August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Neural efficiency in an aviation task with different levels of difficulty: Assessing different biometrics during a performance task
Author Affiliations & Notes
  • Mohammad Javad Darvishi Bayazi
    Faubert Lab, Université de Montréal, Montréal, QC, Canada
    Mila - Québec AI Institute, Montréal, QC, Canada
    Université de Montréal, Montreal, QC, Canada
  • Andrew Law
    National Research Council Canada (NRC), Ottawa, OT, Canada
  • Sergio Mejia Romero
    Faubert Lab, Université de Montréal, Montréal, QC, Canada
  • Sion Jennings
    National Research Council Canada (NRC), Ottawa, OT, Canada
  • Irina Rish
    Mila - Québec AI Institute, Montréal, QC, Canada
    Université de Montréal, Montreal, QC, Canada
  • Jocelyn Faubert
    Faubert Lab, Université de Montréal, Montréal, QC, Canada
    Université de Montréal, Montreal, QC, Canada
  • Footnotes
    Acknowledgements  We thank Alain Bourgon and David Bowness. This work was supported by National Research council Canada, Natural Sciences and Engineering Research Council (NSERC-CAE-CRIAC-CARIQ, NSERC discovery grant RGPIN-2022-05122), Doctoral Research Microsoft Diversity Award (Microsoft-Mila).
Journal of Vision August 2023, Vol.23, 5647. doi:https://doi.org/10.1167/jov.23.9.5647
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      Mohammad Javad Darvishi Bayazi, Andrew Law, Sergio Mejia Romero, Sion Jennings, Irina Rish, Jocelyn Faubert; Neural efficiency in an aviation task with different levels of difficulty: Assessing different biometrics during a performance task. Journal of Vision 2023;23(9):5647. https://doi.org/10.1167/jov.23.9.5647.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Monitoring mental states during high-demand tasks are critical for improving human performance. Metrics representing these states should be robust, reliable and automatic. In recent years biomedical signals have become advantageous quantifiers. This study investigates several metrics for cognitive workload and affective states. Twenty-four participants conducted an aviation task with three difficulty levels to induce workload and stress and were asked to rate their performance and feelings. We recorded EEG brain activity, pupil size dynamics, and heart rate activity with ECG to analyze participants' mental and physiological states during the task. We estimate the performance with a quantization method based on the difference between actual and idealized maneuvers. Subjective rating of cognitive demand and heart rate increase monotonically as a function of task difficulty, while electroencephalogram theta band and pupil size are better discrimination of performance. Our results suggest that heart rate and subjective demands are indicators of general Effort, while pupil size and electroencephalogram theta activity are better performance descriptors.

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