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 ndings suggest that people have difficulty learning in semisupervised 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. is 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 manymodels, we nd little evidence that unlabeled items inuenced categorization behavior when labeled items were also present.