Threshold interaural time differences under optimal conditions
Bibliographic record
Abstract
Klumpp and Eady (1956, J Acoust Soc Am 28, p.859-860) reported preliminary data on human sensitivity to interaural time differences (ITD) with various stimuli. At 10 μs ITD the best discrimination of 79% correct was reported for band-pass filtered (150-1700 Hz) noise. Despite the preliminary nature, and presentation methods different from todays, the above is still the best available reference for optimal ITD discrimination. The goal of the current study is to systematically determine the stimulus and the experimental paradigm that results in the smallest threshold ITD and to provide an accurate reference value. We varied seven stimulus and procedure parameters: stimulus waveform, stimulation level, stimulus duration, adaptive versus constant stimulus procedure, alternative-forced-choice (AFC) procedure, inter-stimulus pause duration, and complete waveform versus ongoing ITD. The condition yielding the lowest threshold ITD was Gaussian noise band-pass filtered from 20 to 1400 Hz, presented at 70 dB SPL, with a short inter-stimulus pause of 50 ms, and an interval duration of 0.5 s. Averaged across 8 trained subjects, the threshold ITD for this condition at the 79% correct level was 7 μs. The influence of each parameter will be discussed together with the obstacles of accurately determining this value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".