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Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.

Code ↔ Paper

4 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 4 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › EEG data collection › EEG recording and preprocessing ↔ analysis/fast_eeg/step1_preprocessing_fast_eeg.m, the whole file · a weak match · score 0.89 · low pass filter, high pass filter, 100–800 ms, EEGLAB, stimulus onset, preprocessing
  2. [2] § Materials and methods › EEG data collection › EEG recording and preprocessing ↔ analysis/slow_eeg/step1_preprocessing_slow_eeg.m, the whole file · a weak match · score 0.88 · low pass filter, high pass filter, 100–800 ms, EEGLAB, stimulus onset, preprocessing
  3. [3] § Materials and methods › Representational similarity analysis › Experiment 1 neural RDMs ↔ analysis/fast_eeg/step1_preprocessing_fast_eeg.m, the whole file · a weak match · score 0.70 · 100–800 ms, stimulus onset, raw, epoch, 100 ms, EEG
  4. [4] § Materials and methods › Representational similarity analysis › Experiment 1 neural RDMs ↔ analysis/slow_eeg/step1_preprocessing_slow_eeg.m, the whole file · a weak match · score 0.70 · 100–800 ms, stimulus onset, raw, epoch, 100 ms, EEG

Paper

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The authors' code

MATLAB · 78 lines · 3.3 KB · no license · 2 matches

  1. % The data can be found on OSF: https://openneuro.org/datasets/ds003825/versions/1.2.0
  2. function step1_preprocessing_fast_eeg(sub_num)
  5. %% eeglab
  6. eeglab;
  7. data_dir = '../data';
  8. model_dir = '../../models';
  10. %% Load the EEG data, filter, and downsample
  11. contfn = sprintf('%s/derivatives/eeglab/sub-%02i_task-rsvp_continuous.set',data_dir,sub_num);
  13. if isfile(contfn)
  14. fprintf('Using %s\n',contfn)
  15. EEG_cont = pop_loadset(contfn);
  16. else
  17. % load EEG file
  18. EEG_raw = pop_loadbv(fullfile(data_dir,num2str(sub_num,'sub-%02i'),'eeg'),sprintf('sub-%02i_task-rsvp_eeg.vhdr',sub_num));
  19. EEG_raw = eeg_checkset(EEG_raw);
  20. EEG_raw.setname = sub_num;
  21. EEG_raw = eeg_checkset(EEG_raw);
  23. % Re-reference to average reference and add Cz data back in, to make 46 channels total
  24. if ismember(sub_num,[49 50]) %these were recorded with a 128 workspace and different ref, so remove the extra channels
  25. EEG_raw = pop_select(EEG_raw,'channel',1:63);
  26. EEG_raw = pop_chanedit(EEG_raw, 'append',1,'changefield',{2 'labels' 'FCz'},'setref',{'' 'FCz'});
  27. EEG_raw = pop_reref(EEG_raw, [],'refloc',struct('labels',{'FCz'},'type',{''},'theta',{0},'radius',{0.1278},'X',{0.3907},'Y',{0},'Z',{0.9205},'sph_theta',{0},'sph_phi',{67},'sph_radius',{1},'urchan',{[]},'ref',{''},'datachan',{0}));
  28. else
  29. EEG_raw = pop_chanedit(EEG_raw, 'append',1,'changefield',{2 'labels' 'Cz'},'setref',{'' 'Cz'});
  30. EEG_raw = pop_reref(EEG_raw, [],'refloc',struct('labels',{'Cz'},'type',{''},'theta',{0},'radius',{0},'X',{0},'Y',{0},'Z',{85},'sph_theta',{0},'sph_phi',{90},'sph_radius',{85},'urchan',{[]},'ref',{''},'datachan',{0}));
  31. end
  32. EEG_raw = eeg_checkset(EEG_raw);
  34. % high pass filter
  35. EEG_raw = pop_eegfiltnew(EEG_raw, 0.1,[]);
  37. % low pass filter
  38. EEG_raw = pop_eegfiltnew(EEG_raw, [],100);
  40. % downsample
  41. EEG_cont = pop_resample(EEG_raw, 250);
  42. EEG_cont = eeg_checkset(EEG_cont);
  44. % save the data
  45. pop_saveset(EEG_cont,contfn);
  46. end
  48. %% add eventinfo to events
  49. eventsfntsv = sprintf('%s/sub-%02i/eeg/sub-%02i_task-rsvp_events.tsv',data_dir,sub_num,sub_num);
  51. % Load the event list
  52. eventlist = readtable(eventsfntsv,'filetype','text','Delimiter','\t');
  54. %% create epochs
  55. % Trigger number 1 marks stim on
  56. % Epoch from -100ms to 800ms after stimulus onset
  57. EEG_epoch = pop_epoch(EEG_cont, {'E 1'}, [-0.100 0.800]);
  58. EEG_epoch = eeg_checkset(EEG_epoch);
  60. %% convert to cosmo
  61. ds = cosmo_flatten(permute(EEG_epoch.data,[3 1 2]),{'chan','time'},{{EEG_epoch.chanlocs.labels},EEG_epoch.times},2);
  62. ds.a.meeg=struct(); %or cosmo thinks it's not a meeg ds
  63. ds.sa = table2struct(eventlist,'ToScalar',true); % Add sample attributes from event list
  65. %% Select the concepts we used for semantics
  66. load(sprintf('%s/names.mat',model_dir));
  67. % Remove scoop
  68. names = names(~strcmp('scoop',names));
  69. % Select the concepts we used for semantics
  70. ds = cosmo_slice(ds,ismember(ds.sa.object,names));
  72. % Check the dataset
  73. cosmo_check_dataset(ds,'meeg');
  75. %% save epochs
  76. save(sprintf('%s/derivatives/cosmomvpa/sub-%02i_task-rsvp_cosmomvpa.mat',data_dir,sub_num),'ds','-v7.3')
  78. end

