Generalizing timbre space data across stimulus contexts: The meta-analytic approach
Bibliographic record
Abstract
Studies of the multidimensionality of the complex auditory attribute called timbre often represent the perceptual structure in terms of a spatial model in which distance has a monotonic relation to perceived dissimilarity. Acoustical correlates are sought for the dimensions of these timbre spaces in order to quantify them psychophysically. An important question concerns the extent to which the results obtained with one stimulus set generalize to other stimulus sets. A meta-analytic study of data from several timbre spaces using musical tones was performed with the same multidimensional scaling algorithm. The effect of changing a subset of the sounds on the perceptual relations among common sounds was evaluated in conditions where the degree of variation along perceptual dimensions across sets was similar or different (new salient dimensions added). A change in the sounds present in the stimulus set, keeping salient dimensions constant, had little effect on perceptual structure of the common sounds in the two sets. Changing salient acoustical correlates present, while resulting in new dimensions, did not strongly affect the perceptual structure of the common sounds. Results demonstrate that, while a change in stimulus set may introduce new perceptual dimensions, the perceptual structure among sounds common to two sets remains similar.
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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 | 0.155 | 0.287 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".