Age, music, and driving performance: Detection of external warning sounds in vehicles.
Notice bibliographique
Résumé
The growing elderly population increases the number of older drivers. Driving requires detection of information against an acoustic background. Modern vehicles are designed to reduce external noises (including warning signals) and provide audio entertainment options that capture auditory attention. The present study investigated the effects of age on the detection of external vehicular warning sounds (car horn or police siren) in the presence of road noise and road noise combined with music. Older listeners required higher warning sound intensities in background noise relative to younger listeners. For both age groups, intensity for detecting acoustically similar stimuli (siren and music) was higher than for dissimilar (car horn and music). Music in vehicles potentially increases accident risk for older drivers and suggests the need for technology that will augment rather than attenuate external warning sounds. Safe and effective driving necessitates detection of auditory information embedded in a background of continuously changing sounds. Music is an ubiquitous contributor of such sounds (DeNora, 2000). As much as 91% of exposure to music occurs during transportation (Sloboda,1999;Sloboda,0'Neill, & Vivaldi, 2001). Several factors might influence the effects of music on driving performance. They include (a) variation in musical tone, rhythm, tempo, melody contour, timbre etc. (Bregman, 1990); (b) complexity of sounds and their organizational layout (Brodsky, 2002; Schreiner, 1998); and (c) age-related loss of the ability to hear high frequency sounds (Willott, 1991). The present study investigated the effects of age on the detection of warning sounds of vehicles {car horn or police siren) in the presence of road noise and road noise combined with music. In developed countries, the elderly segment of the population is projected to increase dramatically in the next 45 years, especially in the age categories of 60 years and above. Thus, it is imperative that the contribution of age-related sensory, perceptual, and cognitive losses to car accidents be understood (Botwinick, 1985; Caird, 2001). While much of the information used in driving is visual, auditory information also plays a role (Sivak, 1996). Because modern vehicles are replete with systems that either filter out external noises, provide for entertaining distractions, or require increased auditory attention (Solomon, 2000), it is important to assess how the detection of warning sounds changes with age. It is also important to determine the type of information that affects the ability of older drivers to detect and identify warning sounds. A listener's ability to detect a warning sound within background sounds depends upon acoustic characteristics. When the characteristics of the warning signal are similar to those of a distracter, the detection and identification ofthat signal are degraded. Performance is particularly likely to deteriorate in the presence of uncertain distracters and informational masking (Grose & Hall, 1996; Leek, & Watson, 1984; Neff, Dethlefs, & Jestead, 1993; Slawinski, Hartel, & Kline, 1993). Informational masking refers to the effects of presentation of many unpredicted sounds on detection on a target sound. It is associated with selective attention and cognition, and it plays an important role in the processing of auditory information (Alien & Wightman, 1995; Slawinski & Scharf, 1998). An increase in the intensity of background noise also adversely affects the detection of a warning sound, Usually, older as compared to younger drivers require a higher ratio of the intensity of the target sound to the background noise (Yost, 1994). The emotional interest of music can also decrease sensitivity to a warning sound (Kassam, 2002). Some information processing abilities decline with increasing age (e.g., Murphy, Craik, Li, & Schneider, 2000) though in the case of audition, much of this is due to declining sensory ability (Schneider, Daneman, Pichora-Fuller, 2002). …
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| 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) | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».