Dual-task costs of listening while driving in older adults with and without audiometric hearing loss: Behavioural and neurophysiological outcomes
Bibliographic record
Abstract
Age-related hearing loss may increase listening difficulties in challenging listening conditions (e.g., speech-in-noise), limiting cognitive resources available to perform common, complex multitasking behaviours like listening while driving. Older adults with hearing loss may compensate by increasing prefrontal cortex (PFC) activation in response to multitasking demands. Few realistic, controlled studies have examined how competing attentional demands of listening while driving affect performance and brain activation, and how these patterns may differ between older adults with and without audiometric hearing loss. This study examined dual-task costs and neural activation levels during a listening-while-driving task in 28 older adults with normal hearing (Mage = 71.79 years) and 22 older adults with hearing loss (Mage=74.00 years) using functional near-infrared spectroscopy (fNIRS). Participants completed a driving task in a high-fidelity driving simulator under simpler (Rural) and more complex (City) conditions and the Connected Speech Test (CST) at +4 dB and 0 dB signal-to-noise ratios (SNR; easier and harder listening respectively). They also performed both tasks simultaneously to examine dual-task costs. fNIRS was recorded during all conditions. Results demonstrated that older adults with hearing loss showed poorer listening accuracy, poorer driving performance, and greater oxygenation concentration in the PFC than those with normal hearing. Both groups showed poorer listening and driving performance in the dual-task compared to the single-task conditions, with the greatest dual-task costs observed during the most difficult condition (City driving, 0 dB SNR). Broadly, these findings could inform strategies to optimize vehicle acoustics and reduce auditory distractions, thereby supporting driving performance in challenging driving conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".