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Rotation-tolerant representations elucidate the time-course of high-level object processing.

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The 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § 2. Methods › 2.3. EEG acquisition and pre-processing ↔ code/A_preprocessing.m, lines 19–115 · score 0.86 · interpolated bad channels, 100–800 ms, created epochs, EEGLAB, filtered, sequence
  2. [2] § 2. Methods › 2.3. EEG acquisition and pre-processing ↔ design_and_analysis_plans_2020_03/preprocessing_invariance.m, the whole file · a weak match · score 0.62 · created epochs, EEGLAB, filtered, 100 ms, onset
  3. [3] § 2. Methods › 2.5. Exploratory analyses › 2.5.2. Representational similarity analysis. ↔ code/C3_RSA_correlate_eeg_models.m, lines 34–60 · score 0.62 · lower triangle, model RDM, RSA, matrices, correlated, EEG

Paper

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The authors' code

MATLAB · 115 lines · 4.7 KB · no license · 1 match

  1. %--------------------------------------------------------------------------
  2. % [] = A_preprocessing(sub_num)
  3. %
  4. % Input: participant number
  5. % Output: none
  6. %
  7. % Pre-processed data is saved in 'derivatives' folder
  8. %
  9. % EEG pre-processing:
  10. % - Interpolate bad channels
  11. % - Re-reference to the average reference
  12. % - High-pass (0.1 Hz) and low-pass (100 Hz) filters
  13. % - Down-sample to 250 Hz
  14. % - Epoch from -100 to 800 ms relative to stimulus onset
  15. %
  16. % Toolboxes needed: eeglab, cosmomvpa
  17. %--------------------------------------------------------------------------
  19. function [] = A_preprocessing(sub_num)
  21. %% Add eeglab & cosmomvpa to the path
  22. if ~ismac
  23. addpath('../../CoSMoMVPA/mvpa')
  24. addpath('../../eeglab')
  25. end
  26. eeglab;
  28. data_dir = '../data';
  30. %% Load the EEG data, filter, and downsample
  31. contfn = sprintf('%s/derivatives/eeglab/sub-%02i_task-rsvp_continuous.set',data_dir,sub_num);
  33. if isfile(contfn)
  34. fprintf('Using %s\n',contfn)
  35. EEG_cont = pop_loadset(contfn);
  36. else
  37. % load EEG file
  38. EEG_raw = pop_loadbv(fullfile(data_dir,num2str(sub_num,'sub-%02i'),'eeg'),sprintf('sub-%02i_task-rsvp_eeg.vhdr',sub_num));
  39. EEG_raw = eeg_checkset(EEG_raw);
  40. EEG_raw.setname = sub_num;
  41. EEG_raw = eeg_checkset(EEG_raw);
  43. % get/interpolate bad channels - needed for CSD
  44. [~, badidx] = pop_rejchan(EEG_raw, 'elec',1:EEG_raw.nbchan ,'threshold',5,'norm','on','measure','kurt');
  45. EEG_raw = eeg_interp(EEG_raw,badidx);
  46. EEG_raw = eeg_checkset(EEG_raw);
  48. % Re-reference to average reference ana add FCz data back in, to make 128 channels total
  49. EEG_raw=pop_chanedit(EEG_raw, 'append',127,'changefield',{128 'labels' 'FCz'},'changefield',{128 'theta' '0'},...
