The object of perception in impacted sound sources: The role of perceptual accessibility and information accuracy
Bibliographic record
Abstract
Two independent principles could potentially govern integration of information in the perception of a sound source. First, higher perceptual weights should be given to acoustical parameters that specify accurately and unambiguously a sound source property. Second, lower perceptual weights should be given to acoustical parameters related to less perceptually accessible information (e.g., impaired discrimination). The relevance of these principles to source perception was investigated. Hardness perception was investigated for the two objects whose interaction generates an impact sound: a highly damped hammer and a freely vibrating sounding object. Acoustical analyses identified unambiguous and relatively accurate acoustical specifiers of the impacted sound source. A first experiment assessed the ability of a listener to learn to discriminate hammer or sounding object hardness, providing a measure of the perceptual accessibility of the two source properties. A second experiment investigated hardness estimation in trained and untrained listeners, providing quantification of the relative perceptual relevance of the informational sources. A final experiment conducted on simulated sound sources further characterized weighting profiles in untrained listeners, decorrelating otherwise covarying source properties. Results concerning both mechanical and acoustical correlates suggest a relevance of both principles.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".