Dual-task costs of listening while driving in older and younger adults
Bibliographic record
Abstract
Age-related changes in hearing and cognition result in increased listening difficulties, which could affect the ability to perform common, complex, multitasking behaviours, such as listening while driving. However, there are very few realistic and controlled studies examining how the competing attentional demands of listening while driving affect performance and how performance may differ between younger and older adults. The primary objective of this study was to examine dual-task costs of listening while driving in older (N = 24, Mage = 68.29 years, 15 female) and younger (N = 24, Mage = 26.42 years, 12 female) licensed drivers with normal hearing, vision, and cognition. Participants completed a driving task in a high-fidelity driving simulator during simpler (Rural driving) and more complex (City driving) driving conditions. They also completed the Connected Speech Test (CST) at +4 signal-to-noise ratio (SNR; easier listening) and at 0 SNR (harder listening) conditions. Finally, they completed both tasks simultaneously to examine the dual-task costs of listening while driving. Results demonstrated that only older adults showed significantly poorer listening accuracy on the CST in the dual-task compared to the single-task condition, particularly during the more difficult driving conditions (City driving). Both older and younger adults showed poorer driving performance (greater variability in lane position) in the dual-task compared to the single-task condition, which was most pronounced under the most challenging conditions (City driving, 0 dB SNR listening). However, the magnitude of these dual-task costs during the most challenging conditions was greater in older adults than younger adults. Findings may inform mitigation strategies to reduce the effects of difficult listening conditions on driving performance by optimizing vehicle acoustics or by minimizing auditory distractions, particularly during more challenging driving conditions.
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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 | 0.000 | 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) | 0.000 | 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".