Localization of real and tangent-law panned phantom sound sources in the frontal horizontal plane
Bibliographic record
Abstract
Auditory source separations of as little as 1 degree are detectable. However, presenting auditory stimuli at small separations presents technical challenges, with loudspeaker separation limited by transducer diameter. An alternative procedure is to utilize phantom sources, with the perceived position of a single source determined by the relative output levels of two spatially separated loudspeakers. Therefore, it is important to determine whether real and phantom sources can be localized with the same precision. In the present experiment, listeners localized real (individual) sources and phantom sources computed using a tangent-law model giving the same nominal azimuthal angles as the real sources. Listeners used a laser pointer to indicate perceived source location. Infrared cameras detected pointer position with responses stored in terms of azimuth. Signals were broadband or narrowband (300-700 Hz and 3800–4200 Hz) noise, 100 or 500 ms in duration. Generally, phantom sources were localized with less precision than real sources, and high-frequency signals were localized with less precision than broadband or low-frequency signals, with no effect of duration. Results show that phantom sources are localized with sufficient accuracy and precision to be useful in assessing auditory spatial acuity, but they are not localized with the same precision as real sources.
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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 | 0.000 | 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".