Maximum Likelihood Signal-Detection Model Fits.
Since ROC analysis is an atheoretical measure, we validated our findings with theoretical measures, fitting signal-detection models to the data (To estimate summary discriminability (ability to discriminate innocent from guilty suspects), correlation, and confidence criteria parameters, the model was fit to the target, filler, and reject decisions in the three lineup conditions. The mean and standard deviation of the innocent distribution was set to 0 and 1, by convention. An unequal variance model was used, because σDiscriminability (Fitted models for the interactive, photo, and video lineup d Comparisons for front- and profile-encodingNote. In the The independent observation model that allows for a positive correlation among the memory signals of a lineup effectively operates like diagnostic feature detection theory under certain conditions by reducing the impact of the shared nondiagnostic features and increasing the pAUC. One way to reduce the impact of shared features is by increasing the correlated memory signals between faces (hereafter, the To investigate the foregoing, we fit a model allowing μFitted models for the interactive, photo and video lineups estimating correlation (σNote. σOverall, the signal-detection modeling validates the ROC analysis results, though for front-encoding, increased discriminability for the interactive compared to the photo lineup did not reach statistical significance in the modeling. The discriminability improvement observed for interactive lineups is most parsimoniously explained by the distributional account.