Black Hole Illusion In Aviation – A Simulator Experiment to examine Predominant Criteria in a Real–Life Environment
Bibliographic record
Abstract
Black Hole Illusion (BHI) is the cause of a significant number of fatal aviation accidents, Controlled Flight Into Terrain (CFIT) events due to “lack of vertical and/or horizontal position awareness in relation to terrain” [1] and is listed as an “environmental threat”, owning a specific section in annual worldwide safety reports [2] [3]. To date, relevant studies focused on whether and how much a single factor affects the pilot’s disorientation, whereas this study considers all the known factors plus runway illumination levels in the same simulation. The simulations conducted in these studies involved one pilot each time and were conducted with a single computer screen and a joystick or in a non-movable fixed-base simulator whereas this study aims to explore BHI in the realistic environment of a movable airline-approved A-320 full-flight simulator with a real-life aircraft cockpit operated by two pilots (captain and first officer). We examine all the factors that have been thus far explored as – causes of BHI and we added light illumination levels [4] and lateral deviation of the flight path that have never been examined before. We followed an exploratory approach with active airline pilots in a simulation where conditions causing BHI were replicated. To measure deviations from the standard approach path, all crews were asked to attempt visual starless night approaches to a predetermined set of airports. While one participant was performing the approaches, the other silently observed, taking notes to assess agreement or disagreement with the flown approach path. In each scenario, landing approaches were attempted by both captain and first officer to establish whether the pilot’s position also affects BIH. We used mix-ed methods to record the outcomes [5]. Quantitative data were generated by specific measurements from deviations of the standard flight path generated by the participants attempting the landing. Qualitative data were collected from the co-pilots’ observations and post-simulation interviews. The initial analysis indicates that the occurrence of BHI in general can be confirmed at a certain distance in the final approach sector (~ 2,5nm – 0,3nm before the runway). Light intensities and the shape of the runway could be confirmed as contributing factors due to significant variations in altitude deviations. The pilot flying the aircraft and the pilot monitoring the approach seem to not have suffered from the illusion to the same extent. Significant lateral deviations could not be observed.It is anticipated that the final findings will affect both flight situational awareness training standards and flight operational policies, contributing towards reducing human error in aviation and increasing flight safety.This study is expected to contribute to the minimisation of human errors and will consequently help increase flight safety. Originality factors are:-common commercial aviation standard full-flight simulator to generate real-life pilot environment and landing conditions-two-subject-participation to represent a complete cockpit crew to assess differences in landing the aircraft from the captain´s or first officer´s perspective-different runway lighting illumination levels in combination with varying lengths of runway and widths References[1] Kelly, D. and Efthymiou, M. (2019). An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017. Journal of Safety Research, 69, pp. 155–165.[2] IATA (2020). Safety Report: 2019 Edition, Montreal (CA): International Air Transport Association, p.234. [3] IATA (2021). Safety Report: 2020 Edition, Montreal (CA): International Air Transport Association, p.222[4] Socha, V. et al. (2020). Black Hole Approach: A Systematic Review, Proceedings of the 22nd International Conference on New Trends in Civil Aviation 2020, pp. 117–121.[5] Almalki, S. (2016). Integrating Quantitative and Qualitative Data in Mixed Methods Research—Challenges and Benefits. Journal of Education and Learning, 5(3), p. 288.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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; both teacher heads agree on what is shown here.
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".