Proceedings of the Annual Meeting of the Cognitive Science Society

How do we learn the internal feature co-occurrence structure of a new category? We constructed novel animal categoriesusing a network science framework in order to examine category structure learning. Two categories were defined bydistinct graph structures in which nodes corresponded to features (e.g., bushy tail, black fur) and edges captured within-category feature co-occurrences. The graphs contained isomorphic core structures, in which certain features occurred inall category exemplars. In a high-modularity graph, additional features formed clusters of co-occurring features, whereasin the low-modularity graph additional features were randomly distributed. Participants learned about these categories ina missing-feature task which probed different kinds of category structure knowledge. Though core structure was identicalacross categories, core structure was better learned in the high- relative to low-modularity category. This suggests thatlearning features of a new category is influenced by the global structure of the concept.