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Delivery and Attitude Modulate Shared Neural Processing of Speech Through Distinct Frequency Channels.

Code ↔ Paper

30 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 30 matches · 7 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Study 2: Listener Attitude and Social Context › Results ↔ study2_isc_stats.py, lines 1 to 45 · score 0.93 · left paracentral lobule, orbital superior frontal, superior temporal gyrus, right perisylvian regions, sided FDR, Regionally · Report a mismatch
  2. [2] § Study 2: Listener Attitude and Social Context › Results ↔ study2_trait_synchrony.py, lines 1 to 18 · score 0.89 · stable individual trait, extreme point, signal quality, Neural synchronizability, cue video, attitude video · Report a mismatch
  3. [3] § General Methods › Inferential Approach ↔ study1_feature_families.py, lines 1 to 32 · score 0.87 · peakRate, log mel, amplitude envelope, syllable rate, phase scrambled, derivative · Report a mismatch
  4. [4] § Study 2: Listener Attitude and Social Context › Results ↔ roi_stage_grid.py, lines 1 to 45 · score 0.87 · 13–51 %, inflates gamma, feature regression, absorb, loss, baseline · Report a mismatch
  5. [5] § Study 1: Delivery Style › Method › Questionnaires ↔ study1_behaviour.py, the whole file · a weak match · score 0.87 · Conger Kanungo charismatic, collective efficacy, manipulation check, modified, leadership, cognition · Report a mismatch
  6. [6] § Study 1: Delivery Style › Results ↔ study1_behaviour.py, the whole file · a weak match · score 0.84 · standard deviation, perceived charisma, Collective efficacy, manipulation check, dz, PANAS · Report a mismatch
  7. [7] § General Discussion ↔ matlab/render_convergence.m, the whole file · a weak match · score 0.82 · sylvian fissure, lateral view, 0–0.09, right auditory perisylvian, buried, unthresholded · Report a mismatch
  8. [8] § General Methods › Preprocessing ↔ matlab/ft_ica_full3.m, the whole file · a weak match · score 0.81 · FieldTrip, source reconstruction, classified, kurtosis, Ocular, preprocessing · Report a mismatch
  9. [9] § Study 2: Listener Attitude and Social Context › Results ↔ study2_feature_regression.py, lines 1 to 43 · score 0.80 · right rolandic operculum, stimulus locked, stimulus driven, phase scrambled, focal, uncorrected · Report a mismatch
  10. [10] § Study 2: Listener Attitude and Social Context › Results ↔ study2_feature_regression.py, lines 1 to 43 · score 0.80 · right inferior frontal, right rolandic operculum, regions survived, focal, uncorrected, insula · Report a mismatch
  11. [11] § Study 1: Delivery Style › Results › Inter‐Subject Correlation ↔ study1_isc.py, lines 1 to 48 · score 0.79 · phase scrambled feature, word onsets, aperiodic removed, feature regression, cingulate, frontal · Report a mismatch
  12. [12] § Study 2: Listener Attitude and Social Context › Results ↔ study2_isc_stats.py, lines 1 to 45 · score 0.78 · superior temporal gyrus, sided FDR, Global ISC, right perisylvian, regions surviving, audience cue · Report a mismatch
  13. [13] § General Methods › Inferential Approach ↔ study1_isc.py, lines 1 to 48 · score 0.71 · phase scrambled feature, forced alignment, word onsets, components, aperiodic, regressed · Report a mismatch
  14. [14] § Study 1: Delivery Style › Method › Stimuli ↔ stimulus_acoustic_features.py, lines 1 to 20 · score 0.70 · loudness normalized, spectral centroid, playback, modulation, stimulus, matched · Report a mismatch
