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Explaining Systematicity: A Reply to Kenneth Aizawa

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Published:01 November 1997Publication History
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Abstract

In his discussion of results which I (with Michael Hayward) recently reported in this journal, Kenneth Aizawa takes issue with two of our conclusions, which are: (a) that our connectionist model provides a basis for explaining systematicity “within the realm of sentence comprehension, and subject to a limited range of syntax”; (b) that the model does not employ structure-sensitive processing, and that this is clearly true in the early stages of the network‘s training. Ultimately, Aizawa rejects both (a) and (b) for reasons which I think are ill-founded. In what follows, I offer a defense of our position. In particular, I argue (1) that Aizawa adopts a standard of explanation that many accepted scientific explanations could not meet, and (2) that Aizawa misconstrues the relevant meaning of ’structure-sensitive process‘.

References

  1. Aizawa, K. (1997), 'Exhibiting versus Explaining Systematicity: A Reply to Hadley and Hayward', Minds and Machines. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Aizawa, K. (forthcoming), 'Explaining Systematicity,' Mind and Language.Google ScholarGoogle Scholar
  3. Fodor, J.A. and Pylyshyn, Z.W. (1988), 'Connectionism and Cognitive Architecture: A Critical Analysis,' Cognition, 28, 3-71.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Fodor, J.A. and McLaughlin, B.P. (1990), 'Connectionism and the Problem of Systematicity: Why Smolensky's Solution Doesn't Work', Cognition, 35, 183-204.Google ScholarGoogle ScholarCross RefCross Ref
  5. Hadley, R.F. (1996), 'Connectionism, Systematicity, and Nomic Necessity,' Proceedings of the Eighteenth Annual Conference of the Cognitive Society, San Diego, CA.Google ScholarGoogle Scholar
  6. Hadley, R.F. (forthcoming), 'Cognition, Systematicity and Nomic Necessity', Mind and Language.Google ScholarGoogle Scholar
  7. Hadley, R.F. and Hayward, M.B. (1997), 'Strong Semantic Systematicity from Hebbian Connectionist Learning,' Minds and Machines. Google ScholarGoogle ScholarDigital LibraryDigital Library

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