Age differences in detecting gaps in speech and non-speech
Bibliographic record
Abstract
Ability to detect gaps in speech and non-speech stimuli was measured in children, young adults, and older adults with good audiograms. The markers varied in duration (40 vs 250 msec) and in spectral symmetry. In spectrally symmetrical conditions, the leading and lagging markers were the same: the vowel [u] in speech conditions and a 500-Hz tone in non-speech conditions. In asymmetrical speech conditions, the lagging marker was the same as in the symmetrical conditions, but the leading marker was the consonant [s] in the speech conditions and a broadband noise (1 to 6 kHz) in the non-speech conditions. For all groups, gap detection thresholds in spectrally symmetrical markers were far smaller than in spectrally asymmetrical markers. Thresholds were significantly smaller in young adults than in either children or older adults. Gaps between spectrally asymmetrical speech markers were detected better than gaps between analogous non-speech stimuli. It is argued that phonological knowledge compensates for auditory processing difficulties. [Research funded by the International Dyslexia Association, the Natural Sciences and Engineering Research Council of Canada, and the Canadian Institutes of Health Research.]
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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".