  15. [15] § Study 1: Delivery Style › Results › Spectral Power ↔ study2_power_specparam.py, lines 1 to 33 · score 0.70 · spectral power, aperiodic exponent, band power, broadband, oscillatory, occipital · Report a mismatch
  16. [16] § General Methods › Preprocessing ↔ matlab/ft_source_broadband_ica.m, the whole file · a weak match · score 0.70 · source reconstruction, FieldTrip, kurtosis, Ocular, preprocessing, concatenated · Report a mismatch
  17. [17] § Study 2: Listener Attitude and Social Context › Results ↔ fig_trait_synchrony.py, lines 1 to 37 · score 0.70 · stable individual trait, signal quality, alpha ISC, audience cue, Spearman, extreme · Report a mismatch
  18. [18] § General Discussion ↔ study1_feature_families.py, lines 1 to 32 · score 0.69 · log mel, amplitude envelope, syllable rate, spectral, regression · Report a mismatch
  19. [19] § Study 1: Delivery Style › Results › Inter‐Subject Correlation ↔ study1_envelope_control.py, lines 1 to 25 · score 0.68 · circular shift, Envelope tracking, 1–8 Hz, coupling, S3, acoustic · Report a mismatch
  20. [20] § General Discussion ↔ roi_contiguity_null.py, lines 1 to 76 · score 0.68 · right lateralization, right hemisphere, adjacent, enrichment, zero, auditory · Report a mismatch
  21. [21] § Study 2: Listener Attitude and Social Context › Results ↔ fig_trait_synchrony.py, lines 1 to 37 · score 0.67 · general signal quality, cross task, audience cue, trait, synchronization, delta · Report a mismatch
  22. [22] § Study 2: Listener Attitude and Social Context › Results ↔ study2_trait_synchrony.py, lines 1 to 18 · score 0.66 · signal quality, survived partialing, trait, synchronization, ask, stimulus · Report a mismatch
  23. [23] § Study 1: Delivery Style › Results › Inter‐Subject Correlation ↔ study1_is_rsa.py, lines 41 to 45 · score 0.66 · nearest neighbor, Anna Karenina, scores, model, behaviorally · Report a mismatch
  24. [24] § General Methods › Inter‐Subject Correlation Pipeline ↔ matlab/source_reconstruction_iscex.m, the whole file · a weak match · score 0.64 · Voxel, LCMV, beamforming, covariance, Hilbert, linearly · Report a mismatch
  25. [25] § General Discussion ↔ roi_contiguity_null.py, lines 1 to 76 · score 0.64 · right lateral, right auditory perisylvian, enumerated, enrichment, maps, atlas · Report a mismatch
  26. [26] § General Methods › Inter‐Subject Correlation Pipeline ↔ matlab/ft_ica_full3.m, the whole file · a weak match · score 0.63 · trace, LCMV, beamforming, covariance, Hilbert, linearly · Report a mismatch
  27. [27] § Study 1: Delivery Style › Results › Inter‐Subject Correlation ↔ study1_anova.py, lines 1 to 27 · score 0.61 · Post hoc, global ISC, ANOVA, intermediate, silent, delta · Report a mismatch
  28. [28] § Study 2: Listener Attitude and Social Context › Results ↔ study2_behaviour.py, lines 1 to 61 · score 0.59 · Attitude manipulation check, scoring lower, memory, lecture, correlation · Report a mismatch
  29. [29] § Study 2: Listener Attitude and Social Context › Results ↔ roi_against_zero.py, lines 1 to 50 · score 0.57 · feature regression, departs, answer, S7, zero, concentrated · Report a mismatch
  30. [30] § Study 1: Delivery Style › Results › Inter‐Subject Correlation ↔ isc_within_condition_null.py, lines 1 to 46 · score 0.51 · circular shift, envelope, signal, temporal, correlation, ISC · Report a mismatch

