Dissociation Between Mental Workload, Performance, and Task Awareness in Pilots of High Performance Aircraft

Mansikka, Heikki Petteri; Virtanen, Kai; Harris, Don

doi:10.1109/thms.2018.2874186

Cited by 33 publications

(27 citation statements)

References 65 publications

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“…'Entropy' and 'Gini', are tried as the search space of the parameter 'partition standard. The search space of parameter 'maximum depth' and parameter 'Minimum sample number required to segment internal nodes is [10,30,60,100] and [2,5,10,15] respectively. RF is an ensemble classification model with good generalization.…”

Section: Classification Methodsmentioning

confidence: 99%

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Chu¹,

Cao²,

Jiang³

et al. 2021

Preprint

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Background: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating EEG and fNIRS signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.Method: The signal acquisition configuration was optimized and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical MATB task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.Results: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 78.25±4.71%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of 𝛽1 and 𝛽2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.Conclusions: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 78.25±4.71% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

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Section: Classification Methodsmentioning

confidence: 99%

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Chu¹,

Cao²,

Jiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This finding was also consistent with previous theory and empirical research on mental workload. Cognitive overload is coupled with poor performance as it indicates insufficient cognitive resources for the current task (Itoh et al, 1990; Mansikka et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Predicting aviation training performance with multimodal affective inferences

Lajoie

2021

Int J Training Development

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Affect influences learning and training through various cognitive, psychomotor and motivational processes. This research aims to examine the role of affect in aviation training. Participants’ (N = 19) affect and performance were examined in simulated aviation training while they performed ten tasks. Affective states were inferred from electrodermal activity, facial expression and NASA Taskload Index. Performance accuracy was graded with the rubrics provided by pilot instructors in CAE Inc. We found that arousal (inferred from electrodermal activity) positively predicted performance in the level 2 (easy) task (F(1, 17) = 7.408, p < 0.05, std β = 0.55). Mental workload (as measured from self‐report) negatively predicted performance in the level 3 (medium difficulty) (F(1, 15) = 4.598, p < 0.05, std β = −0.54) and level 4 (difficult) tasks (F(1, 15) = 12.85, p < 0.01, std β = −0.73), controlling for affect valence and arousal. This research is a preliminary step to a reconsideration of affect in theoretical frameworks in aviation. It demonstrates a comprehensive assessment of affect in aviation training, which could provide guidelines for instructional interventions to improve the overall training experience and pilot performance.

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“…Pilots’ SA, MWL, and performance have been extensively studied – both separately and together (see e.g., Mansikka et al 8 and Endsley 42 ). Unlike the previous studies, the LVC simulation framework explicitly illustrates how NP, SA, and MWL results, together with the HMP output of V- and L-simulations, can be used to identify the qualitative and quantitative rule candidates for revision, and to support the formulation of optimization criteria and constrains for C-simulations.…”

Section: Discussionmentioning

confidence: 99%

“…To ensure the efficacy of TTP, it is necessary to measure the components of HMI during TTP T&E. Adequate MWL, NP, and SA during TTP T&E ensure that pilots are capable of maintaining the required level of HMP output even when the task complexity and stressors exceed those seen during TTP T&E. MWL, NP, and SA are largely dissociated but inter-related components of HMI, and therefore it is not possible to determine any of their values by measuring the values of one of the others. 8 If one chooses to measure MP output with P k and P s , human limitations must be excluded from the HMP by assuming full SA, perfectly balanced MWL, and faultless NP. As outlined in Figure 1, the difference between the MP and the HMP outputs describes HMP loss caused by the MWL, NP, and SA of the pilots.…”

Section: Introductionmentioning

confidence: 99%

Live–virtual–constructive simulation for testing and evaluation of air combat tactics, techniques, and procedures, Part 1: assessment framework

Mansikka

Virtanen

Harris

et al. 2019

Journal of Defense Modeling & Simulation

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This paper advances live (L), virtual (V), and constructive (C) simulation methodologies by introducing a new LVC simulation framework for the development of air combat tactics, techniques, and procedures (TTP). In the framework, TTP is developed iteratively in separate C-, V-, and L-simulation stages. This allows the utilization of the strengths of each simulation class while avoiding the challenges of pure LVC simulations. The C-stage provides the optimal TTP with respect to the probabilities of survival ( Ps) and kill ( Pk) of aircraft without considering the human–machine interaction (HMI). In the V-stage, the optimal TTP is modified by assessing its applicability with Pk and Ps, as well as HMI measures regarding pilots’ situation awareness, mental workload, and TTP adherence. In the L-stage, real aircraft are used to evaluate whether the developed TTP leads to acceptable Pk, Ps, and HMI measures in a real-life environment. The iterative nature of the framework enables that V- or L-stages can reveal flaws of the TTP and an inadequate TTP can be returned to the C- or V-stage for revision. This paper is Part 1 of a two-part study. Part 2 demonstrates the use of the framework with operationally used C- and V-simulators as well as real F/A-18C aircraft and pilots.

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Dissociation Between Mental Workload, Performance, and Task Awareness in Pilots of High Performance Aircraft

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 33 publications

References 65 publications

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Predicting aviation training performance with multimodal affective inferences

Live–virtual–constructive simulation for testing and evaluation of air combat tactics, techniques, and procedures, Part 1: assessment framework

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