Fig. 1.

ROCs (A and D), zROCs (B and E), and probability density functions (C and F) for the unequal variance signal detection (UVSD) and dual process (DP) models, respectively. Recognition memory ROCs in humans are typically curvilinear and asymmetrical to the negative diagonal (black curves in A and D). The UVSD model assumes that novel and repeated (old) items are represented by two Gaussian functions along the dimension of memory strength, with the variance of the old-item distribution being greater than the variance of the novel-item distribution (C). The DP model assumes that two independent processes, recollection and familiarity, contribute to recognition memory functions. Recollection is assumed to be a threshold process, whereas familiarity is assumed to be an equal variance signal detection process (F). Specifically, a certain proportion of old items (the distribution of all old items is indicated by the gray shading in F) is assumed to exceed that threshold and, therefore, is recognized with high confidence on the basis of recollection. When recollection fails, recognition is assumed to be based on familiarity. Purely on their own, an equal variance signal detection process will produce an ROC that is curvilinear and symmetrical to the negative diagonal, and a threshold process will produce a linear ROC (D; both ROCs shown in gray). The UVSD model predicts linear zROCs (B), with a slope smaller than one, reflecting the ratio of the SDs of the new- and old-item distributions. The DP model predicts U-shaped zROCs (E) as a result of the threshold process.