Independence of frequency channels in auditory temporal gap detection
Bibliographic record
Abstract
The ability of listeners to detect a temporal gap in a 1600-Hz-wide noiseband (target) was studied as a function of the absence and presence of concurrent stimulation by a second 1600-Hz-wide noiseband (distractor) with a nonoverlapping spectrum. Gap detection thresholds for single noisebands centered on 1.0, 2.0, 4.0, and 5.0 kHz were in the range from 4 to 6 ms, and were comparable to those described in previous studies. Gap thresholds for the same target noisebands were only modestly improved by the presence of a synchronously gated gap in a second frequency band. Gap thresholds were unaffected by the presence of a continuous distractor that was either proximate or remote from the target frequency band. Gap thresholds for the target noiseband were elevated if the distractor noiseband also contained a gap which "roved" in time in temporal proximity to the target gap. This effect was most marked in inexperienced listeners. Between-channel gap thresholds, obtained using leading and trailing markers that differed in frequency, were high in all listeners, again consistent with previous findings. The data are discussed in terms of the levels of the auditory perceptual processing stream at which the listener can voluntarily access auditory events in distinct frequency channels.
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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 | 0.001 | 0.005 |
| 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".