Mixing process and descriptive runners in the race model of response inhibition: a hybrid approach to the stop-signal paradigm

Response inhibition is frequently measured using the stop-signal paradigm, where responses must be withheld when a “stop” signal appears. This paradigm assumes that go and stop stimuli trigger competing runners. The first runner crossing a boundary wins, and determines whether a response is performed. A tension exists between two categories of models: descriptive and process models. Descriptive models define the speed of the runners, whereas process models express the latency of going (go RT) and stopping (stop-signal RT) in terms of psychological mechanisms and explain how their distributions emerge. One drawback of the process approach is an inability to recover data-generating parameters and thereby not qualifying as a measurement model. In contrast, the descriptive BEESTS approach recovers these parameters, but the psychological interpretation of its parameters is ambiguous which hampers the understanding of RT differences between groups or manipulations. We propose to mix a process “evidence-accumulation” account of the go runners and a descriptive approach of the stop runner. To instantiate this hybrid approach, we assumed Wald distributions for the finishing times of the go runners and, similar to BEESTS, an ex-Gaussian distribution for the stop runner. This approach results in a practically useful measurement model, with good parameter recovery by Bayesian hierarchical methods in realistic designs. By mixing racers, we garner advantages of both process and descriptive models: all parameters are interpretable in a measurement sense, parameters describing go runners are interpretable psychologically, and the stop parameters can be used to reliably and validly estimate stop-signal RTs.