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Modeling pilot mental workload using information theory

Published online by Cambridge University Press:  03 June 2019

X. Zhang ,
X. Qu ,
H. Xue Open the ORCID record for H. Xue [Opens in a new window] ,
H. Zhao ,
T. Li  and
D. Tao
X. Zhang
Affiliation:
Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China Key laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, China School of Aeronautics, Northwestern Polytechnical University, Xi’an, China
X. Qu
Affiliation:
Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China Key laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, China
H. Xue*
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi’an, China
H. Zhao
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi’an, China
T. Li
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi’an, China
D. Tao
Affiliation:
Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China Key laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, China
*
[email protected]
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Abstract

Predicting mental workload of pilots can provide cockpit designers with useful information to reduce the possibility of pilot error and cost of training, improve the safety and performance of systems, and increase operator satisfaction. We present a theoretical model of mental workload, using information theory, based on review investigations of how effectively task complexity, visual performance, and pilot experience predict mental workload. The validity of the model was confirmed based on data collected from pilot taxiing experiments. Experiments were performed on taxiing tasks in four different scenarios. Results showed that predicted values from the proposed mental workload model were highly correlated to actual mental workload ratings from the experiments. The findings indicate that the proposed mental workload model appears to be effective in the prediction of pilots’ mental workload over time.

Keywords

Mental workloadTask complexityTaxiingpilotInformation theoryShannon’s Law
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1264 , June 2019 , pp. 828 - 839
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Caldwell, J.A., Mallis, M.M., Caldwell, J.L., Paul, M.A., Miller, J.C. and Neri, D.F. Fatigue countermeasures in aviation, Aviat Space Envir MD, 2009, 80, (1), pp 29–59. doi: 10.3357/asem.2435.2009.Google Scholar
Carolina, D.P., Hector, R., Juan, S., Francisco, R.T., Andres, C. and Leandro, L.D.S. Fatigue in the military: towards a fatigue detection test based on the saccadic velocity, Physiol Meas, 2016, 37, (9), pp N62–75. doi: 10.1088/0967-3334/37/9/N62.Google Scholar
Gawron, V.J. Summary of fatigue research for civilian and military pilots, IIE Trans, 2016, 4, pp 1–18. doi: 10.1080/21577323.2015.1046093.Google Scholar
Lassiter, D.L., Morrow, D.G., Hinson, G.E., Miller, M. and Hambrick, D.Z. Expertise and age effects on pilot mental workload in a simulated aviation task, Hum Factors Ergon Soc Annu Meet Proc, 1996, 40, (3), pp 133–137. doi: 10.1177/154193129604000306.CrossRefGoogle Scholar
Sohn, S.Y. and JO, Y.K. A study on the student pilot’s mental workload due to personality types of both instructor and student, Ergonomics, 2003, 46, (15), pp 1566–1577. doi: 10.1080/0014013031000121633.Google Scholar
Noel, J.B. and Lanning, J.W. Improving pilot mental workload classification through feature exploitation and combination: a feasibility study, Comput Oper Res, 2005, 32, (10), pp 2713–2730. doi: 10.1016/j.cor.2004.03.022.CrossRefGoogle Scholar
Mansikka, H., Simola, P., Virtanen, K., Harris, D. and Oksama, L. Fighter pilots’ heart rate, heart rate variation and performance during instrument approaches, Ergonomics, 2016, 59, (10), pp 1344–1352. doi: 10.1080/00140139.2015.1136699.CrossRefGoogle ScholarPubMed
Wanyan, X., Zhuang, D., Lin, Y., Xiao, X. and Song, J.W. Influence of mental workload on detecting information varieties revealed by mismatch negativity during flight simulation, Int J Ind Ergonom, 2018, 64, pp 1–7. doi: 10.1016/j.ergon.2017.08.004.CrossRefGoogle Scholar
Hart, S.G. and Staveland, L.E. Development of NASA-TLX (task load index): results of empirical and theoretical research, Adv Psychol, 1988, 52, (6), pp 139–183. doi: 10.1016/S0166-4115(08)62386-9.CrossRefGoogle Scholar
Reid, G.B. and Nygren, T.E. The subjective workload assessment technique: a scaling procedure for measuring mental workload, Human Mental Workload, 1988, 52, (8), pp 185–218. doi: 10.1016/S0166-4115(08)62387-0.CrossRefGoogle Scholar
Roscoe, A.H. The Practical Assessment of Pilot Workload, Agard-AG-282, Neuilly Sur Seine, France: Advisory Group for Aerospace Research and Development, 1987.Google Scholar
Roscoe, A.H. and Ellis, G.A. A subjective rating scale assessing pilot workload in flight, A decade of practical use. Royal Aerospace Establishment, Technical Report 90019, 1990, Farnborough.Google Scholar
Wierwille, W.W. and Connor, S.A. Evaluation of twenty workload assessment measures using a psychomotor task in a motion-base simulator, Hum Factors, 1985, 25, pp 1–16.CrossRefGoogle Scholar
Wierwille, W.W., Rahii, M. and Casali, J.G. Evaluation of sixteen measures of mental workload using a simulated flight task emphasizing mediational activities, Hum Factors, 1985, 27, pp 489–502. doi: 10.1177/001872088502700501.CrossRefGoogle Scholar
Widyanti, A., Johnson, A. and de Waard, D. Adaptation of the rating scale mental effort (RSME) for use in Indonesia, Int J Ind Ergonom, 2013, 43, (1), pp 70–76. doi: 10.1016/j.ergon.2012.11.003.CrossRefGoogle Scholar
Ahlstrom, U. and Friedman, B.F.J. Using eye movement activity as a correlate of cognitive workload, Int J Ind Ergonom, 2006, 36, (7), pp 623–636. doi: 10.3758/s13428-010-0055-7.CrossRefGoogle Scholar
Hankins, T.C. and Wilson, G.F. A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight, Aviat Space Envir MD, 1998, 69, (4), pp 360–367.Google ScholarPubMed
Evstigneev, A.L., Filipenkov, S.N., Klochkov, A.M. and Vasilevsky, N. The EEG indicators of mental workload in pilots during the dynamic flight simulation on the human centrifuge, Int J Psychophysiol, 2008, 69, (3), pp 276–316. doi: 10.1016/j.ijpsycho.2008.05.292.CrossRefGoogle Scholar
Wanyan, X., Zhuang, D. and Zhang, H. Improving pilot mental workload evaluation with combined measures, Bio-Med Mater Eng, 2014, 24, (6), pp 2283–2290. doi: 10.3233/Bme-141041.Google ScholarPubMed
Nocera, F.D., Camilli, M. and Terenzi, M. Using the distribution of eye fixations to assess pilots’ mental workload, Hum Factors Ergon Soc Annu Meet Proc, 2006, 50, (1), pp 63–65. doi: 10.1177/154193120605000114.Google Scholar
Nocera, F.D., Camilli, M. and Terenzi, M. A random glance at the flight deck: pilots’ scanning strategies and the real-time assessment of mental workload, J Cogn Eng Decis Mak, 2007, 1, (3), pp 271–285. doi: 10.1518/155534307X255627.CrossRefGoogle Scholar
Zhang, X., Xue, H., QU, X. and Li, T. Can fixation frequency be used to assess pilots’ mental workload during taxiing? Eng Psychol Cognit Ergonomics: Perform, Emotion Situat Awareness, 2017, 10275, pp 76–84. doi: 10.1007/978-3-319-58472-5403316120_7.Google Scholar
Marinescu, A.C., Sharples, S., Ritchie, A.C. and Morvan, H.P. Physiological parameter response to variation of mental workload, Hum Factors, 2018, 60, (1), pp 31–56. doi: 10.1177/0018720817733101.CrossRefGoogle Scholar
Fukuda, K., Stern, J.A., Brown, T.B. and Russo, M.B. Cognition, blinks, eye-movements, and pupillary movements during performance of a running memory task, Aviat Space Envir MD, 2005, 76, (7), pp 75–85.Google ScholarPubMed
Stern, J.A., Boyer, D. and Schroeder, D. Blink rate: a possible measure of fatigue, Hum Factors, 1994, 36, (2), pp 285–297. doi: 10.1177/001872089403600209.CrossRefGoogle Scholar
Veltman, J.A. and Gaillard, A.W.K. Physiological indices of workload in a simulated flight task, Biol Psychol, 1996, 42, (3), pp 323–342. doi: 10.1016/0301-0511(95)05165-1.CrossRefGoogle Scholar
Beatty, J. and Lucero-Wagoner, B. The pupillary system. In Cacioppo, J.T., Tassinary, L.G. and Berntson, G.G. (Eds), Handbook of Psychophysiology (pp 142–162), New York, NY, US: Cambridge University Press, 2000.Google Scholar
Van Orden, K.F., Limbert, W., Makeig, S. and Jung, T.P. Eye activity correlates of workload during a visuospatial memory task, Hum Factors, 2001, 43, (1), pp 111–121. doi: 10.1518/001872001775992570.CrossRefGoogle ScholarPubMed
Colle, H.A. and Reid, G.B. A framework for mental workload research and applications using formal measurement theory, Int J Cognit Ergon, 1997, 1, (4), pp 303–313.Google Scholar
Ryu, K. and Myung, R. Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic, Int J Ind Ergonom, 2005, 35, (11), pp 991–1009. doi: 10.1016/j.ergon.2005.04.005.CrossRefGoogle Scholar
