A crucial component of language acquisition involves organizing words into grammatical categories and discovering relations between them. Many studies have argued that phonological or semantic cues or multiple correlated cues are required for learning. Here we examine how distributional variables will shift learners from forming a category of lexical items to maintaining lexical specificity. In a series of artificial language learning experiments, we vary a number of distributional variables to category structure and test how adult learners use this information to inform their hypotheses about categorization. Our results show that learners are sensitive to the contexts in which each word occurs, the overlap in contexts across words, the non-overlap of contexts (or systematic gaps), and the size of the data set. These variables taken together determine whether learners fully generalize or preserve lexical specificity.