Plate shape and colour interact to influence taste and quality judgments
Bibliographic record
Abstract
Research has demonstrated that factors external to the food source can influence consumers’ perceptions of food. Contextual factors including cutlery or tableware (for example, size and composition), the atmosphere (for example, noise levels and odours), and packaging (for example, shape and colour) have all been shown to influence the perceptual experience. Plateware has also been shown to influence taste perception since ratings of a dessert (strawberry mousse) were modified by plate colour but not by plate shape. In the current study, which used a 2 × 2 between-subjects design, the effect of plate colour (black versus white) and plate shape (round versus square) on taste perception is re-examined. Through sweetness, intensity, quality, and liking ratings of cheesecake, the current study extends the previous investigation to include an examination of the plate colour by plate shape interaction while using plates with more angular corners. Judgments made on simple elemental properties (sweetness and flavour intensity) and higher level compound property judgments (food quality or food liking) were shown to be differentially influenced by the interaction of plate colour and plate shape. Both elemental and compound property judgments were heightened by white round plates while compound judgments were also increased when food was presented on black square plates. The results suggest that plate colour and shape influence taste perception but not in a straight-forward manner and instead the influence depends on the interaction of the two variables. Depending on which attribute of the perceptual experience is more important, knowledge of this interaction could be used advantageously by the culinary community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".