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  1. Adele A. Abrahamsen (1993). Cognizers' Innards and Connectionist Nets: A Holy Alliance? Mind and Language 8 (4):520-530.
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  2. Jarmo J. Ahonen (1994). On Qualitative Modelling. AI and Society 8 (1):17-28.
    Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.
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  3. Kenneth Aizawa (1999). Connectionist Rules: A Rejoinder to Horgan and Tienson's Connectionism and the Philosophy of Psychology. Acta Analytica 22 (22):59-85.
  4. John R. Anderson & Christian Lebiere (2003). The Newell Test for a Theory of Cognition. Behavioral and Brain Sciences 26 (5):587-601.
    Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories (...)
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  5. Michael A. Arbib (ed.) (2002). The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
    A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.
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  6. Murat Aydede, The Language of Thought Hypothesis. Stanford Encyclopedia of Philosophy.
  7. William P. Bechtel (1996). The Churchlands and Their Critics. Oup.
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  8. William P. Bechtel (1996). What Should a Connectionist Philosophy of Science Look Like? In Robert N. McCauley (ed.), The Churchlands and Their Critics. Oup. 121--144.
    The reemergence of connectionism2 has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations.
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  9. William P. Bechtel (1993). The Case for Connectionism. Philosophical Studies 71 (2):119-54.
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  10. William P. Bechtel (1993). The Path Beyond First-Order Connectionism. Mind and Language 8 (4):531-539.
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  11. William P. Bechtel (1988). Connectionism and Rules and Representation Systems: Are They Compatible? Philosophical Psychology 1 (1):5-16.
    The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not totally (...)
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  12. William P. Bechtel (1987). Connectionism and the Philosophy of Mind. Southern Journal of Philosophy Supplement 26:17-41.
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  13. William P. Bechtel (1986). What Happens to Accounts of Mind-Brain Relations If We Forgo an Architecture of Rules and Representations? Philosophy of Science Association 1986:159 - 171.
    The notion that the mind is a physical symbol system (Newell) with a determinate functional architecture (Pylyshyn) provides a compelling conception of the relation of cognitive inquiry to neuroscience inquiry: cognitive inquiry explores the activity within the symbol system while neuroscience explains how the symbol system is realized in the brain. However, the view the the mind is a physical symbol system is being challenged today by researchers in artificial intelligence who propose that the mind is a connectionist system and (...)
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  14. William P. Bechtel (1985). Contemporary Connectionism: Are the New Parallel Distributed Processing Models Cognitive or Associationist? Behaviorism 13 (1):53-61.
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  15. William P. Bechtel (1985). Are the New PDP Models of Cognition Cognitivist or Associationist? Behaviorism 13:53-61.
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  16. William P. Bechtel & Adele A. Abrahamsen (1992). Connectionism and the Future of Folk Psychology. In Robert G. Burton (ed.), Minds: Natural and Artificial. SUNY Press.
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  17. William P. Bechtel & A. Abrahamson (1990). Beyond the Exclusively Propositional Era. Synthese 82 (2):223-53.
    Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of (...)
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  18. István S. N. Berkeley, Some Myths of Connectionism.
    Since the emergence of what Fodor and Pylyshyn (1988) call 'new connectionism', there can be little doubt that connectionist research has become a significant topic for discussion in the Philosophy of Cognitive Science and the Philosophy of Mind. In addition to the numerous papers on the topic in philosophical journals, almost every recent book in these areas contain at least a brief reference to, or discussion of, the issues raised by connectionist research (see Sterelny 1990, Searle, 1992, and O Nualláin, (...)
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  19. Istvan S. N. Berkeley, What is Connectionism?
    Connectionism is a style of modeling based upon networks of interconnected simple processing devices. This style of modeling goes by a number of other names too. Connectionist models are also sometimes referred to as 'Parallel Distributed Processing' (or PDP for short) models or networks.1 Connectionist systems are also sometimes referred to as 'neural networks' (abbreviated to NNs) or 'artificial neural networks' (abbreviated to ANNs). Although there may be some rhetorical appeal to this neural nomenclature, it is in fact misleading as (...)
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  20. Istvan S. N. Berkeley, A Revisionist History of Connectionism.
    According to the standard (recent) history of connectionism (see for example the accounts offered by Hecht-Nielsen (1990: pp. 14-19) and Dreyfus and Dreyfus (1988), or Papert's (1988: pp. 3-4) somewhat whimsical description), in the early days of Classical Computational Theory of Mind (CCTM) based AI research, there was also another allegedly distinct approach, one based upon network models. The work on network models seems to fall broadly within the scope of the term 'connectionist' (see Aizawa 1992), although the term had (...)
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  21. John Bickle (1995). Connectionism, Reduction, and Multiple Realizability. Behavior and Philosophy 23 (2):29-39.
