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

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.