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Enhanced word learning and neural synchrony during children's storytelling.

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 without given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Child-child Inter-subject cross-correlation (ISC) analysis ↔ code/ISC/phase_shuf_group_data.m, the whole file · a weak match · score 0.74 · phase scrambled, FFT, Fourier, amplitude, domain, randomizing · Report a mismatch
  2. [2] § Methods › Child-instructor cross-correlation analysis ↔ code/Sync comparison/def_euclidean_distance.py, lines 10 to 60 · score 0.55 · Euclidean distance, alarm, hit, prime, Passive, Scaffolding · Report a mismatch

Paper

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The authors' code

MATLAB · 45 lines · 2.1 KB · no license · 1 match

  1. function shuf_data = phase_shuf_group_data(real_data)
  2. % This function randomizes the signal phase with *different* randomization across subjects (columns)
  3. % and same randomization of time/frequency samples across ROIs.
  4. % shuf_data : signal_time_samples X subjs X ROIs
  6. % written by: ?
  7. % Tested in March 2023 (YZ)
  9. [numTRs, numSubjects, numRois] = size(real_data); % Extract number of samples and number of ROIs
  11. % Transform the vectors-to-be-scrambled to the frequency domain
  12. Fx = fft(real_data, [], 1);
  13. clear real_data
  15. % Identify indices of positive and negative frequency components of the fft
  16. % We need to know these so that we can symmetrize the phase of negative and positive frequencies
  17. if mod(numTRs, 2) == 0
  18. posfreqs = 2:(numTRs/2);
  19. negfreqs = numTRs : -1 : (numTRs/2) + 2;
  20. else
  21. posfreqs = 2:(numTRs + 1) / 2;
  22. negfreqs = numTRs : -1 : (numTRs + 1) / 2 + 1;
  23. end
  25. x_amp = abs(Fx); % Get the amplitude of the Fourier components
  26. x_phase = atan2(imag(Fx), real(Fx)); % Get the phases of the Fourier components [NB: must use 'atan2', not 'atan' to get the sign of the angle right]
  27. J = 1i; % Define the vertical vector in the complex plane
  29. % Random phase and symmetrized phase arrays
  30. rand_phase = zeros(numTRs, numSubjects, numRois); % Will contain the randomized phases for each input channel on each bootstrap
  31. sym_phase = zeros(numTRs, numSubjects, numRois); % Will contain symmetrized randomized phases for each bootstrap
  32. [~, index] = sort(rand(numTRs, numSubjects)); % Generate a set of column vectors each of which is a separate random permutation
  34. for s = 1:numSubjects
  35. rand_phase(:, s, :) = x_phase(index(:, s), s, :);
  36. end
  38. clear Fx index x_phase
  39. sym_phase(posfreqs, :, :) = rand_phase(posfreqs, :, :) - rand_phase(negfreqs, :, :); % Symmetrize permuted phases so phi(w) = -phi(-w)
  40. sym_phase(negfreqs, :, :) = rand_phase(negfreqs, :, :) - rand_phase(posfreqs, :, :); % This guarantees the iFFT is real
  41. clear rand_phase
  42. z = x_amp .* exp(J .* sym_phase); % Generate (symmetric)-phase-scrambled Fourier components
  43. shuf_data = ifft(z); % Invert the fft to generate a phase-scrambled version of the data
  45. end

phase_shuf_group_data.m, no license · at the source

Overview

Authors: Nina Besser Ilan1,2, Hadas Shavit3,4, Nofar Zohar3,5, Sagi Jaffe-Dax1,3
ORCID iDs: Nina Besser Ilan
  1. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
  2. Faculty of Psychology, University of Vienna, Vienna, Austria
  3. School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
  4. The Miriam and Aaron Gutwirth Medical School, The Weizmann Institute of Science, Rehovot, Israel
  5. Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel
Institutions: University of Vienna (Austria); Tel Aviv University (Israel); Weizmann Institute of Science (Israel); Reichman University (Israel)
Journal: NPJ science of learning, volume 11, issue 1, article 35
Dates: received 19 August 2025; accepted 12 March 2026; published online 28 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41539-026-00417-7 · PMID 41904173 · PMCID PMC13246770 · OpenAlex W7141966568
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: Neuroscience, Psychology
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: Tel Aviv University (Vice-President for Research Fund); Council for Higher Education (Alon Scholarship); Israel Science Foundation (336/22)
Citations: not cited yet (Europe PMC); 115 references in the paper

