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Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability.

Code ↔ Paper

8 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 8 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 › Data Acquisition and Preprocessing ↔ Code/gmdlDataset_preprocess.m, lines 1–68 · score 0.88 · passband attenuation, stopband attenuation, Noisy channels, deviant trials, kurtosis, downsampled
  2. [2] § Results › EEG Signatures of Acoustic Speech Processing Are Stronger for Less Predictable Words, Beyond Variations in Speaker Articulation ↔ Code/gmdlDataset_fw_lme_4D_array_calc.m, lines 1–36 · score 0.69 · 100–200 ms, 0–100 ms, stage regression, NP surprisal, window, LME
  3. [3] § Methods › Modeling the Relationship Between Speech Features and EEG Responses ↔ Code/gmdlDataset_fw_lme_4D_array_calc.m, lines 1–36 · score 0.66 · 100–300 ms, stage regression, surprisal models, forward models, trained, 100 ms
  4. [4] § Results › EEG Signatures of Acoustic Speech Processing Are Stronger for Less Predictable Words, Beyond Variations in Speaker Articulation ↔ Code/gmdlDataset_fw_lme_prep.m, lines 1–34 · score 0.63 · 100–200 ms, 0–100 ms, LME model, NP surprisal, window, envelope
  5. [5] § Results › EEG Signatures of Acoustic Speech Processing Are Stronger for Less Predictable Words, Beyond Variations in Speaker Articulation ↔ Code/singleWordProsody.m, the whole file · a weak match · score 0.61 · single word, 0–100 ms, resolvability, rows, onset, envelope
  6. [6] § Methods › Data Acquisition and Preprocessing ↔ Code/gmdlDataset_preprocess.m, lines 71–181 · score 0.56 · EEG space, PCA, denoise, MCCA, Preprocessing, neural
  7. [7] § Methods › Assessing the Influence of Context‐Based Predictions on Acoustic Encoding ↔ Code/gmdlDataset_fw_mod.m, lines 1–46 · score 0.54 · word onset, 100–300 ms, speech envelope, lags, 100 ms, surprisal
  8. [8] § Methods › Modeling the Relationship Between Speech Features and EEG Responses ↔ Code/gmdlDataset_fw_mod.m, lines 1–46 · score 0.53 · 100–700 ms, 100–300 ms, lags, trained, TRF, envelope

