A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task

Abstract

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our present goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide twelve easy-to-implement consensus recommendations and point out the problems that can arise when these are not followed. This article is furthermore accompanied by user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

Temporal cascade of frontal, motor and muscle processes underlying human action-stopping

“…Stop Signal Reaction Time. SSRT from the behavioral responses (SSRT Beh ) was determined using the integration method (Verbruggen et al, 2019). When calculating SSRT using the EMG responses, SSRT EMG , as the P(Respond|Stop) was often much more than 0.5, we calculated the SSRT individually for all SSDs and then averaged it (Verbruggen and Logan, 2009).…”

“…one important process is response inhibitionthe prefrontal (top-down) stopping of initiated response tendencies (Aron, 2007). In the laboratory, response inhibition is often studied with the stop-signal task (Verbruggen et al, 2019). On each trial, the participant initiates a motor response, and then, when a subsequent Stop signal occurs, tries to stop.…”

“…On each trial, the participant initiates a motor response, and then, when a subsequent Stop signal occurs, tries to stop. From the behavioral data one can estimate a latent variable; the latency of stopping known as Stop Signal Reaction Time (SSRT), which is typically 200-250 ms in healthy adults (Verbruggen et al, 2019). SSRT has been useful in neuropsychiatry where it is often longer for patients vs. controls (Alderson et al, 2007; Bari and Robbins, 2013;Lavagnino et al, 2016;Lijffijt et al, 2005;Smith et al, 2014;Snyder et al, 2015).…”

“…But recent research shows that this is not the case (Skippen et al, 2019b), and that failing to account for "trigger failures" inflates SSRT. Second, while the standard "race model" assumes that the Go and Stop processes are independent (Verbruggen et al, 2019), recent research show that violations of this independence underestimates SSRT . Finally, the standard ways of computing SSRT likely do not account for electromechanical delays between muscle activity and the response.…”

Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development [ABCD] study

“…The primary way to evaluate context independence is to compare reaction times on go trials to reaction times on stop-failure trials (but note that the prediction of the race model concerning faster stop-failure than go responses is conditioned on both context independence and stochastic independence, Colonius & Diederich, 2018). If the former is longer than the latter then context independence is taken to hold (Bissett et al, 2021;Logan & Cowan, 1984;Verbruggen et al, 2019). On average across all subjects, stop-failure RT (M = 456ms, SD = 109ms) was shorter than overt responses on go trials (M = 543ms, SD = 95ms, 95% confidence interval of the difference [85.9, 88.8].…”