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The authors propose a rule-plus-exception (RULEX) model for how observers classify stimuli re- siding in continuous-dimension spaces. The model follows in the spirit of the discrete-dimension ver- sion of RULEX developed by Nosofsky, Palmeri, and McKinley (1994). According to the model, ob- servers learn categories by forming simple logical rules along single dimensions and by remembering occasional exceptions to those rules. In the continuous-dimension version of RULEX, the rules are for- malized in terms of linear decision boundaries that are orthogonal to the coordinate axes of the psy- chological space. In addition, a similarity-comparison process governs whether stored exceptions are used to classify an object. The model provides excellent quantitative fits both to averaged classifica- tion transfer data and to distributions of generalizations observed at the individual-participant level. The modeling analyses suggest that, when multiple rules are available for solving a problem, averaged classification data often represent a probabilistic mixture of idiosyncratic rule-plus-exception strategies.

A rule-plus-exception model for classifying objects in continuous-dimension spaces