Proceedings of the Annual Meeting of the Cognitive Science Society

People and other animals are very adept at categorizing stimuli even when many features cannot be perceived. Many psychological models of categorization, on the other hand, assume that an entire set of features is known. We present a new model of categorization, called Categorization by Elimination, that uses as few features as possible to make an accurate category assignment. This algorithm demonstrates that it is possible to have a categorization process that is fast and frugal--using fewer features than other categorization methods--yet still highly accurate in its judgments. W e show that Categorization by Elimination does as well as human subjects on a multi-feature categorization task, judging intention from animate motion, and that it does as well as other categorization algorithms on data sets from machine learning. Specific predictions of the Categorization by Elimination algorithm, such as the order of cue use during categorization and the time-course of these decisions, still need to be tested against human performance.