Failure to Detect Critical Auditory Alerts in the Cockpit
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to test whether inattentional deafness to critical alarms would be observed in a simulated cockpit. BACKGROUND: The inability of pilots to detect unexpected changes in their auditory environment (e.g., alarms) is a major safety problem in aeronautics. In aviation, the lack of response to alarms is usually not attributed to attentional limitations, but rather to pilots choosing to ignore such warnings due to decision biases, hearing issues, or conscious risk taking. METHOD: Twenty-eight general aviation pilots performed two landings in a flight simulator. In one scenario an auditory alert was triggered alone, whereas in the other the auditory alert occurred while the pilots dealt with a critical windshear. RESULTS: In the windshear scenario, II pilots (39.3%) did not report or react appropriately to the alarm whereas all the pilots perceived the auditory warning in the no-windshear scenario. Also, of those pilots who were first exposed to the no-windshear scenario and detected the alarm, only three suffered from inattentional deafness in the subsequent windshear scenario. CONCLUSION: These findings establish inattentional deafness as a cognitive phenomenon that is critical for air safety. Pre-exposure to a critical event triggering an auditory alarm can enhance alarm detection when a similar event is encountered subsequently. APPLICATION: Case-based learning is a solution to mitigate auditory alarm misperception.
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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 | 0.001 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".