Shared mental representations underlie metaphorical sound concepts
Bibliographic record
Abstract
Communication between sound and music experts is based on the shared understanding of a metaphorical vocabulary derived from other sensory modalities. Yet, the impact of sound expertise on the mental representation of these sound concepts remains blurry. To address this issue, we investigated the acoustic portraits of four metaphorical sound concepts (brightness, warmth, roundness, and roughness) in three groups of participants (sound engineers, conductors, and non-experts). Participants (N = 24) rated a corpus of orchestral instrument sounds (N = 520) using Best-Worst Scaling. With this data-driven method, we sorted the sound corpus for each concept and population. We compared the population ratings and ran machine learning algorithms to unveil the acoustic portraits of each concept. Overall, the results revealed that sound engineers were the most consistent. We found that roughness is widely shared while brightness is expertise dependent. The frequent use of brightness by expert populations suggests that its meaning got specified through sound expertise. As for roundness and warmth, it seems that the importance of pitch and noise in their acoustic definition is the key to distinguishing them. These results provide crucial information on the mental representations of a metaphorical vocabulary of sound and whether it is shared or refined by sound expertise.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".