Speech Comprehension Training and Auditory and Cognitive Processing in Older Adults
Bibliographic record
Abstract
PURPOSE: To provide a brief history of speech comprehension training systems and an overview of research on auditory and cognitive aging as background to recommendations for future directions for rehabilitation. METHOD: Two distinct domains were reviewed: one concerning technological and the other concerning psychological aspects of training. Historical trends and advances in these 2 domains were interrelated to highlight converging trends and directions for future practice. RESULTS: Over the last century, technological advances have influenced both the design of hearing aids and training systems. Initially, training focused on children and those with severe loss for whom amplification was insufficient. Now the focus has shifted to older adults with relatively little loss but difficulties listening in noise. Evidence of brain plasticity from auditory and cognitive neuroscience provides new insights into how to facilitate perceptual (re-)learning by older adults. CONCLUSIONS: There is a new imperative to complement training to increase bottom-up processing of the signal with more ecologically valid training to boost top-down information processing based on knowledge of language and the world. Advances in digital technologies enable the development of increasingly sophisticated training systems incorporating complex meaningful materials such as music, audiovisual interactive displays, and conversation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".