step1_preprocessing_fast_eeg.m, no license · at the source

Overview

Authors: Ariel H. Kim1,2, Genevieve L. Quek1,3, Denise Moerel1,3, Olivia Gorton1, Thomas A. Carlson1
ORCID iDs: Genevieve L. Quek, Denise Moerel, Thomas A. Carlson
  1. School of Psychology, University of Sydney, Sydney, NSW, Australia
  2. Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
  3. The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW, Australia
Institutions: The University of Sydney (Australia); The University of Melbourne (Australia); Western Sydney University (Australia)
Journal: Journal of vision, volume 26, issue 8, article 2
Dates: received 29 May 2025; accepted 2 June 2026; published online 3 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1167/jov.26.8.2 · PMID 42545066 · PMCID PMC13440622 · OpenAlex W4410891429
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.29.656895
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Single-unit activity, calcium imaging
Keywords: object perception, object similarity, electroencephalography, neural decoding, representational similarity analysis
MeSH: Concept Formation*, Form Perception*, Pattern Recognition, Visual*, Recognition, Psychology*, Adult, Electroencephalography, Female, Humans, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Neural Networks and Applications (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Australian Research Council (DP200101787)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Humans effortlessly relate what they see to what they know, drawing on existing knowledge of the perceptual, conceptual, and contextual attributes of objects while searching for and recognizing objects. Although prior studies have investigated the temporal dynamics of perceptual and conceptual object properties in the neural signal, it remains unclear whether and when contextual associations are uniquely represented. In this study, we used representational similarity analysis on electroencephalography (EEG) data to explore how the brain processes the perceptual, conceptual, and contextual dimensions of object knowledge over time. Using human similarity judgments of 190 naturalistic object concepts presented as either images or words, we constructed separate behavioral models of the perceptual, conceptual, and contextual properties of objects. We correlated these models with neural patterns from two EEG datasets, one publicly available and one newly collected, both recorded while participants passively viewed the same object stimuli. Across both datasets, we found that perceptual features dominated the early EEG response to object images, and conceptual features emerged later. Contextual associations were also reflected in neural patterns, but their explanatory power largely overlapped with that of conceptual models, suggesting limited unique representation of the contextual attributes of objects under passive viewing conditions. These results highlight the integration of perceptual and conceptual information by the brain when processing visual objects. By combining high temporal resolution EEG with behaviorally derived models, this study advances our understanding of how distinct dimensions of object knowledge are encoded in the human brain.

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 4 matches between paragraphs and lines of code.

OSF jy284

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: Python (8), MATLAB (6)
Size: 20 files, 14 scripts
Software Heritage: not checked
Found in: the text, “Correlation between behavioral model RDMs and EE”
Holds: environment (analysis/behaviour/requirements.txt)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: CoSMoMVPA (4 files), NumPy (3 files), pandas (3 files), EEGLAB (2 files), Statistics and Machine Learning Toolbox (2 files), Matplotlib (2 files), SciPy (2 files), seaborn (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
14 files
At the source: doi.org/10.17605/OSF.IO/JY284

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  • 14 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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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 3, 28 September 2026

  • Authors: added Genevieve L. Quek (0000-0002-5905-8405); Denise Moerel (0000-0001-9677-0170); Thomas A. Carlson (0000-0002-3953-4195); removed Genevieve L. Quek; Denise Moerel; Thomas A. Carlson
  • Funding: added Australian Research Council: DP200101787

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 11 MeSH terms, 64 references.

Cite

This paper

Kim, A. H., Quek, G. L., Moerel, D., Gorton, O., & Carlson, T. A. (2026). Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding. Journal of vision, 26(8), 2. https://doi.org/10.1167/jov.26.8.2

BibTeX

@article{kim2026disentangling,
author = {Kim, Ariel H. and Quek, Genevieve L. and Moerel, Denise and Gorton, Olivia and Carlson, Thomas A.},
title = {{Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding}},
journal = {Journal of vision},
year = {2026},
month = aug,
volume = {26},
number = {8},
pages = {2},
publisher = {Association for Research in Vision and Ophthalmology},
issn = {1534-7362},
doi = {10.1167/jov.26.8.2},
url = {https://doi.org/10.1167/jov.26.8.2},
pmid = {42545066},
pmcid = {PMC13440622}
}

RIS

TY - JOUR
AU - Kim, Ariel H.
AU - Quek, Genevieve L.
AU - Moerel, Denise
AU - Gorton, Olivia
AU - Carlson, Thomas A.
TI - Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding
T2 - Journal of vision
J2 - J Vis
PY - 2026
DA - 2026/08/01
VL - 26
IS - 8
SP - 2
SN - 1534-7362
PB - Association for Research in Vision and Ophthalmology
DO - 10.1167/jov.26.8.2
UR - https://doi.org/10.1167/jov.26.8.2
LA - en
ER -

CSL-JSON

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"family": "Kim",
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"volume": "26",
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"page": "2",
"DOI": "10.1167/jov.26.8.2",
"PMID": "42545066",
"PMCID": "PMC13440622",
"ISSN": "1534-7362",
"publisher": "Association for Research in Vision and Ophthalmology",
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