Mixture-models for stimulus-selective stopping

Stimulus-selective stopping extends the standard stop signal task by occasionally presenting an ignore signal instead of a stop signal, in which case participants are instructed to continue responding to the go signal. Here we present several model classes that are based on the idea that responses observed under an ignore signal are the result of a probabilistic mixture from the processing distributions of the go and the ignore signal. Earlier work ensures that the mixture hypothesis is statistically testable. We derive quantitative predictions and parameter estimation for model classes that differ in the way the mixture is introduced. The results are illustrated with an application to a published dataset for stimulus-selective stopping.