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  1. Modeling Mechanisms.Stuart Glennan - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):443-464.
    Philosophers of science increasingly believe that much of science is concerned with understanding the mechanisms responsible for the production of natural phenomena. An adequate understanding of scientific research requires an account of how scientists develop and test models of mechanisms. This paper offers a general account of the nature of mechanical models, discussing the representational relationship that holds between mechanisms and their models as well as the techniques that can be used to test and refine such models. The analysis is (...)
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  • Neural Computation and the Computational Theory of Cognition.Gualtiero Piccinini & Sonya Bahar - 2013 - Cognitive Science 37 (3):453-488.
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...)
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  • The Role of Representation in Computation.Gerard O'Brien & Jon Opie - 2009 - Cognitive Processing 10 (1):53-62.
    Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show that representation (...)
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  • Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience.Carl F. Craver - 2007 - Oxford University Press, Clarendon Press.
    Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.
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  • Thinking About Mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
    The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.
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  • A Computational Foundation for the Study of Cognition.David Chalmers - 2011 - Journal of Cognitive Science 12 (4):323-357.
    Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation (...)
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  • Computational Models.Paul Humphreys - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form (...)
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  • Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science.Gualtiero Piccinini & Andrea Scarantino - 2010 - Studies in History and Philosophy of Science Part A 41 (3):237-246.
    Since the cognitive revolution, it has become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful (...)
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  • Levels Indeed! A Response to Broadbent.David E. Rumelhart & James L. McClelland - 1985 - Journal of Experimental Psychology: General 114 (2):193-197.
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  • Computing Mechanisms.Gualtiero Piccinini - 2007 - Philosophy of Science 74 (4):501-526.
    This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...)
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  • Computation and Cognition: Toward a Foundation for Cognitive Science.John Haugeland - 1987 - Philosophy of Science 54 (2):309-311.
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  • A Question of Levels: Comment on McClelland and Rumelhart.Donald Broadbent - 1985 - Journal of Experimental Psychology: General 114 (2):189-192.
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  • Computational Models.Paul Humphreys - 2002 - Philosophy of Science 69 (S3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form (...)
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  • Transparent Computationalism.Ronald L. Chrisley - unknown
    Summary. A distinction is made between two senses of the claim “cognition is computation”. One sense, the opaque reading, takes computation to be whatever is described by our current computational theory and claims that cognition is best understood in terms of that theory. The transparent reading, which has its primary allegiance to the phenomenon of computation, rather than to any particular theory of it, is the claim that the best account of cognition will be given by whatever theory turns out (...)
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  • Non-Turing Computations Via Malament-Hogarth Space-Times.Gábor Etesi & István Németi - 2002 - International Journal of Theoretical Physics 41:341--70.
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  • Beyond Formal Structure: A Mechanistic Perspective on Computation and Implementation.Marcin Miłkowski - 2011 - Journal of Cognitive Science 12 (4):359-379.
    In this article, after presenting the basic idea of causal accounts of implementation and the problems they are supposed to solve, I sketch the model of computation preferred by Chalmers and argue that it is too limited to do full justice to computational theories in cognitive science. I also argue that it does not suffice to replace Chalmers’ favorite model with a better abstract model of computation; it is necessary to acknowledge the causal structure of physical computers that is not (...)
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  • The Ontological Status of Representations.Matthias Scheutz - 1999 - In Alexander Riegler, Markus F. Peschl & A. von Stein (eds.), Understanding Representation in the Cognitive Sciences. Kluwer Academic Publishers.
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  • Levels Indeed! A Response to Broadbent.J. L. McClelland & D. E. Rumelhart - 1985 - Journal of Experimental Psychology 114:193-7.
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  • A Question of Levels: Comment on McClelland and Rumelhart.D. Broadbent - 1985 - Journal of Experimental Psychology 114:189-92.
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  • Computation in Physical Systems.Gualtiero Piccinini - 2010 - Stanford Encyclopedia of Philosophy.
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