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Two time scales of adaptation in human learning rates.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › fMRI data analyses ↔ Experiment 2/Neuroimaging Analyses/1_Data_Smoothing.m, the whole file · a weak match · score 0.52 · global signal, SPM, fMRI
  2. [2] § Results › Experiment 2 › fMRI results › Dissociating the representation of spatial location and learning rate during island presentation ↔ Experiment 2/Neuroimaging Analyses/10_ROI_RSA.py, lines 122–211 · score 0.52 · central OFC, neural RDM, ROI, ANOVA, 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 · 76 lines · 2.3 KB · no license · 1 match

  1. spm('defaults', 'fmri');
  2. spm_jobman('initcfg');
  4. subs = [1:11 13:19 21:25 27:51 53];
  5. nsubs = length(subs);
  6. nruns = 4;
  7. jobs = cell(nsubs, 1);
  9. for sub = subs
  11. mkdir(sprintf('C:/Users/.../Experiment 2/Analyses/first_level/sub-%d', sub))
  13. end
  15. for sub = subs
  17. dir = sprintf('C:/Users/...Experiment 2/Data/sub-%d/', sub);
  18. confounds_of_interest = {'^rot_[xyz]$', '^trans_[xyz]$', '^global_signal$'};
  20. for run = 1:nruns
  22. content = readtable([dir sprintf('sub-%d_task-ep2dboldrun%d_desc-confounds_timeseries.tsv', sub, run)], "FileType","text",'Delimiter', '\t');
  23. confounds_names = fieldnames(content);
  24. confounds_to_keep = regexp(confounds_names, strjoin(confounds_of_interest, '|'));
  25. confounds_to_keep = ~cellfun('isempty', confounds_to_keep);
  26. confounds_names = confounds_names(confounds_to_keep);
  28. for j = 1:numel(confounds_names)
  30. names{j} = confounds_names{j};
  31. R(:, j) = content.(confounds_names{j});
  33. end
  35. R = R(6:end,:);
  36. output_file_name = sprintf('sub-%d_task-ep2dboldrun%d_desc-confounds.txt', sub, run);
  37. output_file = array2table(R, 'VariableNames', names);
  38. writetable(output_file, [dir, output_file_name], 'WriteVariableNames', false);
  39. save([dir, sprintf('sub-%d_task-ep2dboldrun%d_desc-confounds.mat', sub, run)], 'R', 'names')
  41. clear R;
  42. clear names;
  43. clear output_file;
  45. end
  46. end
  48. for sub = subs
  50. input_dir = sprintf('C:/Users/.../Experiment 2/Data/sub-%d/func/', sub);
  51. output_dir = sprintf('C:/Users/.../Experiment 2/Analyses/first_level/sub-%d/', sub);
  53. for run = 1:nruns
  55. cd(input_dir);
  56. gunzip(sprintf('sub-%d_task-ep2dboldrun%d_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz', sub, run), output_dir);
  58. end
  59. end
  61. for sub = subs
  63. dir = sprintf('C:/Users/...Experiment 2/Analyses/first_level/sub-%d', sub);
  64. files = cellstr(spm_select('FPList', dir, '\.nii$'));
  66. clear matlabbatch
  68. matlabbatch{1}.spm.spatial.smooth.data = files;
  69. matlabbatch{1}.spm.spatial.smooth.fwhm = [5 5 5];
  70. matlabbatch{1}.spm.spatial.smooth.dtype = 0;
  71. matlabbatch{1}.spm.spatial.smooth.im = 0;
  72. matlabbatch{1}.spm.spatial.smooth.prefix = 'smoothed_';
  74. spm_jobman('run', matlabbatch)
  76. end

