Peer Review History
Original SubmissionNovember 12, 2020 |
---|
PONE-D-20-35580 Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach PLOS ONE Dear Dr. Harada, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Two experts in the field reviewed your work. I also read the paper myself, as I find this an intriguing topic, related to my own research. The reviewers seemed to find the topic of your study interesting, and perhaps intriguing; however, they both note major issues with the paper that will need to be addressed, if this paper is to reach the bar for publication. It seems a major issue is to improve the modeling work and data analysis to where it is consistent with similar work in the reinforcement learning and mathematical modeling literature. You also need to better explain the key points and main goals for your study. Please include the following items when submitting your revised manuscript:
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Darrell A. Worthy, Ph.D Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1.) Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 2.) Please change "female” or "male" to "woman” or "man" as appropriate, when used as a noun (see for instance https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/gender). 3.) For this single-authored manuscript, please replace "we" with "I". 4.) We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. 5.) Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables should be uploaded as separate "supporting information" files. 6.) We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed: - https://www.emerald.com/insight/content/doi/10.1108/S0065-2830(2010)0000032004/full/html - https://www.sciencedirect.com/science/article/abs/pii/S0022249616301523?via%3Dihub - http://www.wjh.harvard.edu/~cfc/Publications.html? - https://www.sciencedirect.com/science/article/abs/pii/S0049089X13000884?via%3Dihub In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: No ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Summary The paper examines the question whether cooperative decision making can improve performance in 2-arm bandit task (TAB). Interactive decision making experiments are conducted online using ZOOM meeting software. A variant of Q-Learning is implemented to examine learning parameters (total reward, risk seeking, loss aversion, action selection noise) across the three conditions. Results are complex and do not present a straightforward interpretation by themselves. The discussion is, correspondingly, unclear and offers little clarity. In the spirit of paper’s main research question, a colleague of mine and I reviewed the paper separately and exchanged comments to arrive at a joint review. The paper has proposed a good question and used a remarkably inventive methodology to implement joint decision making in the time of pandemic. The paper could potentially make a valuable contribution. But there is a long way to go. Major comments There is no theory. We do not know what to expect and WHY to expect it. Benefit in joint decision making is not the default (eg many works in joint Memory show that 2 people are worse than one). Could the author provide a formal model of dyadic and triadic performance that could produce some anticipated outcomes? In this regard, a very relevant paper to consider is Migdal et al (J Math. Psych. 2012). The introduction discusses previous findings and theoretical claims regarding collective cognition and group intelligence. However, very little is written about reinforcement learning in general, and Q-learning in particular. Relevant issues include the reasons for choosing to focus on learning (as opposed to perceptual decision-making in previous research), the ecological validity of this task, and what we already know about Q-learning in individuals. Moreover, the hypotheses and predictions should be fleshed-out and justified in the introduction. It is currently unclear what the hypotheses are. One can imagine that under a reasonable model of individual performance, it should be possible to simulate the dyadic and triadic decisions and have some predictions that could be directly compared to the data. At present, the only motivation to do the experiments seems to be to see “What could happen” and that can certainly be improved up on. There is no model comparison. We wondered to what extent the result depend on the exact implementation of Q-learning used in this study. It would be more convincing to show that the general pattern of results is consistent across a family of Q-learning models. For example, Equation 1 includes a constant noise that is not always present in Q-learning models. This equation also involves two learning rate parameters, one for negative and one for positive prediction errors. Do the results also hold in models with one learning rate, and without random noise? In a similar vein, do they also hold in a model that uses raw rewards, rather than prospect-utility-transformed values? Moreover, it can be illuminating to show potential differences in the best-fit model between the group sizes. For example, did a model with 2 learning rates fit the data better than a single-alpha model in all group sizes? This could be interesting, since it could be that not all group sizes weigh gains and losses differently (or equally). The design is unclear More clarity about design is needed. All subjects did the individual condition. Some did the dyadic (130 dyadic groups) and some did the triadic (110 triadic). These numbers show, and the paper indeed indicates that some participants did all three conditions but we do not know how many they were. Did any participants take part in more than 1 dyadic or triadic group? All of these issues make a big difference to establishing the right baseline. For example, when we see the comparison of total collected reward in Figure 2, it makes more sense if the dyadic condition is compared to individual performance of the subjects who took part in dyadic condition and exclude subjects who did not. A similar issue applies to triadic condition which should have its own individual control. The above would then allow the analysis of performance to be made not only between groups each group and the average of its individual but also between group and the best participant within a group. Also, if possible, it would be advisable to try out comparing triads to the best dyad pairing within a group (see Wahn et al., 2018, PLoS one, for a similar approach studying visual search). These analyses will clarify whether the group-size differences reflect statistical aggregation or genuine group dynamics. Such an analysis should not only be made on the level of overall performance, but also on the level of model parameters. In other words, understanding the relationship between individual-level and group-level model parameters can illuminate the dynamics of group learning. But since there is a discrepancy between the number of people who took the dyadic and triadic conditions, I am not sure how feasible this option is. More minor points: 1. p. 2: “causes of synergy” -> the term causes is too strong. Correlates? Factors associated with? 2. p. 4: “To convert the group from…”: sentence unclear. Please elaborate. 3. The author refers to a single individual as a group. This is a strange decision. It is more natural to use the terms “individual” for N=1, and “group” for N>1. Accordingly 4. P. 6 and onward: It is better to regard the study as comprised of a single, multi-condition experiment, rather than 3 different experiments. 5. P.7: how were the number of trials per run (before the probability reversals) distributed? How were the reversal points determined? 6. Procedure: who was the participant that responded in each trial? For example, did the participants take turns, or was the decision determined by the first participant who answered? 7. P. 10: what were the specific parameters of the distributions that were used as priors for parameter estimation? 