An inclusive approach to art appreciation through visual-taste cross-sensory design by exploration with Kandinsky’s Grey Circle
Bibliographic record
Abstract
Cross-sensory translation enables people with diverse abilities to perceive and experience art through sensory modalities other than vision alone. While it is not difficult to find museums or art galleries that are beginning to incorporate auditory, tactile, olfactory, or even gustatory senses into their curatorial practice, findings from recent work on cross-modal correspondences have not been applied to the challenge of “translating” a specific visual artwork to taste sensations. This project seeks to explore a more inclusive approach to artwork appreciation by transforming visual experiences into tasting experiences, thus expanding the perceptual dimensions of artworks for a broader audience. First, this study synthesizes prior work on plastic semiotics with empirical findings from research on cross-modal correspondences to produce a conceptual model that suggests how a sensorial reading of a painting can inform a chef’s “translation” from visual cues of an artwork into a culinary experience. Then, this study practically examined the validity of the conceptual model through interviews and a co-design session with culinary professionals, seeking to understand how they recommend translating Wassily Kandinsky’s painting Grey Circle into a culinary experience. The research findings show how the interpretations of the specific painting through taste modalities echoed the cross-modal mappings from visual to gustatory experiences suggested by the conceptual model. Finally, the study tests the extent to which people can perceive the properties of the given painting through a gustatory experience by hosting a tasting session. The result reveals that participants can receive the intended interpretations mediated by the curated foods’ properties, which are designed to reflect and afford the sensorial expressions in the painting. Furthermore, the gustatory experience also helps visitors dive deeper into the artwork to a certain extent.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".