Task difficulty and physiological measures of mental workload in air traffic control: a scoping review

Pagnotta, Murillo; Jacobs, David M.; Frutos, Patricia López de; Rodríguez, Rubén Rodríguez; Ibáñez-Gijón, Jorge; Travieso, David

doi:10.1080/00140139.2021.2016998

Cited by 18 publications

(7 citation statements)

References 79 publications

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“…It is widely understood that there are three main classes of measures of mental workload: self-report measures, physiological (and neurophysiological) measures, and primary task performance measures. A number of systematic reviews have been already published in this space, including (Kramer, 1991 ; Cain, 2007 ; Morris et al, 2007 ; Whelan, 2007 ; Antonenko et al, 2010 ; Byrne, 2011 ; Lean and Shan, 2012 ; Marquart et al, 2015 ; Young et al, 2015 ; Butmee et al, 2018 ; Orru and Longo, 2018 ; Charles and Nixon, 2019 ; Tao et al, 2019 ; Hancock et al, 2021 ; Marchand et al, 2021 ; Pagnotta et al, 2021 ). Therefore, conducting a new systematic review for mental workload measures not only is not feasible, but unnecessary.…”

Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

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Section: Measuring Mental Workloadmentioning

confidence: 99%

Human Mental Workload: A Survey and a Novel Inclusive Definition

et al. 2022

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“…An electroencephalogram (EEG) is the recording of the brain's spontaneous electrical activity over a period of time, which can be decomposed into the following five frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (>30 Hz) [37]. EEG has been considered one of the most commonly used physiological measures of MW in air traffic control studies [38]. A recent study used EEG to assess cognitive performance under construction noise conditions, which showed a negative correlation between cognitive function and noise exposure [39].…”

Section: Introductionmentioning

confidence: 99%

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

Fan

Jin²,

Cao

et al. 2022

IJERPH

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Twelve healthy male students were recruited to investigate the physiological response to different noise exposure and mental workload (MW) conditions, while performing multi-attribute task battery (MATB) tasks. The experiments were conducted under three noise exposure conditions, with different sound pressure levels and sharpness. After adaptation to each noise condition, the participants were required to perform the resting test and the MATB task tests with low, medium, and high MW. The electroencephalogram (EEG), electrocardiogram (ECG), and eye movement data were obtained, during the periods when participants were in the resting and task taking state. The results showed that subjects’ physiological responses at rest were unaffected by noise exposure conditions. However, during the execution of MATB tasks, the elevated sound pressure level and increased sharpness were significantly correlated with increased mean pupil diameter and heart rate variability (HRV). These responses suggested that the human body defends itself through physiological regulation when noise causes adverse effects. If the negative effects of noise were more severe, this could damage the body’s health and result in a significant drop in task performance. The elevated mental demands led to increased stress on the subjects, which was reflected in a considerable increase in theta relative power. Either high or low MW was related with reduced saccade amplitude and a decrease in weighted task performance, indicating an inverted U-shaped relationship between workload level and work performance.

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“…Also, some researchers measure MWL under some working conditions to develop suitable work/rest schedules [3,[59][60][61][62].…”

Section: Effect Of Pauses During the Execution Of Cognitive Tasks On ...mentioning

confidence: 99%

Real-time mental workload measurement for four break configurations in cognitive tasks using the EEG-based workload index (EWI)

Arana-De-Las-Casas,

Sáenz-Zamarrón,

Maldonado-Macías

et al. 2024

Preprint

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The current real tasks in work environments are complex, they demand a large amount of cognitive work, which causes the need to carry out Mental Workload (MWL) studies during the execution of those tasks. In high-tech manufacturing processes, the human-machine systems adopted are technologically more complex than ever and, consequently, the MWL of employees has increased during their work activities. A high MWL can adversely affect employee well-being, causing new health problems, mainly cognitive. MWL has become a relevant workplace concern due to its relationship with poor performance, low productivity, human errors, accidents, and stress. In this project, a design of experiments framework was created to measure MWL through a graphical user interface (GUI) that contains the Dual N-back method, it is developed by subjects while they performed a set of cognitive tasks; and it is measured physiologically by electroencephalogram (EEG). The design integrated hardware (Cyton© card as EEG measurement equipment) and software (OpenBCI, MATLAB, EEGLAB) that automatically synchronizes the electroencephalogram (EEG) with the activity performed (stimulus), this was done by using the Lab Streaming Layer (LSL)© system, which allows real-time data transmission between applications in a computer. This study validated the EWI model as an index of MWL performance.

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Task difficulty and physiological measures of mental workload in air traffic control: a scoping review

Cited by 18 publications

References 79 publications

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human Mental Workload: A Survey and a Novel Inclusive Definition

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

Real-time mental workload measurement for four break configurations in cognitive tasks using the EEG-based workload index (EWI)

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