Paper

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The authors' code

Python · 92 lines · 4.7 KB · MIT · 2 matches

  1. """
  2. 01 - Inter-subject correlation for Study 2, tested at the SUBJECT level.
  4. Study 2 crossed two manipulations in one cohort: ATTITUDE (a positive or negative description of
  5. the speaker read before an identical lecture; field S2_band) and the AUDIENCE CUE (silhouettes
  6. present or absent during a second, different lecture; field S3_band). Each participant has one
  7. level of each factor, so leave-one-out ISC is computed WITHIN group and compared BETWEEN groups by
  8. permuting the group labels. Under permutation the leave-one-out ISC is RE-COMPUTED within the
  9. permuted groups, via the per-region ISC Gram matrix -- permuting frozen subject-level values
  10. instead treats them as exchangeable when they are not, and returns p values several times too
  11. small.
  13. Reports, per band:
  14. - global ISC (the 72-region mean) for the group difference, with the within-cohort baseline
  15. - the per-region difference with both the two-sided and the one-sided (effect > control)
  16. permutation p, and FDR across the 72 regions under each
  18. Both sidedness conventions are reported because Section 4.3 uses both: three right-perisylvian
  19. regions survive two-sided FDR in alpha, and the superior temporal gyrus, the orbital superior
  20. frontal gyrus and the left paracentral lobule survive only under the a priori direction. Section
  21. 2.5 fixes the permutation count at 10,000 (lib2.NPERM).
  23. The global test uses the same permutation as the regional one. An independent-samples t-test on
  24. the leave-one-out values, holding them fixed, is not equivalent: those values are computed against
  25. a group mean that each participant contributes to, so they are not independent observations, and
  26. the t-test returns p values several times too small. The difference is not academic here -- it
  27. moves the attitude contrast in theta from p < .001 to .004, in alpha from .0001 to .012, and in
  28. delta from .015 to .062.
  30. Writes <DERIV>/study2_isc_results.npz, which is read by cross_study_convergence.py,
  31. roi_contiguity_null.py and fig_export_render_stats.py.
  33. Requires: source_iscex/ from matlab/source_reconstruction_iscex.m.
  35. Usage: python study2_isc_stats.py
  36. """
  37. import gc, os, sys
  38. import numpy as np
  39. from lib2 import load, perm_between, perm_global, fdr, labels, BANDS, NPERM
  41. sys.stdout.reconfigure(encoding="utf-8", errors="replace")
  42. ROOT = r"G:/Barak1"
  43. DERIV = os.path.join(ROOT, "reanalysis") # reconstruction outputs and intermediate results; point anywhere
  45. OUT = os.path.join(DERIV, "study2_isc_results.npz")
  47. # One stream for the regional test, consumed study by study and band by band, so the reported p
  48. # values are reproducible from the seed alone. The global test draws from its own stream, so adding
  49. # or removing it cannot move the regional numbers.
  50. RNG_REGION = np.random.RandomState(0)
  51. RNG_GLOBAL = np.random.RandomState(1)
  53. # Result keys stay S2_*/S3_*: they name the MATLAB fields the two tasks are stored in
  54. # (S2_band, S3_band). Both belong to Study 2 of the manuscript, which is how they are labelled in
  55. # everything a reader sees.
  56. CONTRASTS = [
  57. ("S2", "S2_band", "include_S2", "attitude_grp", "Positive", "Negative",
  58. "Study 2: listener attitude (positive - negative)"),
  59. ("S3", "S3_band", "include_S3", "cue_grp", "With", "Without",
  60. "Study 2: audience cue (cue - no cue)"),
  61. ]
  63. out = {"labels": np.array(labels)}
  65. # The two region time-course sets are several gigabytes each, so only one is held at a time and
  66. # the run is dominated by reading them rather than by the permutations.
  67. for tag, field, inc, gcol, A, B, title in CONTRASTS:
  68. X = None; gc.collect()
  69. X, grp, _ = load(field, inc, gcol)
  70. gA = np.where(grp == A)[0]; gB = np.where(grp == B)[0]
  71. print(f"\n===== {title} ===== n={X.shape[0]} ({A}={len(gA)}, {B}={len(gB)}), "
  72. f"T={X.shape[2]}, {NPERM:,} permutations")
  74. print("\n global ISC, 72-region mean (one-sided, effect > control):")
  75. for bi, b in enumerate(BANDS):
  76. d, p, base = perm_global(X[:, :, :, bi], gA, gB, rng=RNG_GLOBAL)
  77. print(f" {b:6s} d={d:+.4f} p={p:.4f}{' *' if p < .05 else ''} (baseline ISC={base:.3f})")
  79. print("\n regional (FDR across 72 regions):")
  80. for bi, b in enumerate(BANDS):
  81. obs, p2, p1 = perm_between(X[:, :, :, bi], gA, gB, rng=RNG_REGION)
  82. sig2, sig1 = fdr(p2), fdr(p1)
  83. out[f"{tag}_{b}_obs"] = obs; out[f"{tag}_{b}_p2"] = p2; out[f"{tag}_{b}_p1"] = p1
  84. print(f" {b:6s}: two-sided {sig2.sum():2d} sig | one-sided {sig1.sum():2d} sig")
  85. for r in np.argsort(-obs):
  86. if sig2[r] or sig1[r]:
  87. print(f" {labels[r]:22s} d={obs[r]:+.4f} p2={p2[r]:.4f} p1={p1[r]:.4f}"
  88. f"{' [2s]' if sig2[r] else ''}{' [1s]' if sig1[r] else ''}")
  89. del X
  91. np.savez(OUT, **out)
  92. print(f"\nsaved {OUT}")

