Abstract
Symbolic processing rests on a computational technology that includes dynamic memory management, virtual pointers to created structured objects, and the use of variables to propagate bindings. Connectionist models have lacked these features, but create distributed representations that are “rich,” i.e., that encode numerous statistically based expectations, acquired from experience. It is argued here that a synthesis of both symbolic and connectionist features will make important contributions to our understanding of high-level cognition. In what follows, a motivation is given for the need of such a synthesis, along with several, novel techniques used in connectionist systems designed to perform high-level, language-related processing tasks.
The research reported here was supported in part by grants from the JTF Program of the DoD (monitored by JPL), the ITA Foundation, the Office of Naval Research, the W. M. Keck Foundation and the Hughes Artificial Intelligence Center. Hardware grants in support of this research were supplied by Apollo Computer, Hewlett-Packard, the W. M. Keck Foundation and the National Science Foundation.
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Dyer, M.G. (1991). Connectionism Versus Symbolism in High-Level Cognition. In: Horgan, T., Tienson, J. (eds) Connectionism and the Philosophy of Mind. Studies in Cognitive Systems, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3524-5_17
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DOI: https://doi.org/10.1007/978-94-011-3524-5_17
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