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Research Articles

A frequentist mixture modeling of stop signal reaction times

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 90-108 | Received 19 Oct 2018, Accepted 07 Jun 2019, Published online: 03 Sep 2019
 

Abstract

The stop signal reaction time (SSRT), a measure of the latency of the stop signal process, has been theoretically formulated using a horse race model of go and stop signal processes by the American scientist Gordon Logan (1994). The SSRT assumes equal impact of the preceding trial type (go/stop) on its measurement. In the case of a violation of this assumption, we consider estimation of SSRT based on the idea of earlier analysis of cluster type go reaction times (GORT) and linear mixed model (LMM) data analysis results. Two clusters of trials were considered including those trials preceded by a go trial and other trials preceded by a stop trial. Given disparities between cluster type SSRTs, we need to consider some new indexes considering the unused cluster type information in the calculations. We introduce mixture SSRT and weighted SSRT as two new distinct indexes of SSRT that address the violated assumption. Mixture SSRT and weighted SSRT are theoretically asymptotically equivalent under special conditions. An example of stop single task (SST) real data is presented to show equivalency of these two new SSRT indexes and their larger magnitude compared to Logan's single 1994 SSRT.

Abbreviations: ADHD: attention deficit hyperactivity disorder; ExG: Ex-Gaussiandistribution; GORT: reaction time in a go trial; GORTA: reaction time in a type A gotrial; GORTB: reaction time in a type B go trial; LMM: linear mixed model; SWAN:strengths and weakness of ADHD symptoms and normal behavior rating scale; SSD: stop signal delay; SR: signal respond; SRRT: reaction time in a failedstop trial; SSRT: stop signal reaction times in a stop trial; SST: stop signaltask.

Acknowledgments

The authors are grateful to the reviewers for their constructive comments on the original manuscript draft.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been funded by University of Toronto open doctoral fellowship 2015–2017.

Notes on contributors

Mohsen Soltanifar

Mohsen Soltanifar (MSc) is a PhD candidate in biostatistics jointly working at the University of Toronto and the Hospital of Sick Children, Toronto. His research area includes quantitative psychology with statistical focus on multilevel, frequentist and Bayesian mixture modeling and time series.

Annie Dupuis

Annie Dupuis (PhD) is an adjunct professor of biostatistics at the University of Toronto and an independent biostatistical consultant in Toronto, Canada. Her research areas include attention deficit hyperactivity disorder, autism spectrum disorders, and cystic fibrosis.

Russell Schachar

Russell Schachar (MD) is a professor of psychiatry at the University of Toronto, and a senior scientist leading a cognitive neurosciences laboratory in the Research Institute at the Hospital for Sick Children, Toronto. His research focuses on attention deficit hyperactivity disorder.

Michael Escobar

Michael Escobar (PhD) is a professor of biostatistics at the University of Toronto. His research interests include nonparametric Bayesian modeling, mixture modeling, Dirichlet process modeling and applying statistical methods in psychiatric research.

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