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.
