In order to be adaptive, cognition requires knowledge about the statistical structure of the environment. We show that decision performance and the selection between cue-based and exemplar-based inference mechanisms can depend critically on how this knowledge is acquired. Two types of learning tasks are distinguished: "learning by comparison", by which the decision maker learns which of two objects has a higher criterion value, and "direct criterion learning", by which the decision maker learns an object's criterion value directly. In three experiments, participants were trained either with learning by comparison or with direct criterion learning and subsequently tested with paired-comparison, classification, and estimation tasks. Experiments 1 and 2 showed that although providing less information, learning by comparison led to better generalization (at test), both when generalizing to new objects and when the task format at test differed from the task format during training. Moreover, learning by comparison enabled participants to provide rather accurate continuous estimates. Computational modeling suggests that the advantage of learning by comparison is due to differences in strategy selection: whereas direct criterion learning fosters the reliance on exemplar processing, learning by comparison fosters cue-based mechanisms. The pattern in decision performance reversed when the task environment was changed from a linear (Experiments 1 and 2) to a nonlinear structure (Experiment 3), where direct criterion learning led to better decisions. Our results demonstrate the critical impact of learning conditions for the subsequent selection of decision strategies and highlight the key role of comparison processes in cognition. (Contains 6 tables and 7 figures.)