The development of automaticity refers to the changes that in how people perform tasks after sufficient practice. In the initial stages of acquiring a new cognitive skill, people must consciously control the individual steps of a task, are easily distracted by extraneous information, cannot carry on another task simultaneously, and perform rather slowly. Automatic performance, by contrast, allows one to easily and quickly perform a task with little concern for being distracted and while performing other tasks (Palmeri, 2002).

The EBRW model is unique in that it attempts to bridge between the traditionally separate research areas of perceptual categorization and the development of automaticity (Palmeri, 1997). Theoretically, it does so by combining elements of Nosofsky’s (1986) exemplar model of categorization with elements of Logan’s (1988) instance theory of automaticity. According to instance theory, automaticity in some cognitive skill, such as categorization, reflects shifts from strategic to memory-based processes. Although elegant in its simplicity, and powerful in its predictions, instance theory was limited in that it does not take into account the graded similarities among exemplars and does not allow for response competition to emerge (Palmeri, 1997, 2001).

Whereas automaticity studies have generally neither manipulated nor measured similarities between objects, similarity manipulations are a defining characteristic of most categorization studies. To create an empirical bridge between categorization and automaticity, mirroring the structure of categorization tasks, I tested for generalization once automaticity was achieved by presenting new objects of varying similarity to old ones, and I examined the effects of similarity on learning by manipulating the similarity of objects within the same response class and between different response classes. These studies showed pronounced similarity effects, as predicted and accounted for by the EBRW (Palmeri, 1997, 2001). Not only is EBRW one of the first process models to allow quantitative predictions of both categorization response time and accuracy, it is also unique in that it provides a unified account of both categorization and automaticity. In a subsequent paper (Palmeri, 1999), I addressed some criticisms raised by Rickard (1999), demonstrating that both instance theory and the EBRW can account for fairly dramatic deviations from the so-called power law of practice (Newell & Rosenbloom, 1981). Later work looked more closely at shifts from rule-based to similarity-based object categorization as a function of experience (Johansen & Palmeri, 2002).

Johansen, M.K., & Palmeri, T.J. (2002). Are there representational shifts during category learning? Cognitive Psychology, 45, 482-553.

Palmeri, T.J. (2002). Automaticity. In L. Nadel et al. (Eds.), Encyclopedia of Cognitive Science (pp. 390-401), Nature Publishing Group, London.

Palmeri, T.J. (1999). Theories of automaticity and the power law of practice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 543–551.

Palmeri, T.J. (1997). Exemplar similarity and the development of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 324-354.