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

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 some of 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 goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

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

10.31219/osf.io/8mzdu ◽

2019 ◽

Cited By ~ 1

Author(s):

Frederick Verbruggen ◽

Adam Robert Aron ◽

Guido Band ◽

Christian Beste ◽

Patrick Bissett ◽

...

Keyword(s):

Data Analysis ◽

Response Inhibition ◽

Statistical Power ◽

Inhibition Efficiency ◽

Task Design ◽

Stop Signal ◽

Stop Signal Task ◽

Wide Range ◽

User Friendly ◽

Correct Implementation

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.

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Online Independent Versus Laboratory-Based Stop-Signal Task Performance: A Within-Subjects Counterbalanced Comparison Study (Preprint)

10.2196/preprints.32922 ◽

2021 ◽

Author(s):

Antoinette Poulton ◽

Li Peng Evelyn Chen ◽

Michael Fox ◽

Robert Hester

Keyword(s):

Response Inhibition ◽

Statistical Power ◽

Reaction Times ◽

Positive Association ◽

Stop Signal ◽

Stop Signal Task ◽

Laboratory Conditions ◽

Independent Task ◽

Paired Samples ◽

Dependent Samples

BACKGROUND Considered a facet of behavioural impulsivity, response inhibition facilitates adaptive and goal-directed behaviour. It is often assessed using the Stop-Signal Task (SST), which is presented on stand-alone computers under controlled laboratory conditions. Sample size may consequently be a function of cost/time and sample diversity constrained to those willing/able to attend the lab. Statistical power and generalisability of results might, in turn, be impacted. Such limitations may potentially be overcome via the implementation of online testing. OBJECTIVE The aim of this study was to investigate if there were differences between variables derived from an online SST when it was undertaken independently – that is, outside the laboratory, on any computer, and in the absence of researchers – versus when it was performed under laboratory conditions. METHODS We programmed a web-based SST in HTML and JavaScript and employed a counter-balanced design. A total of 166 individuals (Mage = 19.72, SD = 1.85, range: 18-36, 88% female) were recruited. Of these, n = 79 undertook the independent task prior to visiting the laboratory and n = 78 completed the independent task following their laboratory visit. Average time between SST testing was 3.72 days (SD = 2.86). Dependent samples and Bayesian paired samples t-tests were utilised to examine differences between lab-based and independent SST variables. Correlational analyses were conducted on stop-signal reaction times (SSRT). RESULTS After exclusions, 123 participants (Mage = 19.73, SD = 1.97) completed the SST both in the laboratory and independently. While participants were less accurate on go trials and exhibited reduced inhibitory control when undertaking the independent – as compared to the lab-based – SST, there was a positive association between the SSRT of each condition (r = .48, P < .001, 95% CI [0.33, 0.61]). CONCLUSIONS Findings suggest an online SST, which participants undertake on any computer, in any location, and in the absence of the researcher, is a suitable measure of response inhibition.

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Cortical silent period reflects individual differences in action stopping performance

Scientific Reports ◽

10.1038/s41598-021-94494-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mario Paci ◽

Giulio Di Cosmo ◽

Mauro Gianni Perrucci ◽

Francesca Ferri ◽

Marcello Costantini

Keyword(s):

Individual Differences ◽

Response Inhibition ◽

Primary Motor Cortex ◽

Silent Period ◽

Intracortical Inhibition ◽

Stop Signal ◽

Stop Signal Task ◽

Behavioral Differences ◽

Cortical Silent Period ◽

Stop Signal Reaction Time

AbstractInhibitory control is the ability to suppress inappropriate movements and unwanted actions, allowing to regulate impulses and responses. This ability can be measured via the Stop Signal Task, which provides a temporal index of response inhibition, namely the stop signal reaction time (SSRT). At the neural level, Transcranial Magnetic Stimulation (TMS) allows to investigate motor inhibition within the primary motor cortex (M1), such as the cortical silent period (CSP) which is an index of GABAB-mediated intracortical inhibition within M1. Although there is strong evidence that intracortical inhibition varies during action stopping, it is still not clear whether differences in the neurophysiological markers of intracortical inhibition contribute to behavioral differences in actual inhibitory capacities. Hence, here we explored the relationship between intracortical inhibition within M1 and behavioral response inhibition. GABABergic-mediated inhibition in M1 was determined by the duration of CSP, while behavioral inhibition was assessed by the SSRT. We found a significant positive correlation between CSP’s duration and SSRT, namely that individuals with greater levels of GABABergic-mediated inhibition seem to perform overall worse in inhibiting behavioral responses. These results support the assumption that individual differences in intracortical inhibition are mirrored by individual differences in action stopping abilities.

