Localization-in-noise and binaural medial olivocochlear functioning in children and young adults
Bibliographic record
Abstract
Children as young as 5 yr old localize sounds as accurately as adults in quiet in the frontal hemifield. However, children's ability to localize in noise and in the front/back (F/B) dimension are scantily studied. To address this, the first part of this study investigated localization-in-noise ability of children vs young adults in two maskers: broadband noise (BBN) and speech-babble (SB) at three signal-to-noise ratios: -12, -6, and 0 dB. In the second part, relationship between binaural medial olivocochlear system (MOC) function and localization-in-noise was investigated. In both studies, 21 children and 21 young adults participated. Results indicate, while children are able to differentiate sounds arriving in the F/B dimension on par with adults in quiet and in BBN, larger differences were found for SB. Accuracy of children's localization in noise (for both maskers) in the lateral plane was also poorer than adults'. Significant differences in binaural MOC interaction (mBIC; the difference between the sum of two monaural- and binaural-MOC strength) between adults and children were also found. For reasons which are not clear, adult F/B localization in BBN correlates better with mBIC while children's F/B localization in SB correlated better with binaural MOC strength.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".