Detection of static and dynamic changes in interaural correlation
Bibliographic record
Abstract
This study examines the relation between a static and a dynamic measure of interaural correlation discrimination: (1) the just noticeable difference (JND) in interaural correlation and (2) the minimum detectable duration of a fixed interaural correlation change embedded within a single noise-burst of a given reference correlation. For the first task, JNDs were obtained from reference interaural correlations of + 1, -1, and from 0 interaural correlation in either the positive or negative direction. For the dynamic task, duration thresholds were obtained for a brief target noise of +1, -1, and 0 interaural correlation embedded in reference marker noise of +1, -1, and 0 interaural correlation. Performance with a reference interaural correlation of +1 was significantly better than with a reference correlation of -1. Similarly, when the reference noise was interaurally uncorrelated, discrimination was significantly better for a target correlation change towards +1 than towards -1. Thus, for both static and dynamic tasks, interaural correlation discrimination in the positive range was significantly better than in the negative range. Using the two measures, the length of a binaural temporal window was estimated. Its equivalent rectangular duration (ERD) was approximately 86 ms and independent of the interaural correlation configuration.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".