Connectionism, classical cognitive science and experimental psychology

AI and Society 4 (1):73-90 (1990)
Abstract
Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this behaviour. In addition to capturing the gross human performance profile, Connectionist systems seem well suited to accounting for the systematic patterns of errors observed in the human data. We take these arguments to counter Fodor and Pylyshyn's (1988) recent claim that Connectionism is, in principle, irrelevant to psychology
Keywords Connectionism  Classical cognitive science  Experimental psychology  Memory  Inference
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DOI 10.1007/BF01889765
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References found in this work BETA
Criticism and the Growth of Knowledge.Imre Lakatos & Alan Musgrave (eds.) - 1970 - Cambridge University Press.
The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - University of Chicago Press.
The Language of Thought.Jerry A. Fodor - 1975 - Harvard University Press.

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Against Logicist Cognitive Science.Mike Oaksford & Nick Chater - 1991 - Mind and Language 6 (1):1-38.

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