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Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task.

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  1. [1] § Materials and methods › Representational similarity analysis ↔ Code/correlation_rdm_newpca.m, lines 84–158 · score 0.51 · dissimilarity matrices, PCA, Spearman, RDMs, correlation, EEG

Paper

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The authors' code

MATLAB · 159 lines · 4.7 KB · no license · 1 match

  1. close all
  2. clear all
  3. clc
  5. ss=[3:13,15:30];
  6. addpath('/Users/sanjeev/Documents/Projects/toolboxes/fieldtrip-20220416');
  7. ft_defaults()
  8. addpath(genpath('/Users/sanjeev/Documents/Projects/toolboxes/CoSMoMVPA-master'));
  9. for s=ss
  11. %Load data
  12. if s<10
  13. load(['../Data/preprocessed/sub-0' num2str(s) '/timelock/timelock',num2str(s) '.mat']);
  14. else
  15. load(['../Data/preprocessed/sub-' num2str(s) '/timelock/timelock',num2str(s) '.mat']);
  16. end
  18. for ii =1:length(timelock.trialinfo)
  20. if timelock.trialinfo(ii,2)== 1;
  21. timelock.trialinfo(ii,5) = timelock.trialinfo(ii,3);
  23. else
  24. timelock.trialinfo(ii,5) = timelock.trialinfo(ii,3) + 50;
  25. end
  27. end
  29. %Baseline correct
  30. cfg=[];
  31. cfg.baseline=[-0.5,0];
  32. cfg.parameter='trial';
  33. timelock=ft_timelockbaseline(cfg,timelock);
  34. res.time=timelock.time;
  36. %choose time window
  37. cfg=[];
  38. cfg.latency=[-0.25,1.45];
  39. timelock=ft_selectdata(cfg,timelock);
  41. %get original time values
  42. res.time_original=timelock.time;
  44. %convert to cosmomvpa struct
  45. ds=cosmo_meeg_dataset(timelock);
  47. %Assign all possible trial info
  48. ds.sa.targets=timelock.trialinfo(:,5);
  50. %Store intact DS
  51. ds1=ds;
  53. %Remove useless feature data
  54. ds=cosmo_remove_useless_data(ds);
  56. %preassign stuff
  57. ds0=ds;
  58. nch=2;
  59. nIter=50;
  60. res.time=-0.225:0.01:1.425;
  61. res.diss=zeros(length(unique(ds.sa.targets)),length(unique(ds.sa.targets)),length(res.time),nch*nIter);
  62. res.n_feat=zeros(nch*nIter,length(res.time));
  64. %start loop across iterations
  65. ix=0;
  66. tic
  67. for iter=1:nIter
  69. %increase counter
  70. ix=ix+2;
  72. %get ds
  73. ds=ds0;
  75. %build chunks
  76. ds.sa.chunks=[1:length(ds.sa.targets)]';
  77. for cond=unique(ds.sa.targets)'
  78. cond_ind=find(ds.sa.targets==cond);
  79. chks=repmat([1:2]',ceil(length(cond_ind)/2));
  80. chks=Shuffle(chks);
  81. ds.sa.chunks(ds.sa.targets==cond)=chks(1:length(cond_ind));
  82. end
  84. %start loop across time
  85. for t=1:length(res.time)
  87. tp=find(res.time_original>res.time(t)-0.025&res.time_original<=res.time(t)+0.025);
  89. for cv=1:2
  91. %put results in the right place
  92. if cv==1
  93. res_ind=ix-1;
  94. else
  95. res_ind=ix;
  96. end
  98. if cv==1
  99. ds_class=cosmo_slice(ds,ds.sa.chunks==1,1);
  100. ds_pca=cosmo_slice(ds,ds.sa.chunks==2,1);
  101. else
  102. ds_class=cosmo_slice(ds,ds.sa.chunks==2,1);
  103. ds_pca=cosmo_slice(ds,ds.sa.chunks==1,1);
  104. end
  105. ds_class=cosmo_slice(ds_class,ismember(ds_class.fa.time,tp),2);
  106. ds_pca=cosmo_slice(ds_pca,ismember(ds_pca.fa.time,tp),2);
  108. %do a pca one one dataset
  109. n_feat=length(unique(ds_pca.fa.chan));
  110. [coeff,x,LATENT,TSQUARED,x_exp,mu]=pca(ds_pca.samples);
  111. for ccx=1:length(x_exp)
  112. if sum(x_exp(1:ccx))>=99
  113. n_feat=ccx;
  114. break
  115. end
  116. end
  117. res.n_feat(res_ind,t)=n_feat;
  119. %apply pca to other dataset
  120. ds_class.samples=(ds_class.samples-mu)*coeff(:,1:n_feat);
  121. ds_class.fa.chan=1:n_feat;
  122. ds_class.fa.time=ones(size(ds_class.fa.chan));
  123. ds_class.a.fdim.values{1}=1:n_feat;
  125. %average samples
  126. ds_class.sa.chunks=ones(size(ds_class.sa.targets));
  127. cx=0;
  128. for cond=unique(ds_class.sa.targets)'
  129. dsx=cosmo_slice(ds_class,ds_class.sa.targets==cond);
  130. if not(isempty(dsx.samples))
  131. cx=cx+1;
  132. ds_cond{cx}=cosmo_average_samples(dsx);
  133. end
  134. end
  135. ds_class=cosmo_stack(ds_cond,1);
  136. clear ds_cond
  138. %get similarity
  139. args.metric='Spearman';
  140. res_ds=cosmo_dissimilarity_matrix_measure(ds_class,args);
  141. [d,l,v]=cosmo_unflatten(res_ds,1);
  142. res.diss(:,:,t,res_ind)=d;
  144. end
  146. end
  148. display(['subject #',num2str(s),' - analysis #',num2str(iter),'/',num2str(nIter),' - ',num2str(round(toc/60,1)),'min passed.']);
  150. end
  152. res.n_feat=mean(res.n_feat,1);
  153. res.diss=mean(res.diss,4);
  155. %Save
  156. save(['../results/RDMs_EEG_mod/correlation_rdm_newpca',num2str(s)],'res');
  158. end

