Correlated symptoms and simulated medical classification.

Abstract

Category learning theories can be separated into those that expect judgments to be sensitive to configural information and those that expect judgments to be based on a weighted, additive summation of information. Predictions of these two classes of models were investigated in a simulated medical diagnosis task. Subjects learned about a fictitious disease or about two diseases from hypothetical case studies in which some symptoms were correlated with each other and others were independent. Following this initial training, subjects were presented either with pairs of new cases and asked to judge which was more likely to have the disease or with a single case and asked which disease was present. Across four experiments, subjects proved to be sensitive to configural information. When choosing between pairs of new cases, subjects tended to choose the case that preserved the correlation over the case that broke the correlation, even when the case with correlated symptoms contained fewer typical symptoms. When judging which disease was present in a single case, subjects' diagnoses were determined primarily by the correlated symptoms. Implications of these findings to process models of categorization are discussed.