Investigating Potential Negative Age-bias in a Virtual Reality Cognitive Assessment Tool for General Aviation Pilots
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".