The Effects of Age, Hearing Loss, and Dual Tasking on Behavioural Performance and Brain Activation in Realistic, Complex Listening and Driving Environment
Bibliographic record
Abstract
Age-related sensory and cognitive changes, particularly age-related hearing loss, can increase listening difficulties and reduce multitasking ability in complex situations like listening while driving. However, few realistic, controlled studies have examined how competing task demands affect differences in behavioural performance and brain activation patterns between younger (YANH) compared to older (OANH) adults with normal hearing and older adults with hearing loss (OAHL). Using a discourse -like speech-in-noise task (Connected Speech Test; CST), a high-fidelity driving simulator, and functional near-infrared spectroscopy (fNIRS), this dissertation investigated how age and hearing loss influence listening and driving performance and brain activation patterns under controlled yet realistic conditions.Few studies have used fNIRS to measure brain activation in older adults during discourse-like speech-in-noise tasks. Therefore, in Chapter 2, under controlled conditions, I explored whether different levels of listening difficulty (signal-to-noise ratios [SNR]) differently affected word recognition accuracy on the CST, brain oxygenation (HbO), and self-reported fatigue in YANH, OANH, and OAHL. Findings showed that YANH had better word recognition, higher HbO, and greater fatigue across SNRs than both older groups. Among older adults, OANH had better word recognition than OAHL, although HbO and fatigue were similar. With foundational understanding of these behavioural and neurophysiological outcomes under highly controlled conditions described in Chapter 2, Chapters 3 and 4 examined how age (Chapter 3) and hearing loss (Chapter 4) influenced behavioural performance and brain activation patterns during the more realistic and complex dual task of listening while driving. Results from Chapter 3 demonstrated that OANH experienced greater dual-task costs to both listening and driving performance than YANH, particularly during complex conditions (lower SNR, City driving). Chapter 4 replicated these findings in OANH and further showed that, although the magnitude of dual-task costs was similar for OAHL and OANH, OAHL performed worse overall on both tasks and exhibited greater increases in neural activation from single- to dual-tasking than OANH. Results suggest that aging and hearing loss affect cognitive resource allocation, reflected in neural activation differences and increased multitasking costs, especially under complex conditions. Findings highlight the need for strategies to reduce cognitive load on listening and driving performance.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".