Global matching: A comparison of the SAM, Minerva II, Matrix, and TODAM models

Abstract

The pair recognition paradigm was chosen as a means to evaluate and compare different ways to implement a global matching process in which the matches with all list items are combined into a single value. Simplified versions of the SAM, Minerva II, Matrix, and TODAM models are shown to be specific instances of a more general model and are shown to share a parameter-free prediction. This prediction is then shown to be unaffected by the inclusion of additional processes into the models such as variable encoding, cue weights, forgetting, single item matches, context, and background memories. Furthermore, it is only slightly affected by the formation of interpair associations. A test of the prediction using word pairs was inconclusive due either to the use of a recall strategy or to a low level of interitem similarity. Ways to differentiate between the different models and to test the global matching assumption rigorously are discussed.