Peer Review History

Original SubmissionMarch 3, 2021
Decision Letter - Wolfgang Einhäuser, Editor, Alireza Soltani, Editor

Dear Prof. Sridharan,

Thank you very much for submitting your manuscript "Neurally-constrained modeling of human gaze strategies in a change blindness task­" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Alireza Soltani

Associate Editor

PLOS Computational Biology

Wolfgang Einhäuser

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note that the review by reviewer #2 is uploaded as an attachment.

Reviewer #1: Authors have done an interesting study on the pattern of saccades for detection of change in consecutive images and showed that there is a variability in the performance of subjects. besides they provided a model based on evidence accumulation that they claimed has better performance than Deep gaze neural networks.

I believe that though the authors did a very good job in their analyses and modeling, but there is some gaps between behavioral model and study that should be addressed. my suggested extra analyses can be listed as below:

1- in behavioral analysis, though they clustered saccade related areas, but at each image, different clusters are associated with change location, so, we can't infer how much subjects fixated on areas near to change location. so please add an extra analysis that show good performers and poor performers how much fixated in areas near to change location; it can be shown by the total fixation duration and frequency at different locations based on the distance of fixation location relative to the center of change location.

2- the time to first saccade to change location and also the time to fixate 3 sec on change area can be provided for each image and each subject. it can help to analyze the behavior relative to model; because if we see poor performers fixate on change location but don't detect the change, it means that fixation duration or drift rate were not enough for change detection or their threshold was high; but if they did not fixate on areas near change location it means that their weakness is on finding relevant area.

3- one hypothesis can be that the threshold is decaying with different rate in different subjects and so subjects with lower decaying fixate more on each location, so it will be great if you can add threshold decaying that can account for an important mechanism in the model for poor performers.

4- in feature list, saccade amplitude variance should be divided to its components; in other words, because saccade amplitude distribution probably is bimodal and short and long saccades have different means and also different probability, using variance cause to miss important information on the strategy of subjects, so please fit distribution of saccade amplitude by gaussian mixture model and investigate the mean of each cluster and their frequency. please check bimodality for fixation duration too, if it is bimodal the same issue should be chacked for fixations before large saccade and small saccades.

5- study has two parts, first part studied individual differences that is very important and can extend this study for many applications such as clinical applications, but in modeling part analyses are mostly n images' difficulty (performance on each image). so the coherence of paper has been reduced by missing the link between model and individual differences; I understand that data is small and fitting on each subject has some difficulties, but using hierarchical bayesian modeling you can use data from all subjects and have two clusters of poor and good performers on that, and provide the value of each parameter for each subject, it will help to analyze what aspects of model caused to good or poor performance in different subjects. this will help to make paper more coherent and will help others to find important applications for your model to study individual differences.

6- to be more fair on deep gaze, it is good to compare it based on just large saccades, because it seems that deep gaze is good in predicting large saccades and not small saccades.

minor points:

in some of images your change is related to color change, but you have just mentioned normal vision, please add that they have not any color blindness too.

in fig. S1, you said that circle highlighted... while in figure you have square for that.

in the model, inhibition of return has not been included, it seems that it may help to enhance the model; as a suggestion if you'd like you can test it too.

Reviewer #2: See attachment

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

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Abdol-Hossein Vahabie

Reviewer #2: No

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Attachments
Attachment
Submitted filename: 2021-04 Paper Review.docx
Revision 1

Attachments
Attachment
Submitted filename: Rebuttal v4.docx
Decision Letter - Wolfgang Einhäuser, Editor, Alireza Soltani, Editor

Dear Prof. Sridharan,

We are pleased to inform you that your manuscript 'Neurally-constrained modeling of human gaze strategies in a change blindness task­' 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.

Also, Reviewer # 2 has a final useful suggestion that could be included in the final manuscript.

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

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Alireza Soltani

Associate Editor

PLOS Computational Biology

Wolfgang Einhäuser

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I am satisfied with the revision.

Reviewer #2: The authors did a great job addressing my concerns. I especially like the higher-level engagement with mechanisms both in the results and the discussion. I very much look forward to seeing the article in print.

One comment: In figure 1D, several measures are listed for feature importance. It seems a bit strange to label one "Treebagger," as it is a specific implementation of an algorithm. For example, Fisher score, info gain and AUC change are all mathematical technics, while Treebagger is a proprietary implementation of a machine learning method. It might be better to label it as the exact method Treebagger utilized to compute feature importance (usually change in OOB Error, as described in the methods), rather than the algorithm.

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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: None

Reviewer #2: Yes

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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: Abdol-Hossein Vahabie

Reviewer #2: No

Formally Accepted
Acceptance Letter - Wolfgang Einhäuser, Editor, Alireza Soltani, Editor

PCOMPBIOL-D-21-00403R1

Neurally-constrained modeling of human gaze strategies in a change blindness task­

Dear Dr Sridharan,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Zsofi Zombor

PLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom ploscompbiol@plos.org | Phone +44 (0) 1223-442824 | ploscompbiol.org | @PLOSCompBiol

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