A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations

Oscar Hernández; Eduardo Zurek; John Barbosa; Minaya Villasana

doi:10.1371/journal.pone.0274713

Peer Review History

Original SubmissionSeptember 1, 2022
27 Feb 2023 Decision Letter - Moo K. Chung, Editor PONE-D-22-24455A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equationsPLOS ONE Dear Dr. Hernández, 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. Please submit your revised manuscript by Apr 13 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: 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, Moo K. Chung 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 https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please provide additional details regarding ethical approval in the body of your manuscript. In the Methods section, please ensure that you have specified the name of the IRB/ethics committee that approved your study." 3. Please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. 4. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts: a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. [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: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes 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: Yes ****** 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: This article investigates brain activity recorded from EEG using a graph theoretical approach during mathematical and programming tasks. The manuscript is very interesting and well-written. However, I am not confident about the inferences made to brain areas. EEG activity is recorded from the scalp. It is therefore unclear to me how inferences to Brodmann areas can be made. This part of the manuscript therefore needs extensive methodological description or should be removed. The last paragraph of the introduction should conclude by clearly stating the aims and hypotheses of the manuscript, based on previous research. It is not necessary to summarize the organization of a research article as this is always predetermined. Why was the age range of 18-21 years set as a selection criterion? Was the EEG data pre-processed at all? I.e., ocular correction, artefact rejection etc. Otherwise some of the finding might be due to noise in the data Please present some basic descriptives of the sample, ie. Age (mean, SD), gender etc EEG is recorded from the scalp. How were inferences to brain areas (Brodmann areas) made? Was source localization performed? Even if this was the case, source localization is unlikely to determine precise brain areas. Please elaborate on how this analysis was conducted and valid these results are. Reviewer #2: Motivation, novelty and conclusion This submission has a clear and strong motivation to understand the cortical function difference, using EEG, during mathematical and computational challenges. The novelty of the paper is clear and I am happy that the conclusion can be made via a clearly designed empirical study and a relatively solid statistical analysis. Metrics, and research methodology The demonstration of design, shown in Figure 1, is very clear. A minor note, I am very curious about how the order of challenging matters for the final conclusion (e.g., will some level of task randomization influence the final conclusion). I am happy to see researchers apply solid graphical analysis via metrics to define and interpret their conclusions and findings. A few minor notes: I understand the SWN is a very useful metric to understand connectivity. However, it might be very easy to understand for most readers. Maybe we can demonstrate a few examples (either real data examples, or toy examples) to show a better visualization. SWN is known to be very sensitive to network size. This might need further clarification. A similar metric can be L_r/L - C / C_l where r is for randomized graph and l is for lattice graph. The definition of C_rand is not very clear – is it generated through Erdos-Renyi with a fixed number of nodes/edges or with respect to some random weighting? Eg seems to have the same definition of C? I am not suggesting removing the discussion of Eg and El, but it seems to be the SWN analysis is more interesting. Statistical analysis and result This submission uses solid statistical analysis methods to support the majority of their findings, which are mostly innovative and convincing. Some major or minor comments below. [Major] It is not clarified in the paper how replication was performed for each sample. As there are only 16 subjects enrolled in this study, it would be helpful to understand whether the sample replication was performed and so how the sample-level heterogeneity/fixed effect influenced the research conclusion. [Major] Authors applied pairwise non-parametric tests as evidence to show their conclusion. I am curious to understand how the multiple group anova or mixed effect generalized linear model tells us. For example, the difficulty can be a categorical variable, band can be another covariate, computing/mathematical task can be another dummy variable. A few interactions can also be useful (e.g., interaction between problem types and difficulties, etc). This can help us to gather more information related to interactions, rather than the pairwise comparison. [Major] In the supplementary table S1 - S5, which supports the main conclusion