Skip to main content

Performance and Brain Activity During a Spatial Working Memory Task: Application to Pilot Candidate Selection

  • Conference paper
  • First Online:
Advances in Neuroergonomics and Cognitive Engineering (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 953))

Included in the following conference series:

Abstract

For 18 ab initio airline pilots, we assessed the possibility of predicting flight simulator performance with the performance and the prefrontal activity measured during a spatial working memory (SWM) task. Behavioral results revealed that a better control of the aircraft altitude in the flight simulator was correlated with better strategy during the SWM task. In addition, neuroimaging results suggested that participants that recruited more neural resources during the SWM task were more likely to accurately control their aircraft. Taken together, our results emphasized that spatial working memory and the underlying neural circuitries are important for piloting. Ultimately, SWM tasks may be included in pilot selection tests as it seems to be a good predictor of flight performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wiegmann, D.A., Shappell, S.A.: Human error and crew resource management failures in naval aviation mishaps: a review of U.S. Naval Safety Center data, 1990-96. Aviat. Space Env. Med 70(12), 1147–1151 (1999)

    Google Scholar 

  2. Parasuraman, R.: Neuroergonomics: research and practice. Theor. Issues Ergon. Sci. 4(1), 5–20 (2003)

    Article  Google Scholar 

  3. Burke, E., Hobson, C., Linsky, C.: Large sample validations of three general predictors of pilot training success. Int. J. Aviat. Psychol. 7(3), 225–234 (1997)

    Article  Google Scholar 

  4. Carretta, T.R.: Pilot candidate selection method: still an effective predictor of US air force pilot training performance. Aviat. Psychol. Appl. Hum. Factors 1, 3–8 (2011)

    Article  Google Scholar 

  5. Damos, D.L.: Pilot selection batteries: shortcomings and perspectives. Int. J. Aviat. Psychol. 6(2), 199–209 (1996)

    Article  Google Scholar 

  6. Martinussen, M.: Psychological measures as predictors of pilot performance: a meta-analysis. Int. J. Aviat. Psychol. 6(1), 1–20 (1996)

    Article  Google Scholar 

  7. Causse, M., Dehais, F., Arexis, M., Pastor, J.: Cognitive aging and flight performances in general aviation pilots. Aging Neuropsychol. Cogn. 18(5), 544–561 (2011)

    Article  Google Scholar 

  8. Causse, M., Dehais, F., Pastor, J.: Executive functions and pilot characteristics predict flight simulator performance in general aviation pilots. Int. J. Aviat. Psychol. 21(3), 217–234 (2011)

    Article  Google Scholar 

  9. Taylor, J., O’Hara, R., Mumenthaler, M., Yesavage, J.: Relationship of CogScreen-AE to flight simulator performance and pilot age. Aviat. Space Environ. Med. 71(4), 373 (2000)

    Google Scholar 

  10. Benthem, K.V., Herdman, C.M.: Cognitive factors mediate the relation between age and flight path maintenance in general aviation. Aviat. Psychol. Appl. Hum. Factors 6, 81–90 (2016). https://doi.org/10.1027/2192-0923/a000102

    Article  Google Scholar 

  11. Wang, H., Su, Y., Shang, S., Pei, M., Wang, X., Jin, F.: Working memory: a criterion of potential practicality for pilot candidate selection. Int. J. Aerosp. Psychol. 28, 1–12 (2019)

    Google Scholar 

  12. Dror, I.E., Kosslyn, S.M., Waag, W.L.: Visual-spatial abilities of pilots. J. Appl. Psychol. 78(5), 763 (1993)

    Article  Google Scholar 

  13. De Luca, C.R., et al.: Normative data from the Cantab. I: development of executive function over the lifespan. J. Clin. Exp. Neuropsychol. 25(2), 242–254 (2003)

    Article  Google Scholar 

  14. Lu, C.-M., Zhang, Y.-J., Biswal, B.B., Zang, Y.-F., Peng, D.-L., Zhu, C.-Z.: Use of fNIRS to assess resting state functional connectivity. J. Neurosci. Methods 186(2), 242–249 (2010)

    Article  Google Scholar 

  15. Roche-Labarbe, N., Zaaimi, B., Berquin, P., Nehlig, A., Grebe, R., Wallois, F.: NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children. Epilepsia 49(11), 1871–1880 (2008)

    Article  Google Scholar 

  16. White, B.R., et al.: Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage 47(1), 148–156 (2009)

    Article  Google Scholar 

  17. Cui, X., Bray, S., Reiss, A.L.: Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. NeuroImage 49(4), 3039–3046 (2010)

    Article  Google Scholar 

  18. Ayaz, H., Shewokis, P., Bunce, S., Izzetoglu, K., Willems, B., Onaral, B.: Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59(1), 36–47 (2012)

    Article  Google Scholar 

  19. Durantin, G., Gagnon, J.-F., Tremblay, S., Dehais, F.: Using near infrared spectroscopy and heart rate variability to detect mental overload. Behav. Brain Res. 259, 16–23 (2014)

    Article  Google Scholar 

  20. Foy, H.J., Runham, P., Chapman, P.: Prefrontal cortex activation and young driver behaviour: a fNIRS study. PLoS ONE 11(5), e0156512 (2016)

    Article  Google Scholar 

  21. Lee, A., Archer, J., Wong, C.K.Y., Chen, S.-H.A., Qiu, A.: Age-related decline in associative learning in healthy Chinese adults. PLoS ONE 8(11), e80648 (2013)

    Article  Google Scholar 

  22. Gateau, T., Durantin, G., Lancelot, F., Scannella, S., Dehais, F.: Real-time state estimation in a flight simulator using fNIRS. PLoS ONE 10(3), e0121279 (2015)

    Article  Google Scholar 

  23. Mandrick, K., Peysakhovich, V., Rémy, F., Lepron, E., Causse, M.: Neural and psychophysiological correlates of human performance under stress and high mental workload. Biol. Psychol. 121, 62–73 (2016)

    Article  Google Scholar 

  24. Takeuchi, Y.: Change in blood volume in the brain during a simulated aircraft landing task. J. Occup. Health 42(2), 60–65 (2000)

    Article  Google Scholar 

  25. Causse, M., Chua, Z., Peysakhovich, V., Del Campo, N., Matton, N.: Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS. Sci. Rep. 7, 5222 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mickaël Causse .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Causse, M., Chua, Z., Matton, N. (2020). Performance and Brain Activity During a Spatial Working Memory Task: Application to Pilot Candidate Selection. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_5

Download citation

Publish with us

Policies and ethics