Merat, N., Jamson, A.H., Lai, F.C. and Carsten, O. Highly automated driving, secondary task performance, and driver state, Hum Factors, 2012, 54, (5), pp 762–771. doi: 10.1177/0018720812442087.Google ScholarPubMed
Liao, Y., Li, G., Li, S.E., Cheng, B. and Green, P. Understanding driver response patterns to mental workload increase in typical driving scenarios, IEEE Access, 2018, 6, pp 35890–35900.CrossRefGoogle Scholar
Ebbatson, M., Harris, D., Huddlestone, J. and Sears, R. The relationship between manual handling performance and recent flying experience in air transport pilots, Ergonomics, 2010, 53, (2), pp 268–277. doi: 10.1080/00140130903342349.CrossRefGoogle ScholarPubMed
Kennedy, Q., Taylor, J.L., Reade, G. and Yesavage, J.A. Age and expertise effects in aviation decision making and flight control in a flight simulator, Aviat Space Envir MD, 2010, 81, (5), pp 489–497. doi: 10.3357/Asem.2684.2010.Google Scholar
Mccann, R., Parke, B., Andre, A., Hooey, B., Foyle, D. and Kanki, B. An evaluation of the taxiway navigation and situation awareness (T-Nasa) system in high-fidelity simulation, Int J Aerospace Eng, 1998, 107, pp 1612–1625. doi: 10.2514/6.1998-5541.Google Scholar
Hooey, B.L. and Foyle, D.C. Pilot navigation errors on the airport surface: identifying contributing factors and mitigating solutions, NT J Aviat Psychol, 2006, 16, (1), pp 51–76. doi: 10.1207/s15327108ijap1601_3.CrossRefGoogle Scholar
Li, X., Fang, W. and Zhou, Y. Mental workload prediction model based on information entropy, Comput Assist Surg, 2016, 21, (1), pp 116–123. doi: 10.1080/24699322.2016.1240298.Google Scholar
Klemmer, E.T. and Muller, P.F. The rate of handling information, J Motor Behav, 1969, 1, (2), pp 135–147. doi: 10.1080/00222895.1969.10734841.CrossRefGoogle ScholarPubMed
Haga, S., Shinoda, H. and Kokubun, M. Effects of task difficulty and time on task on mental workload, Jpn Psychol Res, 2010, 44, (3), pp 134–143. doi: 10.1111/1468-5884.00016.CrossRefGoogle Scholar
Pulat, B.M. Fundamentals of Industrial Ergonomics, Prentice Hall, 1992, New Jersey.Google Scholar
Jacob, R.J. and Karn, K.S. Eye tracking in human-computer interaction and usability research: ready to deliver the promises, Mind’s Eye: Cognit Appl Aspects Eye Mov Res, 2003, 41:2, (3), pp 573–603.CrossRefGoogle Scholar
Sarter, N.B., Mumaw, R.J. and Wickens, C.D. Pilots’ monitoring strategies and performance on automated flight decks: an empirical study combining behavioral and eye-tracking data, Hum Factors, 2007, 49, (3), pp 347–357. doi: 10.1518/001872007X196685.CrossRefGoogle ScholarPubMed
Yang, B., Lin, Y. and Sun, Y. Transient effects of harsh luminous conditions on the visual performance of aviators in a civil aircraft cockpit, Appl Ergon, 2012, 44, (2), pp 185–191. doi: 10.1016/j.apergo.2012.07.005.Google Scholar
Wu, X., Wanyan, X. and Zhuang, D. Pilot’s visual attention allocation modeling under fatigue, Technol Health Care, 2015, 23, (2), pp S373–S381. doi: 10.3233/Thc-150974.CrossRefGoogle ScholarPubMed
Haslbeck, A. and Zhang, B. I spy with my little eye: analysis of airline pilots’ gaze patterns in a manual instrument flight scenario, Appl Ergon, 2017, 63, pp 62–71. doi: 10.1016/j.apergo.2017.03.015.CrossRefGoogle Scholar
Schonefeld, J. and Moller, D.P.F. Runway incursion prevention systems: a review of runway incursion avoidance and alerting system approaches, Prog Aerosp Sci, 2012, 51, (51), pp 31–49. doi: 10.1016/j.paerosci.2012.02.002.CrossRefGoogle Scholar
Wilke, S., Majumdar, A. and Ochieng, W.Y. Airport surface operations: a holistic framework for operations modeling and risk management. Safety Sci, 2013, 63, (3), pp 18–33. doi: 10.1016/j.ssci.2013.10.015.CrossRefGoogle Scholar
Johnson, M.E., Zhao, X., Faulkner, B. and Young, J.P. Statistical models of runway incursions based on runway intersections and taxiways. J Aviat Tech Eng, 2016, 5, (2), pp 15–26. doi: 10.7771/2159-6670.1121.Google Scholar
Ericsson, K.A. and Lehmann, A.C. Expert and exceptional performance: evidence of maximal adaptation to task constraints, Annu Rev Psychol, 1996, 47, pp 273–305. doi: 10.1146/annurev.psych.47.1.273.CrossRefGoogle ScholarPubMed
Glaser, R. and Chi, M.T.H. Overview. In Chi, M.T.H., Farr, M.J. and Glaser, R. (Eds), The Nature of Expertise (pp. xv–xviii), Hillsdale, NJ: Erlbaum, 1988.Google Scholar