of the paper, it includes a large amount of p-values. There is a concern of multiplicity (e.g., false positive with multiple tests). Those p-values less than 0.05 (highlighted) are very tiny (e.g., <10^-5) so I am supportive of most of the key findings but it is worth checking or at least clarifying how the result will change if we apply multiplicity adjustment. [Minor] F3 and F4 are informative but there might be a few ways to improve. E.g., 1 is a relatively important cutoff for small world parameters, so it would be helpful to align with the same y axes range, and to clarify/interpret the result. [Minor] I am not seeing an informative interpretation on Fig 5. However, as you find some significant differences in SWN, I am very keen to see a few demonstrations or toy examples showing how visually the connectivity differs. ****** 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. Attachments Attachment Submitted filename: Review on PLOS one [PONE-D-22-24455].docx https://doi.org/10.1371/journal.pone.0274713.r001
Revision 1
3 May 2023 Author Response We would like to thank the anonymous reviewers for their comments and insights, some of which found their way into the manuscript improving the overall exposition. Here we would like to address some of the issues and in some cases how these were included in the main text (and highlighted in this letter). Reviewer No. 1. 1) Comment: The last paragraph of the introduction should conclude by clearly stating the aims and hypotheses of the manuscript, based on previous research. It is not necessary to summarize the organization of a research article as this is always predetermined. Answer: The last paragraph of the introduction was reformed to include a clear expression of the objectives instead of outlining the paper organization. Specifically, the following paragraph was added: “This work stems from the statement that algorithmic thinking is intimately related to mathematical thinking, thus it can be speculated that these tasks may give rise to cortical networks that share similarities in terms of the measured parameters of the cortical networks such as global and local efficiencies and smallworldness. To assess this speculation, we propose the measurement of these parameters while mathematical and programming tasks were performed to determine its characteristics, similarities, and differences between them while performing mathematical tasks and algorithmic tasks with varying levels of difficulty.” 2) Comment: Why was the age range of 18-21 years set as a selection criterion? Answer: The age range between 18 and 21 years is because the recruited subjects are students of fifth semester of System’s Engineering from the university where this study was conducted. The ages of these subjects lie within this range and the selection of the semester is since the subjects must have the necessary programming abilities to complete the algorithmic tasks. A brief explanation was introduced in the article to clarify this information (see section 1.1) “All the participants met the following selection criteria: students that dominate the programming requisites for the tasks which is typically attained after the 4th semester of System’s engenieering thus inducing an age range between 18 and 21 years , with normal or corrected vision, right-handed, not having consumed (prior to recordings) coffee, tea or other nervous system stimulant, not having drunk alcohol in the 48 hours prior to test day.” 3) Comment: Was the EEG data pre-processed at all? I.e., ocular correction, artefact rejection etc Answer: Ocular correction and artefact rejection was performed through the ICA from the EEGLAB software. The signal adjustments from the EEG tick marks were performed in Matlab with in-house programming. Section 1.2 now includes this information. 4) Comment: Please present some basic descriptives of the sample, ie. Age (mean, SD), gender etc Answer: Age distribution for the 16 subject sample used in this study is: 1 participant was 18 years old, 9 were 19 years old, while 3 participants were 20 and 21 years old respectively.The mean age is 19.5 and the gender distribution was 2 female and 14 male subjects. This information was included in section 1.1. 5) Comment: I am not confident about the inferences made to brain areas. EEG activity is recorded from the scalp. It is therefore unclear to me how inferences to Brodmann areas can be made . Answer: The use of EEG electrodes placed at corresponding positions on the scalp to make inferences about the function of Brodmann's areas is a valid approach supported by numerous studies (See the references listed below). While the use of source localization algorithms can provide more precise information about the location of the underlying cortical activity, these algorithms are not necessary to establish a reliable correspondence between EEG signals and cortical function. • Arsalidou, M., Pawliw-Levac, M., Sadeghi, M., & Pascual-Leone, J. (2018). Brain areas associated with numbers and calculations in children: Meta-analyses of fMRI studies. Developmental cognitive neuroscience, 30, 239-250. • Damiani, D., Nascimento, A. M., & Pereira, L. K. (2020). Cortical Brain Functions–The Brodmann Legacy in the 21st Century. Arquivos Brasileiros de Neurocirurgia: Brazilian Neurosurgery, 39(04), 261-270. • Koechlin, E., & Hyafil, A. (2007). Anterior prefrontal function and the limits of human decision-making. Science, 318(5850), 594-598. • Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave de Peralta, R. (2004). EEG source imaging. Clinical neurophysiology, 115(10), 2195-2222. doi: 10.1016/j.clinph.2004.06.001 • Vrba, J., & Robinson, S. E. (2001). Signal processing in magnetoencephalography. Methods, 25(2), 249-271. doi: 10.1006/meth.2001.1238 No source localization was done but common practices were followed to make it easier to understand. Reviewer No. 2. 