Efficiency of the Brain Network Is Associated with the Mental Workload with Developed Mental Schema

Author:

Gu Heng1,Chen He12,Yao Qunli1,He Wenbo1,Wang Shaodi1,Yang Chao1,Li Jiaxi1,Liu Huapeng3,Li Xiaoli1ORCID,Zhao Xiaochuan3,Liang Guanhao1

Affiliation:

1. Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China

2. School of Systems Science, Beijing Normal University, Beijing 100875, China

3. Institute of Computer Applied Technology of China North Industries Group Corporation Limited, Beijing 100821, China

Abstract

The study of mental workload has attracted much interest in neuroergonomics, a frontier field of research. However, there appears no consensus on how to measure mental workload effectively because the mental workload is not only regulated by task difficulty but also affected by individual skill level reflected as mental schema. In this study, we investigated the alterations in the functional brain network induced by a 10-day simulated piloting task with different difficulty levels. Topological features quantifying global and local information communication and network organization were analyzed. It was found that during different tests, the global efficiency did not change, but the gravity center of the local efficiency of the network moved from the frontal to the posterior area; the small-worldness of the functional brain network became stronger. These results demonstrate the reconfiguration of the brain network during the development of mental schema. Furthermore, for the first two tests, the global and local efficiency did not have a consistent change trend under different difficulty levels, but after forming the developed mental schema, both of them decreased with the increase in task difficulty, showing sensitivity to the increase in mental workload. Our results demonstrate brain network reconfiguration during the motor learning process and reveal the importance of the developed mental schema for the accurate assessment of mental workload. We concluded that the efficiency of the brain network was associated with mental workload with developed mental schema.

Funder

the National Defense Basic Scientific Research Program of China

Publisher

MDPI AG

Subject

General Neuroscience

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning;2023 IEEE Signal Processing in Medicine and Biology Symposium (SPMB);2023-12-02

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