Abstract
Retrospective revaluation refers to an increase (or decrease) in responding to conditioned stimulus (CS X) as a result of decreasing (or increasing) the associative strength of another CS (A) with respect to the unconditioned stimulus (i.e., A-US) that was previously trained in compound with the target CS (e.g., AX–US or just AX). We discuss the conditions under which retrospective revaluation phenomena are most apt to be observed and their implications for various models of learning that are able to account for retrospective revaluation (e.g., Dickinson and Burke, 1996; Miller and Matzel, 1988; Van Hamme and Wasserman, 1994). Although retroactive revaluation is relatively parameter specific, it is seen to be a reliable phenomenon observed across many tasks and species. As it is not anticipated by many conventional models of learning (e.g., Rescorla and Wagner, 1972), it serves as a critical benchmark for evaluating traditional and newer models.
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