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Direction selectivity in naturalistic action observation: distributed representations across the action observation network.

Code ↔ Paper

2 matches 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 2 matches
  1. [1] § Experimental design › Searchlight multivariate pattern analysis (MVPA) ↔ MVPA/Decoding_MVPA.m, lines 100–120 · score 0.64 · cross validation, confusion matrices, beta, Decoding, MVPA
  2. [2] § Experimental design › Searchlight multivariate pattern analysis (MVPA) › Searchlight model-based representational similarity analysis ↔ RSA/VIF.m, lines 1–3 · score 0.54 · Variance Inflation Factor, VIF, RSA

Paper

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

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The authors' code

MATLAB · 121 lines · 6 KB · no license · 1 match

  2. clear all
  4. for i = 1:27 %subject numbers to analyze
  6. % This script is a template that can be used for a decoding analysis on
  7. % brain image data. It is for people who have betas available from an
  8. % SPM.mat and want to automatically extract the relevant images used for
  9. % classification, as well as corresponding labels and decoding chunk numbers
  10. % (e.g. run numbers). If you don't have this available, then use
  11. % decoding_template_nobetas.m
  13. % Make sure the decoding toolbox and your favorite software (SPM or AFNI)
  14. % are on the Matlab path (e.g. addpath('/home/decoding_toolbox') )
  15. % % TDT
  16. % addpath('$ADD FULL PATH TO TDT TOOLBOX AS STRING OR MAKE THIS LINE A COMMENT IF IT IS ALREADY$')
  17. % assert(~isempty(which('decoding_defaults.m', 'function')), 'TDT not found in path, please add')
  18. % % SPM/AFNI
  19. % addpath('$ADD FULL PATH TO SPM/AFNI (if you need them) AS STRING OR MAKE THIS LINE A COMMENT IF IT IS ALREADY$')
  20. % assert((~isempty(which('spm.m', 'function')) || ~isempty(which('BrikInfo.m', 'function'))) , 'Neither SPM nor AFNI found in path, please add (or remove this assert if you really dont need to read brain images)')
  22. % Set defaults
  23. cfg = decoding_defaults;
  25. % Set the analysis that should be performed (default is 'searchlight')
  26. cfg.analysis = 'searchlight'; % standard alternatives: 'wholebrain', 'ROI' (pass ROIs in cfg.files.mask, see below)
  27. %cfg.searchlight.radius = 4; % use searchlight of radius 3 (by default in voxels), see more details below
  30. sub_str = sprintf('%02d', i);
  31. loc = ['sub-', sub_str];
  33. % Skip subject if SPM.mat does not exist
  34. if ~exist(fullfile(beta_loc, 'SPM.mat'), 'file')
  35. fprintf('Skipping subject %s: SPM.mat not found.\n', loc);
  36. continue;
  37. end
  40. % Set the output directory where data will be saved
  41. cfg.results.dir = ['D:/Zelal\Direction_Invariance\x_y_z_analysis\Decoding/',loc];
  44. % Set the filepath where your SPM.mat and all related betas are
  45. beta_loc = ['D:\Zelal/Direction_Invariance\x_y_z_analysis\Analysis\Subjects\', loc, '\1stLevel'];
  46. % set the mask file
  47. cfg.files.mask = ['D:\Zelal/Direction_Invariance\x_y_z_analysis\Analysis\Subjects\',loc,'\1stLevel\mask.nii'];
  50. % Set the label names to the regressor names which you want to use for
  51. % decoding
  52. % don't remember the names? -> run display_regressor_names(beta_loc)
  53. % infos on '*' (wildcard) or regexp -> help decoding_describe_data
  54. labelname1 = 'Actions_X';
  55. labelname2 = 'Actions_Y';
  56. labelname3 = 'Actions_Z';
  59. %% Set additional parameters
  60. % Set additional parameters manually if you want (see decoding.m or
  61. % decoding_defaults.m). Below some example parameters that you might want
  62. % to use a searchlight with radius 4 mm that is spherical:
  64. cfg.searchlight.unit = 'mm';
  65. cfg.searchlight.radius = 4;
  66. cfg.searchlight.spherical = 1;
  67. cfg.verbose = 2; % you want all information to be printed on screen
  68. % cfg.decoding.train.classification.model_parameters = '-s 0 -t 0 -c 1 -b 0 -q';
  70. % Enable scaling min0max1 (otherwise libsvm can get VERY slow)
  71. % if you dont need model parameters, and if you use libsvm, use:
  72. cfg.scale.method = 'min0max1';
  73. cfg.scale.estimation = 'all'; % scaling across all data is equivalent to no scaling (i.e. will yield the same results), it only changes the data range which allows libsvm to compute faster
  75. % if you like to change the decoding software (default: libsvm):
  76. % cfg.decoding.software = 'liblinear'; % for more, see decoding_toolbox\decoding_software\.
  77. % Note: cfg.decoding.software and cfg.software are easy to confuse.
  78. % cfg.decoding.software contains the decoding software (standard: libsvm)
  79. % cfg.software contains the data reading software (standard: SPM/AFNI)
  81. % Some other cool stuff
  82. % Check out
  83. % combine_designs(cfg, cfg2)
  84. % if you like to combine multiple designs in one cfg.
  86. %% Decide whether you want to see the searchlight/ROI/... during decoding
  87. cfg.plot_selected_voxels = 0; % 0: no plotting, 1: every step, 2: every second step, 100: every hundredth step...
  89. %% Add additional output measures if you like
  90. % See help decoding_transform_results for possible measures
  92. cfg.results.output = {'confusion_matrix','accuracy_minus_chance'}; % 'accuracy_minus_chance' by default
  94. % You can also use all methods that start with "transres_", e.g. use
  95. % cfg.results.output = {'SVM_pattern'};
  96. % will use the function transres_SVM_pattern.m to get the pattern from
  97. % linear svm weights (see Haufe et al, 2015, Neuroimage)
  99. %% Nothing needs to be changed below for a standard leave-one-run out cross
  100. %% validation analysis.
  102. % The following function extracts all beta names and corresponding run
  103. % numbers from the SPM.mat
  104. regressor_names = design_from_spm(beta_loc);
  106. % Extract all information for the cfg.files structure (labels will be [1 -1] if not changed above)
  107. analysis = decoding_describe_data(cfg,{labelname1 labelname2 labelname3},[1 2 3],regressor_names,beta_loc);
  110. cfg = analysis;
  112. % This creates the leave-one-run-out cross validation design:
  113. cfg.design = make_design_cv(cfg);
  114. %cfg.design = make_design_boot_cv(cfg,1);
  116. % Run decoding
  117. cfg.results.overwrite = 1;
  118. results = decoding(cfg);
  119. end
  120. %figure; heatmap(results.confusion_matrix.output{1}, 'Colormap', jet)

