The diagnosticity of individual data for model selection: comparing signal-detection models of recognition memory

We tested whether the unequal-variance signal-detection (UVSD) and dual-process signal-detection (DPSD) models of recognition memory mimic the behavior of each other when applied to individual data. Replicating previous results, there was no mimicry for an analysis that fit each individual, summed the goodness-of-fit values over individuals, and compared the two sums (i.e., a single model selection). However, when the models were compared separately for each individual (i.e., multiple model selections), mimicry was substantial. To quantify the diagnosticity of the individual data, we used mimicry to calculate the probability of making a model selection error for each individual. For nondiagnostic data (high model selection error), the results were compatible with equal-variance signal-detection theory. Although neither model was justified in this situation, a forced-choice between the UVSD and DPSD models favored the DPSD model for being less flexible. For diagnostic data (low model selection error), the UVSD model was selected more often.