The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis of reinforcement learning. The theory assumes that individuals develop subjective expectations for the strategies they have and select strategies proportional to their expectations, which are then updated on the basis of subsequent experience. The learning assumption was supported in 4 experimental studies. Participants substantially improved their inferences through feedback. In all 4 studies, the best-performing strategy from the participants' repertoires most accurately predicted the inferences after sufficient learning opportunities. When testing SSL against 3 models representing extensions of SSL and against an exemplar model assuming a memory-based inference process, the authors found that SSL predicted the inferences most accurately.