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Antagonism shapes social maps in the human brain.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Whole-brain Searchlight RSA ↔ RSA/functions/ACF_noiseSim.m, the whole file · a weak match · score 0.68 · Mahalanobis distances, dissimilarity matrices, noise, RDMs, searchlight

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 54 lines · 2.2 KB · no license · 1 match

  1. function ACF_noiseSim(save_folder, sub_num)
  3. % ACF_noiseSim - Calculates SAC between adjacent searchlight RDMs via random simulation and saves the result
  4. % Syntax:
  5. % ACF_noiseSim(save_folder, sub_num)
  6. %
  7. % Inputs:
  8. % save_folder - Path to the directory where the result (SAC.mat) will be saved (string)
  9. % sub_num - Number of subjects to be used in the simulation (scalar)
  10. %
  11. % Outputs (Saved File):
  12. % <save_folder>/SAC.mat
  13. % SAC - The average correlation coefficient between two adjacent
  14. % searchlight RDMs, obtained from 10,000 simulation iterations (scalar)
  15. %
  16. % Created by Ryo Ishibashi, updated by Isato Chikazawa 2026/04/06
  18. clusterSize = [208 257];
  19. num_items = 8;
  20. num_iterations = 10000;
  22. % Preallocate array for speed
  23. corval = zeros(1, num_iterations);
  25. for cycle = 1:num_iterations % Number of iterations
  26. for sub = 1:sub_num
  27. % Generate random numbers for all voxels and items simultaneously
  28. D = randn(clusterSize(2), num_items);
  30. D2 = D;
  31. % Update non-overlapping part with new random numbers
  32. num_update_voxels = clusterSize(2) - clusterSize(1) + 1;
  33. D2(clusterSize(1):clusterSize(2), :) = randn(num_update_voxels, num_items);
  35. % The Mahalanobis distance of random numbers following a standard normal distribution is equal to the Euclidean distance
  36. if sub == 1
  37. % Calculate dissimilarity using Euclidean distance
  38. dissimA = pdist(D', 'euclidean'); % Euclidean distance for D
  39. dissimB = pdist(D2', 'euclidean'); % Euclidean distance for D2
  40. else
  41. dissimA = [dissimA; pdist(D', 'euclidean')]; % Concatenate distances across all subjects
  42. dissimB = [dissimB; pdist(D2', 'euclidean')]; % Concatenate distances for D2 similarly
  43. end
  44. end
  46. corval(cycle) = corr2(dissimA, dissimB); % Correlation between the two dissimilarity matrices
  47. end
  49. SAC = mean(corval);
  51. % Save the results
  52. save(fullfile(save_folder, 'SAC.mat'), 'SAC');
  54. end

ACF_noiseSim.m, no license · at the source

Overview

Authors: Isato Chikazawa1, Ryo Ishibashi2, Tamami Nakano1,2,3
ORCID iDs: Tamami Nakano
  1. Graduate School of Frontiers Bioscience, The University of Osaka, Suita, Japan
  2. Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Koganei, Japan
  3. Graduate School of Information and Science Technology, The University of Osaka, Suita, Japan
Institutions: The University of Osaka (Japan); National Institute of Information and Communications Technology (Japan)
Journal: Communications psychology, volume 4, issue 1, article 100
Dates: received 4 January 2026; accepted 15 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s44271-026-00491-y · PMID 42410040 · PMCID PMC13338177 · OpenAlex W7167492449
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, fMRI & imaging
Keywords: Cognitive neuroscience, Human behaviour
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: MEXT | Japan Society for the Promotion of Science (JSPS) (25K03424, 23H04832)
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF ygde3

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (16)
Size: 30 files, 16 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (9 files), SPM (8 files), Image Processing Toolbox (2 files), Parallel Computing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
16 files
At the source: osf.io/ygde3/overview?view_only=d5085e21e5a746c88bcf2f01b4dd2871

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s44271-026-00491-y.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s44271-026-00491-y.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 1 funder, 24 references.

Cite

This paper

Chikazawa, I., Ishibashi, R., & Nakano, T. (2026). Antagonism shapes social maps in the human brain. Communications psychology, 4(1), 100. https://doi.org/10.1038/s44271-026-00491-y

BibTeX

@article{chikazawa2026antagonism,
author = {Chikazawa, Isato and Ishibashi, Ryo and Nakano, Tamami},
title = {{Antagonism shapes social maps in the human brain}},
journal = {Communications psychology},
year = {2026},
month = jul,
volume = {4},
number = {1},
pages = {100},
publisher = {Nature Publishing Group},
issn = {2731-9121},
doi = {10.1038/s44271-026-00491-y},
url = {https://doi.org/10.1038/s44271-026-00491-y},
pmid = {42410040},
pmcid = {PMC13338177}
}

RIS

TY - JOUR
AU - Chikazawa, Isato
AU - Ishibashi, Ryo
AU - Nakano, Tamami
TI - Antagonism shapes social maps in the human brain
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/07/06
VL - 4
IS - 1
SP - 100
SN - 2731-9121
PB - Nature Publishing Group
DO - 10.1038/s44271-026-00491-y
UR - https://doi.org/10.1038/s44271-026-00491-y
LA - en
ER -

CSL-JSON

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"title": "Antagonism shapes social maps in the human brain",
"container-title": "Communications psychology",
"author": [
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"family": "Chikazawa",
"given": "Isato"
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{
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"given": "Tamami"
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"container-title-short": "Commun Psychol",
"volume": "4",
"issue": "1",
"page": "100",
"DOI": "10.1038/s44271-026-00491-y",
"PMID": "42410040",
"PMCID": "PMC13338177",
"ISSN": "2731-9121",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s44271-026-00491-y",
"language": "en",
"issued": {
"date-parts": [
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