Neural mechanisms underlying the effects of cognitive fatigue on physical effort-based choice
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Updated: Nov 1, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
Published on: August 2, 2018
Michael H Dryzer1, Vikram S Chib2,3,4
1Department of Biomedical Engineering, Johns Hopkins School of Medicine, 720 Rutland Avenue, Baltimore, MD, 21205, USA.
Abstract:
Fatigue is a state of exhaustion that influences our willingness to engage in effortful tasks. While both physical and cognitive exertion can cause fatigue, there is a limited understanding of how fatigue in one exertion domain (e.g., cognitive) affects decisions to exert in another (e.g., physical). We first trained human participants to associate their physical exertions with a numerical scale of effort levels. Participants then completed a forced-choice task for prospective physical effort before and after completing bouts of a cognitively fatiguing working memory (3-back) task. Functional magnetic resonance imaging was used to measure brain activity during the choice and 3-back tasks. Using computational modeling of choice behavior, we found that participants reported increasing levels of cognitive and physical fatigue, but it was only the increasing feelings of cognitive fatigue that made them more risk-averse for high levels of physical effort. An analysis of the functional magnetic resonance imaging findings showed that right anterior insula encodes cognitive fatigue-induced increases in participants' subjective costs of physical effort compared with a baseline rested state, while increasing activity in right dorsolateral prefrontal cortex tracked time spent on the 3-back task. A psychophysiological interaction analysis revealed that fatigue-related activations right anterior insula and right dorsolateral prefrontal cortex are correlated and that these regions are linked in an exertion-domain general fatigue network. Our results support the idea of a general fatigue signal that integrates exertion-specific information to guide effort-based choice.
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