Bibliographic record
Abstract
Many adjectives for musical timbre reflect cross-modal correspondence, particularly with vision and touch (e.g., “dark–bright,” “smooth–rough”). Although multisensory integration between visual/tactile processing and hearing has been demonstrated for pitch and loudness, timbre is not well understood as a locus of cross-modal mappings. Are people consistent in these semantic associations? Do cross-modal terms reflect dimensional interactions in timbre processing? Here I designed two experiments to investigate crosstalk between timbre semantics and perception through the use of Stroop-type speeded classification. Experiment 1 found that incongruent pairings of instrument timbres and written names caused significant Stroop-type interference relative to congruent pairs, indicating bidirectional crosstalk between semantic and auditory modalities. Pre-Experiment 2 asked participants to rate natural and synthesized timbres on semantic differential scales capturing luminance (brightness) and texture (roughness) associations, finding substantial consistency for a number of timbres. Acoustic correlates of these associations were also assessed, indicating an important role for high-frequency energy in the intensity of cross-modal ratings. Experiment 2 used timbre adjectives and sound stimuli validated in the previous experiment in two variants of a semantic-auditory Stroop-type task. Results of linear mixed-effects modeling of reaction time and accuracy showed slight interference in semantic processing when adjectives were paired with cross-modally incongruent instrument timbres (e.g., the word “smooth” with a “rough” timbre). Taken together, I conclude by suggesting that semantic crosstalk in timbre processing may be partially automatic and could reflect weak synesthetic congruency between interconnected sensory domains.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".