    I sketch a theory of cognitive representation from recent "connectionist" cognitive science. I then argue that (i) this theory is reducible to neuroscientific theories, yet (ii) its kinds are multiply realized at a neurobiological level. This argument demonstrates that multiple realizability alone is no barrier to the reducibility of psychological theories. I conclude that the multiple realizability argument, the most influential argument against psychophysical reductionism, should be abandoned.
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  22. Horst Bischof (1997). Locality, Modularity, and Computational Neural Networks. Behavioral and Brain Sciences 20 (3):516-517.
    There is a distinction between locality and modularity. These two terms have often been used interchangeably in the target article and commentary. Using this distinction we argue in favor of a modularity. In addition we also argue that both PDP-type networks and box-and-arrow models have their own strengths and pitfalls.
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  23. Susan J. Blackmore (2003). The Case of the Mysterious Mind: Review of Radiant Cool, by Dan Lloyd. [REVIEW] New Scientist 13:36-39.
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  24. Denny E. Bradshaw (1991). Connectionism and the Specter of Representationalism. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer. 417--436.
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  25. Robert G. Burton (ed.) (1992). Minds: Natural and Artificial. SUNY Press.
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  26. Drew Christie (1993). Comments on Bechtel's The Case for Connectionism. Philosophical Studies 71 (2):155-162.
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  27. Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press. 343-382.
  28. Paul M. Churchland (1989). On the Nature of Explanation: A PDP Approach. In A Neurocomputational Perspective. MIT Press.
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  29. Paul M. Churchland (1989). On the Nature of Theories: A Neurocomputational Perspective. Minnesota Studies in the Philosophy of Science 14:59--101.
  30. Andy Clark (1995). Connectionism: Debates on Psychological Explanation. Cambridge: Blackwell.
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  31. Andy Clark (1995). Connectionist Minds. In Connectionism: Debates on Psychological Explanation. Cambridge: Blackwell. 83 - 102.
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  32. Andy Clark (1991). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge: MIT Press.
  33. Andy Clark (1990). Connectionism, Competence and Explanation. British Journal for the Philosophy of Science 41 (June):195-222.
    A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some (...)
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  34. Andy Clark (1989). Microfunctionalism: Connectionism and the Scientific Explanation of Mental States. In A. Clark (ed.), Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. MIT Press.
    This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999).
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  35. Andy Clark (1989). Microcognition. MIT Press.
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  36. Andy Clark & Chris Eliasmith (2002). Philosophical Issues in Brain Theory and Connectionism. In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. Mit Press.
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  37. Mark Collier (1999). Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects&Quot;. Hume Studies 25 (1 and 2):155-170.
  38. Jack Copeland (1996). On Alan Turing's Anticipation of Connectionism. Synthese 108 (3):361-377.
    It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...)
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  39. Robert C. Cummins (1995). Connectionist and the Rationale Constraint on Cognitive Explanations. Philosophical Perspectives 9:105-25.
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  40. Robert C. Cummins & Georg Schwarz (1991). Connectionism, Computation, and Cognition. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer. 60--73.
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  41. Robert C. Cummins & Georg Schwarz (1987). Radical Connectionism. Southern Journal of Philosophy Supplement 26 (S1):43-61.
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  42. Martin Davies (1989). Connectionism, Modularity and Tacit Knowledge. British Journal for the Philosophy of Science 40 (December):541-55.
    In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...)
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  43. S. Davis (ed.) (1992). Connectionism: Theory and Practice (Volume III of The Vancouver Studies in Cognitive Science. Oxford University press.
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  44. Jane Duran & Ruth Doell (1993). Naturalized Epistemology, Connectionism and Biology. Dialectica 47 (4):327-336.
  45. Manuel García-Carpintero (1995). The Philosophical Import of Connectionism: A Critical Notice of Andy Clark's Associative Engines. Mind and Language 10 (4):370-401.
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  46. Gordon G. Globus (1992). Derrida and Connectionism: Differance in Neural Nets. Philosophical Psychology 5 (2):183-97.
    A possible relation between Derrida's deconstruction of metaphysics and connectionism is explored by considering diff rance in neural nets terms. First diff rance , as the crossing of Saussurian difference and Freudian deferral, is modeled and then the fuller 'sheaf of diff rance is taken up. The metaphysically conceived brain has two versions: in the traditional computational version the brain processes information like a computer and in the connectionist version the brain computes input vector to output vector transformations non-symbolically. The (...)
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  47. Robert F. Hadley (1999). Connectionism and Novel Combinations of Skills: Implications for Cognitive Architecture. [REVIEW] Minds and Machines 9 (2):197-221.
    In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just (...)
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  48. Gary Hatfield (1990). Gibsonian Representations and Connectionist Symbol-Processing: Prospects for Unification. Psychological Research 52:243-52.
  49. Dianne D. Horgan & Douglas J. Hacker (1999). Beginning a Theoretician-Practitioner Dialogue About Connectionism. Acta Analytica 22 (22):261-273.
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  50. Terence E. Horgan (1999). Short Prcis of Connectionism and the Philosophy of Psychology. Acta Analytica 22 (22):9-21.
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