  50. 'changefield',{128 'radius' '0.12778'},'changefield',{128 'X' '0.39073'},'changefield',...
  51. {128 'Y' '0'},'changefield',{128 'Z' '0.9205'},'changefield',{128 'sph_theta' '0'},...
  52. 'changefield',{128 'sph_phi' '67'},'changefield',{128 'sph_radius' '1'},'setref',{'128' 'FCz'});
  53. EEG_raw = eeg_checkset(EEG_raw);
  55. % Re-reference to average reference
  56. EEG_raw=pop_chanedit(EEG_raw, 'setref',{'128' 'FCz'});
  57. EEG_raw = eeg_checkset(EEG_raw);
  58. EEG_raw = pop_reref( EEG_raw, [],'refloc',struct('labels',{'FCz'},'sph_radius',{1},'sph_theta',{0},'sph_phi',{67},...
  59. 'theta',{0},'radius',{0.12778},'X',{0.39073},'Y',{0},'Z',{0.9205},'type',{''},'ref',{'FCz'},'urchan',{[]},...
  60. 'datachan',{0})); %the reference channel is returned to FCz
  61. EEG_raw = eeg_checkset(EEG_raw);
  63. % high pass filter
  64. EEG_raw = pop_eegfiltnew(EEG_raw, 0.1,[]);
  66. % low pass filter
  67. EEG_raw = pop_eegfiltnew(EEG_raw, [],100);
  69. % downsample
  70. EEG_cont = pop_resample(EEG_raw, 250);
  71. EEG_cont = eeg_checkset(EEG_cont);
  73. % save the data
  74. pop_saveset(EEG_cont,contfn);
  75. end
  77. %% add eventinfo to events
  78. eventsfncsv = sprintf('%s/sub-%02i/eeg/sub-%02i_task-rsvp_events.csv',data_dir,sub_num,sub_num);
  79. eventsfntsv = sprintf('%s/sub-%02i/eeg/sub-%02i_task-rsvp_events.tsv',data_dir,sub_num,sub_num);
  81. % Read the event list
  82. eventlist = readtable(eventsfncsv);
  84. % Check if we have all the events
  85. idx = find(strcmp({EEG_cont.event.type},'E 1'));
  86. nrem=0;
  87. if length(idx) < size(eventlist,1)
  88. idx_seq = strcmp({EEG_cont.event.type},'E 3');
  89. nseq = sum(idx_seq);
  90. nseqr = length(unique(eventlist.sequencenumber));
  91. warning(sprintf('sub-%02i: found only %i sequences instead of %i. Removing first %i sequences',partid,nseq,nseqr,nseqr-nseq))
  92. eventlist = eventlist(eventlist.sequencenumber>=(nseqr-nseq),:);
  93. nrem = length(idx)-size(eventlist,1);
  94. idx = idx((nrem+1):end);
  95. end
  96. onset = vertcat(EEG_cont.event(idx).latency);
  97. duration = ones(size(onset));
  98. neweventlist = [table(onset,duration,'VariableNames',{'onset','duration'}) eventlist];
  100. writetable(neweventlist,eventsfntsv,'filetype','text','Delimiter','\t')
  102. %% create epochs
  103. EEG_epoch = pop_epoch(EEG_cont, {'E 1'}, [-0.100 0.800]);
  104. EEG_epoch = eeg_checkset(EEG_epoch);
  106. %% convert to cosmo
  107. ds = cosmo_flatten(permute(EEG_epoch.data(:,:,(nrem+1):end),[3 1 2]),{'chan','time'},{{EEG_epoch.chanlocs.labels},EEG_epoch.times},2);
  108. ds.a.meeg=struct(); %or cosmo thinks it's not a meeg ds
  109. ds.sa = table2struct(eventlist,'ToScalar',true);
  110. cosmo_check_dataset(ds,'meeg');
  112. %% save epochs
  113. save(sprintf('%s/derivatives/cosmomvpa/sub-%02i_task-rsvp_cosmomvpa.mat',data_dir,sub_num),'ds','-v7.3')
  115. end

