Effects of Age on Auditory and Cognitive Processing: Implications for Hearing Aid Fitting and Audiologic Rehabilitation
Bibliographic record
Abstract
Recent advances in research and clinical practice concerning aging and auditory communication have been driven by questions about age-related differences in peripheral hearing, central auditory processing, and cognitive processing. A "site-of-lesion'' view based on anatomic levels inspired research to test competing hypotheses about the contributions of changes at these three levels of the nervous system. A "processing'' view based on psychologic functions inspired research to test alternative hypotheses about how lower-level sensory processes and higher-level cognitive processes interact. In the present paper, we suggest that these two views can begin to be unified following the example set by the cognitive neuroscience of aging. The early pioneers of audiology anticipated such a unified view, but today, advances in science and technology make it both possible and necessary. Specifically, we argue that a synthesis of new knowledge concerning the functional neuroscience of auditory cognition is necessary to inform the design and fitting of digital signal processing in "intelligent'' hearing devices, as well as to inform best practices for resituating hearing aid fitting in a broader context of audiologic rehabilitation. Long-standing approaches to rehabilitative audiology should be revitalized to emphasize the important role that training and therapy play in promoting compensatory brain reorganization as older adults acclimatize to new technologies. The purpose of the present paper is to provide an integrated framework for understanding how auditory and cognitive processing interact when older adults listen, comprehend, and communicate in realistic situations, to review relevant models and findings, and to suggest how new knowledge about age-related changes in audition and cognition may influence future developments in hearing aid fitting and audiologic rehabilitation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".