PCOMPBIOL-D-26-00505

Coordinative structures as scale-free networks: cascade and percolation dynamics in motor learning with empirical validation

PLOS Computational Biology

Dear Dr. Park,

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Pramod Shinde

Guest Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

Additional Editor Comments (if provided):

Thank you for submitting your manuscript to PLOS Computational Biology. Two reviewers have assessed it, and both recommend publication after revision. Their full reports are appended with this letter.

My decision is major revision. Neither reviewer requests new experiments. Their concerns center on clarity, justification, and overstatement of claims, and I am confident you can resolve them.

Please address every point in both appended reports. Three themes run through both reviews, and I ask you to give them particular attention. First, justify your key metrics. Explain why you chose degree heterogeneity and the eigenvector centrality Gini coefficient, state what each one represents for coordinative structures, and show how they support your claims. Add the short conceptual explanations the reviewers ask for so the paper stands on its own. Second, define your target properties up front and add a summary table. State the characteristic properties of coordinative structures before the Results, and include a table that links each property to its network measurement, the reason for that measurement, and the statistic you compute. Third, improve the readability of the Results. Both reviewers found this section hard to follow, so rewrite it for clarity, number your sections, and cross-reference your earlier metric definitions where you report results.

Please also fix the specific line, figure, and table items listed in the appended reports, including the typo in Table 1.

Return your revised manuscript with a point-by-point response to each reviewer comment. State clearly what you changed and where.

I look forward to your revision.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I recommend this article for publication with minor comments. Please find the comments attached.

Reviewer #2: The manuscript presents an insightful study that explores the connection between scale-free networks and coordinative structures, highlighting their characteristic properties. The authors propose an interesting simulation framework that demonstrates these properties and validate their predictions against published studies on motor learning.

1.In the Results section, several methodological choices are not sufficiently explained. For example, it is unclear why degree heterogeneity is selected as a key measurement. Why does a higher degree heterogeneity in the BA model indicate a better or more meaningful result? Similarly, the interpretation of the eigenvector centrality Gini coefficient is not well articulated. The authors should clarify what these metrics represent in the context of coordinative structures and how they support the main claims.

2.Since the goal is to establish a connection between scale-free networks and the characteristic properties of coordinative structures, these properties should be explicitly defined before presenting the experimental results. It would be helpful to include a summary table that outlines: (i) the specific properties of interest, (ii) the corresponding network measurements used to capture each property, (iii) the rationale for selecting these measurements, and (iv) what is the statistics calculated in the experiment. This may improve the logical flow of the paper.

3. The topic is interesting, and the simulation model appears reasonable. However, the manuscript needs to improve its readability. The current description in the Results section makes it difficult to grasp the main idea.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

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Dear Dr. Park,

We are pleased to inform you that your manuscript 'Coordinative structures as scale-free networks: cascade and percolation dynamics in motor learning with empirical validation' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Pramod Shinde

Guest Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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- Section 2.7 states that pairwise topology comparisons used two-sided t-tests, Cohen's d for effect size, and Bonferroni correction across topology pairs. I could not find any p-value, t-statistic, or effect size for any topology comparison, anywhere in the manuscript or across the Supporting Information tables (S1–S8). Please report these statistics, either in the main text where each comparison is made or in a dedicated supporting information table, with a pointer from the text.

- S1 Fig and S2 Fig are properly captioned within S1 Appendix and S4 Appendix in the Supporting Information, but the manuscript's own Supporting Information list only names “S1 Appendix” through “S6 Appendix.” Please add S1 Fig and S2 Fig as their own line items in that list, consistent with the PLOS convention of listing each Fig separately from each appendix. This is not required before acceptance.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have satisfactorily addressed my concerns, and I am happy to recommend this article for publication.

Reviewer #2: The latest version has improved readability a lot. I have no further questions.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: None

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review?  For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Saptarshi Ghosh

Reviewer #2: No