study2_isc_stats.py at commit ae45996, under MIT · at the source

Overview

Authors: Barak Atia1, Yair Berson2, Abraham Goldstein1
ORCID iDs: Abraham Goldstein
  1. Department of Psychology and Gonda Brain Research Center Bar‐Ilan University Ramat Gan Israel
  2. DeGroote School of Business McMaster University Hamilton Ontario Canada
Institutions: Bar-Ilan University (Israel); McMaster University (Canada)
Journal: Psychophysiology, volume 63, issue 10, article e70407
Dates: received 5 March 2026; accepted 15 September 2026; published online 28 September 2026; in print October 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/psyp.70407 · PMID 42806684 · PMCID PMC13620440 · OpenAlex W7214709310
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Physiology & signal measures
MeSH: Attitude*, Brain*, Speech Perception*, Adult, Female, Humans, Magnetoencephalography, Male, Young Adult (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 90 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.

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Its files are read in the Code ↔ Paper reader above, with 30 matches between paragraphs and lines of code.

Zenodo 22808469

License: MIT
State: the link answers, verified on 1 October 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (39 files), SciPy (28 files), Matplotlib (8 files), FieldTrip (6 files), pandas (6 files), specparam (formerly FOOOF) (5 files), Signal Processing Toolbox (2 files), Pillow (2 files), Statistics and Machine Learning Toolbox (1 file), MNE-Python (1 file), PyTorch (1 file), Hugging Face Transformers (1 file)
Availability: 1 check, the latest on 1 October 2026: the link answers (HTTP 200)
  • 1 October 2026: the link answers (HTTP 200)
48 files
At the source: doi.org/10.5281/zenodo.22808469

avigoldstein-biu/delivery-attitude-isc

License: MIT
State: the link answers, verified on 1 October 2026
Evidence: files inventoried
Commit: ae45996cc16ae80b1d041ef2de0b46bdef313a07, 17 September 2026
Languages: Python (40), MATLAB (6)
Size: 51 files, 46 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: NumPy (39 files), SciPy (28 files), Matplotlib (8 files), FieldTrip (6 files), pandas (6 files), specparam (formerly FOOOF) (5 files), Signal Processing Toolbox (2 files), Pillow (2 files), Statistics and Machine Learning Toolbox (1 file), MNE-Python (1 file), PyTorch (1 file), Hugging Face Transformers (1 file)
Availability: 1 check, the latest on 1 October 2026: the link answers
  • 1 October 2026: the link answers
48 files
At the source: github.com/avigoldstein-biu/delivery-attitude-isc

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

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Data

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Read it in the paper: doi.org/10.1111/psyp.70407.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 9 MeSH terms, 76 references.

Cite

This paper

Atia, B., Berson, Y., & Goldstein, A. (2026). Delivery and Attitude Modulate Shared Neural Processing of Speech Through Distinct Frequency Channels. Psychophysiology, 63(10), e70407. https://doi.org/10.1111/psyp.70407

BibTeX

@article{atia2026delivery,
author = {Atia, Barak and Berson, Yair and Goldstein, Abraham},
title = {{Delivery and Attitude Modulate Shared Neural Processing of Speech Through Distinct Frequency Channels}},
journal = {Psychophysiology},
year = {2026},
month = oct,
volume = {63},
number = {10},
pages = {e70407},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70407},
url = {https://doi.org/10.1111/psyp.70407},
pmid = {42806684},
pmcid = {PMC13620440}
}

RIS

TY - JOUR
AU - Atia, Barak
AU - Berson, Yair
AU - Goldstein, Abraham
TI - Delivery and Attitude Modulate Shared Neural Processing of Speech Through Distinct Frequency Channels
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/10/01
VL - 63
IS - 10
SP - e70407
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70407
UR - https://doi.org/10.1111/psyp.70407
LA - en
ER -

CSL-JSON

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"container-title": "Psychophysiology",
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"family": "Atia",
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"given": "Abraham"
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"container-title-short": "Psychophysiology",
"volume": "63",
"issue": "10",
"page": "e70407",
"DOI": "10.1111/psyp.70407",
"PMID": "42806684",
"PMCID": "PMC13620440",
"ISSN": "0048-5772",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/psyp.70407",
"language": "en",
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"date-parts": [
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}
}

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