Proceedings of the Annual Meeting of the Cognitive Science Society

In learning categories and rules from observation without external feedback, people must makeuse of the structure intrinsic to the instances observed. Success in learning complex categoriesfix)m observation, as in language acquisition, suggests that learners must be equipped withprocedures for efficiently using structure in input to guide learning. W e propose one way thatlearners make use of the correlational structure available in input to facilitate observationallearning: increase reliance on those features discovered to make good predictions about thevalues of other features. Such a mechanism predicts two types of facilitation from multiplecorrelations among input features. This contrasts with the effects of correlated features whichhave been suggested by models addressing learning with explicit feedback. Three experimentsinvestigated learning the syntactic categories of artifical grammars, without external feedback,and tested for the predicted pattern of facilitation. Subjects did show the predicted facilitation.In addition, a simulation of the learning mechanism investigated the conditions when it wouldprovide most benefit to learning. This research program begins investigation of procedureswhich might underlie efficient learning of complex, natural categories and niles finomobservation of examples.