Proceedings of the Annual Meeting of the Cognitive Science Society

Two major techniques in machine learning, explanation-based learning and explanation completion, are both superficially plausible models for ChJ's self-explanation effect, wherein the amount of explanation given to examples while studying them correlates with the amount the subject learns from them. W e attempted to simulate Chi's protocol data with the simpler of the two learning processes, explanation completion, in order to find out how much of the self-explanation effect it could account for. Although explanation completion did not turn out to be a good model of the data, we discovered a new learning technique, called explanation-based learning of correctness, that combines explanation-based learning and explanation completion and does a much better job of explaining the protocol data. The new learning process is based on the assumption that subjects use a certain kind of plausible reasoning.