Emotions shape taste perception in a real restaurant environment
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it