Titlebook: Connectionism in Context; Andy Clark,Rudi Lutz Book 1992 Springer-Verlag London Limited 1992 Connectionism.artificial intelligence.behavio

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Connectionism and Cognition: Why Fodor and Pylyshyn Are Wrong, Pylyshyn (1988). The position which they develop assumes that an acceptable account of brain structure must be able to sustain an appropriate model of cognitive function. They introduce what they take to be the conditions that must be satisfied by an adequate model of cognitive function and contend

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Connecting Object to Symbol in Modelling Cognition,onnectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the “language of thought” has been . computationalism (Dietrich 1990; Fodor 1975; Hamad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is non-symbolic (Fodor and Pylyshyn

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Active Symbols and Internal Models: Towards a Cognitive Connectionism,ve heading of “connectionism” has become the single most credible competitor to three decades of research in artificial intelligence (AI). Originally a response to the failures of the “symbolic” paradigm to live up to the expectations of the 1960s and 1970s, connectionist networks have shown promise

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A Brief History of Connectionism and Its Psychological Implications,ledge pioneers such as Hebb (1949) and Rosenblatt (1962), most of the impetus behind this form of connectionism has built up in the last ten years (Hinton and Anderson 1981; McClelland et al. 1986; cf. Anderson and Rosenfeld 1988; Papert 1988). My intention here, however, is to look at a selection o

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Connectionism and Artificial Intelligence as Cognitive Models,ty of theoretical discussions indicate the emergence of a new research domain. Yet it is not at all clear what connectionism is about. At first sight it may be defined as highly parallel computation with networks of simple neuron-like elements, often called neural networks. But the goals and status

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