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Latent neural architecture organising shared aesthetic evaluations of visual artworks.

Code ↔ Paper

16 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 16 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Neuroimaging analyses › Specificity and generalisability of neural signatures ↔ Main_codes/m4_Model_generalization.m, lines 16–43 · score 0.81 · working memory task, gambling task, contrast maps, signature response, cosine, valence
  2. [2] § Methods › Trial-wise fMRI response estimation ↔ Validation_codes/Validation_RSA/run_RSAsearchlight_validation.m, lines 48–50 · score 0.78 · FITHRF GLMDENOISE RR, single trial betas, GLMsingle, Art
  3. [3] § Methods › Equipment and software ↔ Utilities/cluster_identification.m, lines 19–76 · score 0.74 · Melbourne Subcortex Atlas, Cluster identification, CIFTI, mmp, locate, HCP
  4. [4] § Methods › Neuroimaging analyses › Specificity and generalisability of neural signatures ↔ Main_codes/m3_Predict_crossmodel.m, lines 36–104 · score 0.65 · cross prediction accuracies, fold cross validation, dissociation, permutation, models
  5. [5] § Methods › Behavioural data analysis › Construction of the aesthetic agreement matrix ↔ Main_codes/m1_AAT_Grades.m, lines 1–54 · score 0.64 · standard deviations, negative correlations, AI, behavioural, Pearson, validated
  6. [6] § Methods › Trial-wise fMRI response estimation ↔ Validation_codes/Validation_RSA/Validation_distance_RSA_subcortical.m, lines 33–36 · score 0.64 · FITHRF GLMDENOISE RR, GLMsingle, Art
  7. [7] § Methods › Neuroimaging analyses › Quantification of regional importance ↔ Utilities/fast_haufe.m, the whole file · a weak match · score 0.63 · Haufe transformation, forward model, backward, fitted, weights
  8. [8] § Results › Neural signatures of aesthetic experience are linked to distributed neural systems ↔ Utilities/fast_haufe.m, the whole file · a weak match · score 0.63 · Haufe transformed, forward models, model weights, fitted
  9. [9] § Methods › Neuroimaging analyses › Multivariate neural decoding ↔ Main_codes/m3_Predict_crossmodel.m, lines 36–104 · score 0.61 · fold cross validation, predictive accuracy, error, permutation, correlations
  10. [10] § Methods › Behavioural data analysis › Decomposition of shared aesthetic experience ↔ Utilities/parallel_analysis.m, the whole file · a weak match · score 0.60 · principal component, simulations, parallel, Jolliffe, eigenvalues, variance
  11. [11] § Results › Neural representation of the latent aesthetic space is modulated by expertise in arts ↔ Validation_codes/Validation_RSA/Validation_RSA_TFCE_correction.m, lines 55–101 · score 0.57 · Threshold Free Cluster, Enhancement, TFCE, fMRI, RSA
  12. [12] § Methods › Behavioural data analysis › Construction of the aesthetic agreement matrix ↔ Validation_codes/Validation_matrixPCA_reliability.m, lines 1–8 · score 0.54 · aesthetic agreement matrix, aesthetic ratings, reliability, Pearson, stimulus, correlation
  13. [13] § Results › Neural representation of the latent aesthetic space is modulated by expertise in arts ↔ Main_codes/m6_RSA_trials.m, lines 136–157 · score 0.54 · Threshold Free Cluster, Enhancement, TFCE, RSA
  14. [14] § Results › Neural signatures of aesthetic experience generalise to core cognitive processes ↔ Main_codes/m4_Model_generalization.m, lines 16–43 · score 0.54 · gambling tasks, working memory, valence, HCP, signatures, semantics
  15. [15] § Results › Neural representation of the latent aesthetic space is modulated by expertise in arts ↔ Utilities/cluster_identification.m, lines 19–76 · score 0.51 · Melbourne Subcortex Atlas, located, medial, cortical, RSA
  16. [16] § Methods › Neuroimaging analyses › Multivariate neural decoding ↔ Main_codes/m2_Predict_Dimension1.m, lines 27–52 · score 0.50 · fold cross validation, RMSE, error, permutation, predictive, correlations

