Frances Egan

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware.

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware.

The chapter discusses David Marr's theory of vision, which likens the visual system to an information-processing system with three levels: the topmost “theory of computation,” the algorithmic level, and the implementation level. Marr's work, which is based on computational theory, has been assumed by many acolytes of this field of study to be “intentional.” This chapter aims to refute this assumption utilizing the broad tenets of computational methodology. It argues that, in utilizing the formal, mathematical paradigms of computational theory, Marr's theory is rendered essentially neutral, with no accompanying interpretations. Of course, interpretations based on underlying, external factors can also be valid, but are not essential, even if assigning intentional content can have practical uses for the researcher. The chapter ends the discourse by countering the proposed argument of “narrow content,” which posits that the primitives of Marr's vision theory are intentional, while their content is independent of external, causal factors.

A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes the exercise of the cognitive capacity (in the system's normal environment). Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a function-theoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware.

The chapter discusses David Marr's theory of vision, which likens the visual system to an information-processing system with three levels: the topmost “theory of computation,” the algorithmic level, and the implementation level. Marr's work, which is based on computational theory, has been assumed by many acolytes of this field of study to be “intentional.” This chapter aims to refute this assumption utilizing the broad tenets of computational methodology. It argues that, in utilizing the formal, mathematical paradigms of computational theory, Marr's theory is rendered essentially neutral, with no accompanying interpretations. Of course, interpretations based on underlying, external factors can also be valid, but are not essential, even if assigning intentional content can have practical uses for the researcher. The chapter ends the discourse by countering the proposed argument of “narrow content,” which posits that the primitives of Marr's vision theory are intentional, while their content is independent of external, causal factors.

Philosophers of mind tend to hold one of two broad views about mental representation: they are either robustly realist about mental representations, taking them to have determinate, objective content independent of attributors’ explanatory interests and goals, or they embrace some form of anti-realism, holding that mental representations are at best useful fictions. It is becoming increasingly clear that neither view is satisfactory. Realists disagree about the basis for objective content, and computational neuroscientists and psychologists, while making widespread use of representational talk in articulating their theories, seem indifferent to the efforts of these philosophers to articulate a secure foundation for such talk, suggesting a disconnect between the preoccupations of philosophers of cognitive science and actual practice in these sciences. Fictionalists, for their part, must deny that representational mental states can be causes of cognition and behavior, and that appeal to mental representation can be genuinely explanatory, views that flatly contradict assumptions central to both science and everyday life. I develop and defend a distinctive ‘third way’ – a view I call a deflationary account of mental representation – that both resolves philosophical worries about content and best fits actual practice in science and everyday life. According to the deflationary account, appeal to mental representation does indeed purport to pick out causes of behavior, but the attribution of content to these causes is best understood as a pragmatically motivated gloss, justified in part by attributors’ explanatory interests and goals. Content plays an explanatory role in the deflationary account, but one quite different than that assumed by robust representational realists.

In the philosophy of perception, representationalism is the view that all phenomenological differences among mental states are representational differences, in other words, differences in content. In this paper I defend an alternative view which I call external sortalism, inspired by traditional adverbialism, and according to which experiences are not essentially representational. The central idea is that the external world serves as a model for sorting, conceptualizing, and reasoning surrogatively about perceptual experience. On external sortalism, contents are construed as a kind of gloss on experiences themselves. We can retain what is attractive about representationalism, namely, that perceptual experiences can be evaluated for accuracy, without problematic commitment to the idea that they bear a substantive, representational relation to external objects and properties and that this relation determines the phenomenal character of experience.

Successful sports teams are able to adopt what is known as the 'we-perspective,' forming intentions and making decisions, somewhat as a unified mind does, to achieve their goals. In this paper I consider what is involved in establishing and maintaining the we-perspective on a racing sailboat. I argue that maintaining the we-perspective contributes to the success of the boat in at least two ways: (1) it facilitates the smooth execution of joint action; and (2) it increases the chance that individual crew members will exert their best effort in fulfilling their particular roles on the boat.

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware.

Mohan Matthen has failed to understand the position I develop and defend in “How to Think about Mental Content.” No doubt some of the fault lies with my exposition, though Matthen often misconstrues passages that are clear in context. He construes clarifications and elaborations of my argument to be “concessions.” Rather than dwell too much on specific misunderstandings of my explanatory project and its attendant claims, I will focus on the main points of disagreement.RepresentationalismMy project in the paper is to argue for a particular construal of the role of representational content in computational models of cognition. The view is committed to two kinds of representational content—mathematical content, which characterizes the mathematical function computed by a device and subsumes both biological and artifactual computers, and cognitive content. The latter is determined in part by external, so-called ‘naturalistic’ factors (for example, visual mechanisms represent such distal pro

Introduction: representationalismMost theorists of cognition endorse some version of representationalism, which I will understand as the view that the human mind is an information-using system, and that human cognitive capacities are representational capacities. Of course, notions such as ‘representation’ and ‘information-using’ are terms of art that require explication. As a first pass, representations are “mediating states of an intelligent system that carry information” (Markman and Dietrich 2001, p. 471). They have two important features: (1) they are physically realized, and so have causal powers; (2) they are intentional, in other words, they have meaning or representational content. This presumes a distinction between a representational vehicle—a physical state or structure that has causal powers and is responsible for producing behavior—and its content. Consider the following characterization of a device that computes the addition functionReaders will recognize the similarity t

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature and function of representation in computational cognitive models.

“Naturalistic” semantic theories attempt to specify, in nonintentional and nonsemantic terms, a sufficient condition for a mental representation’s having a particular meaning. Such theories have trouble accounting for the possibility of representational error. In his latest book, Robert Cummins traces the problem to the fact that the theories currently on offer identify the meaning of a representation with certain features of its use. Only a theory that takes meaning to be an intrinsic feature of a representation, Cummins argues, can both accommodate representational error and play a genuinely explanatory role in an account of rational capacities. In the second half of the book he develops and defends a proposal that he calls the “picture theory of representation.”

Stephen Stich has argued that psychological theories that instantiate his Syntactic Theory of Mind are to be preferred to content-based or representationalist theories, because the former can capture and explain a wider range of generalizations about cognitive processes than the latter. Stich's claims about the relative merits of the Syntactic Theory of Mind are unfounded. Not only is it false that syntactic theories can capture psychological generalizations that content-based theories cannot, but a large class of behavioral regularities, readily explained by content-based theories, appear to be beyond their explanatory reach

Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of mental content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts.

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.

Representationalism, in its most widely accepted form, is the view that the human mind is an information-using system, and that human cognitive capacities are to be understood as representational capacities. This chapter distinguishes several distinct theses that go by the name "representationalism," focusing on the view that is most prevalent in cogntive science. It also discusses some objections to the view and attempts to clarify the role that representational content plays in cognitive models that make use of the notion of representation.