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Computation, San Diego Style

Philosophy of Science 77 (5):862-874 (2010)

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  1. Meaning and Mental Representation.Robert Cummins - 1990 - Mind 99 (396):637-642.
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  • Brains as analog-model computers.Oron Shagrir - 2010 - Studies in History and Philosophy of Science Part A 41 (3):271-279.
    Computational neuroscientists not only employ computer models and simulations in studying brain functions. They also view the modeled nervous system itself as computing. What does it mean to say that the brain computes? And what is the utility of the ‘brain-as-computer’ assumption in studying brain functions? In previous work, I have argued that a structural conception of computation is not adequate to address these questions. Here I outline an alternative conception of computation, which I call the analog-model. The term ‘analog-model’ (...)
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  • Methodological solipsism considered as a research strategy in cognitive psychology.Jerry A. Fodor - 1979 - Behavioral and Brain Sciences 3 (1):63-73.
    The paper explores the distinction between two doctrines, both of which inform theory construction in much of modern cognitive psychology: the representational theory of mind and the computational theory of mind. According to the former, propositional attitudes are to be construed as relations that organisms bear to mental representations. According to the latter, mental processes have access only to formal (nonsemantic) properties of the mental representations over which they are defined.The following claims are defended: (1) That the traditional dispute between (...)
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  • Marr on computational-level theories.Oron Shagrir - 2010 - Philosophy of Science 77 (4):477-500.
    According to Marr, a computational-level theory consists of two elements, the what and the why . This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: ( a ) that the Why element plays an explanatory role in computational-level theories, ( b ) that its goal is to explain why the computed function (specified by the What element) is appropriate for a given visual task, and ( c ) that the (...)
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  • Computers.Gualtiero Piccinini - 2008 - Pacific Philosophical Quarterly 89 (1):32–73.
    I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the system's mechanistic (...)
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  • Structural representation and surrogative reasoning.Chris Swoyer - 1991 - Synthese 87 (3):449 - 508.
    It is argued that a number of important, and seemingly disparate, types of representation are species of a single relation, here called structural representation, that can be described in detail and studied in a way that is of considerable philosophical interest. A structural representation depends on the existence of a common structure between a representation and that which it represents, and it is important because it allows us to reason directly about the representation in order to draw conclusions about the (...)
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  • The semantic challenge to computational neuroscience.Rick Grush - 2001 - In Peter McLaughlin, Peter Machamer & Rick Grush (eds.), Theory and Method in the Neurosciences. Pittsburgh University Press. pp. 155--172.
    I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that the authors provide no (...)
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  • Does a rock implement every finite-state automaton?David J. Chalmers - 1996 - Synthese 108 (3):309-33.
    Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the (...)
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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 without representation.Gualtiero Piccinini - 2008 - Philosophical Studies 137 (2):205-241.
    The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...)
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  • Why we view the brain as a computer.Oron Shagrir - 2006 - Synthese 153 (3):393-416.
    The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...)
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  • Connectionism and cognitive architecture: A critical analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
    This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...)
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  • Physical symbol systems.Allen Newell - 1980 - Cognitive Science 4 (2):135-83.
    On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...)
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  • Content, computation and externalism.Oron Shagrir - 2001 - Mind 110 (438):369-400.
    The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...)
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  • A logical calculus of the ideas immanent in nervous activity.Warren S. McCulloch & Walter Pitts - 1943 - The Bulletin of Mathematical Biophysics 5 (4):115-133.
    Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions (...)
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  • Did Tarski commit “Tarski's fallacy”?G. Y. Sher - 1996 - Journal of Symbolic Logic 61 (2):653-686.
    In his 1936 paper,On the Concept of Logical Consequence, Tarski introduced the celebrated definition oflogical consequence: “The sentenceσfollows logicallyfrom the sentences of the class Γ if and only if every model of the class Γ is also a model of the sentenceσ.” [55, p. 417] This definition, Tarski said, is based on two very basic intuitions, “essential for the proper concept of consequence” [55, p. 415] and reflecting common linguistic usage: “Consider any class Γ of sentences and a sentence which (...)
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  • Did Tarski commit "Tarski's fallacy"?Gila Sher - 1996 - Journal of Symbolic Logic 61 (2):653-686.
    In his 1936 paper,On the Concept of Logical Consequence, Tarski introduced the celebrated definition oflogical consequence: “The sentenceσfollows logicallyfrom the sentences of the class Γ if and only if every model of the class Γ is also a model of the sentenceσ.” [55, p. 417] This definition, Tarski said, is based on two very basic intuitions, “essential for the proper concept of consequence” [55, p. 415] and reflecting common linguistic usage: “Consider any class Γ of sentences and a sentence which (...)
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  • The Bounds of Logic: A Generalized Viewpoint.Gila Sher - 1991 - British Journal for the Philosophy of Science 45 (4):1078-1083.
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  • Vision: Variations on Some Berkeleian Themes.Robert Schwartz & David Marr - 1985 - Philosophical Review 94 (3):411.
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  • A Neurocomputational Perspective: The Nature of Mind and the Structure of Science.Lynne Rudder Baker & Paul M. Churchland - 1992 - Philosophical Review 101 (4):906.
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  • Measurement-Theoretic Representation and Computation-Theoretic Realization.Eli Dresner - 2010 - Journal of Philosophy 107 (6):275-292.
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  • 1. Marr on Computational-Level Theories Marr on Computational-Level Theories (pp. 477-500).Oron Shagrir, John D. Norton, Holger Andreas, Jouni-Matti Kuukkanen, Aris Spanos, Eckhart Arnold, Elliott Sober, Peter Gildenhuys & Adela Helena Roszkowski - 2010 - Philosophy of Science 77 (4):477-500.
    According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...)
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