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The shape of attention reflects flexible filtering of natural speech modulations.

Code ↔ Paper

3 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 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] § Methods › MPS and MRF ↔ GetOmegas.m, the whole file · a weak match · score 0.65 · spectral modulation scale, temporal modulation rate, MPS, cycles, Hz, acoustic
  2. [2] § Methods › Envelope and spectrogram processing ↔ GetSpectrogram.m, lines 11–84 · score 0.61 · gammatone filterbank, cochlear, compression, bands, signal, octave
  3. [3] § Results › MRF across tasks ↔ example.m, lines 1–20 · score 0.51 · reconstructed spectrograms, Modulation Response Function, cognitive, cortical, acoustic, MRF

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 135 lines · 5.1 KB · no license · 1 match

  1. % =========================================================================%
  2. % Author: Huet, M.-Ph., & Elhilali, M.
  3. % Contact: [email hidden]
  4. % If used, please cite:
  5. % Huet & Elhilali (2025), bioRxiv, https://doi.org/10.1101/2025.05.22.655464
  6. % =========================================================================
  7. function [omegas_t, omegas_f, params] = GetOmegas(rate_params, scale_params, FS, FMIN, FMAX, NCHAN, TMIN)
  8. % GETOMEGAS Create temporal and spectral modulation axes for MPS/MRF analysis.
  9. %
  10. % Minimal use:
  11. % [omegas_t, omegas_f] = GetOmegas(rate_params, scale_params)
  12. %
  13. % Recommended use:
  14. % [omegas_t, omegas_f, params] = GetOmegas(rate_params, scale_params, FS, FMIN, FMAX, NCHAN, TMIN)
  15. %
  16. % Inputs:
  17. % rate_params = [MIN_RATE, MAX_RATE, STEP_RATE] in log2(Hz)
  18. % scale_params = [MIN_SCALE, MAX_SCALE, STEP_SCALE] in log2(cycles/octave)
  19. % TMIN = shortest signal duration in seconds
  20. % FS = spectrogram sampling rate in Hz
  21. % FMIN = minimum acoustic frequency in Hz
  22. % FMAX = maximum acoustic frequency in Hz
  23. % NCHAN = number of spectrogram frequency channels
  24. %
  25. % Outputs:
  26. % omegas_t = temporal modulation rates in Hz
  27. % omegas_f = spectral modulation scales in cycles/octave
  28. % params = corrected log2 parameters
  30. NCYCLE = 2;
  32. MIN_RATE = rate_params(1);
  33. MAX_RATE = rate_params(2);
  34. STEP_RATE = rate_params(3);
  36. MIN_SCALE = scale_params(1);
  37. MAX_SCALE = scale_params(2);
  38. STEP_SCALE = scale_params(3);
  40. has_limits = nargin >= 7 && ...
  41. ~isempty(FS) && ~isempty(FMIN) && ~isempty(FMAX) && ...
  42. ~isempty(NCHAN) && ~isempty(TMIN);
  44. if ~has_limits
  45. warning(['No signal/spectrogram constraints provided. ' ...
  46. 'Axes generated without automatic checks. To avoid aliasing, use ' ...
  47. 'MAX_RATE < FS/2 and MAX_SCALE < NCHAN/2; minimum rates/scales should allow >=2 cycles.']);
  48. else
  49. % ---------------------------------------------------------------------
  50. % Temporal rate limits
  51. % ---------------------------------------------------------------------
  53. min_rate_recommended = NCYCLE / TMIN;
  54. max_rate_recommended = FS / 2;
  56. if 2^MIN_RATE < min_rate_recommended
  57. new_MIN_RATE = ceil(log2(min_rate_recommended) / STEP_RATE) * STEP_RATE;
  59. warning(['[MPS] MIN_RATE too low: %.3f log2 Hz (%.3f Hz).\n' ...
  60. 'Recommended minimum is %.3f log2 Hz (%.3f Hz), based on %d cycles over %.3f s.\n' ...
  61. 'Using MIN_RATE = %.3f instead.'], ...
  62. MIN_RATE, 2^MIN_RATE, ...
  63. log2(min_rate_recommended), min_rate_recommended, NCYCLE, TMIN, ...
  64. new_MIN_RATE);
  66. MIN_RATE = new_MIN_RATE;
  67. end
  69. if 2^MAX_RATE > max_rate_recommended
  70. new_MAX_RATE = floor(log2(max_rate_recommended) / STEP_RATE) * STEP_RATE;
  72. warning(['[MPS] MAX_RATE too high: %.3f log2 Hz (%.3f Hz).\n' ...
  73. 'Recommended maximum is %.3f log2 Hz (%.3f Hz), based on FS/4.\n' ...
  74. 'Using MAX_RATE = %.3f instead.'], ...
  75. MAX_RATE, 2^MAX_RATE, ...
  76. log2(max_rate_recommended), max_rate_recommended, ...
  77. new_MAX_RATE);
  79. MAX_RATE = new_MAX_RATE;
  80. end
  82. % ---------------------------------------------------------------------
  83. % Spectral scale limits
  84. % ---------------------------------------------------------------------
  86. B_oct = log2(FMAX / FMIN);
  88. min_scale_recommended = NCYCLE / B_oct;
  89. max_scale_recommended = NCHAN / 2;
  91. if 2^MIN_SCALE < min_scale_recommended
  92. new_MIN_SCALE = ceil(log2(min_scale_recommended) / STEP_SCALE) * STEP_SCALE;
  94. warning(['[MPS] MIN_SCALE too low: %.3f log2 cyc/oct (%.3f cyc/oct).\n' ...
  95. 'Recommended minimum is %.3f log2 cyc/oct (%.3f cyc/oct), based on %d cycles over %.3f octaves.\n' ...
  96. 'Using MIN_SCALE = %.3f instead.'], ...
  97. MIN_SCALE, 2^MIN_SCALE, ...
  98. log2(min_scale_recommended), min_scale_recommended, NCYCLE, B_oct, ...
  99. new_MIN_SCALE);
  101. MIN_SCALE = new_MIN_SCALE;
  102. end
  104. if 2^MAX_SCALE > max_scale_recommended
  105. new_MAX_SCALE = floor(log2(max_scale_recommended) / STEP_SCALE) * STEP_SCALE;
  107. warning(['[MPS] MAX_SCALE too high: %.3f log2 cyc/oct (%.3f cyc/oct).\n' ...
  108. 'Recommended maximum is %.3f log2 cyc/oct (%.3f cyc/oct), based on NCHAN/4.\n' ...
  109. 'Using MAX_SCALE = %.3f instead.'], ...
  110. MAX_SCALE, 2^MAX_SCALE, ...
  111. log2(max_scale_recommended), max_scale_recommended, ...
  112. new_MAX_SCALE);
  114. MAX_SCALE = new_MAX_SCALE;
  115. end
  116. end
  118. % -------------------------------------------------------------------------
  119. % Create modulation axes
  120. % -------------------------------------------------------------------------
  122. omegas_t = 2.^(MIN_RATE:STEP_RATE:MAX_RATE);
  123. omegas_f = 2.^(MIN_SCALE:STEP_SCALE:MAX_SCALE);
  125. params = struct();
  126. params.MIN_RATE = MIN_RATE;
  127. params.MAX_RATE = MAX_RATE;
  128. params.STEP_RATE = STEP_RATE;
  129. params.MIN_SCALE = MIN_SCALE;
  130. params.MAX_SCALE = MAX_SCALE;
  131. params.STEP_SCALE = STEP_SCALE;
  132. params.NCYCLE = NCYCLE;
  133. params.has_limits = has_limits;
  135. end

