Abstract
Recognition memory is commonly modeled as either a single, continuous process within the theory of signal detection, or with two-process models such as Yonelinas’ dual-process model. Previous attempts to determine which model provides a better account of the data have relied on fitting the models to data that are averaged over items. Because such averaging distorts conclusions, we develop and compare hierarchical versions of competing single and dual-process models that account for item variability. The dual-process model provides a superior account of a typical data set when models are compared with the deviance information criterion. Parameters of the dual-process model are highly correlated, however, suggesting that a single-process model may exist that can provide a better account of the data.
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