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Volume 49 Issue 11
Nov.  2023
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Journal of Beijing University of Aeronautics and Astronautics  > 2023  >  49(11): 2921-2928.
LIU C P，XIAO X，ZHAO J Q. Pilots’ mental workload dynamic prediction based on cognitive process[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：2921-2928 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0053
Citation: LIU C P，XIAO X，ZHAO J Q. Pilots’ mental workload dynamic prediction based on cognitive process[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：2921-2928 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0053
shu

Pilots’ mental workload dynamic prediction based on cognitive process

doi: 10.13700/j.bh.1001-5965.2022.0053
  • 1.

    School of Aeronautic Science and Engineering，Beihang University，Beijing 100191，China

  • 2.

    China Academy of Electronics and Information Technology of China Electronics Technology Group Corporation，Beijing 100041，China

Funds:  Joint Fund of the National Natural Science Foundation of China and the Civil Aviation Administration of China (U1733118); National Natural Science Foundation of China (71301005)
More Information
  • Corresponding author: E-mail：zjq206@buaa.edu.cn
  • Received Date: 27 Jan 2022
  • Accepted Date: 04 Mar 2022
  • Publish Date: 11 Mar 2022
    Abstract

  • Modern military flight systems are highly information-intensive and their tasks are complex and changeable. In order to explore the influence of information processing types and multi-task coordination on pilots’ mental workload, a quantitative prediction model based on the cognitive process was proposed. The ACT-R cognitive module and the mental workload determinants were used to separate the pilot’s mental workload into perceptual workload and cognitive burden. The multi-task resource interference for mental workload was calculated based on multiple resources. 16 subjects were selected to complete the multi-factor mental workload experiment. The results showed that the main effects of flight performance, NASA-TLX, average saccade time and scanning rate were significant (P<0.05). Subjective evaluation, RRCV and HR were significantly positively correlated with the total mental workload. And the average mental workload was significantly positively correlated with flight performance, pupil diameter and average saccade time. In order to anticipate and assess the mental workload of pilots, the prediction model offered a certain application value.

     

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