Skip to main content Accessibility help
×
Home

Modeling pilot mental workload using information theory

  • X. Zhang (a1) (a2) (a3), X. Qu (a1) (a2), H. Xue (a3), H. Zhao (a3), T. Li (a3) and D. Tao (a1) (a2)...

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.

Copyright

Corresponding author

References

Hide All
1.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 2959. doi: 10.3357/asem.2435.2009.
2.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 N6275. doi: 10.1088/0967-3334/37/9/N62.
3.Gawron, V.J. Summary of fatigue research for civilian and military pilots, IIE Trans, 2016, 4, pp 118. doi: 10.1080/21577323.2015.1046093.
4.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 133137. doi: 10.1177/154193129604000306.
5.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 15661577. doi: 10.1080/0014013031000121633.
6.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 27132730. doi: 10.1016/j.cor.2004.03.022.
7.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 13441352. doi: 10.1080/00140139.2015.1136699.
8.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 17. doi: 10.1016/j.ergon.2017.08.004.
9.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 139183. doi: 10.1016/S0166-4115(08)62386-9.
10.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 185218. doi: 10.1016/S0166-4115(08)62387-0.
11.Roscoe, A.H. The Practical Assessment of Pilot Workload, Agard-AG-282, Neuilly Sur Seine, France: Advisory Group for Aerospace Research and Development, 1987.
12.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.
13.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 116.
14.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 489502. doi: 10.1177/001872088502700501.
15.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 7076. doi: 10.1016/j.ergon.2012.11.003.
16.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 623636. doi: 10.3758/s13428-010-0055-7.
17.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 360367.
18.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 276316. doi: 10.1016/j.ijpsycho.2008.05.292.
19.Wanyan, X., Zhuang, D. and Zhang, H. Improving pilot mental workload evaluation with combined measures, Bio-Med Mater Eng, 2014, 24, (6), pp 22832290. doi: 10.3233/Bme-141041.
20.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 6365. doi: 10.1177/154193120605000114.
21.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 271285. doi: 10.1518/155534307X255627.
22.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 7684. doi: 10.1007/978-3-319-58472-5403316120_7.
23.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 3156. doi: 10.1177/0018720817733101.
24.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 7585.
25.Stern, J.A., Boyer, D. and Schroeder, D. Blink rate: a possible measure of fatigue, Hum Factors, 1994, 36, (2), pp 285297. doi: 10.1177/001872089403600209.
26.Veltman, J.A. and Gaillard, A.W.K. Physiological indices of workload in a simulated flight task, Biol Psychol, 1996, 42, (3), pp 323342. doi: 10.1016/0301-0511(95)05165-1.
27.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 142162), New York, NY, US: Cambridge University Press, 2000.
28.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 111121. doi: 10.1518/001872001775992570.
29.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 303313.
30.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 9911009. doi: 10.1016/j.ergon.2005.04.005.
31.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 762771. doi: 10.1177/0018720812442087.
32.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 3589035900.
33.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 268277. doi: 10.1080/00140130903342349.
34.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 489497. doi: 10.3357/Asem.2684.2010.
35.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 16121625. doi: 10.2514/6.1998-5541.
36.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 5176. doi: 10.1207/s15327108ijap1601_3.
37.Li, X., Fang, W. and Zhou, Y. Mental workload prediction model based on information entropy, Comput Assist Surg, 2016, 21, (1), pp 116123. doi: 10.1080/24699322.2016.1240298.
38.Klemmer, E.T. and Muller, P.F. The rate of handling information, J Motor Behav, 1969, 1, (2), pp 135147. doi: 10.1080/00222895.1969.10734841.
39.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 134143. doi: 10.1111/1468-5884.00016.
40.Pulat, B.M. Fundamentals of Industrial Ergonomics, Prentice Hall, 1992, New Jersey.
41.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 573603.
42.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 347357. doi: 10.1518/001872007X196685.
43.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 185191. doi: 10.1016/j.apergo.2012.07.005.
44.Wu, X., Wanyan, X. and Zhuang, D. Pilot’s visual attention allocation modeling under fatigue, Technol Health Care, 2015, 23, (2), pp S373S381. doi: 10.3233/Thc-150974.
45.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 6271. doi: 10.1016/j.apergo.2017.03.015.
46.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 3149. doi: 10.1016/j.paerosci.2012.02.002.
47.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 1833. doi: 10.1016/j.ssci.2013.10.015.
48.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 1526. doi: 10.7771/2159-6670.1121.
49.Ericsson, K.A. and Lehmann, A.C. Expert and exceptional performance: evidence of maximal adaptation to task constraints, Annu Rev Psychol, 1996, 47, pp 273305. doi: 10.1146/annurev.psych.47.1.273.
50.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. xvxviii), Hillsdale, NJ: Erlbaum, 1988.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed