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

The authors propose a rule-plus-exception (RULEX) model for how observers classify stimuli residing in continuous-dimension spaces. The model follows in the spirit of the discrete-dimension version of RULEX developed by Nosofsky, Palmeri, and McKinley (1994). According to the model, observers 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 formalized in terms of linear decision boundaries that are orthogonal to the coordinate axes of the psychological 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 classification 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.