Detection and Discrimination of Auditory Alerts in Single- and Dual-Task Conditions: Use of a Free-Response Method
Bibliographic record
Abstract
INTRODUCTION: In military operations, the ability to detect, identify, and respond to auditory alerts in complex and dynamic environments is crucial for safety and mission success. Typical alert designs, however, often fail to account for characteristics of noisy and cognitively demanding conditions, so that the levels of alerts required to support desired levels of performance are minimized. To redress those shortcomings, we developed a pair of alerts, one having consonant harmony ("friendly"), the other dissonant harmony ("enemy"). Those alerts were placed strategically within the spectrum of the masker to minimize masking while maintaining high levels of detection and discrimination performance. MATERIALS AND METHODS: The detectability and discriminability of the "friendly" and "enemy" alerts was assessed as a function of signal-to-noise-masker ratio (S/N) while employing a masker consisting of continuous military "truck noise." Both of the alerts occupied a narrow spectral region within the masker around 500-Hz. Subjects (n = 20) performed an auditory detection/discrimination task in isolation or with a simultaneous visual "N-Back task." The N-Back task was also run in isolation. The auditory task employed a free-response vigilance paradigm with underlying temporal "trials" that were unknown to the subjects. They experienced temporal uncertainty regarding when an alert might be added to the masker. This approach afforded measures of "hit" and "false-alarm" rates and the computation of bias-free measures of sensitivity (d'). Trials were blocked by S/N with values of S/N visited via descending and ascending series. Stimuli were presented at an overall level of 70 dB SPL (in the absence of alerts) via Sennheiser HD 280 headphones. RESULTS: Values of d' (sensitivity) indicated that high levels of detection performance were obtained despite the harmonic "friendly" and inharmonic "enemy" alerts occupying a common spectral locus. That outcome likely occurred because subjects discriminated the alerts on the basis of perceived consonance or dissonance. Values of ß (response bias) revealed that subjects adopted conservative response criteria. Turning to discrimination performance, differences between obtained values of p(c) and p(c)max also indicated that subjects did not adopt neutral criteria. In the presence of a simultaneous, visual N-Back task (dual-task condition), auditory detection and discrimination performance was not degraded. In contrast, N-Back performance was poorer in the dual-task condition than when it was measured in isolation. CONCLUSIONS: The results establish "proof of concept" regarding our approach to evaluating detection and discrimination of auditory alerts within a situationally realistic vigilance paradigm. The findings reveal the advantages of employing a Theory of Signal Detection (TSD)-based free-response paradigm to evaluate human performance in such a setting. In addition, the results highlight the potential advantages of employing alerts tailored to the specific spectral profile of the ambient acoustic environment. Overall, our findings can be applied to enhance both the performance and evaluation of practitioners who must respond appropriately to critical alerts in high-consequence settings. The potential enhancements extend beyond military applications, for example, to situations in which clinicians must monitor multiple metrics of patient status in environments with potentially distracting auditory and visual information.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".