Proceedings of the Annual Meeting of the Cognitive Science Society

In an experiment, subjects learned about new categories for wbich tbey had prior beliefs, and made probability judgments at various points during the course of learning. The responses were analyzed in terms of bias due to prior beliefs and in terms of sensitivity to the content of the new categories. These results were compared to the predictions of four models of belief revision or categorization: (1) a Bayesian estimation procedure (Raiffa & Schlaifer, 1961); (2) the integration model (Heit, 1993, 1994), a categorization model that is a generalization of the Bayesian model; (3) a linear operator model that performs serial averaging (Bush & Mosteller, 1955); and (4) a simple adaptive network model of categorization (Gluck & Bower, 1988) that is a generalization of the hnear operator model. Subjects were conservative in terms of sensitivity to new information, compared to the predictions of the Bayesian model and the linear operator model. The network model was able to account for this conservatism, however this model predicted an extreme degree of forgetting of prior beliefs compared to that shown by human subjects. Of the four models, the integration model provided the closest account of bias due to prior beliefs and sensitivity to new information over the course of category learning.