Why do people eat the same breakfast every day? Goals and circadian rhythms of variety seeking in meals
Bibliographic record
Abstract
People exhibit a circadian rhythm in the variety of foods they eat. Many people happily eat the same foods for breakfast day after day, yet seek more variety in the foods they eat for lunch and dinner. We identify psychological goals as a driver of this diurnal pattern of variety seeking, complementing other biological and cultural drivers. People are more likely to pursue hedonic goals for meals as the day progresses, which leads them to seek more variety for dinners and lunches than breakfasts. We find evidentiary support for our theory in studies with French and American participants (N = 4481) using diary data, event reconstruction methods, and experiments. Both endogenously and exogenously induced variation in hedonic goal activation modulates variety seeking in meals across days. Hedonic goal activation predicts variety seeking for meals when controlling for factors including time devoted to meal preparation and eating, the presence or absence of other people, and whether people ate a meal inside or outside their home. Goal activation also explain differences in time spent on meals, whereas increasing time spent on meals does not increase variety seeking. Finally, we observed that a similar increase in hedonic goal activation enacts a larger increase in variety seeking at breakfast than at lunch than at dinner, suggesting a diminishing marginal effect of hedonic goal activation on variety seeking.
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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 | 0.002 |
| 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) | 0.001 | 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 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".