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Connecting Object to Symbol in Modelling Cognition

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Book cover Connectionism in Context

Part of the book series: Artificial Intelligence and Society ((HCS))

Abstract

Connectionism and computationalism are currently vying for hegemony in cognitive modelling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the “language of thought” has been symbolic computationalism (Dietrich 1990; Fodor 1975; Hamad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is non-symbolic (Fodor and Pylyshyn 1988 — or, as some have hopefully dubbed it, “subsymbolic” — Smolensky 1988). This chapter will examine what is and is not a symbol system. A hybrid non-symbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system’s capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analogue sensory projection on which successful categorization is based. “Categorical perception” (Hamad 1987a), in which similarity space is “warped” in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analogue world of objects on the formal world of symbols.

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Authors
  1. Stevan Harnad
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Editors and Affiliations

  1. School of Cognitive and Computing Sciences, University of Sussex, Falmer, Brighton, BN1 9QH, UK

    Andy Clark  & Rudi Lutz  & 

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© 1992 Springer-Verlag London Limited

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Harnad, S. (1992). Connecting Object to Symbol in Modelling Cognition. In: Clark, A., Lutz, R. (eds) Connectionism in Context. Artificial Intelligence and Society. Springer, London. https://doi.org/10.1007/978-1-4471-1923-4_5

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  • DOI: https://doi.org/10.1007/978-1-4471-1923-4_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19716-4

  • Online ISBN: 978-1-4471-1923-4

  • eBook Packages: Springer Book Archive

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