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

  1. Frederick Verbruggen  Is a corresponding author
  2. Adam R Aron
  3. Guido PH Band
  4. Christian Beste
  5. Patrick G Bissett
  6. Adam T Brockett
  7. Joshua W Brown
  8. Samuel R Chamberlain
  9. Christopher D Chambers
  10. Hans Colonius
  11. Lorenza S Colzato
  12. Brian D Corneil
  13. James P Coxon
  14. Annie Dupuis
  15. Dawn M Eagle
  16. Hugh Garavan
  17. Ian Greenhouse
  18. Andrew Heathcote
  19. René J Huster
  20. Sara Jahfari
  21. J Leon Kenemans
  22. Inge Leunissen
  23. Chiang-Shan R Li
  24. Gordon D Logan
  25. Dora Matzke
  26. Sharon Morein-Zamir
  27. Aditya Murthy
  28. Martin Paré
  29. Russell A Poldrack
  30. K Richard Ridderinkhof
  31. Trevor W Robbins
  32. Matthew Roesch
  33. Katya Rubia
  34. Russell J Schachar
  35. Jeffrey D Schall
  36. Ann-Kathrin Stock
  37. Nicole C Swann
  38. Katharine N Thakkar
  39. Maurits W van der Molen
  40. Luc Vermeylen
  41. Matthijs Vink
  42. Jan R Wessel
  43. Robert Whelan
  44. Bram B Zandbelt
  45. C Nico Boehler
  1. Ghent University, Belgium
  2. University of California, San Diego, United States
  3. Leiden University, Netherlands
  4. Dresden University of Technology, Germany
  5. Stanford University, United States
  6. University of Maryland, United States
  7. Indiana University, United States
  8. University of Cambridge, United Kingdom
  9. Cardiff University, United Kingdom
  10. Oldenburg University, Germany
  11. University of Western Ontario, Canada
  12. Monash University, Australia
  13. University of Toronto, Canada
  14. University of Vermont, United States
  15. University of Oregon, United States
  16. University of Tasmania, Australia
  17. University of Oslo, Norway
  18. Spinoza Centre Amsterdam, Netherlands
  19. Utrecht University, Netherlands
  20. KU Leuven, Belgium
  21. Yale University, United States
  22. Vanderbilt University, United States
  23. University of Amsterdam, Netherlands
  24. Anglia Ruskin University, United Kingdom
  25. Indian Institute of Science, India
  26. Queen's University, Canada
  27. King's College London, United Kingdom
  28. Michigan State University, United States
  29. University of Iowa, United States
  30. Trinity College Dublin, Ireland
  31. Donders Institute, Netherlands
Tools and Resources
Cite this article as: eLife 2019;8:e46323 doi: 10.7554/eLife.46323
10 figures, 2 tables, 1 data set and 1 additional file

Figures

Depiction of the sequence of events in a stop-signal task (see https://osf.io/rmqaw/ for open-source software to execute the task).

In this example, participants respond to the direction of green arrows (by pressing the corresponding arrow key) in the go task. On one fourth of the trials, the arrow is replaced by ‘XX’ after a variable stop-signal delay (FIX = fixation duration; SSD = stop signal delay; MAX.RT = maximum reaction time; ITI = intertrial interval).

https://doi.org/10.7554/eLife.46323.002
Box 1—figure 1
The independent race between go and stop.
https://doi.org/10.7554/eLife.46323.004
Main results of the simulations reported in Appendix 2.

Here, we show a comparison of the integration method (with replacement of go omissions) and the mean method, as a function of percentage of go omissions, skew of the RT distribution (τgo), and number of trials. Appendix 2 provides a full overview of all methods. (A) The number of excluded ‘participants’ (RT on unsuccessful stop trials > RT on go trials). As this check was performed before SSRTs were estimated (see Recommendation 7), the number was the same for both estimation methods. (B) The average difference between the estimated and true SSRT (positive values = overestimation; negative values = underestimation). SD = standard deviation of the difference scores (per panel). (C) Correlation between the estimated and true SSRT (higher values = more reliable estimate). Overall R = correlation when collapsed across percentage of go omissions and τgo. Please note that the overall correlation does not necessarily correspond to the average of individual correlations.

https://doi.org/10.7554/eLife.46323.008
Appendix 1—figure 1
The number of stop-signal publications per research area (Panel A) and the number of articles citing the ‘stop-signal task’ per year (Panel B).

