Validating the unequal-variance assumption in recognition memory using response time distributions instead of ROC functions: A diffusion model analysis

• Response time (RT) distributions validate the unequal-variance assumption in memory.
• An unequal-variance diffusion model fits better than an equal-variance version.
• Parameters are largely consistent for equal- and unequal-variance fits.

Recognition memory z-transformed Receiver Operating Characteristic (zROC) functions have a slope less than 1. One way to accommodate this finding is to assume that memory evidence is more variable for studied (old) items than non-studied (new) items. This assumption has been implemented in signal detection models, but this approach cannot accommodate the time course of decision making. We tested the unequal-variance assumption by fitting the diffusion model to accuracy and response time (RT) distributions from nine old/new recognition data sets comprising previously-published data from 376 participants. The η parameter in the diffusion model measures between-trial variability in evidence based on accuracy and the RT distributions for correct and error responses. In fits to nine data sets, η estimates were higher for targets than lures in all cases, and fitting results rejected an equal-variance version of the model in favor of an unequal-variance version. Parameter recovery simulations showed that the variability differences were not produced by biased estimation of the η parameter. Estimates of the other model parameters were largely consistent between the equal- and unequal-variance versions of the model. Our results provide independent support for the unequal-variance assumption without using zROC data.