8. Given the impressive sample-size used in this study, it would be very valuable to provide the readers access to the raw data, as well as the analysis code. 9. P. 11 and onward: exact p-values should be given for all statistical tests (significant or not). 10. P. 12, top: the difference between individuals and dyads does not meet the standard .05 criterion. The same is true for p. 14, top. However, I do not think that the Bonferroni correction is needed when having only 2 comparisons, so that the uncorrected p-value can be used in the former case. 11. P. 13, top paragraph: mode details should be given regarding the parameters that did not vary across group sizes (i.e., descriptive and inferential statistics). 12. A section on parameter recovery is missing. It is important to show the precision in which the parameter values could be recovered using the number of trials and participants used in this study. This can strengthen the claims regarding group-size invariance in some of the parameters. 13. Figures 2-4 do not provide much more information than is already given in the text, and hence should be omitted. 14. The bar graphs used to show the data are quite outdated compared to what is acceptable and standard practice these days which includes superimposing the data points on top of the bars and/or showing the distributions using violin plots and similar tools Reviewer #2: This is an interesting study with (to my knowledge) a novel finding. In this study people performed a two-armed bandit task. They did this first individually, and later in a separate session they performed the same task as in groups of two or three people. The results suggest that groups of two people did worse than either individuals or groups of three people. This is a good experiment and the main behavioral result is interesting and appears to be novel. But, there are some serious issues with the paper. It is not ready for publication as it is. In particular many aspects of the modeling are unclear. Without more detail it is not clear whether the model is appropriate, or whether it accurately characterizes the data. Equation 1, phi is never explained. What does it represent and what function does it play in the model? It is also listed on pg. 10 as one of the parameters, but nothing is said about its prior. I am also confused about the learning rate alpha. I’m guessing that the plus/minus superscript means there are separate learning rates for when the prediction error is positive or negative, but this should be stated explicitly (or explained if it is doing something else). Also, alpha has a t subscript which implies it is dependent on the trial somehow, but if so, it is never explained. I think that equation 3 is supposed to specify that you use the top part if R(t) is greater than 0, and the bottom part if it is less than 0. But, on that note, the experiment only ever has positive rewards, so the bottom half of the equation would never be used. This means that with the present design the parameter v is never used and serves no purpose. It also means that (because there are never losses) this study cannot assess risk attitudes, so all sections of the paper related to risk are not valid. This is a major issue for the interpretations of the paper. It also calls into question whether the model is fitting well, and therefore whether analyses of other parameters (like inverse temperature) are meaningful. The priors should also be better specified. The type of distributions are noted, but the parameters of those distributions are not mentioned. Overall, the model as it was applied does not appear to be appropriate, and a number of parts of it are not adequately explained. It is also a relatively complicated model for a somewhat simple task—which is not necessarily a problem, but some of the choices need to be justified--such as using different learning rates for positive and negative prediction errors. Results of the model fit also needs more detail (i.e., fit statistics) so that we can assess how well it is characterizing the data. --------------------------------------------------- Other issues and typos: The paper implies that triads do better than individuals, but the difference is not significant, so the authors need to be more careful about how the results are presented. I noticed this in the abstract, but it might say it elsewhere as well pg. 8 – the text jumps into explaining the modeling in the section that explains the task. It should probably be its own section top of pg. 4 'put' should be 'puts' pg. 5 "single" maybe should be "signal" pg. 13. first sentence of 2nd paragraph. 'the' should be 'a' ********** 6. 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: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. |
Revision 1 |
PONE-D-20-35580R1 Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach PLOS ONE Dear Dr. Harada, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. I sent your paper back to the two original reviewers; R2 was satisfied with the revisions you made, but R1 still noted some concerns. It seems the concerns center on the lack of attention to detail, as well as emphasizing the novel theoretical advances made by your paper. I invite you to submit a revision, but please pay special attention to the points raised by R1. An overarching concern is that this paper feels as though it was written in a hasty manner, simply to get another publication, and it needs to reach a certain level of quality before it is published. Please be candid about noting the strengths and limitations of your study, so that the conclusions are supported by the data. If you choose to submit a revision, I will evaluate the manuscript and decide whether to ask R1 to review it once again. Please submit your revised manuscript by May 22 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Darrell A. Worthy, Ph.D Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: No Reviewer #2: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The paper has been substantially changed. But improved, I am not sure. 1. In the introduction, we see an attempt at motivating a theoretical justification in pp. 6-7. Frankly, I did not understand any of the notation or its relationship to the study or how it could then be a motivation for the experiment. 2. Two RL models are presented, and applied to the data and their fits are equally good and cannot be differentiated. In addition, the models do not make any different predictions for the experiments either. One is left wondering what the purpose of the exercise is. 3. In the first round, we asked for clarification about which subjects did the 1, 2 or 3-person experiments and how many rounds they did. Here, this request has been addressed but the rebuttal does not really solve any problem. On pp 10-11 (line 172-185) the descriptions are more confusing than helping. For example, in line 184, we are told that 161 individuals participated in triadic experiments but 161 is not divisible by 3. This leaves the reader with the impression that there was no real systematicity to the arrangement of the experimental participation. I am afraid I cannot be positive Reviewer #2: The author addressed all of my previous concerns well. Particularly, the modeling approach used and the way the modeling is explained are both much improved. ********** 7. 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: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. |
Revision 2 |
Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach PONE-D-20-35580R2 Dear Dr. Harada, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Darrell A. Worthy, Ph.D Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: |
Formally Accepted |
PONE-D-20-35580R2 Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach Dear Dr. Harada: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Darrell A. Worthy Academic Editor PLOS ONE |
Open letter on the publication of peer review reports
PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.
We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.
Learn more at ASAPbio .