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 Ψ 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 Ψ 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.

Effects of Emotional Valence on Hemispheric Asymmetries in Response Inhibition

“…Additionally, the Go/Nogo task is only one of several paradigms that allow investigation of prefrontally mediated inhibition processes. Other paradigms targeting prefrontal functions that involve inhibition, e.g., the stop-signal task [63], stop-change task [64], or the task-switching paradigm [65] could yield further evidence for a relationship between emotional lateralisation and prefrontal inhibition.…”

A reaction-time adjusted PSI method for estimating performance in the stop-signal task

“…One disadvantage of the Stop-signal task is its potential length, especially if different conditions are included, because for every Stop-trial several Go-trials have to be included to make the Go-response the prepotent response, and because it takes a relatively large number of Stop-trials to reliably estimate the SSRT [3]. As a possible solution to this problem, Livesey and Livesey [4] proposed a different method to select an upcoming Stop-trial’s SSD and estimate the critical SSD used for computing the SSRT, based on an adaptive Bayesian estimation algorithm known in the psychophysical literature as the PSI method [5].…”

“…In short, the relationship between SSD and the likelihood of a response in a Stop-trial follows a psychometric function, such as the Weibull cumulative density function chosen by Livesey and Livesey [4]: The Threshold parameter governs at which SSD p(error) is 0.5, the Slope determines the rise of the function, and the ErrorRate determines the minimum and maximum the function can take on.…”

“…In a number of Stop-signal task simulations and behavioral experiments, Livesey and Livesey [4] showed that the PSI method can quickly and accurately estimate the SSRT. In terms of correlation with the actually simulated SSRT, as well as mean absolute deviation, the PSI method was comparable to the traditional staircase.…”

“…The purpose of this study is to investigate the influence of Go-response slowing on the PSI method. In Livesey and Livesey [4], the Go-RTs in both the simulations and the behavioral tasks are constant over the course of the experiment. However, it is known that the Stop-signal task can invite a gradual slowing of the Go-RTs over the course of the experiment [7, 8].…”