The psychomechanics of simulated sound sources: Material properties of impacted thin plates
Bibliographic record
Abstract
Sounds convey information about the materials composing an object. Stimuli were synthesized using a computer model of impacted plates that varied their material properties: viscoelastic and thermoelastic damping and wave velocity (related to elasticity and mass density). The range of damping properties represented a continuum between materials with predominant viscoelastic and thermoelastic damping (glass and aluminum, respectively). The perceptual structure of the sounds was inferred from multidimensional scaling of dissimilarity judgments and from their categorization as glass or aluminum. Dissimilarity ratings revealed dimensions that were closely related to mechanical properties: a wave-velocity-related dimension associated with pitch and a damping-related dimension associated with timbre and duration. When asked to categorize sounds, however, listeners ignored the cues related to wave velocity and focused on cues related to damping. In both dissimilarity-rating and identification experiments, the results were independent of the material of the mallet striking the plate (rubber or wood). Listeners thus appear to select acoustical information that is reliable for a given perceptual task. Because the frequency changes responsible for detecting changes in wave velocity can also be due to changes in geometry, they are not as reliable for material identification as are damping cues.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".