1_Data_Smoothing.m, no license · at the source

Overview

Authors: Jonas Simoens1, Senne Braem1, Pieter Verbeke1, Haopeng Chen1, Stefania Mattioni1, Mengqiao Chai1, Nicolas W Schuck2, Tom Verguts1
ORCID iDs: Senne Braem, Pieter Verbeke, Stefania Mattioni, Nicolas W Schuck, Tom Verguts
  1. Department of Experimental Psychology, Ghent University, Ghent, Belgium
  2. Institute of Psychology, Universität Hamburg, Hamburg, Germany
Institutions: Ghent University (Belgium); Universität Hamburg (Germany)
Journal: eLife, volume 14, article RP108223
Dates: published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108223 · PMID 42484629 · PMCID PMC13391084 · OpenAlex W4414756727
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions, Machine learning
Keywords: Human
MeSH: Adaptation, Physiological*, Learning*, Prefrontal Cortex*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Time Factors, Young Adult (* major topic)
Topic: Child and Animal Learning Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: European Research Council (10.3030/852669, 852669, 10.3030/852570); Fonds Wetenschappelijk Onderzoek (G010319N, 11K5121N)
Citations: cited by 1 paper (Europe PMC); 63 references in the paper

Abstract

Different situations may require radically different information updating speeds (i.e., learning rates). Some demand fast learning rates while others benefit from using slower ones. To adjust learning rates, decision makers could rely on either global, meta-learned differences between environments, or faster but transient adaptations to locally experienced prediction errors. Here, we introduce a new paradigm that allows researchers to measure and empirically disentangle both forms of adaptation. Participants performed short blocks of trials of a continuous estimation task – fishing for crabs – on six different islands that required different optimal (initial) learning rates. Across two experiments, participants showed fast adaptations in learning rate within a block. Critically, participants also learned global environment-specific learning rates over the time course of the experiment, as evidenced by computational modelling and by the learning rates calculated on the very first trial when revisiting an environment (i.e., unconfounded by transient adaptations). Using representational similarity analyses of fMRI data, we found that differences in voxel pattern responses in the central orbitofrontal cortex (OFC) correlated with differences in these global environment-specific learning rates. Our findings show that humans adapt learning rates at both slow and fast time scales, and that the central OFC may support meta-learning by representing environment-specific task-relevant features such as learning rates.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF qft2p

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not checked
Found in: the text, “Participants”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: osf.io/qft2p

OSF be4td

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (17), MATLAB (5)
Size: 139 files, 22 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (17 files), NumPy (15 files), SciPy (6 files), Matplotlib (5 files), seaborn (5 files), SPM (5 files), Stan (4 files), ArviZ (3 files), statsmodels (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
22 files
At the source: osf.io/be4td/

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 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.

Data availability

All behavioural and neuroimaging data as well as the code for the analyses of the data presented in this paper are publicly available at https://osf.io/be4td/.

The following dataset was generated:

Simoens J. 2022. The environment-specific regulation of learning rate in continuous choice. Open Science Framework. be4td

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 1 keyword, 10 MeSH terms, 2 funders, 60 references.

Cite

This paper

Simoens, J., Braem, S., Verbeke, P., Chen, H., Mattioni, S., Chai, M., Schuck, N. W., & Verguts, T. (2026). Two time scales of adaptation in human learning rates. eLife, 14, RP108223. https://doi.org/10.7554/elife.108223

BibTeX

@article{simoens2026two,
author = {Simoens, Jonas and Braem, Senne and Verbeke, Pieter and Chen, Haopeng and Mattioni, Stefania and Chai, Mengqiao and Schuck, Nicolas W and Verguts, Tom},
title = {{Two time scales of adaptation in human learning rates}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP108223},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108223},
url = {https://doi.org/10.7554/elife.108223},
pmid = {42484629},
pmcid = {PMC13391084}
}

RIS

TY - JOUR
AU - Simoens, Jonas
AU - Braem, Senne
AU - Verbeke, Pieter
AU - Chen, Haopeng
AU - Mattioni, Stefania
AU - Chai, Mengqiao
AU - Schuck, Nicolas W
AU - Verguts, Tom
TI - Two time scales of adaptation in human learning rates
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/07/22
VL - 14
SP - RP108223
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108223
UR - https://doi.org/10.7554/elife.108223
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Two time scales of adaptation in human learning rates",
"container-title": "eLife",
"author": [
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"family": "Simoens",
"given": "Jonas"
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{
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"container-title-short": "Elife",
"volume": "14",
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"DOI": "10.7554/elife.108223",
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"issued": {
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22
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}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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