How does the number of features impact category learning? One view suggests additional features create a “curse of dimensionality” - where having more features causes the size of the search space to explode such that learning becomes increasingly challenging. The opposing view suggests additional features provides additional information which should be beneficial. Previous research exploring this issue has produced conflicting results: some finding additional features are helpful (Hoffman & Murphy, 2006) or harmful (Minda & Smith, 2001; Edgell et al., 1996). Here we investigate the possibility that category structure may explain this apparent discrepancy – more features are useful in categories with family resemblance structure, but are not in rule-based categories. We find while the impact of having many features depends on category structure, which can be explained by a single unified model that attends to a single feature on any given trial and uses that information to make judgments.