Sparse category labels obstruct generalization of category membership

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). Surprisingly, a number of recent findings suggest that people have difficulty learning in semi-supervised tasks. To further explore this issue, we devised a category learning task in which the distribution of labeled and unlabeled items suggested alternative organizations of a category. This design allowed us to determine whether learners combined information from both types of episodes via their patterns of generalization at test. In contrast with the prediction of many models, we find little evidence that unlabeled items influenced categorization behavior when labeled items were also present.