Dear Prof Sur,

Thank you very much for submitting your manuscript "A state-space algorithm for dissociating mixtures of strategies during reversal learning" 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. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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Sincerely,

Alireza Soltani

Academic Editor

PLOS Computational Biology

Marieke van Vugt

Section 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: This is an interesting study that builds on recent state-based mixture models to describe heterogeneity in rodent reward-based decision making behaviors. The study is well-written and the methodology is sound and can be clearly understood (and the data and code are made freely available for closer inspection). The main conclusions are (1) reward-guided decision making behavior in rodents are composed of a diversity of distinct decision strategies -- *even* in very simple all-or-none reversal learning tasks, and *even* after extensive task exposure in such tasks, and (2) these distinct strategies can be described by model-free and inference-based evaluation models (and mice for the most part transition between these strategies throughout early and late phases of training).

My only major concerns are:

(1) conclusion 1 above is more or less expected (we know that mouse behavior is often suboptimal in nearly all task settings, and that state-space models can capture these suboptimalities is already published). Relatedly, the adaptation of these previously published state-space models here seems quite modest. The authors may want to temper/qualify (or perhaps, fortify/better justify, if authors disagree) claims of novelty around the HMM mixture model itself (i.e. in the abstract, title, less emphasis on the state-space model versus the conculsions drawn for it in combination with the model agent simulations)

(2) Relatedly, the adapted state-space model here seems of somewhat limited utility outside of this study. I imagine the utility of a new model should be a) it's ability to describe behavior in new depth (i think the authors adequately meet this bar, although caveat for point 1 above at the high level), and b) providing new parameters for linking reward-based decisions with their underlying neural correlates. Because the model here describes decisions in blocks of choices, it seems unlikely to be very useful for the latter (perhaps it provides a toehold into the neural basis of block transitions?). In addition, this model is only suitable to describe reversal learning tasks with clear block structures. My main suggestion here: can the authors apply their blockHMM to a seperate dataset from a similar related revesal learning task (perhaps datasets from the Intl Brain Lab?). I imagine this could support their main conclusions and demonstrate a broader utility of the model. If this is not possible or feasible, I would ask to better fortify the more general utility of this modeling framework (compared to prior efforts) with regards to a) understanding the neural basis of mixed strategies and b) behavior in related but distinct contexts.

Minor points/questions:

(1) Fig 3e-- can the authors add the actual values of state transitions rather than colorscale to better evaluate

(2) Fig4 -- is the blockHMM fit to each individual animals or concatenaged data? I would be curious to see the cross-validated log-likliehood for different numbers of states in the data (related to Supp Fig 2a), as well as the transition matrices for individual mice or the group.

Reviewer #2: Review is uploaded as an attachment.

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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: Yes

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Reviewer #1: Yes: Scott Bolkan

Reviewer #2: No

Figure Files:

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Attachments

Attachment

Submitted filename: PCOMPBIOL-D-23-00531.pdf

Dear Prof Sur,

We are pleased to inform you that your manuscript 'Mixtures of strategies underlie rodent behavior during reversal learning' 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.

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

Academic Editor

PLOS Computational Biology

Marieke van Vugt

Section 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 thank and congratulate the authors for a thoughtful reply to all comments.

Reviewer #2: The authors have addressed all of my comments more than adequately, and I especially appreciate all the supplementary analyses (and figures) that have been conducted in response to my questions. I feel that the paper has been strengthened after the revision. I hope that this study will appeal to wider audiences.

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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: Yes

**********

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: Scott S. Bolkan

Reviewer #2: No