Literature DB >> 30266685

Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty.

Kyle J Jaquess1, Li-Chuan Lo1, Hyuk Oh2, Calvin Lu1, Andrew Ginsberg1, Ying Ying Tan3, Keith R Lohse4, Matthew W Miller5, Bradley D Hatfield2, Rodolphe J Gentili6.   

Abstract

The allocation of mental workload is critical to maintain cognitive-motor performance under various demands. While mental workload has been investigated during performance, limited efforts have examined it during cognitive-motor learning, while none have concurrently manipulated task difficulty. It is reasonable to surmise that the difficulty level at which a skill is practiced would impact the rate of skill acquisition and also the rate at which mental workload is reduced during learning (relatively slowed for challenging compared to easier tasks). This study aimed to monitor mental workload by assessing cortical dynamics during a task practiced under two difficulty levels over four days while perceived task demand, performance, and electroencephalography (EEG) were collected. As expected, self-reported mental workload was reduced, greater working memory engagement via EEG theta synchrony was observed, and reduced cortical activation, as indexed by progressive EEG alpha synchrony was detected during practice. Task difficulty was positively related to the magnitude of alpha desynchrony and accompanied by elevations in the theta-alpha ratio. Counter to expectation, the absence of an interaction between task difficulty and practice days for both theta and alpha power indicates that the refinement of mental processes throughout learning occurred at a comparable rate for both levels of difficulty. Thus, the assessment of brain dynamics was sensitive to the rate of change of cognitive workload with practice, but not to the degree of difficulty. Future work should consider a broader range of task demands and additional measures of brain processes to further assess this phenomenon.
Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Keywords:  EEG; mental workload; motor learning; motor performance; spectral power

Mesh:

Year:  2018        PMID: 30266685     DOI: 10.1016/j.neuroscience.2018.09.019

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


  4 in total

1.  Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features.

Authors:  Zixuan Cao; Zhong Yin; Jianhua Zhang
Journal:  Cogn Neurodyn       Date:  2020-10-07       Impact factor: 3.473

2.  A Systematic Review of Physiological Measures of Mental Workload.

Authors:  Da Tao; Haibo Tan; Hailiang Wang; Xu Zhang; Xingda Qu; Tingru Zhang
Journal:  Int J Environ Res Public Health       Date:  2019-07-30       Impact factor: 3.390

3.  Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks.

Authors:  Wenjun Jia; Frederic von Wegner; Mengting Zhao; Yong Zeng
Journal:  Sci Rep       Date:  2021-12-20       Impact factor: 4.379

4.  Effects of Noise Exposure and Mental Workload on Physiological Responses during Task Execution.

Authors:  Yurong Fan; Jin Liang; Xiaodong Cao; Liping Pang; Jie Zhang
Journal:  Int J Environ Res Public Health       Date:  2022-09-29       Impact factor: 4.614

  4 in total

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