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Peer-Review Record

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

Mathematics 2023, 11(3), 500; https://doi.org/10.3390/math11030500
Reviewer 1: Omid Kharazmi
Reviewer 2: Anonymous
Reviewer 3: Rupesh Kumar Chikara
Mathematics 2023, 11(3), 500; https://doi.org/10.3390/math11030500
Received: 20 December 2022 / Revised: 13 January 2023 / Accepted: 14 January 2023 / Published: 17 January 2023
(This article belongs to the Special Issue Advances in Computational Statistics and Applications)

Round 1

Reviewer 1 Report

 

In this manuscript, the author have presented the R package "SimSST," available in CRAN, to simulate stop signal 19 task(SST) data. The simulation process was conducted based on the independent horse race model, enabling the researchers to simulate eight scenarios of the SST data. Moreover, they have provided a realistic example to evaluate the simulations’ precision on parameter estimation.

The paper is interesting to read, however I see the following minor issue that should be resolved before publishing this paper

1-     In general, is it applicable for any arbitrary distribution?

Author Response

Please see the attachment file, thanks.

Author Response File: Author Response.pdf

Reviewer 2 Report

In the manuscript “SimSST: An R Statistical Software Package to Simulate Stop 4 Signal Task Data” the authors presented the R package “SimSST”, that permits to simulate Stop Signs Task (SST) data under independent horse race paradigm.
This package enables one to simulate Exponentially modified Gaussian (ExG) distribution with monotone increasing hazard and Shifted Wald (SW) distribution with peaked hazard reaction times distribution describing GORT and SSRT processes, also with both fixed SSD and tracking methods

The simulated datasets in the study should help researchers to test their hypothesis regarding stopping processes within the stop signal task literature. 

 

The manuscript present an insufficient form, content and structure especially regarding the Abstract and the Introduction.

The Abstract is too synthetic and lacks of some fundamental information about the study.

The Introduction structure is disordered with an incomplete content and some essential citation are missing (such as Logan & Cowan, 1984 and Verbruggen et al., 2019).

The description of the distribution and of the SIMSST package are instead exhaustive and clear.

Furthermore, the fact that the sample studied is limited should be added to the limitations.

 

Line 27-28: The definition of inhibition is not correct, so I suggest to change it.

Line 37: I suggest to use “delay” instead of pause 

In The Horse Race Model paragraph, among the presumptions it is missing that the RT on unsuccessful stop trials was numerically shorter than RT on go trials 

Line 83: “Mathlab” instead of Matlab

Line 86: It is write that “we could not find a stand-alone statistical software that allows researchers to simulate the stop signal task data effectively”. Are you referring to R too?

Line 188: “quasi-significant p-values” is a misleading sentence. If the p-value is greater than 0.05 means that no effect was observed.

Author Response

Please see the attachment file, thanks.

Author Response File: Author Response.pdf

Reviewer 3 Report

I have reviewed this manuscript the overall contents of this manuscript is well organized to give a clear overview of this work. I have suggested some comments about this work are as the following:

Comments to the Authors:

1.     Authors should write clearly abstract including background, method, results, conclusion and significance of this study. It is in very short form.

 

2.     Authors should revise the introduction section in three paragraph, first paragraph for R Statistical Software, second paragraph about stop signal task, third paragraph for objective of this study and hypothesis.   

 

3.     Author should write the discussion section of this study about the R software and stop signal task. Author can refer the latest paper about the stop signal task: Ko L-W, Shih Y-C, Chikara RK, Chuang Y-T and Chang EC (2016) Neural Mechanisms of Inhibitory Response in a Battlefield Scenario: A Simultaneous fMRI-EEG Study. Front. Hum. Neurosci.

4.     The authors should write some limitations of this study and clinical application in more details.

5.     Author should revise the conclusion of this study. It is in very short form. 

Author Response

Please see the attachment file, thanks.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

This new revised version is surely more exhaustive and accurate. 
I appreciated the addition of supplementary material, which allows for more in-deep understanding.

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