least basic minds. But what if it could be shown that representation is itself a form of skilled performance? I sketch the outline of such an account (...) from the perspective of Peirce’s pragmatist semiotics, which theorises signs as habits of associating specific cues with appropriate acts and schemas of ensuing experience. Within this framework, I argue, a naturalistic account of propositional structure can be constructed which transcends the symbolic – and in some instances even the linguistic – sphere, and offers new insights regarding the Information Processing Challenge, and the Hard Problem of Content. (shrink)

What is the content of a mental state? This question poses the problem of intentionality: to explain how mental states can be about other things, where being about them is understood as representing them. A framework that integrates predictive coding and signaling systems theories of cognitive processing offers a new perspective on intentionality. On this view, at least some mental states are evaluations, which differ in function, operation, and normativity from representations. A complete naturalistic theory of intentionality must account for (...) both types of intentional state. (shrink)

Although pragmatism and phenomenology have both contributed significantly to the genealogy of so-called “4E” – embodied, embedded, enactive and extended – cognition, there is benefit to be had from a systematic comparative study of these roots. As existing 4E cognition literature has tended to emphasise one or the other tradition, issues remain to be addressed concerning their commonalities – and possible incompatibilities. We begin by exploring pragmatism and phenomenology’s shared focus on contesting intellectualism, and its key assumption of mindedness as (...) representation. We then outline distinctive insights from both traditions regarding the nature and role of habits, in order to put forward a habit-based epistemology as an alternative to the Cartesian idea-based epistemology that has dominated modern philosophy. We pay particular attention to the work of classical pragmatist C.S. Peirce, arguing that his semiotics, which analyses sign-use as habit, shows how theorists of embodied cognition can break a certain false dichotomy between embodiment and logical or intellectual structure which has prevented them from fully theorising propositional knowledge. In this way, our work both augments and challenges the Dewey/Merleau-Ponty connection that has been much more extensively explored by the field. (shrink)

To account for the explanatory role representations play in cognitive science, Egan’s deflationary account introduces a distinction between cognitive and mathematical contents. According to that account, only the latter are genuine explanatory posits of cognitive-scientific theories, as they represent the arguments and values cognitive devices need to represent to compute. Here, I argue that the deflationary account suffers from two important problems, whose roots trace back to the introduction of mathematical contents. First, I will argue that mathematical contents do not (...) satisfy important and widely accepted desiderata all theories of content are called to satisfy, such as content determinacy and naturalism. Secondly, I will claim that there are cases in which mathematical contents cannot play the explanatory role the deflationary account claims they play, proposing an empirical counterexample. Lastly, I will conclude the paper highlighting two important implications of my arguments, concerning recent theoretical proposals to naturalize representations via physical computation, and the popular predictive processing theory of cognition. (shrink)

This paper offers a modeling account of episodic representation. I argue that the episodic system constructsmental models: representations that preserve the spatiotemporal structure of represented domains. In prototypical cases, these domains are events: occurrences taken by subjects to have characteristic structures, dynamics and relatively determinate beginnings and ends. Due to their simplicity and manipulability, mental event models can be used in a variety of cognitive contexts: in remembering the personal past, but also in future-oriented and counterfactual imagination. As structural representations, (...) they allow surrogative reasoning, supporting inferences about their constituents which can be used in reasoning about the represented events. (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)

Recent developments in the literature suggest cognitive representation can be conceived of as a kind of mechanism that meets the functional profile set out by the S-representation account. However, this approach is threatened by worries that the S-representation account cannot tell a satisfactory story about content determination at the subpersonal level. One solution is to complement the S-representation account with a traditional etiological theory of content determination. This paper argues such a move is unwarranted and threatens the broader project of (...) understanding representation in mechanism-friendly terms. Once properly situated within a mechanistic framework, the S-representation account can address the issue of content determination without supplementing it with an etiological theory. In turn, this strengthens the notion that combing a mechanistic framework with the S-representation account provides a robust and comprehensive defense of representation’s positive role in cognitive science. (shrink)

A novel account of semantic information is proposed. The gist is that structural correspondence, analysed in terms of similarity, underlies an important kind of semantic information. In contrast to extant accounts of semantic information, it does not rely on correlation, covariation, causation, natural laws, or logical inference. Instead, it relies on structural similarity, defined in terms of correspondence between classifications of tokens into types. This account elucidates many existing uses of the notion of information, for example, in the context of (...) scientific models and structural representations in cognitive science. It is poised to open a new research program concerned with various kinds of semantic information, its functions, and its measurement. (shrink)

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation....