Source: Web of Science, 27/01/2019, search term: ‘topic = stop signal task’. The research areas in Panel A are also taken from Web of Science.

https://doi.org/10.7554/eLife.46323.010
Appendix 2—figure 1
Examples of ex-Gaussian (RT) distributions used in our simulations.

For all distributions, μgo = 500 ms, and σgo = 50 ms. τgo was either 1, 50, 100, 150, and 200 (resulting in increasingly skewed distributions). Note that for a given RT cut-off (1,500 ms in the simulations), cut-off-related omissions are rare, but systematically more likely as tau increases. In addition to such ‘natural’ go omissions, we introduced ‘artificial’ ones in the different go-omission conditions of the simulations (not depicted).

https://doi.org/10.7554/eLife.46323.012
Appendix 2—figure 2
Violin plots showing the distribution and density of the difference scores between estimated and true SSRT as a function of condition and estimation method when the total number of trials is 100 (25 stop trials).

Values smaller than zero indicate underestimation; values larger than zero indicate overestimation.

https://doi.org/10.7554/eLife.46323.014
Appendix 2—figure 3
Violin plots showing the distribution and density of the difference scores between estimated and true SSRT as a function of condition and estimation method when the total number of trials is 200 (50 stop trials).

Values smaller than zero indicate underestimation; values larger than zero indicate overestimation.

https://doi.org/10.7554/eLife.46323.015
Appendix 2—figure 4
Violin plots showing the distribution and density of the difference scores between estimated and true SSRT as a function of condition and estimation method when the total number of trials is 400 (100 stop trials).

Values smaller than zero indicate underestimation; values larger than zero indicate overestimation.

https://doi.org/10.7554/eLife.46323.016
Appendix 2—figure 5
Violin plots showing the distribution and density of the difference scores between estimated and true SSRT as a function of condition and estimation method when the total number of trials is 800 (200 stop trials).

Values smaller than zero indicate underestimation; values larger than zero indicate overestimation.

https://doi.org/10.7554/eLife.46323.017
Appendix 3—figure 1
Achieved power for an independent two-groups design as function of differences in go omission, go distribution, SSRT distribution, and the number of trials in the ‘experiments’.
https://doi.org/10.7554/eLife.46323.020

Tables

Appendix 2—table 1
The mean difference between estimated and true SSRT for participants who were included in the main analyses and participants who were excluded (because average RT on unsuccessful stop trials > average RT on go trials).

We did this only for τgo = 1 or 50, p(go omission)=10, 15, or 20, and number of trials = 100 (i.e. when the number of excluded participants was high; see Panel A, Figure 2 of the main manuscript).

https://doi.org/10.7554/eLife.46323.013
Estimation methodIncludedExcluded
Integration with replacement of go omissions−6.4−35.8
Integration without replacement of go omissions−19.4−48.5
Integration with adjusted p(respond|signal)12.5−17.4
Mean−16.0−46.34
Appendix 3—table 1
Parameters of the go distribution for the control group and the three experimental conditions.

SSRT of all experimental groups differed from SSRT in the control group (see below).

https://doi.org/10.7554/eLife.46323.019
Parameters of go distributionControlExperimental 1Experimental 2Experimental 3
μgo500500525575
σgo505052.557.5
τgo505075125
go omission00510

Data availability

The code used for the simulations and all simulated data can be found on Open Science Framework (https://osf.io/rmqaw/).

The following data sets were generated
  1. 1
    Open Science Framework
    1. F Verbruggen
    (2019)
    Race model simulations to determine estimation bias and reliability of SSRT estimates.
    https://doi.org/10.17605/OSF.IO/JWSF9

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