Exploring Decision Rules and Sampling Dynamics in Recognition Memory

Cox and Shiffrin (2012) introduced a dynamic model of recog-nition memory that was capable of making simultaneous pre-dictions for accuracy and mean response time. The present pa-per extends that work by investigating the assumptions under-lying the model’s decision process, in particular those pertain-ing to the process by which features are sampled at test and the processes by which evidence for an “old”/“new ” recognition decision accumulates. These assumptions are tested against empirically collected response time distributions. Evidence is found that sampling dynamics can change in response to instructions, and that competition between accumulators for “old ” and “new ” evidence is not necessary to capture at least the recognition data presented here