Beyond ROC curvature: Strength effects and response time data support continuous-evidence models of recognition memory

A classic question in the recognition memory literature is whether retrieval is best described as a continuous-evidence process consistent with signal detection theory (SDT), or a threshold process consistent with many multinomial processing tree (MPT) models. Because receiver operating characteristics (ROCs) based on confidence ratings are typically curved as predicted by SDT, this model has been preferred in many studies of recognition memory (Wixted, 2007). Recently, Bröder and Schütz (2009) argued that curvature in ratings ROCs may be produced by variability in scale usage; therefore, ratings ROCs are not diagnostic in deciding between the two approaches. From this standpoint, only ROCs constructed via experimental manipulations of response bias (‘binary’ ROCs) are predicted to be linear by threshold MPT models. The authors claimed that binary ROCs are linear, consistent with the assumptions of threshold MPT models. We compared SDT and the double high-threshold MPT model using binary ROCs differing in target strength. Results showed that the SDT model provided a superior account of both the ROC curvature and the effect of strength compared to the MPT model. Moreover, the bias manipulation produced differences in RT distributions that were well described by the diffusion model (Ratcliff, 1978), a dynamic version of SDT.

► We compare continuous and threshold models using binary ROCs and response times.
► We manipulate target strength to add additional constraints to the models.
► We test continuous/SDT assumptions further via the Ratcliff diffusion model.
► Of the two ROC models, only SDT describes the ROCs and strength effects.
► SDT assumptions applied via the diffusion model capture all of our data.