On the dominance of unidimensional rules in unsupervised categorization.

Abstract

In several experiments, observers tried to categorize stimuli constructed from two separable stimulus dimensions in the absence of any trial-by-trial feedback. In all of the experiments, the observers were told the number of categories (i.e., two), they were told that perfect accuracy was possible, and they were given extensive experience in the task (i.e., 800 trials). When the boundary separating the contrasting categories was unidimensional, the accuracy of all observers improved significantly over blocks (i.e., learning occurred), and all observers eventually responded optimally. When the optimal boundary was diagonal, none of the observers responded optimally. Instead they all used some sort of suboptimal unidimensional rule. In a separate feedback experiment, all observers responded optimally in the diagonal condition. These results contrast with those for supervised category learning; they support the hypothesis that in the absence of feedback, people are constrained to use unidimensional rules.