Artificial neural networks as models of stimulus control

We evaluate the ability of artificial neural network models (multilayer perceptrons) to predict stimulus-response relationships. A variety of empirical results are considered, such as generalization, peak shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the experiments considered. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. We conclude that these models are valuable tools in the study of animal behaviour. (c) 1998 The Association for the Study of Animal Behaviour.