David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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AI and Society 4 (1):73-90 (1990)
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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References found in this work BETA
Nick Chater & Mike Oaksford (1990). Autonomy, Implementation and Cognitive Architecture: A Reply to Fodor and Pylyshyn. Cognition 34 (1):93-107.
J. A. Fodor (1980). Methodological Solipsism Considered as a Research Strategy in Cognitive Psychology. Behavioral and Brain Sciences 3 (1):63.
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Citations of this work BETA
Mike Oaksford & Nick Chater (1991). Against Logicist Cognitive Science. Mind and Language 6 (1):1-38.
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