Age, music, and driving performance: Detection of external warning sounds in vehicles.
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".