IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i3p500-d1038556.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/3/500/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/3/500/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Ye, Weijie, 2020. "Dynamics of a revised neural mass model in the stop-signal task," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. 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).
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Garikoitz Lerma-Usabiaga & Noah Benson & Jonathan Winawer & Brian A Wandell, 2020. "A validation framework for neuroimaging software: The case of population receptive fields," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-18, June.
    2. Jeffrey Rouder & Jordan Province & Richard Morey & Pablo Gomez & Andrew Heathcote, 2015. "The Lognormal Race: A Cognitive-Process Model of Choice and Latency with Desirable Psychometric Properties," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 491-513, June.
    3. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    4. Ani Eloyan & Shanshan Li & John Muschelli & Jim J Pekar & Stewart H Mostofsky & Brian S Caffo, 2014. "Analytic Programming with fMRI Data: A Quick-Start Guide for Statisticians Using R," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    5. Cardona Jiménez, Johnatan & de B. Pereira, Carlos A., 2021. "Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    6. Samaddar, Arunava & Jackson, Brooke S. & Helms, Christopher J. & Lazar, Nicole A. & McDowell, Jennifer E. & Park, Cheolwoo, 2022. "A group comparison in fMRI data using a semiparametric model under shape invariance," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    7. Shravan Vasishth & Bruno Nicenboim & Nicolas Chopin & Robin Ryder, 2017. "Bayesian Hierarchical Finite Mixture Models of Reading Times: A Case Study," Working Papers 2017-33, Center for Research in Economics and Statistics.
    8. Sam Efromovich & Jiayi Wu, 2018. "Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1381-1402, December.
    9. Cheng‐Han Yu & Raquel Prado & Hernando Ombao & Daniel Rowe, 2023. "Bayesian spatiotemporal modeling on complex‐valued fMRI signals via kernel convolutions," Biometrics, The International Biometric Society, vol. 79(2), pages 616-628, June.
    10. Marijke Welvaert & Yves Rosseel, 2013. "On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    11. Park, Jun Young & Polzehl, Joerg & Chatterjee, Snigdhansu & Brechmann, André & Fiecas, Mark, 2020. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    12. Jochen Ranger & Jörg-Tobias Kuhn & José-Luis Gaviria, 2015. "A Race Model for Responses and Response Times in Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 791-810, September.
    13. Zhao, Yuxuan & Matteson, David S. & Mostofsky, Stewart H. & Nebel, Mary Beth & Risk, Benjamin B., 2022. "Group linear non-Gaussian component analysis with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).

    More about this item

    Keywords

    R; simulation; stop signal task;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:500-:d:1038556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.