Proceedings of the Annual Meeting of the Cognitive Science Society

When learners explain to themselves as they encounter new in-formation, they recruit a suite of processes that influence sub-sequent learning. One consequence is that learners are morelikely to discover exceptionless rules that underlie what theyare trying to explain. Here we investigate what it is about ex-ceptionless rules that satisfies the demands of explanation. Areexceptions unwelcome because they lower predictive accuracy,or because they challenge some other explanatory ideal, suchas simplicity and breadth? To compare these alternatives, weintroduce a causally rich property explanation task in whichexceptions to a general rule are either arbitrary or predictable(i.e., exceptions share a common feature that supports a “ruleplus exception” structure). If predictive accuracy is sufficientto satisfy the demands of explanation, the introduction of a ruleplus exception that supports perfect prediction should blockthe discovery of a more subtle but exceptionless rule. Acrosstwo experiments, we find that effects of explanation go beyondattaining perfect prediction.