Speech‐in‐noise understanding in older age: The role of inhibitory cortical responses
Bibliographic record
Abstract
Studies of central auditory processing underlying speech-in-noise (SIN) recognition in aging have mainly concerned the degrading neural representation of speech sound in the auditory brainstem and cortex. Less attention has been paid to the aging-related decline of inhibitory function, which reduces the ability to suppress distraction from irrelevant sensory input. In a response suppression paradigm, young and older adults listened to sequences of three short sounds during MEG recording. The amplitudes of the cortical P30 response and the 40-Hz transient gamma response were compared with age, hearing loss and SIN performance. Sensory gating, indicated by the P30 amplitude ratio between the last and the first responses, was reduced in older compared to young listeners. Sensory gating was correlated with age in the older adults but not with hearing loss nor with SIN understanding. The transient gamma response expressed less response suppression. However, the gamma amplitude increased with age and SIN loss. Comparisons of linear multi-variable modeling showed a stronger brain-behavior relationship between the gamma amplitude and SIN performance than between gamma and age or hearing loss. The findings support the hypothesis that aging-related changes in the balance between inhibitory and excitatory neural mechanisms modify the generation of gamma oscillations, which impacts on perceptual binding and consequently on SIN understanding abilities. In conclusion, SIN recognition in older age is less affected by central auditory processing at the level of sensation, indicated by sensory gating, but is strongly affected at the level of perceptual organization, indicated by the correlation with the gamma responses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".