An immersive ecological measure of noise-induced functional interference in adults with hyperacusis
Bibliographic record
Abstract
Noise interfering with everyday activities is a common experience in many daily soundscapes. However, for individuals with intolerance to loud sounds, a condition called hyperacusis, these soundscapes can have a severe impact, greatly impairing lifestyle habits such as work, hobbies and social interactions. Yet, there is little experimental evidence documenting the functional impact of noise in these individuals. This study aims to validate a novel, ecologically relevant task designed to measure the functional impact of noise during a common daily activity: reading. Forty-nine participants (29 controls, 20 with hyperacusis) read a book excerpt while exposed to four different soundscapes. The sound level was gradually increased until participants reported that the noise interfered with their reading ability (called annoyance level), and then further increased until it became uncomfortable (called discomfort level). Participants then performed a 2-back cognitive task both in silence and in noise calibrated to their individual annoyance threshold. On average, individuals with hyperacusis reached these thresholds at sound levels 13 dB LAeq lower than controls. However, at their respective annoyance thresholds, both groups showed similar performance decrements (-3 %) in noise versus quiet. These findings support the validity of a novel ecological measure that integrates subjective annoyance thresholds with cognitive performance on a behavioral task, offering a reproducible approach to quantify the functional impact of hyperacusis.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".