Abstract

Children learn better through shared social experiences like storytelling, where they benefit as both listeners and tellers. Encouraging children to tell stories is a form of scaffolding, an instructional strategy in which learners and instructors are actively engaged. While the educational benefits of storytelling and scaffolding are well-documented, their underlying neural processes remain underexplored. Shared experiences are generally reflected in neural synchrony, which is often linked to comprehension. This study examined learning outcomes and neural synchrony in young school-aged children engaged in storytelling through either scaffolding or passive listening. Results showed that scaffolding improved learning outcomes and was associated with higher synchrony between teacher and young learner. However, these learning benefits disappeared in remote learning environments, highlighting the importance of face-to-face interaction for active learning strategies. Taken together, the heightened cortical synchrony between teacher and learner in face-to-face settings points to the benefits of active interaction as a learning strategy.

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 2 matches between paragraphs and lines of code.

OSF dbw6c

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Python (23), MATLAB (9)
Size: 46 files, 32 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), pandas (10 files), SciPy (7 files), Matplotlib (6 files), seaborn (4 files), Statistics and Machine Learning Toolbox (1 file), Pingouin (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
32 files
At the source:

Code availability

Code for all analyses (Python and MATLAB) is publicly available on the Open Science Framework at: 10.17605/OSF.IO/DBW6C.

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

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;
  • 32 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 the data and the materials (except for the participants’ videos) have been deposited on the Open Science Framework and are publicly available at: 10.17605/OSF.IO/DBW6C.

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, 4 authors, 2 keywords, 3 funders, 81 references.

Cite

This paper

Besser Ilan, N., Shavit, H., Zohar, N., & Jaffe-Dax, S. (2026). Enhanced word learning and neural synchrony during children's storytelling. NPJ science of learning, 11(1), 35. https://doi.org/10.1038/s41539-026-00417-7

BibTeX

@article{besserilan2026enhanced,
author = {Besser Ilan, Nina and Shavit, Hadas and Zohar, Nofar and Jaffe-Dax, Sagi},
title = {{Enhanced word learning and neural synchrony during children's storytelling}},
journal = {NPJ science of learning},
year = {2026},
month = mar,
volume = {11},
number = {1},
pages = {35},
publisher = {Nature Publishing Group},
issn = {2056-7936},
doi = {10.1038/s41539-026-00417-7},
url = {https://doi.org/10.1038/s41539-026-00417-7},
pmid = {41904173},
pmcid = {PMC13246770}
}

RIS

TY - JOUR
AU - Besser Ilan, Nina
AU - Shavit, Hadas
AU - Zohar, Nofar
AU - Jaffe-Dax, Sagi
TI - Enhanced word learning and neural synchrony during children's storytelling
T2 - NPJ science of learning
J2 - NPJ Sci Learn
PY - 2026
DA - 2026/03/28
VL - 11
IS - 1
SP - 35
SN - 2056-7936
PB - Nature Publishing Group
DO - 10.1038/s41539-026-00417-7
UR - https://doi.org/10.1038/s41539-026-00417-7
LA - en
ER -

CSL-JSON

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"container-title": "NPJ science of learning",
"author": [
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"family": "Besser Ilan",
"given": "Nina"
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"given": "Hadas"
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"container-title-short": "NPJ Sci Learn",
"volume": "11",
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"page": "35",
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"PMCID": "PMC13246770",
"ISSN": "2056-7936",
"publisher": "Nature Publishing Group",
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