PONE-D-23-06734Facial mimicry does not influence memory precision of emotion expressions when working memory load is highPLOS ONE

Dear Dr. Holmer,

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 want to begin by apologizing for the lengthy delay in getting a recommendation to you. I had sustained difficulty securing reviewers for your submission, with some reviewers failing to submit their report, requiring me to secure new reviewers and further delaying a final recommendation. None of this affected my judgement of your manuscript, which I personally enjoyed reading, it's just an unfortunate pattern editor have been dealing with since COVID.

I was fortunate to secure two expert reviewers with knowledge of the field, each of whom provided insightful and helpful reports. As you will see, both reviewers and I had a positive opinion on the quality and clarity of the manuscript. Overall, the manuscript is well written, appropriately cited, and clearly identifies a gap in the mimicry literature; I personally enjoyed reading your work. However, both reviewers and I took issue with certain aspects of the narrative and analyses.

There was consensus across reviewers, including myself, that facial mimicry was not the observed variable in the study, and that this needs to be made much clearer to the reader. The narrative makes mimicry a central piece of the story, yet mimicry was not measured (with EMG or video motion); rather, it was facial expression recall accuracy that was measured. Like R2, the results of the interaction are interesting, and it would be helpful to contextualize these findings with typical n-back literature findings. R1 makes the insightful comment that the study had no control condition with no-facial stimuli; a discussion here is warranted. Reporting of your mixed model can also be improved for clarity. Finally, interpretation of the null, as indicated by R2 is a serious point for consideration given the lack of a prior sample size estimate. Below you will find comments of the two reviewers, along with own additional suggestions.

• Alexthymia is considered a personality trait, not a medical condition or disorder. See Ricciardi et al, 2015 (Neuropsychiatry) for reference material, or you can just drop the reference to medical condition.
• Your manuscript is missing a strong justification for sample size. I won’t ask for a posthoc sample size estimate, as this is meaningless. Rather, see Lakens, 2022 for sample size/power justification and follow their advice that matches your situation. In future, please consider the use of simR as a suitable sample size estimate in mixed models. https://humburg.github.io/Power-Analysis/simr_power_analysis.html
• Your mixed model reporting is missing many key details. Did you investigate the assumptions of mixed models? See Harrison et al, 2018. Normality of residuals, homogeneity of variances etc? sjPlot provides straightforward visual diagnostics.
• What form of p-value correction are you reporting, Satterthwaite or Kenward-Roger? Luke 2017 suggests that KR is the gold standard. Either way, please include in your manuscript.
• What was your random effects structure? I note in your script that with (1|fp), you’re not exploring by-participant random slopes for load or interference; yet a maximal RE structure is recommended to avoid Type I errors; Barr et al, 2013.
• Ideally, you would report other aspects of your model (optimiser used, number of iterations). Specify if you optimised with REML or ML.
• Please specify in the manuscript the distribution used (logit)
• I agree with R1’s concerns regarding interpretation of the interaction. You may be better positioned to code the contrasts yourself. You may find this tutorial helpful - https://marissabarlaz.github.io/portfolio/contrastcoding/. Optionally, you may want to consider use of the emmeans package and performing pairwise comparisons.
• Fig 2 axes need better labelling. It is not clear from the Y-axis or caption what your DV is. Please state it clearly (raw accuracy, I believe, from the letter matching n-back task).
• Rating of Radbound images was a good addition, though not really needed given they are validated stimuli. I am curious why you did not tie your AV data to your accuracy data, as also suggested by R2. That is, did you consider adding A/V as continuous predictors in your glmer model?

Please submit your revised manuscript by Sep 29 2023 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:

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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.

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We look forward to receiving your revised manuscript.

Kind regards,

Steven R Livingstone

Academic Editor

PLOS ONE

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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: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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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: Yes

Reviewer #2: No

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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

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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: This experiment examines the role of facial mimicry in working memory for images of facial expressions. Overall, the methods are nice and straightforward, although the lack of non-face stimuli is a definite limitation. I have a few suggestions for improving the regression model specification and interpretation. I also find myself disagreeing about the authors’ takeaway, that “Facial mimicry does not play a critical role in working memory for emotion expressions” (p. 21; see my point #6).

