The Exemplar-Based Random Walk (EBRW) model was one of the first models of object categorization to account for both time and accuracy. EBRW combines key elements of the well-known Generalized Context Model of perceptual categorization of Nosofsky (1986) and the Instance Theory of automaticity of Logan (1988). Building on these previous models, when visual memory is probed with an object representation, stored exemplars race to be retrieved with rates proportional to their similarity to that object. The category associated with a retrieved exemplar is used to provide incremental evidence for a particular categorization decision. Categorization decisions are made via a random walk process that accumulates this incremental evidence (Link, 1975; Luce, 1986; Ratcliff, 1978). Accuracy is given by which response boundary of the random walk is hit first, and time is given by the time to reach that response boundary. The model account for complete response time distributions of perceptual categorizations, speedups in response time with experience, and in recent work has been extended to account for key aspects of the development of perceptual expertise.
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