Failure to report social influences on food intake: Lack of awareness or motivated denial?
Bibliographic record
Abstract
OBJECTIVE: Two studies examined whether people are aware of social influences on food intake, and whether recognition of those influences is driven by the observation of mimicked eating and/or matching the amount of food eaten. METHOD: In Study 1, participants watched a video of 1 person eating alone, or a video of 2 people eating together that varied in the extent to which the target's eating behavior mimicked or matched that of the model. Participants then made attributions for the eating behavior of the target person. In Study 2, each participant watched a video of herself eating with a confederate and made attributions for her own eating behavior. In both studies, the outcome of interest was the extent to which each participant acknowledged the influence of the model's eating behavior on the target's (or her own) food intake. RESULTS: In Study 1, participants accurately recognized social influences on the food intake of the target person, and this recognition was facilitated by the presence of mimicked eating, but not by matching the total amount eaten. Study 2 showed that the extent to which people acknowledge social influences on their own food intake depended on their self-reported general responsiveness to social cues on eating. CONCLUSION: Overall, people seem to be aware that social factors can influence others' food intake. Whereas some people (high social eaters) are able to accurately report social influences on their own food intake, others (low social eaters) seem to deny those influences for reasons that merit future investigation.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".