Validation of a Bayesian adaptive estimation technique in the stop-signal task

Abstract

The Stop Signal Task (SST), a commonly used measure of response inhibition, uses standard psychophysical methods to gain an estimate of the time needed to withhold a prepotent response. Under some circumstances, conventional forms of the SST are impractical to use because of the large number of trials necessary to gain a reliable estimate of the speed of inhibition. Here we applied to the SST an adaptive method for estimating psychometric parameters that can find reliable threshold estimates over a relatively small number of trials. The Φ adaptive staircase, which uses a Bayesian algorithm to find the most likely parameters of a psychophysical function, was used to estimate the critical stop signal delay at which the probability of successful response inhibition equals 0.5. Using computational modeling and adult participants, estimates of stop signal reaction time (SSRT) based on the Φ staircase were compared to estimates using the method of constant stimuli and a standard staircase method of adjustment. Results demonstrate that a reliable estimate of SSRT can be gained very quickly (20-30 stop trials), making the method very useful for testing populations that cannot maintain concentration for long periods or for rapidly obtaining multiple SSRT estimates from healthy adult participants.