What’s in the box? Preference for leftward plating of food in bentos
Bibliographic record
Abstract
Optimizing the aesthetics of plating and understanding where certain elements of the dish should be placed within a bento box will aid chefs and advertisers in learning what side of dishware plating creates the most appetizing appearance to the average diner. Previous literature led us to predict that there would be a leftward bias of plating the largest, most caloric heavy component of the meal on the dish. Canadian participants (n = 204 and n = 279) were presented photographs of single-tiered bento boxes and their mirror image together to elicit and record preferences. Sixty image pairs were presented simultaneously (with one of the images located directly above the other) in random order, and decisions between leftward and rightward biased choices were collected and analysed. The majority of participants were right-handed and had a native reading direction of left-to-right. Participants in both experiments preferred asymmetrical bentos where the majority of the food was plated on the left side of the box, gravitating to a leftward bias. Age and native reading direction were additionally analysed, where regardless of either demographic, left side plating was preferred. Learning how we can optimize plating aesthetics can benefit people in a variety of vocations (e.g. chefs, advertisers, photographers, etc.). It appears that regardless of age or native reading direction, a leftward plating bias is evident when it comes to arranging a bento in the most aesthetic fashion.
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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 | 0.002 |
| 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 | 0.000 |
| 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".