Neural network models of list learning

Abstract

A neural network model is developed which captures the results of human memory experiments on learning lists of items. The psychological experiments on learning lists are reviewed. Hopfield–Parisi type neural networks are used to model many of the simpler features of order effects in serial recall. The recall of items as a function of their number, their position in the list and their similarity is investigated with simulations. More complex experiments involving different categories of items are modelled using correlated patterns of activity. Insight into how the models work is gained by consideration of the distribution of weights and signal-to-noise ratio arguments.