correlation_rdm_newpca.m, no license · at the source

Overview

Authors: Sanjeev Nara1,2,3, Lara Becker4, Lilly Hillebrand4, Rongming Xiang1, Daniel Kaiser1,5,6,7
ORCID iDs: Sanjeev Nara
  1. Present Address: Neural Computation Group, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Giessen,Giessen, Germany
  2. School of Biosciences and Bioengineering, IIT Mandi,Mandi, 175075 Himachal Pradesh India
  3. Centre for Human-Computer Interaction (CHCi), IIT Mandi,Mandi, Himachal Pradesh India
  4. Department of Psychology, Justus Liebig University Giessen,Giessen, Germany
  5. Center for Mind, Brain and Behavior (CMBB), Universities of Giessen, Marburg, and Darmstadt,Marburg, Germany
  6. Center for Applied Computer Science and Data Science (ZAD), Justus Liebig University Giessen,Giessen, Germany
  7. Cluster of Excellence “The Adaptive Mind”, Universities of Giessen, Marburg, and Darmstadt,Giessen, Germany
Institutions: Justus-Liebig-Universität Gießen (Germany); Indian Institute of Technology Mandi (India)
Journal: Scientific reports, volume 16, issue 1, article 15217
Dates: received 30 April 2025; accepted 24 March 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-46149-x · PMID 42141016 · PMCID PMC13179325 · OpenAlex W4409836820
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.22.649954
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, Connectivity, Physiology & signal measures, Machine learning
Keywords: Neuroscience, Cognitive neuroscience, Perception
MeSH: Beauty*, Visual Perception*, Adult, Electroencephalography, Female, Humans, Judgment, Male, Photic Stimulation, Time Factors, Young Adult (* major topic)
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (533717223, KA4683/6-1, EXC 3066/1, 536053998)
Citations: cited by 1 paper (Europe PMC); 30 references in the paper

Abstract

Understanding the neural correlates of aesthetic experiences in natural environments is a central question in neuroaesthetics. A previous EEG study (Kaiser, 2022) identified early and temporally sustained neural representations of visual scene beauty. These results were obtained with long presentation durations (1450 ms) and with explicit beauty judgments, rendering it unclear how presentation time and task demands shape the neural correlates of scene beauty. In two EEG experiments, we replicated this study while varying presentation time and task. Experiment 1 tested whether reducing stimulus presentation time from 1450 ms to 100 ms altered neural representations of beauty. Experiment 2 examined whether beauty-related representations prevailed when participants performed an orthogonal task instead of explicitly judging beauty. Representational similarity analysis revealed that beauty-related neural representations emerged early (within 150–200 ms post-stimulus) and were sustained over time, in line with previous findings. Critically, we found that neither reduced presentation time nor the absence of an explicit beauty judgment significantly altered beauty-related neural dynamics. These results suggest that the neural correlates of scene beauty are relatively robust to stimulus presentation and task regimes, providing a potential correlate of the spontaneous perception of beauty in natural environments.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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

OSF fj6sq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (6)
Size: 67 files, 6 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 (2 files), FieldTrip (2 files), Statistics and Machine Learning Toolbox (2 files), boundedline (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
6 files
At the source: osf.io/fj6sq

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;
  • 6 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

Data and code are publicly accessible on the Open Science Framework (OSF) via this link: https://osf.io/fj6sq.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added Deutsche Forschungsgemeinschaft: 533717223, KA4683/6-1, EXC 3066/1, 536053998

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 11 MeSH terms, 30 references.

Cite

This paper

Nara, S., Becker, L., Hillebrand, L., Xiang, R., & Kaiser, D. (2026). Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task. Scientific reports, 16(1), 15217. https://doi.org/10.1038/s41598-026-46149-x

BibTeX

@article{nara2026dynamic,
author = {Nara, Sanjeev and Becker, Lara and Hillebrand, Lilly and Xiang, Rongming and Kaiser, Daniel},
title = {{Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {15217},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-46149-x},
url = {https://doi.org/10.1038/s41598-026-46149-x},
pmid = {42141016},
pmcid = {PMC13179325}
}

RIS

TY - JOUR
AU - Nara, Sanjeev
AU - Becker, Lara
AU - Hillebrand, Lilly
AU - Xiang, Rongming
AU - Kaiser, Daniel
TI - Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/15
VL - 16
IS - 1
SP - 15217
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-46149-x
UR - https://doi.org/10.1038/s41598-026-46149-x
LA - en
ER -

CSL-JSON

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