The effect of portion size and unit size on food intake: Unit bias or segmentation effect?
Bibliographic record
Abstract
OBJECTIVE: The "unit bias" has been proposed as an explanation for the portion-size effect; people consider a single unit to be an appropriate amount to eat and thus eat more when served a larger unit than when served a smaller unit. We suggest that the unit bias might be better characterized as a "segmentation effect," such that people eat less when a unit of food is separated into smaller subunits, but may eat more than a single unit. Furthermore, we suggest that portion-size effects should be independent of this segmentation effect. METHOD: In Study 1, female participants (n = 87) were served either a small or large portion of food that was either presented in the form of a single unit or multiple individually wrapped units. In Study 2, female participants (n = 42) were served a fixed portion of food that was either presented in the form of a single unit or multiple units presented on separate plates. RESULTS: Across both studies, there was no evidence that participants prefer to eat a single unit. Participants served multiple smaller units did eat less than did participants served a single larger unit, even when the overall portion size was the same, but the amount eaten was consistently more than a single unit. Furthermore, perceived norms of appropriate intake mediated the effect of unit number on food intake. CONCLUSIONS: These findings suggest that a segmentation effect, rather than a unit bias, is driving people's food intake, with implications for designing interventions aimed at reducing excessive food intake.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".