METHOD

1. A non-trivial limitation of the study design is a lack of non-face stimuli. We cannot know whether the effects of facial mimicry interference are specific to facial expression working memory, or whether the manipulation was just generally distracting and would have impacted accuracy for any type of stimuli. This needs to be addressed in the Discussion.

ANALYSES AND RESULTS

2. I think the dependent variable in the regression is better described as “whether the response on a given trial was correct or incorrect” or something like that to make it explicit that it’s at the trial level and dichotomous. The current phrasing, “the probability of a correct response as the dependent variable” (p. 11), makes it sound like you’ve converted raw trial-level data into percentages.

3. The Load variable is a numeric variable coded as 1 and 2—with the interaction term, this means you’re interpreting the main effects for the Interference dummy variables at a nonexistent level of Load, 0 (0-back trial?). If you relevel Load so 1-back is 0 and 2-back is 1, then you can interpret your Interference main effects as being the effect of facial mimicry interference for the 1-back trials. Here’s a blog post about how changing what 0 means for one variable changes the interpretation of another variable when there’s an interaction term: https://www.r-bloggers.com/2018/05/regression-with-interaction-terms-how-centering-predictors-influences-main-effects/

4. It’s really tricky to describe interactions with dummy-coded variables, and I’m not sure that this sentence (p. 12) is complete: “The most important finding of our study was an interaction between working memory load and low interference (β = -.266, df = 34, p < .044) and load with no interference (β = -.270, df =34, p < .042).” It’s really 1) the interaction between Load and the dummy variable comparing low and high interference and 2) the interaction between Load and the dummy variable comparing no interference and high interference. Note that the two interference variables are using “high interference” as the reference level and comparing “low” and “none” to it. Be similarly careful with your wording when explaining the main effects for interference.

5. The labels in Figure 4 are unclear to me. Based on the in-text description, you’ve transformed the model coefficients into probabilities (which I tried doing myself and got different values, so maybe showing the formula would help). I would change the labels. For instance, make it clearer that the probability for “Load” is how much the probability of a correct response changes from low to high load. And the “No” probability is how much the probability of a correct response changes from the “High interference” to the “no interference” conditions. Honestly, I’m not sure if that figure is even worth using since it’s pretty confusing and somewhat redundant with Figure 3. Do we really care about the relative effect sizes of these different variables?

DISCUSSION

6. It’s interesting that you found facial mimicry impairment reduced accuracy on the 1-back trials but not the 2-back trials. One possible interpretation is facial mimicry helps you keep the most recent face in working memory, but the 2-back face’s associated simulation is overridden by the 1-back face. It’s hard to imagine how sensorimotor activity could maintain representations of multiple facial expressions at once when they involve opposing facial actions. This interpretation does not lead me to the conclusion that you “did not find convincing support for the notion incongruent sensorimotor feedback impairs working memory for facial emotion expressions although it might impair precision in the absence of a high working memory load.” (p. 15). As an analogy, let’s say I’m trying to keep a melodic phrase in my working memory by repeating it in my head (which recruits auditory system). If I am unable to do it for two melodies simultaneously, you wouldn’t then conclude that the auditory system wasn’t playing a role—you’d conclude that my auditory working memory can only handle 1 melody at a time. I’m happy to be convinced otherwise if the authors have already ruled out this interpretation for themselves.

7. I’m confused by this interpretation in the discussion section (p. 17): “working memory demands increase the neurocognitive system responds by filtering out potentially distracting information, as proposed in the task-engagement/distraction trade-off (TEDTOFF) model.” How is sensorimotor activity distracting information? Isn’t it (theoretically) aiding task performance? Distracting externally-generated auditory input in the Sörqvist et al. study is different from internally-generated and task-relevant facial mimicry.

8. The discussion is a tad repetitive. For instance, the paragraph starting on p. 19 with the phrase “In addition to the potential role of mimicry” was a restatement of earlier ideas.

9. “…and the sample size was too small to test a three-way interaction” (p. 21). Is this true, given that the additional variable is also within-subject?

