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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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
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