Listening effort assessed using engaging, naturalistic materials
Bibliographic record
Abstract
Hearing loss in older people is typically diagnosed long after they begin to find speech comprehension effortful in the presence of background sound. Progress in measuring listening effort has been slow because the concept is ill defined, the materials typically used to measure it (simple sentences) may not motivate effortful listening the way that richer narratives do, and the cognitive abilities and brain networks that are most related to listening effort have not been systematically identified. We are examining the utility of engaging, naturalistic stories, compared lo isolated sentences, to measure listening effort with novel behavioural and functional magnetic resonance imaging (fMRI) methods that provide a window on the cognitive processes recruited to compensate for masked speech. We exploit the fact that naturalistic and engaging stories are known to activate much of the brain, in specific patterns that are timelocked to a story. The goal of the research is to develop a clearer understanding of the nature of listening effort, and begin to identify relevant markers in cognition and brain network activity, so that we can make more rapid progress on development of sensitive tests that will enable more timely, efficient, and beneficial fitting of hearing aids.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".