GENERAL

10. The paper needs to be proofread, as I encountered the occasional typo or grammar issue throughout. For instance, this sentence on p. 18 is not grammatically correct and is difficult to follow: "Blocker and McIntosh finding of suppressed facial mimicry responses when working memory load increases, speaks for that facial mimicry might not play a crucial role in accurately representing facial emotion expressions when working memory load is high.” Possible rewording: “Blocker and McIntosh’s finding, that increases in working memory load suppress facial mimicry, suggests that facial mimicry might not play a crucial role in accurately representing facial emotion expressions when working memory load is high.”

Reviewer #2: In this manuscript, the authors systematically address a gap in the literature regarding the effects of interfering with facial mimicry of emotions on working-memory performance at different loads. For this purpose, they assessed 36 students in their 20s (72% female) on a 2 x 3 within-subjects design with two levels of working memory and three levels of interference with facial mimicry. The main DV was accuracy on an emotional n-back task. The Introduction is well-written, presenting relevant background to support a clear rationale. The authors expected a cumulative effect of the 2 factors on accuracy, but the observed interaction was such that an interference effect was only significant in the low working-memory load condition, where low and no interference appeared similarly superior to high interference. A main effect of load was also observed. These effects are generally consistent with prior studies. What is most novel is the lack of interference effects in the high working-memory load condition. Although EMG and subjective reports were not included to verify the interference manipulations, the authors adequately address these limitations. Overall, they conclude that facial mimicry does not play a critical role in working memory for emotional expressions, but may influence performance when working-memory demands are low. However, the interpretive claims rest on the null finding at high load, which deserves further attention, along with other aspects regarding the design and analyses.

MAIN COMMENTS

1. As above, the main conclusion that facial mimicry does not play a critical role in working memory for emotional expressions rests on the failure to observe an effect of interference in the 2-back condition. The Discussion briefly notes that performance was still above chance (p.18) and the figures suggest there was still substantial variation at high load. What was chance? Was the distribution skewed? A counter-argument should be made to the alternative explanation that the absence of effect may be due to a floor effect or restricted range. Moreover, Bayes factors would be helpful towards supporting the strong claims in favour of the null; at present, null hypothesis significant testing cannot support the claims as currently phrased.

2. The basis for the sample size should be elaborated or clarified. The methods indicate it was “determined based on previous studies…and thirty-six students participated” (p.7). Does this mean that N=36 was somehow optimal or minimal within a range observed in prior studies? Or did the authors obtain an effect size to calculate and estimated sample size a priori. If so, for what effect(s) of interest? Even if sufficiently powered, it is a relatively small sample size from a reliability / replication standpoint. The Discussion also notes that the “sample size was too small to test a three-way interaction” including valence (p.21). Why wasn’t this considered a priori given the depth of consideration paid to valence in the paper and the field? Additionally, as valence would be embedded within the multilevel model, I’m also not convinced the current design lacks sufficient power.

3. While the basic script is posted on OSF, the analytic models could be further elaborated. For instance, how were the levels of load and interference modeled and coded (dummy variables)? This is important for interpreting the sign of the regression coefficients. Why did the high interference condition serve as the reference category in these analyses? Why not the no-interference condition to allow for potentially more meaningful assessments of the interactions between low and high interference with load (vs none and low)? Why was a mixed-effect model used to assess n-back performance, but a traditional ANOVA used to assess valence and arousal/intensity ratings?

4. Moreover, a fair bit of attention is devoted to validating the valence/arousal ratings. The Discussion notes the role of context (p.18) and potential moderation by expression type (p.21). As above, why not also include it as a factor of interest in the analyses? This may prove important if variation across expression types are masking an effect of interference when averaged across valence.

5. The figures suggest sizable variation across participants in accuracy, particularly under high load (although it’s not clear what the error bars reflect or the underlying distribution; see below). It may be useful to account for some of this variability in the analysis.

a. For example, although counterbalanced, were there any order effects of the load or interference conditions? The same stimuli repeated across trials. Namely, 9 face identities were each presented with the 3 emotional expressions (27 images total), such that across the 6 blocks, each image was presented twice and each facial identity 6 times. Might this have introduced some habituation or mnemonic interference across trials?

b. Sources of individual differences may also be contributing to error variance. A practice task was used to reduce baseline performance variability. However, it may be useful to harness the verbal working-memory scores (LNS subscale) as covariate if it relates to task performance(?); consider the arguments made for verbal working-memory involvement particularly at high loads. Likewise, what about other participant characteristics? For example, all the stimuli were of women; did participant sex moderate the results?

