Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments
Abstract
:1. Introduction
2. Workload and Human Performance Assessment: A Quick Overview of the State of the Art
3. Experimental Design: Methods and Materials
3.1. Materials and Instruments
3.1.1. ATCLab-Advanced Software
3.1.2. Scenarios and Indicators of Task Complexity
- The scenario was programmed with the general purpose of producing task demand variations with time on task (TOT). In other words, the amount of mental workload experienced by participants was different throughout the execution of the task in order to collect the variations caused in the different mental workload indexes and see how they would affect operator performance.
- Nine initial aircrafts were presented to participants, six of which were controlled initially by them.
- Along the execution of the scenario, participants controlled a total amount of 70 aircrafts; 50 of them travelled from external (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) to internal locations (11, 12) and, conversely, 20 travelled from internal to external locations (Figure 1).
3.1.3. Tobii T120 Eye-Tracker
3.1.4. Instantaneous Self-Assessment Scale
3.2. Experimental Conditions and Participants
3.3. Procedure
- Learning stage: This first stage lasted 90 min and the main goal was to achieve proper learning and training of the ATM simulator by participants so that they could handle it comfortably before the data collection stage. First, participants had to read and sign the informed consent document and read a brief manual about the use of the simulator. Then, the participants were informed about their task goal (maintain aviation safety by preventing eventual conflicts between aircrafts) and the manual was reviewed in detail between participants and the researcher to guarantee proper understanding of the simulator use. Finally, participants trained themselves with the simulator by performing 6 training scenarios that were executed in order of difficulty. Participants could always ask questions to the researcher and consult the manual if necessary. At the end of the learning stage, the researcher checked the performance of participants to ensure their learning.
- Data collection stage: This stage took place during the day after the training stage. It lasted 120 min and the main goal was to collect empirical data of the participants during the execution of the experimental ATM scenario. More specifically, we collected performance (no. of conflicts between aircrafts) and psychophysiological (pupil size) objective data, as well as subjective mental workload reports (ISA scale). The procedure of the data collection stage for each participant was as follows: first, the researcher calibrated the eye-tracker system and told the participant to avoid head movements during the execution of the task. Then, the participant was instructed to report perceived mental workload periodically, every 5 min (during experimental condition 1), or every 2 min (during experimental condition 2). A periodic alarm would sound to advise the right time to record the ISA scale. Finally, the participant would start performing the experimental scenario for 120 min while recording. Once the experimental session finished, the participants were thanked for their participation and awarded with credits.
3.4. Experimental Room Conditions
3.5. Variables
3.5.1. Independent Variables
3.5.2. Dependent Variables
4. Overview of Key Results
4.1. Experimental Condition 1
4.2. Experimental Condition 2
5. Discussion: Challenges and Opportunities
5.1. Predicting Performance and Task Complexity through Mental Workload
5.2. Latency Differences between Measurements
5.3. Limitations, Final Conclusions and Further Research
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Performance Ratings | Pupil Size (Right) | Pupil Size (Left) | Subjective Reports | Traffic Density | ||
---|---|---|---|---|---|---|
Performance Ratings | Spearman | 1 | 0.489 * | 0.413 | 0.485 * | 0.695 ** |
Sig. (bilateral) | 0.018 | 0.050 | 0.019 | 0.000 | ||
N | 23 | 23 | 23 | 23 | 23 | |
Pupil Size (Right) | Spearman | 0.489 * | 1 | 0.963 ** | 0.875 ** | 0.656 ** |
Sig. (bilateral) | 0.018 | 0.000 | 0.000 | 0.001 | ||
N | 23 | 23 | 23 | 23 | 23 | |
Pupil Size (Left) | Spearman | 0.413 | 0.963 ** | 1 | 0.867 ** | 0.602 ** |
Sig. (bilateral) | 0.050 | 0.000 | 0.000 | 0.002 | ||
N | 23 | 23 | 23 | 23 | 23 | |
Subjective Reports | Spearman | 0.485 * | 0.875 ** | 0.867 ** | 1 | 0.596 ** |
Sig. (bilateral) | 0.019 | 0.000 | 0.000 | 0.003 | ||
N | 23 | 23 | 23 | 23 | 23 | |
Traffic Density | Spearman | 0.695 ** | 0.656 ** | 0.602 ** | 0.596 ** | 1 |
Sig. (bilateral) | 0.000 | 0.001 | 0.002 | 0.003 | ||
N | 23 | 23 | 23 | 23 | 23 |
Performance Ratings | Pupil Size (Right) | Pupil Size (Left) | Subjective Reports | Traffic Density | ||
---|---|---|---|---|---|---|
Performance Ratings | Spearman | 1 | 0.177 | 0.218 | 0.390 ** | 0.579 ** |
Sig. (bilateral) | 0.184 | 0.100 | 0.003 | 0.000 | ||
N | 58 | 58 | 58 | 58 | 58 | |
Pupil Size (Right) | Spearman | 0.177 | 1 | 0.942 ** | 0.777 ** | 0.540 ** |
Sig. (bilateral) | 0.184 | 0.000 | 0.000 | 0.000 | ||
N | 58 | 58 | 58 | 58 | 58 | |
Pupil Size (Left) | Spearman | 0.218 | 0.942 ** | 1 | 0.769 ** | 0.534 ** |
Sig. (bilateral) | 0.100 | 0.000 | 0.000 | 0.000 | ||
N | 58 | 58 | 58 | 58 | 58 | |
Subjective Reports | Spearman | 0.390 * | 0.777 ** | 0.769 ** | 1 | 0.745 ** |
Sig. (bilateral) | 0.003 | 0.000 | 0.000 | 0.000 | ||
N | 58 | 58 | 58 | 58 | 58 | |
Traffic Density | Spearman | 0.579 ** | 0.540 ** | 0.534 ** | 0.745 ** | 1 |
Sig. (bilateral) | 0.000 | 0.000 | 0.000 | 0.000 | ||
N | 58 | 58 | 58 | 58 | 58 |
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Muñoz-de-Escalona, E.; Leva, M.C.; Cañas, J.J. Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments. Aerospace 2024, 11, 691. https://doi.org/10.3390/aerospace11080691
Muñoz-de-Escalona E, Leva MC, Cañas JJ. Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments. Aerospace. 2024; 11(8):691. https://doi.org/10.3390/aerospace11080691
Chicago/Turabian StyleMuñoz-de-Escalona, Enrique, Maria Chiara Leva, and José Juan Cañas. 2024. "Mental Workload as a Predictor of ATCO’s Performance: Lessons Learnt from ATM Task-Related Experiments" Aerospace 11, no. 8: 691. https://doi.org/10.3390/aerospace11080691