Emotions shape taste perception in a real restaurant environment
Bibliographic record
Abstract
Can emotions make your drink taste sweeter, bitterer, or more sour? Previous laboratory studies show that incidental emotions – emotions that are unrelated to the situation at hand – can influence taste perception. For example, people who recall a happy memory before tasting food may find it sweeter than after recalling a sad memory. However, outside of the confines of the laboratory, little research has examined how integral emotions – emotions that are directly tied to the situation at hand – can be used to shape consumers’ experiences. We recruited 231 participants for a drink-tasting session at Copenhagen’s Alchemist restaurant, where dining is accompanied by a 360-degree immersive visual experience projected into a dome ceiling. Unbeknownst to the participants, there were only two different drinks (one kombucha and one water kefir) that participants tasted each twice, while immersive scenes designed to elicit positive or negative feelings were projected. Results showed that the same beverage tasted less sweet and more bitter and sour when accompanied by an unpleasant emotional scene. These findings demonstrate that emotions, when elicited as part of a real-world multisensory gastronomic experience, can shape our taste perceptions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".