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Pupil- and gaze dynamics track emotion content in naturalistic speech.

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  1. [1] § Materials and methods › Pupil data acquisition and preprocessing ↔ pupilTutorial.m, the whole file · a weak match · score 0.62 · FieldTrip, blink interpolation, accuracy, preprocessing, windows, stimulus

Paper

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The authors' code

MATLAB · 137 lines · 5 KB · no license · 1 match

  1. %% PUPIL PREPROCESSING
  2. %
  3. % 1. convert edf file to asc
  4. % 2. create a FieldTrip-style data structure
  5. % 3. interpolate Eyelink-defined and additionally detected blinks
  6. % (optional): regress out blink- and saccade-linked pupil response
  7. % 4. epoch using a custom trial-definition function
  8. % 5. plot event-related pupil responses
  9. %
  10. % Anne Urai, 2016
  12. % setup path
  13. clear; clc; close all;
  14. thispath = '~/Dropbox/code/PupilPreprocessing/';
  15. addpath(thispath);
  17. % first, define the asc filename
  18. edfFile = 'EL_P22_s5_b1_2015-06-28_11-14-27.edf';
  19. ascFile = regexprep(edfFile, 'edf', 'asc');
  21. % set path to FieldTrip - get this from http://www.fieldtriptoolbox.org/download
  22. addpath('~/Documents/fieldtrip/');
  23. ft_defaults;
  25. % ============================================== %
  26. % 1. convert edf file to asc
  27. % ============================================== %
  29. if ~exist(ascFile, 'file'),
  30. if ismac,
  31. edf2ascPath = [thispath '/edf2asc-mac'];
  32. elseif isunix,
  33. edf2ascPath = [thispath '/edf2asc-linux'];
  34. else
  35. error('Sorry, I don''t have an edf2asc converter for Windows')
  36. end
  37. % use this converter to create the asc file
  38. % failsafe mode avoids errors when some samples are missing
  39. system(sprintf('%s %s -failsafe -input', edf2ascPath, edfFile));
  40. end
  41. assert(exist(ascFile, 'file') > 1, 'Edf not properly converted');
  43. % ============================================== %
  44. % 2. create a FieldTrip-style data structure
  45. % ============================================== %
  47. % read in the asc EyeLink file
  48. asc = read_eyelink_ascNK_AU(ascFile);
  50. % create events and data structure, parse asc
  51. [data, event, blinksmp, saccsmp] = asc2dat(asc);
  53. % ============================================== %
  54. % 3. interpolate Eyelink-defined and additionally detected blinks
  55. % ============================================== %
  57. plotMe = true;
  58. newpupil = blink_interpolate(data, blinksmp, plotMe);
  59. data.trial{1}(find(strcmp(data.label, 'EyePupil')==1),:) = newpupil;
  61. % ============================================== %
  62. % (optional): regress out blink- and saccade-linked pupil response
  63. % http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155574
  64. % ============================================== %
  66. pupildata = data.trial{1}(~cellfun(@isempty, strfind(lower(data.label), 'eyepupil')),:);
  67. newpupil = blink_regressout(pupildata, data.fsample, blinksmp, saccsmp, 1, 1);
  68. % put back in fieldtrip format
  69. data.trial{1}(~cellfun(@isempty, strfind(lower(data.label), 'eyepupil')),:) = newpupil;
  71. % zscore since we work with the bandpassed signal
  72. data.trial{1}(find(strcmp(data.label, 'EyePupil')==1),:) = ...
  73. zscore(data.trial{1}(find(strcmp(data.label, 'EyePupil')==1),:));
  75. % ==================================================================
  76. % define trials
  77. % ==================================================================
  79. cfg = [];
  80. cfg.dataset = ascFile;
  81. cfg.event = event;
  82. cfg.trialfun = 'my_trialfun';
  83. cfg.trialdef.pre = 0;
  84. cfg.trialdef.post = 2;
  85. cfg.fsample = asc.fsample;
  86. cfg.sj = 22;
  87. cfg.session = 5;
  88. cfg.block = 1;
  89. cfg = ft_definetrial(cfg);
  90. % epoch
  91. data = ft_redefinetrial(cfg, data);
  93. % note that this trialfun defines four events per trial: two stimuli (that
  94. % have to be compared), a response, and feedback). We have the trial start
  95. % at fixation and save the samples of each of those events (as well as more
  96. % information about stimulus identity, choice and accuracy) in
  97. % data.trialinfo:
  98. % [fixoffset refoffset stimtype stimoffset resptype respcorrect respoffset ...
  99. % feedbacktype feedbackoffset trlcnt blockcnt session]
  101. % ==================================================================
  102. % downsample before saving
  103. % ==================================================================
  105. cfg = [];
  106. cfg.resamplefs = 100;
  107. cfg.fsample = data.fsample;
  109. % see Niels' message on the FT mailing list
  110. samplerows = find(data.trialinfo(1,:)>100); % indices of the rows with sample values (and not event codes)
  111. data.trialinfo(:,samplerows) = round(data.trialinfo(:,samplerows) * (cfg.resamplefs/cfg.fsample));
  113. % use fieldtrip to resample
  114. data = ft_resampledata(cfg, data);
  116. % ==================================================================
  117. % visualize the pupil timecourse
  118. % ==================================================================
  120. cfg = [];
  121. cfg.channel = 'EyePupil';
  122. cfg.trials(1).name = 'correct';
  123. cfg.trials(1).idx = find(data.trialinfo(:, 8) == 1);
  124. cfg.trials(2).name = 'error';
  125. cfg.trials(2).idx = find(data.trialinfo(:, 8) == 0);
  127. clf; subplot(2,2,[1 2]);
  128. plotEventRelated(cfg, data);
  129. title(ascFile, 'interpreter', 'none');
  130. ylabel('Pupil response (z)');
  132. % ==================================================================
  133. % save file
  134. % ==================================================================
  136. filename = regexprep(ascFile, 'asc', 'mat');
  137. save(filename, 'data');

