A framework for auditory model comparability and applicability
Bibliographic record
Abstract
Many computational models of the auditory system exist, most of which can predict a variety of psychoacoustical, physiological, or other experimental data. However, it is often challenging to apply existing third party models to own experimental paradigms, even if the model code is available. It will be demonstrated that model applicability is increased by providing a framework where the model acts as artificial observer performing exactly the same task as the subject (e.g., adaptive staircase procedure). A possible separation of the actual auditory processing of the model from the decision making stage will be discussed, which allows for testing the auditory processing of one model in a variety of experimental paradigms. The framework will consist of a citable data repository providing the required data for the models as well as toolboxes implementing both the auditory models and a variety of experimental paradigms. The model framework will be demonstrated with exemplary binaural models applied to the three most common binaural psychoacoustic paradigms: just noticeable difference (e.g., interaural time difference), tone in noise detection (e.g., binaural masking level difference), and absolute judgment (e.g., sound source localization). Further development of the framework will be discussed.
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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 | 0.034 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".