This research aimed to discriminate between 2 general approaches to unsupervised category learning, one based on learning explicit correlational rules or associations within a stimulus domain (autocorrelation) and the other based on inventing separate categories to capture the correlational structure of the domain (category invention). An "attribute-listing" paradigm was used to index unsupervi...

Most of machine learning is concerned with learning a single concept from a sequence of examples. In repeat learning the teacher chooses a series of related concepts randomly and independently from a distribution D. A nite sequence of examples is provided for each concept in the series. The learner does not initially know D, but progressively updates a posterior estimation of D as the series pr...