Retrieving dynamically and effectively from memory (D-REM): A recognition memory model with dynamic decision making mechanism

The recognition memory models explain the processes of representation, encoding, and retrieval of items, and make performance predictions. These models are mostly based on the basic stages of familiarity calculation for a probe and a recognition decision based on a threshold value for endorsement of the probe. However, the course of decision making during recognition has been widely ignored in the recognition modeling literature. The research has mostly focused on explaining accuracy data but ignored the response time (RT) findings until the advent of dynamic recognition memory models (e.g. Cox & Shiffrin, 2012, 2017; Diller et al., 2001; Malmberg, 2008; Hockley & Murdock, 1987; Osth, Jansson, Dennis, & Heathcote, 2018). In recent years, dynamic recognition modeling research achieved promising results to account for the major findings on RT data. In the current study, we have been developing a novel dynamic version of Retrieving Effectively from Memory (REM, Shiffrin & Steyvers, 1997), which is one of the major recognition models. The model, called Retrieving Dynamically and Effectively from Memory (D-REM), incorporates the representation, encoding and likelihood calculation mechanisms of REM while including a dynamic decision making process based on sequential sampling. D-REM assumes that items are represented as vectors of item features. According to REM, encoding is a stochastic process with errors. Retrieval is made by comparisons between the test item and the memory traces, and the recognition decision is made by the likelihood calculation based on these comparisons. During retrieval, the features of the memory traces gradually enter into the buffer system in which the likelihood calculations are made. Thus, the evidence as to whether the probe is old or new accumulates in time towards the decision boundaries. The accumulation of evidence continues until it reaches one of the “yes” or “no” decision boundaries. The memory is updated according to the recognition decision. With this mechanism, D-REM proposes a novel account for the course of decision making during recognition. Including a time-varying boundary mechanism and a starting point parameter, the model aims to be the most extensive dynamic model in the REM framework. Examination of alternative variants of the model with differing drift rate and boundary mechanisms will provide further evidence on the time-course of evidence accumulation and response caution during a decision. We will present the simulations for standard yes-no recognition task and recognition with response deadline procedure via the preliminary variants of D-REM model. The model will be revised and improved according to the comparisons between alternative variants.