Proceedings of the Annual Meeting of the Cognitive Science Society

People can learn simply from observation, without explicit feedback. Natural language acquisition is perhaps the most spectacular example, but unsupervised learning occurs in many domains. W e present 1) a task analysis of a broad class of unsupervised learning problems and 2) an initial simulation based on the task analysis which successfully learns all the rule types identified in the analysis. Our task analysis characterizes systems of interpredictive correlational rules which could be the basis for category formation in unsupervised learning. For example, observation of various animals could lead to abstracting covariation rules among wings, feathers, and flight, and also among fins, scales, and swimming. These rules in turn could form the basis for the categories bird and fish.Our analysis identifies three types of predictive features and three types of rules which may be available in input: universal, contrastive, and exception-based rules. This analysis guided design of our learning procedures.Our simulation succeeds in learning Jill three rule types. This is difficult because procedures which facilitate learning one rule type may inhibit learning another. Further, our simulation is restricted in psychologically motivated ways and succeeds despite these requirements. We know of no other simulation or modeling project which addresses exactly this class of learning problems. Our results demonstrate the existence of successful procedures. However, we believe our most valuable contributions are our task analysis and framework for testing the power and limits of domain-general learning procedures applied to unsupervised learning problems.