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Some Advantages of Localist over Distributed Representations

  • Conference paper
4th Neural Computation and Psychology Workshop, London, 9–11 April 1997

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

In this paper, I suggest that localist models in psychology have commonly been underestimated relative to their fully-distributed counterparts. I make a clear distinction between the two types of model and address some of the reasons for this popular bias. I conclude that, contrary to conventional wisdom, a localist approach shows more promise than a fully-distributed approach with regard to the development of both psychological-level and brain-level models.

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Author information

Authors and Affiliations

  1. Medical Research Council Applied Psychology Unit, Cambridge, UK

    Mike Page

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  1. Mike Page
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Editor information

Editors and Affiliations

  1. Centre for Speech and Language, Department of Psychology, Birkbeck College, University of London, Malet Street, WC1E 7HX, London, UK

    John A. Bullinaria BSc, MSc, PhD

  2. Department of Psychology, University College London, Gower Street, WC1E 6BT, London, UK

    David W. Glasspool BSc, Msc  & George Houghton BA, MSc, PhD  & 

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© 1998 Springer-Verlag London Limited

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Page, M. (1998). Some Advantages of Localist over Distributed Representations. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_1

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  • DOI: https://doi.org/10.1007/978-1-4471-1546-5_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76208-9

  • Online ISBN: 978-1-4471-1546-5

  • eBook Packages: Springer Book Archive

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