Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
28 08 2023
Historique:
received: 10 02 2023
accepted: 26 08 2023
medline: 31 8 2023
pubmed: 29 8 2023
entrez: 28 8 2023
Statut: epublish

Résumé

Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient manner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desired level of expertise, training programs should account for individual differences to optimize pilot performance. This study investigates the relationship between task performance and physiological correlates of effort in ab initio pilots. Twenty-four participants conducted a flight simulator task with three difficulty levels and were asked to rate their perceived demand and effort using the NASA TLX. We recorded heart rate, EEG brain activity, and pupil size to assess changes in the participants' mental and physiological states across different task demands. We found that, despite group-level correlations between performance error and physiological responses, individual differences in physiological responses to task demands reflected different levels of participant effort and task efficiency. These findings suggest that physiological monitoring of student pilots might provide beneficial insights to flight instructors to optimize pilot training at the individual level.

Identifiants

pubmed: 37640892
doi: 10.1038/s41598-023-41427-4
pii: 10.1038/s41598-023-41427-4
pmc: PMC10462656
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

14035

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Mohammad-Javad Darvishi-Bayazi (MJ)

Faubert Lab, Université de Montréal, Montréal, QC, Canada.
Mila-Québec AI Institute, Montréal, QC, Canada.
Université de Montréal, Montréal, QC, Canada.

Andrew Law (A)

National Research Council Canada, Ottawa, ON, Canada.

Sergio Mejia Romero (SM)

Faubert Lab, Université de Montréal, Montréal, QC, Canada.

Sion Jennings (S)

National Research Council Canada, Ottawa, ON, Canada.

Irina Rish (I)

Mila-Québec AI Institute, Montréal, QC, Canada.
Université de Montréal, Montréal, QC, Canada.

Jocelyn Faubert (J)

Faubert Lab, Université de Montréal, Montréal, QC, Canada. jocelyn.faubert@umontreal.ca.
Université de Montréal, Montréal, QC, Canada. jocelyn.faubert@umontreal.ca.

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