Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper, I combine the ideas of attention from cognitive psychology with concept formation in machine learning. M y claim is that the use of attention can lead to a more efficient learning system, without sacrificing accuracy. Attention leads to a savings in efficiency because it focuses only on the relevant attributes, retrieves less information from the environment, and is therefore less costly than a system that uses every piece of information available. I present a working dgorithm for attention, built onto the Classit concept formation system, and describe results from three domains.'