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It is generally assumed that when making categorization judgments the cognitive system learns to focus on stimuli features that are relevant for making an accurate judgment. This is a key feature of hybrid categorization systems, which selectively weight the use of exemplar- and rule-based processes. In contrast,  Hahn et al. (2010)  have shown that people cannot help but pay attention to exemplar similarity, even when doing so leads to classification errors. This paper tests, through a series of computer simulations, whether a hybrid categorization model developed in the ACT-R cognitive architecture (by  Anderson and Betz, 2001 ) can account for the Hahn et al. dataset. This model implements  Nosofsky and Palmeri’s (1997)  exemplar-based random walk model as its exemplar route, and combines it with an implementation of  Nosofsky et al. (1994)  rule-based model RULEX. A thorough search of the model’s parameter space showed that while the presence of an exemplar-similarity effect on response times was associated with classification errors it was possible to fit both measures to the observed data for an unsupervised version of the task (i.e., in which no feedback on accuracy was given). Difficulties arose when the model was applied to a supervised version of the task in which explicit feedback on accuracy was given. Modeling results show that the exemplar-similarity effect is diminished by feedback as the model learns to avoid the error-prone exemplar-route, taking instead the accurate rule-route. In contrast to the model, Hahn et al. found that people continue to exhibit robust exemplar-similarity effects even when given feedback. This work highlights a challenge for understanding how and why people combine rules and exemplars when making categorization decisions.

Ignore Similarity If You Can: A Computational Exploration of Exemplar Similarity Effects on Rule Application