title: Abstract and associatively based representations in human sequence learning.
abstract: We give an analysis of performance in an artificial neural network for which the claim had been made that it could learn abstract representations. Our argument is that this network is associative in nature, and cannot develop abstract representations. The network thus converges to a solution that is solely based on the statistical regularities of the training set. Inspired by human experiments that have shown that humans can engage in both associative (statistical) and abstract learning, we present a new, hybrid computational model that combines associative and more abstract, cognitive processes. To cross-validate the model we attempted to predict human behaviour in further experiments. One of these experiments reveals some evidence for the use of abstract representations, whereas the others provide evidence for associatively based performance. The predictions of the hybrid model stand in line with our empirical data.
authors.affiliation: Department of Computing, Goldsmiths College, University of London, Lewisham Way, New Cross, London SE14 6NW, UK. r.spiegel@gold.ac.uk
authors.forename: Rainer
authors.initials: R
authors.lastname: Spiegel
authors.forename: I P L
authors.initials: IP
authors.lastname: McLaren
mesh_terms: D003198:Computer Simulation; D003210:Concept Formation; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer