Research has found that when children or adults attemptto explain novel observations in the course of learning,they are more likely to discover patterns that support idealexplanations: explanations that are maximally simple andbroad. However, not all learning contexts support suchexplanations. Can explaining facilitate discoverynonetheless? We present a study in which participantswere tasked with discovering a rule governing theclassification of items, where the items were consistenttwo non-ideal rules: one correctly classified 66% of cases,the other 83%. We find that when there is no ideal rule tobe discovered (i.e., no 100% rule), participants promptedto explain are better than control participants atdiscovering the best available rule (i.e., the 83% rule).This supports the idea that seeking ideal explanations canbe beneficial in a non-ideal world because the pursuit ofan ideal explanation can facilitate the discovery ofimperfect patterns along the way.

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.