Crossmodal Correspondence Between Auditory Timbre and Visual Shape
Bibliographic record
Abstract
Crossmodal correspondences are defined as associations between crossmodal stimuli based on seemingly irrelevant stimulus features (i.e., bright shapes being associated with high-pitched sounds). There is a large body of research describing auditory crossmodal correspondences involving pitch and volume, but not so much involving auditory timbre, the character or quality of a sound. Adeli and colleagues (2014, Front. Hum. Neurosci. 8, 352) found evidence of correspondences between timbre and visual shape. The present study aimed to replicate Adeli et al.'s findings, as well as identify novel timbre-shape correspondences. Participants were tested using two computerized tasks: an association task, which involved matching shapes to presented sounds based on best perceived fit, and a semantic task, which involved rating shapes and sounds on a number of scales. The analysis of association matches reveals nonrandom selection, with certain stimulus pairs being selected at a much higher frequency. The harsh/jagged and smooth/soft correspondences observed by Adeli et al. were found to be associated with a high level of consistency. Additionally, high matching frequency of sounds with unstudied timbre characteristics suggests the existence of novel correspondences. Finally, the ability of the semantic task to supplement existing crossmodal correspondence assessments was demonstrated. Convergent analysis of the semantic and association data demonstrates that the two datasets are significantly correlated (-0.36) meaning stimulus pairs associated with a high level of consensus were more likely to hold similar perceived meaning. The results of this study are discussed in both theoretical and applied contexts.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".