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 physics of representation” aims to define the word “representation” as used in the neurosciences, argue that such representations as described in neuroscience are related to and usefully illuminated by the representations generated by modern neural networks, and establish that these entities are “representations in good standing”. We suggest that Poldrack succeeds in, exposes some tensions between the broad use of the term in neuroscience and the narrower class of entities that he identifies in the end, and between the meaning (...) of “representation” in neuroscience and in psychology in, and fails in. This results in some hard choices: give up on the broad scope of the term in neuroscience or continue to embrace the broad, psychologically inflected sense of the term, and deny the entities generated by neural nets are representations in the relevant sense. (shrink)

Predictive Processing (PP) framework construes perception and action (and perhaps other cognitive phenomena) as a matter of minimizing prediction error, i.e. the mismatch between the sensory input and sensory predictions generated by a hierarchically organized statistical model. There is a question of how PP fits into the debate between traditional, neurocentric and representation-heavy approaches in cognitive science and those approaches that see cognition as embodied, environmentally embedded, extended and (largely) representation-free. In the present paper, I aim to investigate and clarify (...) the cognitivist or ‘conservative’ reading of PP. I argue that the conservative commitments of PP can be divided into three distinct categories: (1) representationalism, (2) inferentialism, and (3) internalism. I show how these commitments and their relations should be understood and argue for an interpretation of each that is both non-trivial and largely ecumenical towards the 4E literature. Conservative PP is as progressive as conservatism gets. (shrink)

Engrams—physical memory traces resulting from specific experiences—are the central posits of modern memory science. In this paper, I examine engrams through the lens of the theory of mental files. Integrating evidence from a variety of research programs, I argue that engrams exhibit the core functional properties of mental files. I characterize them as discrete informational structures, formed upon individual experiences of events and causally involved in their subsequent recall. Engrams are plausibly structurally complex in a file-like way, consisting of a (...) stable hippocampal index, which may function as an atomic pointer-like component, and a distributed cortical representation of an event’s properties. As such, they afford transmission of content and referential stability during potential content change. Their deployment is constitutive of the capacity for singular reference in episodically remembering particular previously experienced events. This emerging picture of engrams should engender reasonable optimism about the prospects of causal-representational theories of memory. (shrink)

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of (...) two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success. (shrink)

Concept normativity is a prominent subject of inquiry in the philosophical literature on the nature of concepts. Concepts are said to be normative, in that the use of concepts to categorise is associated with an evaluation of the appropriateness of such categorisation measured against some objective external standard. Two broad groups of views have emerged in accounting for the normativity of concepts: a weaker view traces such normativity to the social practice in which the agent using the concept is embedded, (...) while a stronger view traces such normativity to a first-person capacity of reflection. However, both views have drawbacks: the weaker view seems not to do justice to the basic sense of normativity associated with an individual agent using a concept, while the stronger view ties such normativity with the first-person conscious evaluation, which appears to be too strong. Here, we propose a different view of concepts using principles from the Active Inference framework. We reconceive concepts, defining them as Bayesian beliefs—that is, conditional probability distributions—that represent causes and contingencies in the world, their form grounded in the exchange between the agent and its environment. This allows us to present a different view on the source of normativity, with an emphasis on the structure of the agent itself as well as its interaction with the environment. On the Active Inference view, concepts are normative in that they are intrinsically connected to the self-evidencing nature of an agent, whose very structure implies an evaluation of the concepts it employs. (shrink)

Inspired by Ronald Giere’s cognitive approach to scientific models, Cognitive Structural Realism has presented a naturalist account of scientific representation. CSR characterises the structure of theories in terms of cognitive structures. These are informational structures embodied in the brains of scientists. CSR accounts for scientific representation in terms of the dynamical relationship between the organism and its environment. The proposal has been criticised on account of its negligence of social aspects of scientific practice. The present paper aims to chart out (...) a reply to the objection. It shows that cognitive structures do not need to be put inside the brains of single individuals. Cognitive structures are redefined as extended structures in distributed cognitive systems under Free Energy Principle. (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)

