Exploring the Differences Between an Immature and a Mature Human Auditory System Through Auditory Late Responses in Quiet and in Noise
Bibliographic record
Abstract
Children are disadvantaged compared to adults when they perceive speech in a noisy environment. Noise reduces their ability to extract and understand auditory information. Auditory-Evoked Late Responses (ALRs) offer insight into how the auditory system can process information in noise. This study investigated how noise, signal-to-noise ratio (SNR), and stimulus type affect ALRs in children and adults. Fifteen participants from each group with normal hearing were studied under various conditions. The findings revealed that both groups experienced delayed latencies and reduced amplitudes in noise but that children had fewer identifiable waves than adults. Babble noise had a significant impact on both groups, limiting the analysis to one condition: the /da/ stimulus at +10 dB SNR for the P1 wave. P1 amplitude was greater in quiet for children compared to adults, with no stimulus effect. Children generally exhibited longer latencies. N1 latency was longer in noise, with larger amplitudes in white noise compared to quiet for both groups. P2 latency was shorter with the verbal stimulus in quiet, with larger amplitudes in children than adults. N2 latency was shorter in quiet, with no amplitude differences between the groups. Overall, noise prolonged latencies and reduced amplitudes. Different noise types had varying impacts, with the eight-talker babble noise causing more disruption. Children's auditory system responded similarly to adults but may be more susceptible to noise. This research emphasizes the need to understand noise's impact on children's auditory development, given their exposure to noisy environments, requiring further exploration of noise parameters in children.
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 | 0.001 | 0.001 |
| 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".