Presenter:  Roger Ratcliff
Presentation type:  Talk
Presentation date/time:  7/27  2:45-3:10
 
Modeling Confidence Judgments in Recognition Memory
 
Roger Ratcliff, Ohio State University
Jeffrey Starns, Ohio State University
 
We have developed a model of confidence judgments in recognition memory that assumes that evidence for each confidence category is accumulated in a separate diffusion process. The model assumes that activity in each diffusion process cannot fall below zero and there is decay in the process (i.e., an OU diffusion process). The model makes predictions for both the accuracy and response time distributions for each confidence judgment. Stimulus information is assumed to be represented as a normal distribution of values on a familiarity scale, different distributions for old and new items. Confidence criteria are placed on this familiarity dimension and the rate of accumulation for each response category is determined by the area under the distribution between the confidence criteria. The model incorporates several identifiable sources of variability, variability within the decision process, familiarity, decision criteria, and nondecision components of processing across trials. This means that the standard interpretation of the z-ROC function is no longer valid. Deviations of the slope from unity reflects both decision criterion settings across confidence criteria as well as differences in familiarity distribution standard deviations. We present the results from experiments in which instructions to use the different categories are either "be accurate" or "spread responses equally across categories" (the usual instructions) and show how the latter lead to inconsistency in data and fits. We also discuss sequential effects.