6) Comment: I am very curious about how the order of challenging matters for the final conclusion (e.g., will some level of task randomization influence the final conclusion) Answer: Similar studies begin the testing with the control task of white cross observation over black followed by subitizing task. Thereafter, the order of the tasks used here is similar to those used in neuroscience and experimental psychology, from simplest to hardest. These decisions exclude any possible variation of the results seen here due to a change in task ordering. For the same task the order of presentation of the items was random, but the decision to start with the mathematical testing followed by the programming tasks was arbitrary and we consider that no significant change would have been observed if the order were reversed. This is a hypothesis that could be tested in further follow-up studies but are not addressed here. 7) Comment: I understand the SWN is a very useful metric to understand connectivity. However, it might be very easy to understand for most readers. Maybe we can demonstrate a few examples (either real data examples, or toy examples) to show a better visualization. Answer: One way to better understand the SWN metric is to examine the transition of a regular network into a random network. The figure below shows a computational model to obtain small world networks while increasing the probability 𝑝 of randomly connecting nodes. This is, if we connect some nodes randomly from the regular network, C is increased while L remains approximately the same as a small world network. We hope this helps to clarify the matter; however it has not been included into the manuscript to prevent extending the article further, however references to these metrics have been included in the text (section 1.4) when defining the measure. The following references may help readers with a better understanding of this metric and its importance for network characteristics: • Bassett DS, Bullmore E. Small-world brain networks. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry. 2006;12(6):512–523. doi:10.1177/1073858406293182. • Humphries, Mark D., and Kevin Gurney. "Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence." PloS one 3.4 (2008). • Bassett, Danielle S., and Edward T. Bullmore. "Small-world brain networks revisited." The Neuroscientist 23.5 (2017): 499-516. 8) Comment: SWN is known to be very sensitive to network size. This might need further clarification. A similar metric can be L_r/L - C / C_l where r is for randomized graph and l is for lattice graph. Answer: In regard to the SWN sensibility with respect to the size of the network, in Humphries M.D. and Gurney K. (2008) it was shown for social, technological and biological networks that SWN increases linearly with the size of the network. This reference was included in the article and referred to in section 1.4 to emphasize this dependency. Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. The reviewer suggests an alternative for SWN. Such an alternative is plausible, however given the fact that many previous works use SWN as a network measure we think that this can help readers to relate to other published works. 9) Comment: The definition of C_rand is not very clear – is it generated through Erdos-Renyi with a fixed number of nodes/edges or with respect to some random weighting? Answer: The random graphs were created using the HERMES software which states: “The graph created is entirely random, with the probability of connection tested for each pair of vertices (Erdos-Renyi random graph). Only single edges and no self-loops are allowed - hence, P is adjusted to account for the slight reduction in the number of maximum edges”. This explanation is included in section 1.4 10) Comment: It is not clarified in the paper how replication was performed for each sample. As there are only 16 subjects enrolled in this study, it would be helpful to understand whether the sample replication was performed and so how the sample-level heterogeneity/fixed effect influenced the research conclusion. Answer: Replication was not possible due to the costs involved in the experiment both in money and time. However, we observed that the standard deviation for the various metrics is reasonably low for 20 randomized items within each task so there is some homogeneity for each subject. 