A_preprocessing.m, no license · at the source

Overview

Authors: Denise Moerel1,2, Tijl Grootswagers1,2,3, Amanda K. Robinson1,4,5, Patrick Engeler1, Alex O. Holcombe1, Thomas A. Carlson1
ORCID iDs: Denise Moerel, Amanda K. Robinson, Alex O. Holcombe
  1. School of Psychology, University of Sydney, Sydney, Australia
  2. The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
  3. School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
  4. Queensland Brain Institute, The University of Queensland, Brisbane, Australia
  5. School of Psychology, the University of Queensland, Brisbane, Australia
Institutions: The University of Sydney (Australia); Western Sydney University (Australia); The University of Queensland (Australia)
Journal: PloS one, volume 21, issue 4, article e0347992
Dates: received 30 November 2025; accepted 9 April 2026; published online 29 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0347992 · PMID 42054380 · PMCID PMC13127968 · OpenAlex W4292103860
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/wp73u
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Preprocessing, Statistics, Machine learning, Physiology & signal measures
MeSH: Pattern Recognition, Visual*, Visual Perception*, Electroencephalography, Female, Humans, Male, Photic Stimulation, Rotation, Time Factors (* major topic)
Journal subjects: Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Biology and Life Sciences, Physiology, Electrophysiology, Neurophysiology, Neuroscience, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Computational Biology, Computational Neuroscience, Coding Mechanisms, Organisms, Eukaryota, Animals, Vertebrates, Amniotes, Mammals, Primates, Zoology, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Vision, Psychology, Social Sciences, Computer and Information Sciences, Information Technology, Information Processing, Sensory Physiology, Visual System, Sensory Systems, Anatomy, Brain, Visual Cortex, Engineering and Technology, Electronics Engineering, Electronics, Electrodes, Reference Electrodes
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Australian Research Council (DP160101300, DP200101787, DE230100380, DE200101159)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Despite the very different retinal images that result from different viewing conditions, humans have little difficulty recognising visual objects in varying circumstances. One source of variability is 2-D rotation, which results in an object having different orientations. Here, we studied how the brain transforms rotated object images into object representations that are tolerant to rotation. We measured time-varying electroencephalography responses to object images shown in eight different orientations, presented at either 5 Hz or 20 Hz. We used multivariate classification to assess when rotation-tolerant object information emerged, and whether the rotation-tolerant processing would be limited at the faster presentation rate. We compared this to fixed-rotation measures of object decoding, where the classifier is trained and tested on the same orientation. Our results showed that both fixed-rotation and rotation-tolerant object decoding emerged at an early stage of processing, less than 100 ms after stimulus onset. However, rotation-tolerant information peaked later than fixed-rotation information, suggesting rotation-tolerant object representations are most prominent during a late stage of processing, around 200 ms after stimulus onset. Both fixed-rotation and rotation-tolerant object information was lower for the 20 Hz compared to 5 Hz presentation rate, which suggests that object information processing is disrupted, but not eliminated, for fast presentation rates. Our results show that object information arises at similar times in the brain regardless of whether it is investigated with the fixed-rotation or rotation-tolerant object decoding method. An object representation that is tolerant to rotation and generalises across different exemplars of the same object is established in later stages of processing.

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 3 matches between paragraphs and lines of code.

OSF r93es

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (9), Python (1)
Size: 83 files, 10 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CoSMoMVPA (7 files), Statistics and Machine Learning Toolbox (6 files), EEGLAB (2 files), ERPLAB (1 file), Image Processing Toolbox (1 file), NumPy (1 file), pandas (1 file), PsychoPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
10 files
At the source: doi.org/10.17605/OSF.IO/R93ES

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

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  • 10 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data Availability

The experiment code, analysis codes, results, and figures can be found on the Open Science Framework: https://doi.org/10.17605/OSF.IO/R93ES. The analysis plan was not officially pre-registered, but a timestamped document with the experiment design and analysis plan can also be found there. The Representational Similarity Analysis was not part of the original analysis plan but was added as an exploratory analysis to be able to control for visual models. The data for this study can be found via OpenNeuro: https://doi.org/10.18112/openneuro.ds004252.v1.1.0.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 MeSH terms, 1 funder, 51 references.

Cite

This paper

Moerel, D., Grootswagers, T., Robinson, A. K., Engeler, P., Holcombe, A. O., & Carlson, T. A. (2026). Rotation-tolerant representations elucidate the time-course of high-level object processing. PloS one, 21(4), e0347992. https://doi.org/10.1371/journal.pone.0347992

BibTeX

@article{moerel2026rotation,
author = {Moerel, Denise and Grootswagers, Tijl and Robinson, Amanda K. and Engeler, Patrick and Holcombe, Alex O. and Carlson, Thomas A.},
title = {{Rotation-tolerant representations elucidate the time-course of high-level object processing}},
journal = {PloS one},
year = {2026},
month = apr,
volume = {21},
number = {4},
pages = {e0347992},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0347992},
url = {https://doi.org/10.1371/journal.pone.0347992},
pmid = {42054380},
pmcid = {PMC13127968}
}

RIS

TY - JOUR
AU - Moerel, Denise
AU - Grootswagers, Tijl
AU - Robinson, Amanda K.
AU - Engeler, Patrick
AU - Holcombe, Alex O.
AU - Carlson, Thomas A.
TI - Rotation-tolerant representations elucidate the time-course of high-level object processing
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/04/29
VL - 21
IS - 4
SP - e0347992
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0347992
UR - https://doi.org/10.1371/journal.pone.0347992
LA - en
ER -

CSL-JSON

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