GetOmegas.m at commit f3e3820, no license · at the source

Overview

Authors: Moïra-Phoebé Huet1, Mounya Elhilali1
ORCID iDs: Moïra-Phoebé Huet, Mounya Elhilali
  1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD USA
Institutions: Johns Hopkins University (United States)
Journal: Communications biology, volume 9, issue 1, article 1107
Dates: received 15 October 2025; accepted 5 May 2026; published online 24 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10265-1 · PMID 42177335 · PMCID PMC13478475 · OpenAlex W7162190684
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing
Keywords: Cognitive neuroscience, Attention, Perception
MeSH: Attention*, Speech*, Speech Perception*, Acoustic Stimulation, Adult, Comprehension, Electroencephalography, Female, Humans, Male, Noise, Speech Intelligibility, Young Adult (* major topic)
Topic: Multisensory perception and integration (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: United States Department of Defense | United States Navy | Office of Naval Research (N00014-23-1-2050, N00014-23-1-2086)
Citations: not cited yet (Europe PMC); 84 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 3 matches between paragraphs and lines of code.

Zenodo 19830754

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
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)
12 files
At the source:

mphuet/matgaborstm

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f3e3820996ae423b1cd8847512240214824177da, 25 April 2026
Languages: MATLAB (11)
Size: 16 files, 11 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/mphuet/MatGaborSTM/tree/v1.0.0

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 19830754

Read it in the paper: doi.org/10.1038/s42003-026-10265-1.

Tracing map

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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;
  • 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.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-10265-1.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 3 keywords, 13 MeSH terms, 1 funder, 69 references.

Cite

This paper

Huet, M.-P., & Elhilali, M. (2026). The shape of attention reflects flexible filtering of natural speech modulations. Communications biology, 9(1), 1107. https://doi.org/10.1038/s42003-026-10265-1

BibTeX

@article{huet2026shape,
author = {Huet, Moïra-Phoebé and Elhilali, Mounya},
title = {{The shape of attention reflects flexible filtering of natural speech modulations}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1107},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10265-1},
url = {https://doi.org/10.1038/s42003-026-10265-1},
pmid = {42177335},
pmcid = {PMC13478475}
}

RIS

TY - JOUR
AU - Huet, Moïra-Phoebé
AU - Elhilali, Mounya
TI - The shape of attention reflects flexible filtering of natural speech modulations
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/24
VL - 9
IS - 1
SP - 1107
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10265-1
UR - https://doi.org/10.1038/s42003-026-10265-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10265-1",
"type": "article-journal",
"title": "The shape of attention reflects flexible filtering of natural speech modulations",
"container-title": "Communications biology",
"author": [
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"family": "Huet",
"given": "Moïra-Phoebé"
},
{
"family": "Elhilali",
"given": "Mounya"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1107",
"DOI": "10.1038/s42003-026-10265-1",
"PMID": "42177335",
"PMCID": "PMC13478475",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10265-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
24
]
]
}
}

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