Age-related differences in the impact of background noise on neural speech tracking
Bibliographic record
Abstract
Tracking the envelope of speech in the brain is important for speech comprehension. Recent research suggests that acoustic background noise can enhance neural speech tracking, enabling the auditory system to robustly encode speech even under unfavorable conditions. Aging and hearing loss are associated with internal, neural noise in the auditory system, raising the question whether additional acoustic background noise enhances neural speech tracking in older adults. In the current electroencephalography study, younger (~25.5 years) and older adults (~68.5 years) listened to spoken stories in quiet (clear) or in the presence of background noise at a wide range of different signal-to-noise ratios. In younger adults, early neural speech tracking responses (<0.15 s) were enhanced by minimal background noise, indicating response facilitation through noise. In contrast, older adults, compared to younger adults, showed enhanced neural speech tracking for clear speech and speech masked by minimal background noise, but the acoustic noise led to little enhancement of the early neural tracking response in older people. The data demonstrate different sensitivity of the auditory cortex to speech masked by noise between younger and older adults. The results are consistent with the idea that the auditory cortex of older people exhibits more internal, neural noise that enhances neural speech tracking but that additional acoustic noise does not further support speech encoding. The work points to a highly non-linear auditory system that differs between younger and older adults.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".