An Exemplar-Based Random-Walk Model of Speeded Categorization and Absolute Judgment

Nosofsky and Palmeri (in press) proposed and tested an exemplar-based random walk model for predicting response times in tasks of speeded multidimensional perceptual classification. According to the model, test items serve as retrieval cues for category exemplars stored in memory. The exemplars race to be retrieved with rates determined by their similarity to the presented items. The retrieved exemplars then provide information that enters into a random walk process. In this chapter, after first reviewing the model, I present tests of its ability to fit categorization response-time distributions. I also explore extensions of the model designed to account for unidimensional absolute judgment and category same-different judgment.