Localist models are compatible with information measures, sparseness indices and complementary learning systems in the brain

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Abstract

In this paper, I express continued support for localist modelling in psychology and critically evaluate previous studies that have sought to weaken the localist case in favour of models with thoroughgoing distributed representation. I question claims that information measures and sparseness indices derived from single-cell recording data are supportive of distributed representation and show that the patterns observed in those data can be reproduced from simulations of a model that is known to be localist. I also set out some logical objections to the complementary learning hypothesis, particularly in as much as it is used to justify thoroughgoing-distributed models of the cortex.
Original languageEnglish
Pages (from-to)366-379
Number of pages14
JournalLanguage, Cognition and Neuroscience
Volume32
Issue number3
Early online date17 Nov 2016
DOIs
Publication statusPublished - 16 Mar 2017

Keywords

  • localist models; sparseness; information theory; complementary learning hypothesis

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