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Physical and cognitive contributions to fatigue perception: The interplay between local muscle fatigue and sensory prediction error.

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Paper

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

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The authors' code

Python · 13 lines · 403 B · no license

  1. from plot_figure_3_2 import analyze_and_plot_fatigue_data
  3. # Subjective_Fatigue
  4. p_values, plot = analyze_and_plot_fatigue_data("Study1_SF.csv")
  6. # Heart_Rate
  7. p_values, plot = analyze_and_plot_fatigue_data("Study1_HR.csv")
  9. # Muscle_Fatigue_1
  10. p_values, plot = analyze_and_plot_fatigue_data("Study1_MF1.csv")
  12. # Muscle_Fatigue_2
  13. p_values, plot = analyze_and_plot_fatigue_data("Study1_MF2.csv")

Fig2.py, no license · at the source

Overview

Authors: Zihang Xu1, Baichun Wei2, Chifu Yang1, Haiqi Zhu2, Chunyu Zhang3, Zhiyuan Chen4, Shuqing Chen2, Chunzhi Yi2,5,6,7
ORCID iDs: Baichun Wei, Chunzhi Yi
  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
  2. School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
  3. School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China
  4. School of Computer Science, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor, Malaysia
  5. Zhengzhou Research Institute, Harbin Institute of Technology, Harbin, China
  6. Suzhou Research Institute, Harbin Institute of Technology, Harbin, China
  7. Institute for Neural Information Processing, University Medical Center Hamburg- Eppendorf, Hamburg, Germany
Institutions: Harbin Institute of Technology (China); Beijing Sport University (China); University of Nottingham (United Kingdom); University of Nottingham Malaysia Campus (Malaysia); University Medical Center Hamburg-Eppendorf (Germany); Suzhou Research Institute (China)
Journal: iScience, volume 29, issue 5, article 115552
Dates: received 10 August 2025; accepted 27 March 2026; published online 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115552 · PMID 42023143 · PMCID PMC13098603 · OpenAlex W7147668434
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Neuroscience, Behavioral neuroscience, Sensory neuroscience, Cognitive neuroscience
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (62306083); Ministry of Industry and Information Technology of the People's Republic of China; National Key Research and Development Program of China; Key Technologies Research and Development Program (2024YFC3016403)
Citations: not cited yet (Europe PMC); 90 references in the paper

Abstract

Fatigue perception during exercise arises from complex body-brain interactions, but integration of local muscle fatigue with sensory prediction errors remains unclear. Traditional cognitive frameworks overlook dynamic physiological contributions. This study examined how local muscle fatigue and prediction errors jointly shape fatigue perception across spatial-temporal domains. Two experiments used naturalistic running with physiological monitoring, inducing temporal and spatial prediction errors by manipulating performance feedback. Computational models quantified contributions of muscle fatigue, prediction errors, and their interactions. Results showed fatigue perception is driven by both muscle fatigue and prediction errors, with domain-specific interactions: temporal errors linearly amplified muscle fatigue’s impact, while spatial errors modulated it exponentially. These findings challenge purely cognitive models, demonstrating that fatigue perception emerges from domain-dependent integration of physiological signals and sensory discrepancies. The study provides a unified computational framework for body-brain interactions in fatigue, offering insights for personalized training and rehabilitation targeting both physical and cognitive fatigue pathways.

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

Repository

Its files are read in the Code ↔ Paper reader above.

OSF pycxr

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: Python (16), MATLAB (3)
Size: 65 files, 19 scripts
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (7 files), NumPy (7 files), pandas (7 files), SciPy (7 files), seaborn (7 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
19 files
At the source: osf.io/pycxr/?view_only=d3ad2737b8414e208ffef0d06f6ae548

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

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;
  • 19 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 and code availability

All data generated in this study are included in the article.

Analysis codes presented in the text are also available on OSF: https://osf.io/pycxr/?view_only=d3ad2737b8414e208ffef0d06f6ae548.

Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.

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

  • Authors: added Baichun Wei (0000-0002-2407-3410); removed Baichun Wei

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 4 funders, 86 references.

Cite

This paper

Xu, Z., Wei, B., Yang, C., Zhu, H., Zhang, C., Chen, Z., Chen, S., & Yi, C. (2026). Physical and cognitive contributions to fatigue perception: The interplay between local muscle fatigue and sensory prediction error. iScience, 29(5), 115552. https://doi.org/10.1016/j.isci.2026.115552

BibTeX

@article{xu2026physical,
author = {Xu, Zihang and Wei, Baichun and Yang, Chifu and Zhu, Haiqi and Zhang, Chunyu and Chen, Zhiyuan and Chen, Shuqing and Yi, Chunzhi},
title = {{Physical and cognitive contributions to fatigue perception: The interplay between local muscle fatigue and sensory prediction error}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {115552},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115552},
url = {https://doi.org/10.1016/j.isci.2026.115552},
pmid = {42023143},
pmcid = {PMC13098603}
}

RIS

TY - JOUR
AU - Xu, Zihang
AU - Wei, Baichun
AU - Yang, Chifu
AU - Zhu, Haiqi
AU - Zhang, Chunyu
AU - Chen, Zhiyuan
AU - Chen, Shuqing
AU - Yi, Chunzhi
TI - Physical and cognitive contributions to fatigue perception: The interplay between local muscle fatigue and sensory prediction error
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/01
VL - 29
IS - 5
SP - 115552
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115552
UR - https://doi.org/10.1016/j.isci.2026.115552
LA - en
ER -

CSL-JSON

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