The “top-down ” and “bottom-up ” approaches have been thought to exhaust the possibilities for do... more 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 prom-ising 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 pro-cesses 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. Keywords Neuroscientific cognitive modelling · Top-down approach to neuroscience · Bottom-up approach to n...
The Sailing Mind (Roberto Casati, ed., Springer), 2022
Racing in sailboats is for the most part a team sport, sailed in everything from two-person dingh... more Racing in sailboats is for the most part a team sport, sailed in everything from two-person dinghies to super-maxi boats that require well over a dozen crew. Sailing is challenging enough but crewed racing boats present special challenges. 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.
Shea (2018) offers what he takes to be a naturalistic account of representational content in cogn... more Shea (2018) offers what he takes to be a naturalistic account of representational content in cognitive science. I argue that the account secures determinate content only by appeal to pragmatic considerations, and so it fails to respect naturalism. But that is fine, because representational content is not, strictly speaking, necessary for explanation in cognitive science. Even in Shea’s own account, content serves only a variety of heuristic functions.
in the Routledge Handbook of the Computational Mind, M. Sprevak & M. Colombo, eds.
Much of computational cognitive science construes human cognitive capacities as representational ... more 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.
Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cog... more 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, dynam-ical, embodied, enactive, and Bayesian approaches to cognition. In this paper 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 represen-tational content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts.
A common kind of explanation in cognitive neuroscience might be called function-theoretic:with so... more 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 (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 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 “top-down ” and “bottom-up ” approaches have been thought to exhaust the possibilities for do... more 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 prom-ising 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 pro-cesses 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. Keywords Neuroscientific cognitive modelling · Top-down approach to neuroscience · Bottom-up approach to n...
The Sailing Mind (Roberto Casati, ed., Springer), 2022
Racing in sailboats is for the most part a team sport, sailed in everything from two-person dingh... more Racing in sailboats is for the most part a team sport, sailed in everything from two-person dinghies to super-maxi boats that require well over a dozen crew. Sailing is challenging enough but crewed racing boats present special challenges. 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.
Shea (2018) offers what he takes to be a naturalistic account of representational content in cogn... more Shea (2018) offers what he takes to be a naturalistic account of representational content in cognitive science. I argue that the account secures determinate content only by appeal to pragmatic considerations, and so it fails to respect naturalism. But that is fine, because representational content is not, strictly speaking, necessary for explanation in cognitive science. Even in Shea’s own account, content serves only a variety of heuristic functions.
in the Routledge Handbook of the Computational Mind, M. Sprevak & M. Colombo, eds.
Much of computational cognitive science construes human cognitive capacities as representational ... more 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.
Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cog... more 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, dynam-ical, embodied, enactive, and Bayesian approaches to cognition. In this paper 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 represen-tational content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts.
A common kind of explanation in cognitive neuroscience might be called function-theoretic:with so... more 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 (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 function-theoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware.
Uploads
Papers by Frances Egan
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.
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.