Paper

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The authors' code

MATLAB · 45 lines · 2 KB · GPL-3.0 · 2 matches

  1. %% get the prediction models
  2. G1_data = load('Data/ART_G1_4lvl_predictiondata.mat');
  3. G2_data = load('Data/ART_G2_4lvl_predictiondata.mat');
  5. svrobj_G1 = svr({'C=1', 'optimizer="andre"', kernel('linear')});
  6. svrobj_G2 = svr({'C=1', 'optimizer="andre"', kernel('linear')});
  8. dataobj_G1 = data('spider data', double(G1_data.AATpredition.dat)', G1_data.AATpredition.Y);
  9. dataobj_G2 = data('spider data', double(G2_data.AATpredition.dat)', G2_data.AATpredition.Y);
  10. [~, svrobj_G1] = train(svrobj_G1, dataobj_G1, loss);
  11. [~, svrobj_G2] = train(svrobj_G2, dataobj_G2, loss);
  13. weights_G1 = get_w(svrobj_G1)';
  14. weights_G2 = get_w(svrobj_G2)';
  16. %% load contrast maps from HCP dataset
  18. load('Data/HCP_generalization_contrast.mat')
  20. % gambling task
  21. similarity_generalvalence_map_G1 = canlab_pattern_similarity(general_valence_contrast', weights_G1, 'cosine_similarity');
  22. similarity_generalvalence_map_G2 = canlab_pattern_similarity(general_valence_contrast', weights_G2, 'cosine_similarity');
  24. C1_hex = {'#9dc4db','#e9a888'};
  25. C1_RGB = cellfun(@(x) sscanf(x(2:end),'%2x%2x%2x',[1 3])/255, C1_hex, 'UniformOutput', false);
  26. labels = {'Semantic','Value'};
  27. barplot_columns([similarity_generalvalence_map_G1,similarity_generalvalence_map_G2],...
  28. 'names',labels,'color',C1_RGB,'nostars','MarkerSize',5);
  29. ylim([-0.12,0.12])
  30. set(gcf,'position',[10,10,550,750])
  31. ylabel 'Signature Response (Cosine Similarity)'
  33. % working memory task
  34. similarity_placevsface_map_G1 = canlab_pattern_similarity(placevsface_contrast', weights_G1, 'cosine_similarity');
  35. similarity_placevsface_map_G2 = canlab_pattern_similarity(placevsface_contrast', weights_G2, 'cosine_similarity');
  37. C2_hex = {'#b8262b','#316eac'};
  38. C2_RGB = cellfun(@(x) sscanf(x(2:end),'%2x%2x%2x',[1 3])/255, C2_hex, 'UniformOutput', false);
  39. labels = {'Semantic','Value'};
  40. barplot_columns([similarity_placevsface_map_G1,similarity_placevsface_map_G2],'names',labels,'color',C2_RGB,'nostars');
  41. ylim([-0.1,0.2])
  42. set(gcf,'position',[10,10,550,750])
  43. ylabel 'Signature Response (Cosine Similarity)'

m4_Model_generalization.m at commit e6d46da, under GPL-3.0 · at the source

Overview

Authors: Xinyu Liang1, Kaixiang Zhuang1, Yun Wang1, Yueting Su1, Jianfeng Feng1,2, Feng Zhou3, Benjamin Becker4,5, Deniz Vatansever1,2
ORCID iDs: Xinyu Liang, Kaixiang Zhuang, Yun Wang, Yueting Su, Jianfeng Feng, Feng Zhou, Benjamin Becker, Deniz Vatansever
  1. Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
  2. Zhangjiang International Brain Imaging Centre, Fudan University, Shanghai, China
  3. Faculty of Psychology, Southwest University, Chongqing, China
  4. MIND & AI Lab, Department of Psychology, University of Hong Kong, Hong Kong SAR, China
  5. SRT AI, Society & Social Dynamics, Faculty of Social Sciences, University of Hong Kong, Hong Kong SAR, China
Institutions: Shanghai Medical College of Fudan University (China); Fudan University (China); Southwest University (China); University of Hong Kong (Hong Kong SAR China)
Journal: Nature communications, volume 17, issue 1, article 6510
Dates: received 15 September 2025; accepted 30 April 2026; published online 16 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73153-6 · PMID 42143057 · PMCID PMC13377068 · OpenAlex W7161409037
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), pain (population), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Perception, Decision, Functional magnetic resonance imaging, Neural decoding
MeSH: Brain*, Esthetics*, Paintings*, Visual Perception*, Adult, Art, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Semantics, Young Adult (* major topic)
Topic: Aesthetic Perception and Analysis (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: China Postdoctoral Science Foundation (2022M720818); Ministry of Science and Technology of the People's Republic of China (2022ZD0207900); Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology) (2022ZD0207900)
Citations: cited by 2 papers (Europe PMC); 115 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

cognizelab/ARTyficial

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e6d46da781102d6be76b5cc72482bea608e63b74, 9 April 2026
Languages: MATLAB (23), Jupyter (7)
Size: 84 files, 30 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, 7 notebooks
Not found: environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (14 files), cifti-matlab (6 files), Matplotlib (6 files), NumPy (6 files), SciPy (6 files), seaborn (6 files), pandas (5 files), CoSMoMVPA (4 files), GIfTI library for MATLAB (3 files), scikit-learn (3 files), h5py (1 file), Pillow (1 file), Pingouin (1 file), PyTorch (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
32 files
At the source: github.com/cognizelab/ARTyficial

Zenodo 19486642

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-73153-6.

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-73153-6.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 13 MeSH terms, 3 funders, 106 references.

Cite

This paper

Liang, X., Zhuang, K., Wang, Y., Su, Y., Feng, J., Zhou, F., Becker, B., & Vatansever, D. (2026). Latent neural architecture organising shared aesthetic evaluations of visual artworks. Nature communications, 17(1), 6510. https://doi.org/10.1038/s41467-026-73153-6

BibTeX

@article{liang2026latent,
author = {Liang, Xinyu and Zhuang, Kaixiang and Wang, Yun and Su, Yueting and Feng, Jianfeng and Zhou, Feng and Becker, Benjamin and Vatansever, Deniz},
title = {{Latent neural architecture organising shared aesthetic evaluations of visual artworks}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6510},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73153-6},
url = {https://doi.org/10.1038/s41467-026-73153-6},
pmid = {42143057},
pmcid = {PMC13377068}
}

RIS

TY - JOUR
AU - Liang, Xinyu
AU - Zhuang, Kaixiang
AU - Wang, Yun
AU - Su, Yueting
AU - Feng, Jianfeng
AU - Zhou, Feng
AU - Becker, Benjamin
AU - Vatansever, Deniz
TI - Latent neural architecture organising shared aesthetic evaluations of visual artworks
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/16
VL - 17
IS - 1
SP - 6510
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73153-6
UR - https://doi.org/10.1038/s41467-026-73153-6
LA - en
ER -

CSL-JSON

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