Proceedings of the Annual Meeting of the Cognitive Science Society

Global recognition models usually assume recognition is based on a single number, generjilly interpreted as 'familiarity'. Clark, Hori, and Callan (in press), tested the adequacy of such models for associative recognition, a paradigm in which subjects study pairs and must distinguish them from the same words rearranged into other pairs. Subjects chose a tcirget pair from a set of three choices. In one condition all three choices contained a common, shared word (OLAP); in the other condition, all words were unique (NOLAP). Subjects performed slightly better in the NOLAP condition, but global recognition models predict an P advantage, due to the correlation among test pairs. Clark et al. (in press) suggested that the subjects m a y have used cued-recall to supplement their familiarity judgments: the greater number of imique words in the NOLAP case provides extra retrieval chances that can boost performance. We tested this possibility by implementing a retrieval structure that leads to a hybrid of cued-recall and recognition. W e did this for several current memory models, including connectionist and neural net models. For all of the models we explored , the observed NOLAP advantage was difficult to impossible to produce. While some researchers propose that there is a cued-recall component to associative recognition, our modeling shows that this component cannot be realized easily in the extant memory models as they are currently formulated.