Paper

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

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The authors' code

MATLAB · 181 lines · 6.6 KB · no license · 2 matches

  1. %% Shyanthony Synigal Fall 2023
  3. % Preprocessing G.M. di Liberto's 2018 vocoding experiment data (his raw data)
  4. % Applying MCCA using standard trials only
  5. % Uses kurtosis, spectra, and probability to identify noisy channels
  6. % reref to the average of all channels
  8. % Dependencies: EEGLAB, Fieldtrip (for filtfilthd), NoiseTools (Alain de Cheveigne's toolbox)
  10. % This script loads the CND data
  11. % My data wasn't originally in CND format when I analyzed it and saved it
  12. % for the first time, so I tried to write it in at the end
  14. study_dir = pwd;
  16. eegRaw_dir = [study_dir '\rawNeural\'];
  17. eeg_dir = [study_dir '\dataCND\'];
  18. mcca_dir = [eeg_dir '\MCCA\'];
  19. dev_dir = [study_dir '\Data\'];
  21. if ~exist(eeg_dir,'dir'), mkdir(eeg_dir); end
  22. if ~exist(mcca_dir,'dir'), mkdir(mcca_dir); end
  25. % Bandpass filter
  26. fs = 512; % Sampling frequency (Hz)
  27. fs_new = 128; % what we'll downsample to
  29. fstop1 = 0.5; % Lower stopband frequency (Hz)
  30. fpass1 = 1; % Lower passband frequency (Hz)
  31. astop1 = 60; % Stopband attenuation (dB)
  32. apass = 1; % Passband attenuation (dB)
  33. fpass2 = 8; % Upper passband frequency (Hz)
  34. fstop2 = 8.5; % Upper stopband frequency (Hz)
  35. astop2 = 80; % Stopband attenuation (dB)
  37. h = fdesign.highpass(fstop1,fpass1,astop1,apass,fs);
  38. hpf = design(h,'cheby2','MatchExactly','stopband'); clear h
  40. h = fdesign.lowpass(fpass2,fstop2,apass,astop2,fs);
  41. lpf = design(h,'cheby2','MatchExactly','stopband'); clear h
  43. clear fpass2 fstop2 apass astop2 fstop1 fpass1 astop1 h
  46. % Other info: subjects, conditions, etc.
  47. subs = 1:14;
  48. nsubs = length(subs);
  50. conditions = {'np','c','p'}; % the presentation order
  51. ncond = numel(conditions);
  53. nchans = 128;
  54. badchans_all = cell(1,nsubs);
  56. tsec = 10; % 10 sec trials
  57. tlen = fs_new*tsec; %% of samp for 10 sec trials
  59. nPCs = 40; % number of PCs
  60. nMCCs = 110; % numbr of CCs
  63. % load([dev_dir 'chanlocs.mat']) % also saved in CND file
  65. load([dev_dir 'Deviants.mat']);
  66. standards = 1:120;
  67. standards(ismember(standards,deviants))=[]; %removing the deviant trials
  68. ntrials = numel(standards); % should be 93;
  71. %% Preprocess
  73. for cond = 1:ncond
  74. disp(['** Processing condition' int2str(cond) ' **']);
  76. for s = 1:nsubs
  78. allEEG = zeros(tlen,nchans,ntrials); %needs to be time x chann x trials
  80. load([eegRaw_dir, 'dataSub', int2str(s), '_', conditions{cond}, '.mat'],'neural')
  83. row = 1;
  84. for trial = standards
  85. % using standard trials only. even for clean condition.
  87. disp(['subject ' int2str(s) ' trial ' int2str(trial)])
  89. load([eegRaw_dir 'dataSub' int2str(s) '_' conditions{cond} '.mat'],'neural');
  90. eeg = neural.data{trial}; % [neural.data{trial} neural.extChan{1}.data{trial}]; % time x chan
  93. %'** Filtering **'
  94. EEG = filtfilthd(hpf,eeg');
  95. EEG = filtfilthd(lpf,EEG);
  97. EEG = EEG(:,1:nchans);
  100. % What Giovanni used to find trial beginning and endings
  101. startSample = neural.trialStart(1,trial);
  102. endSample = neural.trialEnd(1,trial);
  104. EEG = EEG(startSample:endSample,:); %still tim x chan
  105. % mastoids = mastoids(startSample:endSample,:);
  107. clear startSample endSample
  110. % '** Interpolating bad channels **' % Spline interpolate bad channels (time x chans) w/ eeglab
  111. EEGstruct = create_eegstruct(EEG,fs,neural.chanlocs); % input data: time x chann
  113. elecind1 = []; elecind2 = []; elecind3 = []; badchans = []; elecind4 = []; % for bad channels
  114. [~,elecind1,~,~] = pop_rejchan(EEGstruct, 'elec',1:nchans,'threshold',10,'norm','on','measure','kurt');
  115. [~,elecind2,~,~] = pop_rejchan(EEGstruct, 'elec',1:nchans,'threshold',5,'norm','on','measure','spec');
  116. [~,elecind3,~,~] = pop_rejchan(EEGstruct, 'elec',1:nchans,'threshold',10,'norm','on','measure','prob');
  117. elecind4 = findBadChansMean(EEGstruct,3);
  119. badchans = unique([elecind1 elecind2 elecind3 elecind4]); % indecies of bad channels
  120. badchans_all{row,s} = badchans;
  121. EEGstruct.badchans = badchans; % keep info about bad channels
  122. EEGstruct = eeg_interp(EEGstruct,badchans); % do and save interp data
  124. data = double(EEGstruct.data'); clear EEG eeg % time x chan x trials
  127. % '** Downsampling **'
  128. eegData = downsample(data,(fs/fs_new)); %still time x chan
  131. % '** Rereferening to global avg **'
  132. % input data = chan x time, so transpose
  133. eegData = reref(eegData',[],'keepref','off');
  135. allEEG(1:length(eegData),:,row) = eegData'; %needs to be time x chann x trials
  136. row = row+1;
  138. clear badchans data eegData %mastoids
  140. end
  141. %
  143. disp('** Run PCA and save **')
  144. % save each subject's data here and run MCCA on all subjects at once
  145. % xAll = sub x time x PC x trial; topcs = sub x chan x chan
  146. [xAll(s,:,:,:), topcs(s,:,:)] = alainPCA(allEEG,nPCs);
  148. clear allEEG %startIdx EEGdata
  149. end
  151. save([mcca_dir 'pca_and_badchans_standards_' conditions{cond} '.mat'],'xAll','topcs','badchans_all'); %
  154. disp('** Denoise **')
  155. filename = ([mcca_dir 'MCCA_standards_' conditions{cond} '_subject_']); %fn save name,subject#,mat
  156. alainMCCAdenoise(xAll,nPCs,nMCCs,filename); % xOut (denoised) = sub x chann x pc x trial
  159. % Data is expressed in CC space. map back to EEG space for each subject and each trial and save
  161. for s = 1:nsubs
  162. load([mcca_dir 'MCCA_standards_' conditions{cond} '_subject_' int2str(s) 'mat.mat']);
  163. tt = size(xx,2); % xx = time x trial x PC
  164. eeg_clean = NaN(size(xx,1),nchans,tt); % time, chan, trial
  166. for trial = 1:tt
  167. eeg_clean(:,:,trial) = squeeze(xx(:,trial,:))*squeeze(topcs(s,:,1:nPCs))';
  168. end
  171. disp('** Save **')
  173. eeg.trialPosition = 1:size(eeg_clean,3);
  174. eeg.data = squeeze(num2cell(eeg_clean,[1 2]))';
  175. eeg.chanlocs = chanlocs;
  177. save([eeg_dir, 'dataSub', int2str(s), '_', conditions{cond}, '.mat'],'eeg', '-v7.3')
  179. end
  180. clear xAll topcs badchans_all
  181. end