pupilTutorial.m at commit eb91e68, no license · at the source

Overview

Authors: Sümeyye Şen-Alpay1, Christian Keitel1, Rosanne H Timmerman2, Anne Keitel1
  1. Psychology, University of Dundee, Dundee, UK
  2. CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, UK NE45TG
Institutions: University of Dundee (United Kingdom); Newcastle University (United Kingdom)
Journal: Scientific reports, volume 16, issue 1, article 27855
Dates: received 20 February 2026; accepted 5 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-57256-0 · PMID 42315873 · PMCID PMC13547293 · OpenAlex W7165127149
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Physiology & signal measures
Keywords: Speech perception, Active sensing, Emotion, Naturalistic speech, Pupillometry, Eye movements, Neuroscience, Psychology
MeSH: Emotions*, Eye Movements*, Fixation, Ocular*, Pupil*, Speech*, Speech Perception*, Acoustic Stimulation, Adult, Arousal, Attention, Electrooculography, Female, Humans, Male, Young Adult (* major topic)
Topic: Vestibular and auditory disorders (Neurology, Neuroscience), according to OpenAlex
Funding: Medical Research Council (MR/W02912X/1); Royal Society of Edinburgh (1963); the Republic of Türkiye, Ministry of National Education
Citations: not cited yet (Europe PMC); 128 references in the paper

Abstract

Cortical tracking of speech features is a well-established marker of continuous speech processing, but far less is known about listeners’ ocular responses to speech rhythm. Ocular responses are central to active sensing models in the auditory domain, where motor recruitment guides temporal speech prediction and attention allocation, potentially shaped by non-rhythmic cues, such as emotions. Here, we ask whether listeners’ pupil response and eye movements track the acoustic speech signals and to what extent this tracking is modulated by emotion-related top-down factors, including subjective emotion ratings, mood, and trait empathy. In a validation study (N = 100), participants passively listened to two TED talks and intermittently rated speech segments on valence (negative–positive) and arousal (low–high). This suggested substantial variability in valence and arousal across speech segments in both talks. In the second study (N = 41), participants completed the same task while pupillometry and electrooculography (EOG) were recorded. Mutual information was used to quantify speech tracking in pupil dilation, along with horizontal and vertical eye movements. All ocular signals significantly tracked speech at low frequencies. High-arousal speech was associated with stronger pupil tracking but weaker vertical and horizontal EOG tracking. Negative speech valence was linked to stronger tracking in pupil and vertical eye-movement signals. Interactions between the speech-emotion dimensions, as well as their interactions with listeners’ mood, further shaped these effects, giving rise to distinct patterns across ocular measures. Taken together, our findings provide evidence that ocular activity dynamically aligns to the temporal structure of natural speech and that this tracking is sensitive to both stimulus-driven and listener-dependent emotional factors.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-57256-0.

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 1 match between paragraphs and lines of code.

anne-urai/pupil_preprocessing_tutorial

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: eb91e689aaa1dcf6176181b3208845b9e1804ad5, 9 November 2018
Languages: MATLAB (3), Jupyter (1)
Size: 8 files, 4 scripts
Software Heritage: archived
Found in: the text, “Pupil data acquisition and preprocessing”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (2 files), boundedline (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/anne-urai/pupil_preprocessing_tutorial

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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;
  • 4 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

Data and stimuli are publicly available on the OSF (https://osf.io/dsvzh/files/osfstorage).

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 keywords, 15 MeSH terms, 3 funders, 124 references.

Cite

This paper

Şen-Alpay, S., Keitel, C., Timmerman, R. H., & Keitel, A. (2026). Pupil- and gaze dynamics track emotion content in naturalistic speech. Scientific reports, 16(1), 27855. https://doi.org/10.1038/s41598-026-57256-0

BibTeX

@article{senalpay2026pupil,
author = {Şen-Alpay, Sümeyye and Keitel, Christian and Timmerman, Rosanne H and Keitel, Anne},
title = {{Pupil- and gaze dynamics track emotion content in naturalistic speech}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {27855},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-57256-0},
url = {https://doi.org/10.1038/s41598-026-57256-0},
pmid = {42315873},
pmcid = {PMC13547293}
}

RIS

TY - JOUR
AU - Şen-Alpay, Sümeyye
AU - Keitel, Christian
AU - Timmerman, Rosanne H
AU - Keitel, Anne
TI - Pupil- and gaze dynamics track emotion content in naturalistic speech
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/18
VL - 16
IS - 1
SP - 27855
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-57256-0
UR - https://doi.org/10.1038/s41598-026-57256-0
LA - en
ER -

CSL-JSON

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