A quantitative model of human categorization behavior is proposed, which can be applied to 4-alternative
forced-choice categorization data involving two binary classifications. A number of processing dependencies between
the two classifications are explicitly formulated, such as the dependence of the location, orientation, and steepness of
the class boundary for one classification on the outcome of the other classification. The significance of various types of
dependencies can be tested statistically. Analyses of a data set from the literature shows that interesting dependencies
in human speech recognition can be uncovered using the model.