Exemplar similarity and the development of automaticity

Effects of exemplar similarity on the development of automaticity were investigated with a task in which participants judged the numerosity of random patterns of between 6 and 11 dots. After several days of training, response times were the same at all levels of numerosity, signaling the development of automaticity. In Experiment 1, response times to new patterns were a function of their similarity to old patterns. In Experiment 2, responses to patterns with high within-category similarity became automatized more quickly than responses to patterns with low within-category similarity. In Experiment 3, responses to patterns with high between-category similarity became automatized more slowly than responses to patterns with low between-category similarity. A new theory, the exemplar-based random walk (EBRW) model, was used to explain the results. Combining elements of G. D. Logan's (1988) instance theory of automaticity and R. M. Nosofsky's (1986) generalized context model of categorization, the theory embeds a dynamic similarity-based memory retrieval mechanism within a competitive random walk decision process.