Modeling the relationship between listener factors and signal modification: A pooled analysis spanning a decade
Bibliographic record
Abstract
The overarching goal of our work is to understand how listeners respond to hearing aid processing and consequently inform individualized hearing aid treatment. Our previous work demonstrated that individual cognitive abilities, age, and hearing loss contribute to variability in response to cumulative signal modifications introduced by hearing aid processing and background noise. Specifically, older individuals with poorer working memory and more hearing loss were more susceptible to signal modifications introduced by hearing aid processing. These relationships were established in independent studies involving systematic manipulations of compression, digital noise reduction, frequency lowering, or microphone directionality. In this study, we present a hierarchical pooled analysis of data collected from six previous studies to develop a unified statistical model of the relationships between response to signal modification and individual listener variables. Across studies, signal modification is quantified using a cepstral correlation metric that accounts for cumulative envelope distortions arising from hearing aid processing and background noise. The statistical model will determine how working memory, age, and degree of hearing loss mediate the relationship between signal modification and speech intelligibility in noise across a large dataset. Both inferential and predictive applications of the combined data and model will be discussed. [Work supported by NIDCD.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".