Sensory and affective dimensions in loudness perception: Insights from young adults
Bibliographic record
Abstract
Traditional psychoacoustic measures often lack accuracy in diagnosing hyperacusis and other sound tolerance disorders, possibly due to their reliance on artificial stimuli and unidimensional scales. The aim of this study was to assess loudness across sensory and affective dimensions using natural sounds, drawing on pain research wherein intensity and unpleasantness are assessed separately. We hypothesized that similar distinctions apply to loudness perception. A total of 102 young adults with normal to mild hearing loss rated 32 sound stimuli (pleasant, unpleasant, neutral, and artificial) at 10 intensities (40 to 100 dBA) on sensory and affective scales. They also completed the Hyperacusis Questionnaire, the Noise Sensitivity Scale, and the Hospital Anxiety and Depression Scale. Mixed linear models indicated both correlations and dissociations between scales that varied according to intensity and valence. Louder stimuli were rated as more unpleasant, but only at high intensities. On the sensory scale, sounds were perceived as louder with increasing intensity; however, at low to moderate intensities, pleasant and neutral sounds were rated as louder, whereas at higher intensities, artificial and unpleasant stimuli were rated as louder. On the affective scale, the perception of unpleasantness also increased with intensity, but less steeply. At high intensities, artificial stimuli were rated similarly to unpleasant stimuli. Noise sensitivity scores predicted louder and more unpleasant ratings, whereas depression scores were associated with softer and less pleasant perceptions. This study highlights the need for multidimensional approaches in audiology and suggests that the integration of sensory and affective scales with natural stimuli may improve the diagnosis and treatment of sound tolerance disorders.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".