‘Radical enactivism’ (Hutto and Myin 2013, 2017) eschews representational content for all ‘basic’ mental activities. Critics have argued that this view cannot make sense of the workings of the imagination. In their recent book (2017), Hutto and Myin respond to these critics, arguing that some imaginings can be understood without attributing them any representational content. Their response relies on the claim that a system can exploit a structural isomorphism between two things without either of those things being a semantically evaluable (...) representation of the other. I argue that even if this claim is granted, there remains a problem for radically enactive accounts of imagining, namely that the active establishing and maintenance of a structural isomorphism seems to require representational content even if the exploitation of such an isomorphism, when established, does not. (shrink)

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...) convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...) convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

An influential view in cognitive science is that computation in cognitive systems is semantic, conceptually depending on representation: to compute is to manipulate representations. I argue that accepting the non-semantic teleomechanistic view of computation lays the ground for a promising alternative strategy, in which computation helps to explain and naturalise representation, rather than the other way around. I show that this computation-based approach to representation presents six decisive advantages over the semantic view. I claim that it can improve the two (...) most influential current theories of representation, teleosemantics and structural representation, by providing them with precious tools to tackle some of their main shortcomings. In addition, the computation-based approach opens up interesting new theoretical paths for the project of naturalising representation, in which teleology plays a role in individuating computations, but not representations. (shrink)

This paper aims to elucidate a kind of ignorance that is more fundamental than a momentary lack of information, but also not a kind of ignorance that is built into the subject’s cognitive apparatus such that the subject can’t do anything about it. The paper sets forth the notion of cognitive confinement, which is a contingent, yet relatively stable state of being structurally or systematically unable to gain information from an environment, determined by patterns of interaction between the subject and (...) the world. In order to unpack the idea of cognitive confinement the paper discusses niche construction theory, and then, in greater detail, the notion of cognitive niche once proposed by John Tooby and Irven DeVore. Cognitive confinement is here imagined as a pathologized form of cognitive niche. This posit is substantiated by referring to a case that has come to the fore in recent years and raised debate around the world: the rise of so-called filter bubbles. They turn out to be instantiations of a more general phenomenon of cognitive confinement. (shrink)

Some proponents of structural representations (henceforth, structuralists) claim that no other theory of representation can legitimatize the explanatory appeals that cognitive science makes to mental content. Because other naturalistic approaches to representation purportedly posit an arbitrary relation between representing vehicles and representational content, these approaches must appeal to the role played by a representation, i.e., how it is used by the system in which it is embedded, to ground its content. This is in supposed contrast to structural representations, in which (...) the relation of resemblance results in a non-arbitrary relationship between vehicles and content. Structuralists argue that, as a result, approaches that posit structural representations can, and alternative approaches cannot, explain how representational content can be causally relevant in the production of behavior. In this paper, I will argue that structural representations are susceptible to the very same critiques that proponents level against what they sometimes refer to as “use” theories. This, I contend, is not surprising given that a theory of structural representations is, in fact, just as much a use-theory as alternative approaches. (shrink)

Structural representations are likely the most talked about representational posits in the contemporary debate over cognitive representations. Indeed, the debate surrounding them is so vast virtually every claim about them has been made. Some, for instance, claimed structural representations are different from indicators. Others argued they are the same. Some claimed structural representations mesh perfectly with mechanistic explanations, others argued they can’t in principle mash. Some claimed structural representations are central to predictive processing accounts of cognition, others rebuked predictive processing (...) networks are blissfully structural representation free. And so forth. Here, I suggest this confusing state of affairs is due to the fact that the term “structural representations” is applied to a number of distinct conceptions of representations. In this paper, I distinguish four such conceptions, argue that these four conceptions are actually distinct, and then show that such a fourfold distinction can be used to clarify some of the most pressing questions concerning structural representations and their role in cognitive theorizing, making these questions more easily answerable. (shrink)

There is a long-lasting quest of demarcating a minimally representational behavior. Based on neurophysiologically-informed behavioral studies, we argue in detail that one of the simplest cases of organismic behavior based on low-resolution spatial vision–the visually-guided obstacle avoidance in the cubozoan medusaTripedalia cystophora–implies already a minimal form of representation. We further argue that the characteristics and properties of this form of constancy-employing structural representation distinguish it substantially from putative representational states associated with mere sensory indicators, and we reply to some possible (...) objections from the liberal representationalists camp by defending and qualitatively demarcating the minimal nature of our case. Finally, we briefly discuss the implications of our thesis within a naturalistic framework. (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)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of observation. (...) My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)