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The dominant neuroscientific theory of spatial memory is, like many theories in neuroscience, a multilevel description of a mechanism. The theory links the activities of molecules, cells, brain regions, and whole organisms into an integrated sketch of an explanation for the ability of organisms to navigate novel environments. Here I develop a taxonomy of interlevel experimental strategies for integrating the levels in such multilevel mechanisms. These experimental strategies include activation strategies, interference strategies, and additive strategies. These strategies are mutually reinforcing, (...) |
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Although cognitive scientists have learned a lot about concepts, their findings have yet to be organized in a coherent theoretical framework. In addition, after twenty years of controversy, there is little sign that philosophers and psychologists are converging toward an agreement about the very nature of concepts.Doing without Concepts(Machery 2009) attempts to remedy this state of affairs. In this article, I review the main points and arguments developed at greater length inDoing without Concepts. |
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In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to talk of (...) |
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This paper describes the corner-stones of a means-ends approach to the philosophy of inductive inference. I begin with a fallibilist ideal of convergence to the truth in the long run, or in the 'limit of inquiry'. I determine which methods are optimal for attaining additional epistemic aims (notably fast and steady convergence to the truth). Means-ends vindications of (a version of) Occam's Razor and the natural generalizations in a Goodmanian Riddle of Induction illustrate the power of this approach. The paper (...) |
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Although cognitive scientists have learned a lot about concepts, their findings have yet to be organized in a coherent theoretical framework. In addition, after twenty years of controversy, there is little sign that philosophers and psychologists are converging toward an agreement about the very nature of concepts. Doing without Concepts (Machery 2009) attempts to remedy this state of affairs. In this article, I review the main points and arguments developed at greater length in Doing without Concepts. |
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This paper pursues a thorough-going instrumentalist, or means-ends, approach to the theory of inductive inference. I consider three epistemic aims: convergence to a correct theory, fast convergence to a correct theory and steady convergence to a correct theory (avoiding retractions). For each of these, two questions arise: (1) What is the structure of inductive problems in which these aims are feasible? (2) When feasible, what are the inference methods that attain them? Formal learning theory provides the tools for a complete (...) |
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In this article, I compare the epistemic standing of the function-to-structure inferences found in cognitive neuroscience and of the inferences based on dissociations in neuropsychology. I argue that the former have a poorer epistemic standing than the latter. |
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It is widely accepted among philosophers that neuroscientists are conducting a search for the neural correlates of consciousness, or NCC. Chalmers conceptualized this research program as the attempt to correlate the contents of conscious experience with the contents of representations in specific neural populations. A notable claim on behalf of this interpretation is that the neutral language of “correlates” frees us from philosophical disputes over the mind/body relation, allowing the science to move independently. But the experimental paradigms and explanatory canons (...) |
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The paper makes three points about the role of double dissociation in cognitive neuropsychology. First, arguments from double dissociation to separate modules work by inference to the best, not the only possible, explanation. Second, in the development of computational cognitive neuropsychology, the contribution of connectionist cognitive science has been to broaden the range of potential explanations of double dissociation. As a result, the competition between explanations, and the characteristic features of the assessment of theories against the criteria of probability and (...) |
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Formal learning theory is the mathematical embodiment of a normative epistemology. It deals with the question of how an agent should use observations about her environment to arrive at correct and informative conclusions. Philosophers such as Putnam, Glymour and Kelly have developed learning theory as a normative framework for scientific reasoning and inductive inference. |
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The cognitive neuropsychological understanding of a cognitive system is roughly that of a ‘mental organ’, which is independent of other systems, specializes in some cognitive task, and exhibits a certain kind of internal cohesiveness. This is all quite vague, and I try to make it more precise. A more precise understanding of cognitive systems will make it possible to articulate in some detail an alternative to the Fodorian doctrine of modularity (since not all cognitive systems are modules), but it will (...) |
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What representational state mediates between perception and action? Bence Nanay says pragmatic representations, which are outputs of perceptual systems. This commits him to the view that optic ataxics face difficulty in performing visually guided arm movements because the relevant perceptual systems output their pragmatic representations incorrectly. Here, I argue that it is not enough to say that pragmatic representations are output incorrectly; we also need to know why they are output that way. Given recent evidence that optic ataxia impairs peripersonal (...) |
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The aim of cognitive neuropsychology is to articulate the functional architecture underlying normal cognition, on the basis of congnitive performance data involving brain-damaged subjects. Throughout the history of the subject, questions have been raised as to whether the methods of neuropsychology are adequate to its goals. The question has been reopened by Glymour [1994], who formulates a discovery problem for cognitive neuropsychology, in the sense of formal learning theory, concerning the existence of a reliable methodology. It appears that the discovery (...) |
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Multiple realizability (MR) is traditionally conceived of as the feature of computational systems, and has been used to argue for irreducibility of higher-level theories. I will show that there are several ways a computational system may be seen to display MR. These ways correspond to (at least) five ways one can conceive of the function of the physical computational system. However, they do not match common intuitions about MR. I show that MR is deeply interest-related, and for this reason, difficult (...) |
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The time honoured philosophical issue of how to resolve the mind/body problem has taken a more scientific turn of late. Instead of discussing issues of the soul and emotion and person and their reduction to a physical form, we now ask ourselves how well-understood cognitive and social concepts fit into the growing and changing field of neuropsychology. One of the many projects that have come out of this new scientific endeavour is Zaidel’s (2005) inquiry into the neuropsychological bases of art. |
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Occam’s razor directs us to adopt the simplest hypothesis consistent with the evidence. Learning theory provides a precise definition of the inductive simplicity of a hypothesis for a given learning problem. This definition specifies a learning method that implements an inductive version of Occam’s razor. As a case study, we apply Occam’s inductive razor to causal learning. We consider two causal learning problems: learning a causal graph structure that presents global causal connections among a set of domain variables, and learning (...) |
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“Error Analysis”: The Methodology of Learning from an Object’s Errors, Deficits, and Malfunctions. |
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