High Rate of Inattentional Deafness in Simulated Air Traffic Control Tasks
Bibliographic record
Abstract
The Air Traffic Control (ATC) environment is complex and safety-critical; operators work in dynamic situations and must make high-risk decisions under stress and temporal pressure. The high perceptual load involved in ATC means that controllers’ attention must be shared between several subtasks, with few or no remaining attentional capacity for processing information that is not related directly to the focal task. In this kind of situation, the likelihood of a controller failing to become aware of an auditory alarm, i.e. inattentional deafness, is high. We designed an ecological ATC thanks to the simulation environment called the “LABY” microworld. Twenty participants were required to guide one (low cognitive load) or two planes (high cognitive load) around a given route, while dealing with visual notifications relating to peripheral aircrafts. During the task, participants were played either standard tones which they were told to ignore, or deviant tones (“the alarm”, probability = 0.20) which they were told to report (20 alarms per scenario). We hypothesized that the detection rate of auditory alarms will decrease with cognitive workload. In order to explore this possibility, Behavioral results showed that 28.8% of alarms were not reported when guiding one plane, and up to 46.2% when guiding two planes (high load). The cognitive load increase led to a reduced visual notification detection rate, but the performance to guiding the central aircrafts was maintained, as well as the reaction times to report auditory alarms when perceived. This high rate of inattentional deafness is essential to further physiological studies on alarm omission in aeronautics, such as ERP or eye movement analysis. Potential applications are related to the integrative online detection and prevention of alarm omission, and the online measurement of workload in ecological situation.
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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 | 0.014 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".