11) Comment: Authors applied pairwise non-parametric tests as evidence to show their conclusion. I am curious to understand how the multiple group anova or mixed effect generalized linear model tells us. For example, the difficulty can be a categorical variable, band can be another covariate, computing/mathematical task can be another dummy variable. A few interactions can also be useful (e.g., interaction between problem types and difficulties, etc). This can help us to gather more information related to interactions, rather than the pairwise comparison. Answer: Statistical test show that the data does not follow a normal distribution, therefore anova or manova should not be applied as normality is an underlying hypothesis for the anova. However, in its place and thanks to this comment. The Friedman test was used to determine if one could differentiate the 3 levels of difficulty for each type of task. This is now found in the supplementary materials. We did not evaluate differentiation of difficulty level mixing types of tasks, since we cannot presume that programming and mathematical tasks are of similar nature and is the objective of this manuscript. Furthermore, we considered that given the differences in ranges for SWN, Eg and El, and the existing literature where these metrics are reported segregated by bands, it doesn’t make sense to consider all the bands simultaneously with regards to the two types of tasks and the difficulty levels. The Friedman tables are added in the supplementary materials and are referenced in section 1.5. The supplementary material also includes results from Friedman test to attest the statistical differences between the 3 difficulty levels complementary to the U-Mann Whitney test. 12) Comment: In the supplementary table S1 - S5, which supports the main conclusion of the paper, it includes a large amount of p-values. There is a concern of multiplicity (e.g., false positive with multiple tests). Those p-values less than 0.05 (highlighted) are very tiny (e.g., <10^-5) so I am supportive of most of the key findings but it is worth checking or at least clarifying how the result will change if we apply multiplicity adjustment. Answer: The experiment was carried out with 6 different tasks and 20 exercises in each task (ES, EM, EC, PS, PM and PC), in addition to two control activities (CO and DO). Each of the exercises was performed by 16 volunteer participants, generating a total of 320 samples per task. The power of the Mann-Whitney U test is calculated as indicated in [Sample Size Calculator. Available from: https://homepage.univie.ac.at/robin.ristl/samplesize.php?test=wilcoxon], with parameters of a sample size per group of 320, the obtained p-values and a significance level of 0.05 (alpha two-sided 0.05), and as a result a power greater than 0.99 is obtained. The format of the results shown in tables S1, S2, S3, S4 and S5 in supplementary materials were changed to scientific notation in order to improve readability. 13) Comment: F3 and F4 are informative but there might be a few ways to improve. E.g., 1 is a relatively important cutoff for small world parameters, so it would be helpful to align with the same y axes range, and to clarify/interpret the result. Answer: It was noted that in Figures 3 and 4 the range for each picture panel is different. When placing them on a unifying range, in many cases these were no observable differences in the distributions or boxplots at the overall scale and range because information appeared “lumped” together, but the differences became evident when depicting the values in their natural range. However, a unifying range is shown in panel A for both figures where the medians are plotted and differences between the ranges per bands could be evident. 14) Comment: I am not seeing an informative interpretation on Fig 5. However, as you find some significant differences in SWN, I am very keen to see a few demonstrations or toy examples showing how visually the connectivity differs. Answer: Figure 5 shows heatmaps for El and not for SWN which was addressed in the previous comment. The El heatmaps of figure 5 and the information contained within are further explored using clustering techniques (figures 6, 7 and 8 in section 2.3). The other comment regarding toy examples to demonstrate connectivity was addressed in point 7) in this document. Please do not hesitate to contact us if further clarifications are required. Attachments Attachment Submitted filename: Response_to_Reviewers.pdf https://doi.org/10.1371/journal.pone.0274713.r002
12 Jun 2023 Decision Letter - Fabio Rapallo, Editor A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations PONE-D-22-24455R1 Dear Dr. Hernández, 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, Fabio Rapallo, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Both reviewers agree that the authors have carefully addressed the concerns on the first version. 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: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes 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 authors have adequately addressed by concerns and feedback. I believe the manuscript is now in a format suitable for publication. Reviewer #2: Thanks for carefully addressing my comments. Most of my comments are well addressed and some pushbacks are reasonable. LGTM to accept. ****** 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 ******** https://doi.org/10.1371/journal.pone.0274713.r003
Formally Accepted
16 Jun 2023 Acceptance Letter - Fabio Rapallo, Editor PONE-D-22-24455R1 A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations Dear Dr. Hernández: 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. Fabio Rapallo Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0274713.r004

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 .