The role of stimulus complexity, spectral overlap, and pitch for gap-detection thresholds in young and old listeners
Bibliographic record
Abstract
Thresholds for detecting a gap between two complex tones were determined for young listeners with normal hearing and old listeners with mild age-related hearing loss. The leading tonal marker was always a 20-ms, 250-Hz complex tone with energy at 250, 500, 750, and 1000 Hz. The lagging marker, also tonal, could differ from the leading marker with respect to fundamental frequency (f0), the presence versus absence of energy at f0, and the degree to which it overlapped spectrally with the leading marker. All stimuli were presented with steeper (1 ms) and less steep (4 ms) envelope rise and fall times. F0 differences, decreases in the degree of spectral overlap between the markers, and shallower envelope shape all contributed to increases in gap-detection thresholds. Age differences for gap detection of complex sounds were generally small and constant when gap-detection thresholds were measured on a log scale. When comparing the results for complex sounds to thresholds obtained for pure-tones in a previous study by Heinrich and Schneider [(2006). J. Acoust. Soc. Am. 119, 2316-2326], thresholds increased in an orderly fashion from markers with identical (within-channel) pure tones to different (between-channel) pure tones to complex sounds. This pattern of results was true for listeners of both ages although younger listeners had smaller thresholds overall.
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".