Decoding_MVPA.m, no license · at the source

Overview

Authors: Zelal Eltaş1,2, Murat B Tunca1,2,3, Burcu A Urgen1,4,2
ORCID iDs: Zelal Eltaş, Murat B Tunca, Burcu A Urgen
  1. Department of Neuroscience, Bilkent University, Ankara, Turkey
  2. Aysel Sabuncu Brain Research Center, National Magnetic Resonance Research Center (UMRAM) Bilkent University, 06800 Ankara, Turkey
  3. Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig-Universität Gießen, Gießen, Germany
  4. Department of Psychology, Bilkent University, Ankara, Turkey
Institutions: Bilkent University (Türkiye); Justus-Liebig-Universität Gießen (Germany)
Journal: Brain structure & function, volume 231, issue 5, article 60
Dates: received 18 October 2025; accepted 29 March 2026; published online 5 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1007/s00429-026-03111-x · PMID 42082808 · PMCID PMC13139285 · OpenAlex W4415354505
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.20.683194
Status: code verified
Categories: human (organism)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Visual perception, Direction selectivity, Action observation network (AON), Action perception, Multivariate pattern analysis (MVPA), Representational similarity analysis (RSA)
MeSH: Brain*, Motion Perception*, Visual Perception*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Human Pose and Action Recognition (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: Turkish Health Institutes Presidency (TUSEB) (37734)
Citations: not cited yet (Europe PMC); 86 references in the paper
Research resources: RRID:SCR_001362

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 2 matches between paragraphs and lines of code.

OSF 9sqyd

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (11)
Size: 108 files, 11 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CoSMoMVPA (5 files), Image Processing Toolbox (2 files), SPM (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/9sqyd/](https:/osf.io/9sqyd)

The paper's code and data availability statement is in the Data section.

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;
  • 11 scripts, each with its path and the digest of its content;
  • 2 matches 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.

Code and data availability statement

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

  • it points to the authors' code: OSF 9sqyd
  • it says that the data are available on request

Read it in the paper: doi.org/10.1007/s00429-026-03111-x.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 11 MeSH terms, 1 funder, 83 references, 1 RRID.

Cite

This paper

Eltaş, Z., Tunca, M. B., & Urgen, B. A. (2026). Direction selectivity in naturalistic action observation: distributed representations across the action observation network. Brain structure & function, 231(5), 60. https://doi.org/10.1007/s00429-026-03111-x

BibTeX

@article{eltas2026direction,
author = {Eltaş, Zelal and Tunca, Murat B and Urgen, Burcu A},
title = {{Direction selectivity in naturalistic action observation: distributed representations across the action observation network}},
journal = {Brain structure \& function},
year = {2026},
month = may,
volume = {231},
number = {5},
pages = {60},
publisher = {Springer Science+Business Media},
issn = {1863-2653},
doi = {10.1007/s00429-026-03111-x},
url = {https://doi.org/10.1007/s00429-026-03111-x},
pmid = {42082808},
pmcid = {PMC13139285}
}

RIS

TY - JOUR
AU - Eltaş, Zelal
AU - Tunca, Murat B
AU - Urgen, Burcu A
TI - Direction selectivity in naturalistic action observation: distributed representations across the action observation network
T2 - Brain structure & function
J2 - Brain Struct Funct
PY - 2026
DA - 2026/05/05
VL - 231
IS - 5
SP - 60
SN - 1863-2653
PB - Springer Science+Business Media
DO - 10.1007/s00429-026-03111-x
UR - https://doi.org/10.1007/s00429-026-03111-x
LA - en
ER -

CSL-JSON

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