Detection and Discrimination of Auditory Alerts in Single- and Dual-Task Conditions: Use of a Free-Response Method
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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