Discrete-state and continuous models of recognition memory: Testing core properties under minimal assumptions

A classic discussion in the recognition-memory literature concerns the question of whether recognition judgments are better described by continuous or discrete processes. These two hypotheses are instantiated by the signal detection theory model (SDT) and the 2-high-threshold model, respectively. Their comparison has almost invariably relied on receiver operating characteristic data. A new model-comparison approach based on ranking judgments is proposed here. This approach has several advantages: It does not rely on particular distributional assumptions for the models, and it does not require costly experimental manipulations. These features permit the comparison of the models by means of simple pairedcomparison tests instead of goodness-of-fit results and complex model-selection methods that are predicated on many auxiliary assumptions. Empirical results from 2 experiments are consistent with a continuous memory process such as the one assumed by SDT.