Evidence map›Paper›PMID 42023143›Full record

ArticleiScience2026

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

Zihang Xu, Baichun Wei, Chifu Yang, Haiqi Zhu, Chunyu Zhang, Zhiyuan Chen, Shuqing Chen, Chunzhi Yi

Abstract read
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zihang XuSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Baichun WeiSchool of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
Chifu YangSchool of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Haiqi ZhuSchool of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
Chunyu ZhangSchool of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China.
Zhiyuan ChenSchool of Computer Science, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor, Malaysia.
Shuqing ChenSchool of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
Chunzhi YiSchool of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Behavioral neuroscienceCognitive neuroscienceNeuroscienceSensory neuroscience

Identifiers

PMID42023143
PMCPMC13098603

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.