MINOR POINTS

6. Alexithymia is not typically considered a “medical condition” per se (p.4). Although there are different views on the construct, it can present on a subclinical spectrum and some view it as a personality trait, for example.

7. Did the data meet the assumptions for the main analyses (i.e., normality, homogeneity of variance, no outliers)? Only the data for the valence/arousal ratings are provided on OSF.

8. Related, while the valence/arousal figures are box plots, the main n-back accuracy findings are displayed as predicted probabilities with some margin of error (bars not defined). The distributions are thus unclear – the authors might consider displaying a dot plot or at least a box plot for these findings as well.

9. I don’t see any captions for the figures in the file for review – are they missing?

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Reviewer #1: No

Reviewer #2: No

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PONE-D-23-06734R1Facial mimicry interference reduces working memory accuracy for facial emotion expressions when task load is low but not when it is highPLOS ONE

Dear Dr. Holmer,

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.

 Thank you for your patience while we collected the reviewer's feedback. As one of the original reviewers was unable to accept the revision task, a third reviewer (R3) was brought on to provide a new review. The original Reviewer 1 was happy with your revisions and is ready to accept the manuscript. However, R3 has asked for revisions. Most of these relate to statistical reporting. While they do not require the collection of new data, they are significant enough to be classified as major revisions. Therefore, I invite you to revise and resubmit the manuscript to address R3's concerns.

Please submit your revised manuscript by Apr 27 2024 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:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled '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: https://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,

Steven R Livingstone

Academic Editor

PLOS ONE

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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 #2: All comments have been addressed

Reviewer #3: (No Response)

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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 #2: Yes

Reviewer #3: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: Yes

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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 #2: (No Response)

Reviewer #3: In the revised manuscript titled “Facial mimicry interference reduces working memory accuracy for facial emotion expressions when task load is low but not when it is high,” the authors investigated the effect of blocking spontaneous facial mimicry on working memory recall for facial emotion expressions. They reported that high-level mimicry interference was effective for 1-back but not for 2-back recalls. The authors made great efforts in addressing comments from the editor and reviewers 1 & 2. However, key points about the study’s affordance to provide enough precision for their observed effects were not addressed.

1. The authors stated that they have followed Lakens’ suggestions (Lakens, 2022) in sample size justification. While Lakens provided six principles, the authors only stated reasons for resource limitations and heuristics. I want to emphasize that the authors need to demonstrate whether their present study provided precision in effect estimation for what they expected to observe and what they actually observed. The issue of precision in effect estimation is demonstrated in (Lakens & Evers, 2014) Table 1. Small effects require a large sample size to reach the point of stability in effect estimation. With an insufficient sample size, the observed effects are likely unreliable and unlikely to be reproduced. Therefore, I would like to request explicitly:

a. A sensitivity analysis to determine, at the current design and sample size, what the minimally detectable effect size (MDES) is with 80% power. Note that this has nothing to do with your observed effect size. In Lakens’ terms (Lakens, 2022), this is “which minimal effect size will be statistically significant.”

b. Reports of observed effect sizes for all statistical results (Green & MacLeod, 2016; Nakagawa & Schielzeth, 2013).

c. Considering whether the present study afforded enough precision in effect estimation for the significant and null results they reported in the manuscript. Suppose the effect size reported is much smaller than the present study's precision. In that case, the results reported should not be considered conclusive, and I will have difficulty recommending publishing this study as it is.

2. The discussions focused on the unexpected results and the limitations. While it is okay to speculate that lexico-semantic representations might come into play when working memory demands increase (lines 415-434), the authors should note explicitly that the present study provided no evidence of whether it was the case with their participants.

3. The lack of facial EMG validation of the mimicry interference was apparent. The authors first defended that the placement of electrodes is not compatible with the interference manipulation. Still, they later cited (Davis et al., 2017) who did such validation to strengthen their “assumption” that their manipulation was successful. I suggest it is enough to acknowledge that some validation should have been done, but it is missing in the present study.

4. As mentioned by the editor, participants’ facial video recordings might also help validate the manipulation. While the automated FACS (e.g., OpenFace or Py-Feat) might be ineffective in estimating AU12 and AU4 with the plastic foam rob and tapes in place, human FACS raters might be able to tackle this issue. Thus, facial video recordings could be mentioned as a validation method.

