Proceedings of the Annual Meeting of the Cognitive Science Society

The category variability effect describes assigning objects to high-variability categories. We show that similarity-based categorization theories can predict the category variability effect and conduct a rigorous empirical test. In an optimized categorization experiment, participants learned to assign geometrical figures to a high-variability and a low-variability category and then categorized transfer stimuli located between the categories. We compared a formal model that ignores category variability (Euclidean model) to one that considers category variability (Mahalanobis model) during similarity computation. The data (N = 43) revealed that most participants did not show the category variability effect, in line with the Euclidean model. Nevertheless, the Mahalanobis model consistently described the participants that selected the high-variability category. This demonstrates that—contrary to previous claims—similarity can explain the category variability effect. However, in our data, most people do not seem to show the effect, maybe because the low-variability category was more coherent than the high-variability category.