Bayesian Parametric Estimation of Stop-Signal Reaction Time Distributions

The cognitive concept of response inhibition can be measured with the stop-signal paradigm. In this paradigm, participants perform a 2-choice response time (RT) task where, on some of the trials, the primary task is interrupted by a stop signal that prompts participants to withhold their response. The dependent variable of interest is the latency of the unobservable stop response (stop-signal reaction time, or SSRT). Based on the horse race model (Logan & Cowan, 1984), several methods have been developed to estimate SSRTs. None of these approaches allow for the accurate estimation of the entire distribution of SSRTs. Here we introduce a Bayesian parametric approach that addresses this limitation. Our method is based on the assumptions of the horse race model and rests on the concept of censored distributions. We treat response inhibition as a censoring mechanism, where the distribution of RTs on the primary task (go RTs) is censored by the distribution of SSRTs. The method assumes that go RTs and SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to obtain posterior distributions for the model parameters. The method can be applied to individual as well as hierarchical data structures. We present the results of a number of parameter recovery and robustness studies and apply our approach to published data from a stop-signal experiment.