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Severe violations of independence in response inhibition tasks

Science Advances ◽

10.1126/sciadv.abf4355 ◽

2021 ◽

Vol 7 (12) ◽

pp. eabf4355

Author(s):

Patrick G. Bissett ◽

Henry M. Jones ◽

Russell A. Poldrack ◽

Gordon D. Logan

Keyword(s):

Response Inhibition ◽

Theoretical Foundation ◽

Reaction Times ◽

Stop Signal ◽

Experimental Paradigm ◽

Signal Delay ◽

Independence Assumption ◽

Analysis Techniques ◽

Wide Range ◽

Saccadic Responses

The stop-signal paradigm, a primary experimental paradigm for understanding cognitive control and response inhibition, rests upon the theoretical foundation of race models, which assume that a go process races independently against a stop process that occurs after a stop-signal delay (SSD). We show that severe violations of this independence assumption at short SSDs occur systematically across a wide range of conditions, including fast and slow reaction times, auditory and visual stop signals, manual and saccadic responses, and especially in selective stopping. We also reanalyze existing data and show that conclusions can change when short SSDs are excluded. Last, we suggest experimental and analysis techniques to address this violation, and propose adjustments to extant models to accommodate this finding.

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Effect of variability of sequence length of go trials preceding a stop trial on ability of response inhibition in stop-signal task

Somatosensory & Motor Research ◽

10.1080/08990220.2018.1475351 ◽

2018 ◽

Vol 35 (2) ◽

pp. 95-102 ◽

Cited By ~ 2

Author(s):

Koichi Hiraoka ◽

Atsushi Kinoshita ◽

Hiroshi Kunimura ◽

Masakazu Matsuoka

Keyword(s):

Response Inhibition ◽

Stop Signal ◽

Stop Signal Task ◽

Sequence Length ◽

Stop Trial

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Response inhibition on the stop signal task improves during cardiac contraction

Scientific Reports ◽

10.1038/s41598-018-27513-y ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 15

Author(s):

Charlotte L. Rae ◽

Vanessa E. Botan ◽

Cassandra D. Gould van Praag ◽

Aleksandra M. Herman ◽

Jasmina A. K. Nyyssönen ◽

...

Keyword(s):

Response Inhibition ◽

Stop Signal ◽

Cardiac Contraction ◽

Stop Signal Task

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S76. PROACTIVE AND REACTIVE RESPONSE INHIBITION IN INDIVIDUAL WITH SCHIZOTYPY: AN ERP STUDY

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.142 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S63-S63

Author(s):

Ya Wang ◽

Lu-xia Jia ◽

Xiao-jing Qin ◽

Jun-yan Ye ◽

Raymond Chan

Keyword(s):

Response Inhibition ◽

Reaction Times ◽

Proactive Inhibition ◽

Stop Signal ◽

Event Related Potential ◽

Neural Mechanisms ◽

Stop Signal Task ◽

Reactive Inhibition ◽

Reactive Control ◽

Present Event

Abstract Background Schizotypy, a subclinical group at risk for schizophrenia, have been found to show impairments in response inhibition. Recent studies differentiated proactive inhibition (a preparatory process before the stimuli appears) and reactive inhibition (the inhibition of a pre-potent or already initiated response). However, it remains unclear whether both proactive and reactive inhibition are impaired in schizotypy and what are the neural mechanisms. The present event-related potential study used an adapted stop-signal task to examine the two inhibition processes and the underlying neural mechanisms in schizotypy compared to healthy controls (HC). Methods A total of 21 individuals with schizotypy and 25 matched HC participated in this study. To explore different degrees of proactive inhibition, we set three conditions: a “certain” go condition which no stop signal occurred, a “17% no go” condition in which stop signal would appear in 17% of trials, and a “33% no go” condition in which stop signal would appear in 33% of trials. All participants completed all the conditions, and EEG was recorded when participants completed the task. Results Behavioral results showed that in both schizotypy and HC, the reaction times (RT) of go trials were significantly prolonged as the no go percentage increased, and HC showed significantly longer go RT compared with schizotypy in both “17% no go” and “33% no go” conditions, suggesting greater proactive inhibition in HC. Stop signal reaction times (SSRTs) in “33% no go” condition was shorter than “17% no go” condition in both groups. Schizotypy showed significantly longer SSRTs in both “17% no go” and “33% no go” conditions than HC, indicating schizotypy relied more on reactive inhibition. ERP results showed that schizotypy showed larger overall N1 for go trials than HC irrespective of condition, which may indicate a compensation process in schizotypy. Schizotypy showed smaller N2 on both successful and unsuccessful stop trials in “17% no go” conditions than HC, while no group difference was found in “33% no go” conditions for stop trials, which may indicate impaired error processing. Discussion These results suggested that schizotypy tended to be impaired in both proactive control and reactive control processes.

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