The role of audiovisual congruence in aesthetic appreciation of contemporary music and visual art
Bibliographic record
Abstract
Does congruence between auditory and visual modalities affect aesthetic experience? While cross-modal correspondences between vision and hearing are well-documented, previous studies show conflicting results regarding whether audiovisual correspondence affects subjective aesthetic experience. Here, in collaboration with the Kentler International Drawing Space (NYC, USA), we depart from previous research by using music specifically composed to pair with visual art in the professionally-curated Music as Image and Metaphor exhibition. Our pre-registered online experiment consisted of 4 conditions: Audio, Visual, Audio-Visual-Intended (artist-intended pairing of art/music), and Audio-Visual-Random (random shuffling). Participants (N = 201) were presented with 16 pieces and could click to proceed to the next piece whenever they liked. We used time spent as an implicit index of aesthetic interest. Additionally, after each piece, participants were asked about their subjective experience (e.g., feeling moved). We found that participants spent significantly more time with Audio, followed by Audiovisual, followed by Visual pieces; however, they felt most moved in the Audiovisual (bi-modal) conditions. Ratings of audiovisual correspondence were significantly higher for the Audiovisual-Intended compared to Audiovisual-Random condition; interestingly, though, there were no significant differences between intended and random conditions on any other subjective rating scale, or for time spent. Collectively, these results call into question the relationship between cross-modal correspondence and aesthetic appreciation. Additionally, the results complicate the use of time spent as an implicit measure of aesthetic experience.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".