L'interaction odeur‐goût et ses effets marketing sur la perception et la consommation des produits diététiques
Bibliographic record
Abstract
Abstract The interaction between smell and taste has been widely confirmed in the literature. Exposure to a congruent olfactory stimulus enhances taste, increases palatability and the desire to eat, and promotes food consumption. In this paper, we use laboratory experiments to show that, in addition to its role in taste enhancement, olfaction also plays a role in taste induction. Using the phenomena of cognitive sensory integration and mental imagery, we explain how exposure to a real odor congruent with the expected taste produces a taste sensation for which there is no real taste stimulus. For example, exposure to the smell of vanillin, which is a sugar‐congruent odor, creates a taste sensation of sweetness, increases palatability and food cravings, and increases consumption of sugar‐free biscuits. The lack of exposure to the actual taste stimulus of sugar does not prevent the taste‐congruent odor from creating a taste sensation of sweetness, an olfactory sweetness. Odors are able to induce a taste sensation for which there is no real taste stimulus. The results of this research have implications for the marketing of dietary foods. Tastefulness, which is reduced by the absence of flavor, can be increased by congruent olfactory stimuli in the environment and have a positive effect on the sales of this product category.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".