Strategies to Combat Auditory Overload During Vehicular Command and Control
Bibliographic record
Abstract
Strategies to combat auditory overload were studied. Normal-hearing males were tested in a sound isolated room in a mock-up of a military land vehicle. Two tasks were presented concurrently, in quiet and vehicle noise. For Task 1 dichotic phrases were delivered over a communications headset. Participants encoded only those beginning with a preassigned call sign (Baron or Charlie). For Task 2, they agreed or disagreed with simple equations presented either over loudspeakers, as text on the laptop monitor, in both the audio and the visual modalities, or not at all. Accuracy was significantly better by 20% on Task 2 when the equations were presented visually or audiovisually. Scores were at least 78% correct for dichotic phrases presented over the headset, with a right ear advantage of 7%, given the 5 dB speech-to-noise ratio. The left ear disadvantage was particularly apparent in noise, where the interaural difference was 12%. Relatively lower scores in the left ear, in noise, were observed for phrases beginning with Charlie. These findings underscore the benefit of delivering higher priority communications to the dominant ear, the importance of selecting speech sounds that are resilient to noise masking, and the advantage of using text in cases of degraded audio.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".