Reaction time distributions in recognition conditions were compared to those in cued recall to explore the time course of retrieval, to test current models, and to provide constraints for the development of n e w models (including, to take an example, the class of recurrent neural nets, since they naturally produce reaction time predictions). Two different experimental paradigms were used. Results from a free response procedure showed fundamental differences between the two test modes, both in mean reaction time and the general shape of the distributions. Analysis of data from a signal-to-respond procedure revealed large differences between recognition and recall in the rate of growth of performance. These results suggest the existence of different processes underlying retrieval in recognition and cued recall. One model posits parallel activation of separate memory traces; for recognition, the summed activation is used for a decision, but for recall a search is based on sequential probabilistic choices from the traces. Further constraining models was the observation of nearly identical reaction time distributions for positive and negative responses in recognition, suggesting a single process for recognition decisions for targets and distractors.

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.