A New Paradigm And Computational Framework To Estimate Stop-Signal Reaction Time Distributions From The Inhibition Of Complex Motor Sequences

Inhibitory control is an important component of executive function that allows organisms to abort emerging behavioral plans or ongoing actions on the fly as new sensory information becomes available. Current models treat inhibitory control as a race between a Go- and a Stop process that may be mediated by partially distinct neural substrates, i.e., the direct and the hyper-direct pathway of the basal ganglia. The fact that finishing times of the Stop process (Stop-Signal Reaction Time, SSRT) cannot be observed directly has precluded a precise comparison of the functional properties that govern the initiation (GoRT) and inhibition (SSRT) of a motor response. To solve this problem, we modified an existing inhibitory paradigm and developed a non-parametric framework to measure the trial-by-trial variability of SSRT. A series of simulations verified that the non-parametric approach is on par with a parametric approach and yields accurate estimates of the entire SSRT distribution from as few as similar to 750 trials. Our results show that in identical settings, the distribution of SSRT is very similar to the distribution of GoRT albeit somewhat shorter, wider and significantly less right-skewed. The ability to measure the precise shapes of SSRT distributions opens new avenues for research into the functional properties of the hyper-direct pathway that is believed to mediate inhibitory control.