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Cobo-Lewis, A. B. (1997). An adaptive psychophysical method for subject classification. Perception and Psychophysics, 59, 989–1003.

In psychophysical experiments, one’s goal is usually to measure some continuous parameter hypothesized to determine the statistical properties of a subject’s responses. Methods are well developed that adaptively select stimulus parameters in such a way that the reliability of the parameter estimate is maximized. However, such methods are inapplicable in situations where the goal is to assign subjects to discrete categories, rather than to measure a continuous parameter. Herein is introduced a technique, which is directly applicable to efficient categorization, that adaptively selects stimulus parameters in such a way that the information obtained from each trial is maximized. This technique is based on the principle of minimum estimated expected entropy, whereby stimulus parameters on each trial are chosen in order to minimize the estimated expected entropy of the a posteriori probability distribution across categories. A sample implementation of the technique—the classification of infant subjects according to their audiograms—is then described and evaluated via computer simulation.

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