A Bayesian Mixture Modelling of Stop Signal Reaction Time Distributions: The Second Contextual Solution for the Problem of Aftereffects of Inhibition on SSRT Estimations.

Mohsen Soltanifar,Russell Schachar,Annie Dupuis,Michael Escobar

doi:10.3390/brainsci11081102

Mohsen Soltanifar, Russell Schachar + Show 2 more

Open Access

https://doi.org/10.3390/brainsci11081102

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Journal: Brain sciences	Publication Date: Aug 21, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Hospital for Sick Children, University of Toronto

Abstract

The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) has been modelled with two general methods: a nonparametric method by Hans Colonius (1990) and a Bayesian parametric method by Dora Matzke, Gordon Logan and colleagues (2013). These methods assume an equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the relaxed assumption. This study presents the required model by considering a two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under ex-Gaussian (ExG) distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more varied, more positively skewed, more leptokurtic and larger in stochastic order. The size of the results’ disparities also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This result offers a vital improvement in the SSRT estimations.

Highlights

This study presented a mixture Bayesian parametric approach for a more illuminating Stop Signal Reaction Times (SSRT) distribution estimation by considering two subtype stop-signal task (SST) cluster information suggesting a new estimation of the SSRT distribution
There has been a great deal of interest in the aftereffects of inhibition on the estimation of SSRT in the SST literature from the early 1990s
This study addressed the problem in part and presented a two-state mixture model of SSRT distribution by considering the prior trial type with results consistent with the constant SSRT index results in the literature [15]

Summary

Introduction

Inhibition refers to the ability to suppress actively, interrupt or delay an action [1]. Two paradigms have been suggested to study response inhibition empirically in a laboratory setting: The Go/No-go task and the stop-signal task (SST). The stop-signal paradigm includes two response tasks: the go task and the stop task (e.g., stop 25% of the time). The go reaction time (GORT) is the response to the stimulus such as “X” and “O” presented on the computer screen. The stop signal is presented to the participant after the passage of some time called the stop signal delay [6,7]. The adjustment of stop signal delays (SSD or Td) is made by the more reliable tracking method in which, depending on the previous trial’s success or failure, the

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