Discriminating Famous From Fictional Names Based on Lifetime Experience: Evidence in Support of a Signal-Detection Model Based on Finite Mixture Distributions

It is widely accepted that signal-detection mechanisms contribute to item-recognition memory decisions that involve discriminations between targets and lures based on a controlled laboratory study episode. Here, the authors employed mathematical modeling of receiver operating characteristics (ROC) to determine whether and how a signal-detection mechanism contributes to discriminations between moderately famous and fictional names based on lifetime experience. Unique to fame judgments is a lack of control over participants' previous exposure to the stimuli deemed "targets" by the experimenter; specifically, if they pertain to moderately famous individuals, participants may have had no prior exposure to a substantial proportion of the famous names presented. The authors adopted established models from the recognition-memory literature to examine the quantitative fit that could be obtained through the inclusion of signal-detection and threshold mechanisms for two data sets. They first established that a signal-detection process operating on graded evidence is critical to account for the fame judgment data they collected. They then determined whether the graded memory evidence for famous names would best be described with one distribution with greater variance than that for the fictional names, or with two finite mixture distributions for famous names that correspond to items with or without prior exposure, respectively. Analyses revealed that a model that included a d' parameter, as well as a mixture parameter, provided the best compromise between number of parameters and quantitative fit. Additional comparisons between this equal-variance signal-detection mixture model and a dual-process model, which included a high-threshold process in addition to a signal-detection process, also favored the former model. In support of the conjecture that the mixture parameter captures participants' prior experience, the authors found that it was increased when the analysis was restricted to names in occupational categories for which participants indicated high exposure.