gmdlDataset_preprocess.m, no license · at the source

Overview

Authors: Shyanthony R Synigal1,2, Michael P Broderick3, Edmund C Lalor1,2,4,5
ORCID iDs: Edmund C Lalor
  1. Department of Neuroscience, University of Rochester, Rochester, New York, USA
  2. Del Monte Institute for Neuroscience, University of Rochester, Rochester, New York, USA
  3. School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
  4. Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
  5. Center for Visual Science, University of Rochester, Rochester, New York, USA
Institutions: University of Rochester Medicine (United States); University of Rochester (United States); Trinity College Dublin (Ireland)
Journal: The European journal of neuroscience, volume 64, issue 1, article e70569
Dates: received 6 April 2026; accepted 18 May 2026; published online 2 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70569 · PMID 42390025 · PMCID PMC13325525 · OpenAlex W7167016697
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Preprocessing, Physiology & signal measures
Keywords: EEG, speaker variability, speech, surprisal, vocoding
MeSH: Brain*, Speech Acoustics*, Speech Perception*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Irish Research Council; Simons Foundation Autism Research Initiative; Del Monte Institute for Neuroscience
Citations: cited by 1 paper (Europe PMC); 76 references in the paper

Abstract

There is substantial support for the idea that the listening brain makes predictions about upcoming speech and that these predictions are integrated with sensory input to influence perception. For example, the early auditory encoding of words appears to vary based on how those words semantically relate to their preceding context, suggesting that top‐down information might feed back to affect acoustic speech processing. However, the way in which speakers enunciate words can vary based on how well those words fit with their preceding context. This presents a potential confound to the interpretation of top‐down prediction in the listener. In this study, we address this possibility by assessing the influence of probability‐based predictions (word surprisal) on electroencephalographic (EEG) indices of acoustic speech processing while controlling for variations in speaker dynamics. We analyzed EEG from 14 adults who undertook a perceptual pop‐out task in which prior information enhanced the comprehensibility of degraded speech while acoustic information was held constant. Behavioral results confirmed the manipulation's effectiveness and were mirrored in the neural indices of word surprisal processing. Importantly, a positive relationship between word surprisal and EEG tracking of word acoustics emerged for degraded speech when prior information rendered it intelligible, but was absent when it was unintelligible, despite identical acoustic input across conditions. The difference in neural effects between conditions also correlated with the corresponding difference in behavioral pop‐out. These findings support the claim that top‐down word predictability influences the acoustic encoding of natural speech, independent of variations in speaker enunciation.

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 8 matches between paragraphs and lines of code.

OSF 756xn

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (12), R (1)
Size: 62 files, 13 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), EEGLAB (1 file), emmeans (1 file), ggplot2 (1 file), lmerTest (1 file), psych (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
13 files
At the source: doi.org/10.17605/OSF.IO/756XN

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

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

No dataset and no data link were found in the paper.

Data Availability Statement

The data and scripts associated with this study are available at https://doi.org/10.17605/OSF.IO/756XN.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 9 MeSH terms, 3 funders, 62 references.

Cite

This paper

Synigal, S. R., Broderick, M. P., & Lalor, E. C. (2026). Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability. The European journal of neuroscience, 64(1), e70569. https://doi.org/10.1111/ejn.70569

BibTeX

@article{synigal2026quantifying,
author = {Synigal, Shyanthony R and Broderick, Michael P and Lalor, Edmund C},
title = {{Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability}},
journal = {The European journal of neuroscience},
year = {2026},
month = jul,
volume = {64},
number = {1},
pages = {e70569},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70569},
url = {https://doi.org/10.1111/ejn.70569},
pmid = {42390025},
pmcid = {PMC13325525}
}

RIS

TY - JOUR
AU - Synigal, Shyanthony R
AU - Broderick, Michael P
AU - Lalor, Edmund C
TI - Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/07/01
VL - 64
IS - 1
SP - e70569
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70569
UR - https://doi.org/10.1111/ejn.70569
LA - en
ER -

CSL-JSON

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