Bibliography

Davis, J. D., Winkielman, P., & Coulson, S. (2017). Sensorimotor simulation and emotion processing: Impairing facial action increases semantic retrieval demands. Cognitive, Affective, & Behavioral Neuroscience, 17(3), 652–664. https://doi.org/10.3758/s13415-017-0503-2

Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498. https://doi.org/10.1111/2041-210X.12504

Lakens, D. (2022). Sample Size Justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267

Lakens, D., & Evers, E. R. K. (2014). Sailing From the Seas of Chaos Into the Corridor of Stability: Practical Recommendations to Increase the Informational Value of Studies. Perspectives on Psychological Science, 9(3), 278–292. https://doi.org/10.1177/1745691614528520

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x

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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 #2: No

Reviewer #3: No

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Attachments

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PONE-D-23-06734R2Facial mimicry interference reduces working memory accuracy for facial emotion expressionsPLOS ONE

Dear Dr. Holmer,

Thank you for submitting your manuscript to PLOS ONE. I have now received reviews from two expert reviewers. As you will see, the reviews are positive, with reviewer 1 opting to accept and reviewer 2 raising only minor concerns.  For context, I was lucky secure Reviewer 2 for your revision. However, as Reviewer 1 needed to decline due to personal issues, a new Reviewer 1 was located.

Please submit your revised manuscript by Jul 04 2024 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.

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Steven R Livingstone

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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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 #2: All comments have been addressed

Reviewer #3: (No Response)

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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 #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: Yes

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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 #2: Overall, the authors have addressed the bulk of comments raised by R3 in the last review and the tempering of conclusions and clarifications improve the manuscript.

One item to note is that R3 requested that the authors further present a sensitivity analysis based on their sample size and discuss their precision in effect estimation. The authors argue that they are unaware of a mechanism to accurately address this request for their analytic model and provide a lengthy consideration of this item in their response letter. To speak to the sensitivity, they present a power curve based on simulations for a similar model (without covariates) in the response, but this not in the manuscript. While this may be harnessed to offer a rough estimate of an MDES, the values are not comparable given the unstandardized betas reported. An estimate and reporting of standardized effects might address this issue. Alternatively, I wonder if providing an estimate based on a more basic model (i.e., ANCOVA vs. a mixed model) could at least provide a conservative estimate (given the MLM here is more powered). Regardless, the authors have addressed practical reasons for their sample size and tempered the strength of their interpretations accordingly.

As a minor point, in the new sentence on line 410-411, I believe the authors may have meant to say “…to suppress the negative effect of facial mimicry INTERFERENCE on precision.”

Reviewer #3: Again, the authors made respectable efforts to improve the coherence and transparency of their work. I have only three suggestions.

Major points:

1. Please check whether the r2glmm package (https://cran.r-project.org/web/packages/r2glmm/readme/README.html) (Jaeger et al., 2017) is applicable in providing standardized effect size estimation (semi-partial R2) for fixed effects in GLMM. If applicable, please add the standardized effect size information for all reported effects accordingly. If not, please provide the rationale, which could be judged by the editor, and I will have no further requests on this issue. The authors' simulation using simr was independent of the observed effect size, and I consider this approach valid.

Minor points:

2. Ensuring the R code version on the OSF depository is up to date is crucial for the replicability of your analysis in its latest form. I kindly request you to prioritize this update.

3. Please consider making the review process public during acceptance (I appreciate that PLoS One provides this option). This will provide the reader with the context for why the authors conducted additional analysis or simulation.

Jaeger, B. C., Edwards, L. J., Das, K., & Sen, P. K. (2017). An R 2 statistic for fixed effects in the generalized linear mixed model. Journal of Applied Statistics, 44(6), 1086–1105. https://doi.org/10.1080/02664763.2016.1193725

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Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PONE-D-23-06734_R2_review.pdf

Facial mimicry interference reduces working memory accuracy for facial emotion expressions

PONE-D-23-06734R3

Dear Dr. Holmer,

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.

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Kind regards,

Steven R Livingstone

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #3: All comments have been addressed

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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 #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: 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 #3: Yes

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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 #3: 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 #3: (No Response)

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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 #3: No

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