Novel auditory alerts that foster efficient detection and discrimination in complex auditory environments: Dual-task conditions
Bibliographic record
Abstract
In many complex auditory environments, auditory alerts must be perceived and distinguished accurately while distractions are mitigated. Previously, we measured detection and discrimination performance using four novel alerts: one pair of narrowband and one pair of broadband alerts. Within each pair, one alert was perceived as consonant (labeled “friendly”) and one as dissonant (labeled “enemy”). The alerts were presented along with maskers consisting of “truck noise” or “truck noise” combined with “speech babble.” Separately, for the pairs of alerts, detectability of the alerts and discriminability between “friendly” and “enemy” pairs of alerts were measured as a function of signal-to-noise ratio (S/N). Results indicated that the alerts allow for robust detection and discrimination, even though the “friendly”-“enemy” pairs of alerts occupied similar spectral loci. In the study reported here, we measured the detectability and discriminability of the alerts under single and dual-task “N-back conditions” to simulate more attentionally demanding environments (e.g., in military conflict). The goal was to assess the impact of the N-back task on the robustness of the alerts to convey crucial information. Results will be presented and discussed in terms of the influence of the relevant variables on the form of the dual-task ROC. [Work funded by ONR N000142212184.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".