The recognition-primed decision (RPD) model (Klein, 1993) is an account of expert decision making that focuses on how experts recognize situations as being similar to past experienced events and thus rely on memory and experience to make decisions. A number of computational models exist that attempt to account for similar aspects of expert decision making. In this paper, I briefly review these extant models and propose the Bayesian recognitional decision model (BRDM), a Bayesian implementation of the RPD model based primarily on models of episodic recognition memory (Mueller & Shiffrin, 2006; Shiffrin & Steyvers, 1997). The proposed model accounts for three important factors used by experts to make decisions: evidence about a current situation, the prior base rate of events in the environment, and the reliability of the information reporter. The Bayesian framework integrates these three aspects of information together in an optimal way and provides a principled framework for understanding recognitional decision processes.
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