Proceedings of the Annual Meeting of the Cognitive Science Society

How does the number of features impact category learning?One view suggests that additional features creates a “curse ofdimensionality” - where having more features causes the sizeof the search space to grow so quickly that discovering goodclassification rules becomes increasingly challenging. The op-posing view suggests that additional features provide a wealthof additional information which learners should be able to useto improve their classification performance. Previous researchexploring this issue appears to have produced conflicting re-sults: some find that learning improves with additional features(Hoffman & Murphy, 2006) while others find that it does not(Minda & Smith, 2001; Edgell et al., 1996). Here we inves-tigate the possibility that category structure may explain thisapparent discrepancy – that more features are useful in cate-gories with family resemblance structure, but are not (and mayeven be harmful) in more rule-based categories. We find whilethe impact of having many features does indeed depend on cat-egory structure, the results can be explained by a single unifiedmodel: one that attends to a single feature on any given trialand uses information learned from that particular feature tomake classification judgments.