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

We studied 2 classifiers to determine their ability to discriminate among 4 levels of mental workload during a simulated air traffic control task using psychophysiological measures. Data from 7 air traffic controllers were used to train and test artificial neural network and stepwise discriminant classifiers. Very high levels of classification accuracy were achieved by both classifiers. When the 2 task difficulty manipulations were tested separately, the percentage correct classifications were between 84% and 88%. Feature reduction using saliency analysis for the artificial neural networks resulted in a mean of 90% correct classification accuracy. Considering the data as a 2-class problem, acceptable load versus overload, resulted in almost perfect classification accuracies, with mean percentage correct of 98%. In applied situations, the most important distinction among operator functional states would be to detect mental overload situations. These results suggest that psychophysiological data are capable of such discriminations with high levels of accuracy. Potential applications of this research include test and evaluation of new and modified systems and adaptive aiding.

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Power law model for subjective mental workload and validation through air traffic control human-in-the-loop simulation

Cognition Technology & Work ◽

10.1007/s10111-021-00681-0 ◽

2021 ◽

Author(s):

Norbert Fürstenau ◽

Thea Radüntz

Keyword(s):

Traffic Flow ◽

Power Law ◽

Traffic Control ◽

Air Traffic Control ◽

Mental Workload ◽

Air Traffic ◽

Parameter Estimates ◽

Task Load ◽

Human In The Loop ◽

Power Law Exponent

AbstractWe provide evidence for a power law relationship between the subjective one-dimensional Instantaneous Self Assessment workload measure (five-level ISA-WL scale) and the radio communication of air traffic controllers (ATCOs) as an objective task load variable. It corresponds to Stevens’ classical psychophysics relationship between physical stimulus and subjective response, with characteristic power law exponent γ of the order of 1. The theoretical model was validated in a human-in-the loop air traffic control simulation experiment with traffic flow as environmental stimulus that correlates positively with ATCOs frequency and duration of radio calls (task load, RC-TL) and their reported ISA-WL. The theoretical predictions together with nonlinear regression-based model parameter estimates expand previously published results that quantified the formal logistic relationship between the subjective ISA measure and simulated air traffic flow (Fürstenau et al. in Theor Issues Ergon Sci 21(6): 684–708, 2020). The present analysis refers to a psychophysics approach to mental workload suggested by (Gopher and Braune in Hum Factors 26(5): 519–532, 1984) that was recently used by (Bachelder and Godfroy-Cooper in Pilot workload esimation: synthesis of spectral requirements analysis and Weber's law, SCL Tech, San Diego, 2019) for pilot workload estimation, with a corresponding power law exponent in the typical range of Stevens’ exponents. Based on the hypothesis of cognitive resource limitation, we derived the power law by combination of the two logistic models for ISA-WL and communication TL characteristics, respectively. Despite large inter-individual variance, the theoretically predicted logistic and power law parameter values exhibit surprisingly close agreement with the regression-based estimates (for averages across participants). Significant differences between logistic ISA-WL and RC-TL scaling parameters and the corresponding Stevens exponents as ratio of these parameters quantify the TL/WL dissociation with regard to traffic flow. The sensitivity with regard to work conditions of the logistic WL-scaling parameter as well as the power law exponent was revealed by traffic scenarios with a non-nominal event: WL sensitivity increased significantly for traffic flow larger than a critical value. Initial analysis of a simultaneously measured new neurophysiological (EEG) load index (dual frequency head maps, DFHM, (Radüntz in Front Physiol 8: 1–15, 2017)) provided evidence for the power law to be applicable to the DFHM load measure as well.

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Eye Segment Movements as Indicators of Mental Workload in Air Traffic Control Tasks

Lecture Notes in Mechanical Engineering - iMEC-APCOMS 2019 ◽

10.1007/978-981-15-0950-6_37 ◽

2019 ◽

pp. 238-244

Author(s):

Vivi Triyanti ◽

Hastian Abdul Azis ◽

Hardianto Iridiastadi ◽

Yassierli

Keyword(s):

Traffic Control ◽

Air Traffic Control ◽

Mental Workload ◽

Air Traffic

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Artificial Intelligence for Evaluating the Mental Workload of Air Traffic Controllers

Advances in Psychology, Mental Health, and Behavioral Studies - Evaluating Mental Workload for Improved Workplace Performance ◽

10.4018/978-1-7998-1052-0.ch009 ◽

2020 ◽

pp. 184-212

Author(s):

Tetiana Shmelova ◽

Yuliya Sikirda

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Expert System ◽

Traffic Control ◽

Air Traffic Control ◽

Mental Workload ◽

Air Traffic ◽

Control Center ◽

Air Traffic Controllers ◽

And Control

In this chapter, the authors propose the application of artificial intelligence (namely expert system and neural network) for estimating the mental workload of air traffic controllers while working at different control centers (sectors): terminal control center, approach control center, area control center. At each air traffic control center, air traffic controllers will perform the following procedures: coordination between units, aircraft transit, climbing, and descending. So with the help of the artificial intelligence (AI) and its branches expert system and neural network, it is possible to estimate the mental workload of dispatchers for a different number of aircraft, compare the workload intensity of the air traffic control sectors, and optimize the workload between sectors and control centers. The differentiating factor of an AI system from a standard software system is the characteristic ability to learn, improve, and predict. Real dispatchers, students, graduate students, and teachers of the National Aviation University took part in these researches.

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