Correlations among within-Channel and between-Channel Auditory Gap-Detection Thresholds in Normal Listeners
Bibliographic record
Abstract
We obtained data on within-channel and between-channel auditory temporal gap-detection acuity in the normal population. Ninety-five normal listeners were tested for gap-detection thresholds, for conditions in which the gap was bounded by spectrally identical, and by spectrally different, acoustic markers. Separate thresholds were obtained with the use of an adaptive tracking method, for gaps delimited by narrowband noise bursts centred on 1.0 kHz, noise bursts centred on 4.0 kHz, and for gaps bounded by a leading marker of 4.0 kHz noise and a trailing marker of 1.0 kHz noise. Gap thresholds were lowest for silent periods bounded by identical markers--'within-channel' stimuli. Gap thresholds were significantly longer for the between-channel stimulus--silent periods bounded by unidentical markers (p < 0.0001). Thresholds for the two within-channel tasks were highly correlated (R = 0.76). Thresholds for the between-channel stimulus were weakly correlated with thresholds for the within-channel stimuli (1.0 kHz, R = 0.39; and 4.0 kHz, R = 0.46). The relatively poor predictability of between-channel thresholds from the within-channel thresholds is new evidence on the separability of the mechanisms that mediate performance of the two tasks. The data confirm that the acuity difference for the tasks, which has previously been demonstrated in only small numbers of highly trained listeners, extends to a population of untrained listeners. The acuity of the between-channel mechanism may be relevant to the formation of voice-onset time-category boundaries in speech perception.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".