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SimSST: An R Statistical Software Package to Simulate Stop Signal Task Data

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  • Mohsen Soltanifar

    (Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, 620, 155 College Street, Toronto, ON M5T 3M7, Canada
    Analytics Division, College of Professional Studies, Northeastern University, 1400-410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada)

  • Chel Hee Lee

    (Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
    Department of Critical Care Medicine, Alberta Heath Services, University of Calgary, 3260 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada)

Abstract

The stop signal task (SST) paradigm with its original roots in 1948 has been proposed to study humans’ response inhibition. Several statistical software codes have been designed by researchers to simulate SST data in order to study various theories of modeling response inhibition and their assumptions. Yet, there has been a missing standalone statistical software package to enable researchers to simulate SST data under generalized scenarios. This paper presents the R statistical software package “SimSST”, available in Comprehensive R Archive Network (CRAN), to simulate stop signal task (SST) data. The package is based on the general non-independent horse race model, the copulas in probability theory, and underlying ExGaussian (ExG) or Shifted Wald (SW) distributional assumption for the involving go and stop processes enabling the researchers to simulate sixteen scenarios of the SST data. A working example for one of the scenarios is presented to evaluate the simulations’ precision on parameter estimations. Package limitations and future work directions for its subsequent extensions are discussed.

Suggested Citation

  • Mohsen Soltanifar & Chel Hee Lee, 2023. "SimSST: An R Statistical Software Package to Simulate Stop Signal Task Data," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:500-:d:1038556
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    References listed on IDEAS

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    1. Mohsen Soltanifar, 2022. "A Look at the Primary Order Preserving Properties of Stochastic Orders: Theorems, Counterexamples and Applications in Cognitive Psychology," Mathematics, MDPI, vol. 10(22), pages 1-13, November.
    2. Jeffrey Rouder, 2005. "Are unshifted distributional models appropriate for response time?," Psychometrika, Springer;The Psychometric Society, vol. 70(2), pages 377-381, June.
    3. Welvaert, Marijke & Durnez, Joke & Moerkerke, Beatrijs & Berdoolaege, Geert & Rosseel, Yves, 2011. "neuRosim: An R Package for Generating fMRI Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i10).
    4. Ye, Weijie, 2020. "Dynamics of a revised neural mass model in the stop-signal task," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Lorenz Weise & Maren Boecker & Siegfried Gauggel & Bjoern Falkenburger & Barbara Drueke, 2018. "A reaction-time adjusted PSI method for estimating performance in the stop-signal task," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-28, December.
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    Keywords

    R; simulation; stop signal task;
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