Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.033 | 0.014 |
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 it