Latest revision as of 13:01, 16 February 2021

Abstract

WOS: 000424708100003

Background. Air traffic controllers need to use their cognitive resources to cope with multiple tasks while monitoring air traffic. They are trained through advanced 3D simulators; however, they might demonstrate simulator sickness symptoms during this training. The relationship between multitasking and simulator sickness and the influence of different tasks on these variables can be investigated to inform further training practices for an efficient traffic monitoring. Purpose. The purpose of this quasi-experimental research was to explore the influence of different working positions and multitasking scenarios on simulator sickness and performance in 3D Aerodrome Control Training. Method. Thirteen undergraduate students from a civil aviation school participated in a time-series experimental design. Nausea, disorientation and oculomotor disturbances were measured before and after conducting different multitasking scenarios in an aerodrome control simulator. A 20-item simulation performance scale was also used. Performance differences and the relationship between simulator sickness and performance were addressed through parametric tests. Results. Findings revealed that tower-ground positions created higher levels of nausea. Performance scores varied in different scenarios. Simulator sickness was higher in females. Total sickness correlated negatively with performance in all scenarios. Conclusion. Certain tasks have different influences on both simulator sickness and performance. Concurrent multitasking seems to trigger simulator sickness further. The degree of simulator adaptation, test anxiety and physiological measures of the process should be considered in further research.

Original document

The different versions of the original document can be found in:

• https://hdl.handle.net/11421/14286

• http://journals.sagepub.com/doi/pdf/10.1177/1046878117750417,

http://journals.sagepub.com/doi/full-xml/10.1177/1046878117750417,

http://dx.doi.org/10.1177/1046878117750417 under the license http://journals.sagepub.com/page/policies/text-and-data-mining-license

• https://journals.sagepub.com/doi/full/10.1177/1046878117750417,

https://journals.sagepub.com/doi/pdf/10.1177/1046878117750417,

http://journals.sagepub.com/doi/10.1177/1046878117750417,

https://academic.microsoft.com/#/detail/2780252847