Investigating Potential Negative Age-bias in a Virtual Reality Cognitive Assessment Tool for General Aviation Pilots
Notice bibliographique
Résumé
General aviation accounts for 94% of aviation accidents (National Transportation Safety Board, 2011), many of which are associated with cognitive factors, such as poor situation awareness, and are more likely to occur among older pilots (Flight Safety Foundation, 2016; Li et al., 2007).The association of cognitive factors and older age with accidents motivated the development of CANFLY, a virtual reality (VR) cognitive health assessment tool for pilots across the lifespan.While CANFLY addresses test validity and generalization to real-world risk, it is important to also ensure that older pilots do not experience negative bias arising from the test's content or VR format.Furthermore, older pilots should not disproportionately experience cybersickness or other VR effects, which could negatively affect test performance.In the present study, data from an online study was used to investigate the interest and sentiments of pilots towards a VR-based cognitive assessment.A second study was lab-based and involved pilots flying two sessions, the first in a standard full-scale simulator (2-D graphics) and the second in a VR flight simulator (the CANFLY prototype).Data from the flight simulation experiment investigated the effects of flight simulation environment (standard or VR) and age on performance, and experience in VR.The results from the online survey showed that pilots older than 65 had less intent to use VR flight simulation when compared to younger pilots, citing concerns with fidelity & ecological validity.With respect to potential negative bias in the VR flight environment, the second study found that although older pilots tended to have lower SA and PM scores, when compared to younger pilots, they were not disadvantaged in their performance or experience in the VR platform.Pilots over the age of 65 years also showed a strong preference for the VR platform, as compared to the standard 2-D flight simulator.Findings from the present research show that the use of VR technology does not negatively bias the assessment of older pilots. Results support the are my inspiration.Thank you for being a guiding light through this whole process.Your wise and kind words kept me going on days I felt overwhelmed.Thank you for always encouraging me to pursue my passions and interests.To my sister, Onyeche Audu, thank you for always believing in me, at times even more than I believe in myself.You are my biggest cheerleader and your continuous encouragement serves as a constant reminder that I can achieve everything I put my mind to.To my brother, Emmanuel Audu, thank you for constantly lending a listening ear.You always seem to know the perfect things to say to encourage me.I am so grateful to have you both as siblings.To all my friends and loved ones, I am so grateful for the effort you put into understanding this research and the dedication and time you took to do everything from practicing my presentations with me to reading drafts and providing feedback.Your continuous support and encouragement allowed me to overcome all the obstacles I faced throughout this journey.Thank you all for helping me realize this important and challenging dream.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».