Evidence of a self-serving bias in people's attributions for their food intake
Bibliographic record
Abstract
People often fail to acknowledge external influences on their food intake, but there might be some circumstances in which people are willing to report that those external factors influenced their behavior. This study examined whether participants who believed that they had overeaten would indicate that the portion size they were served influenced their food intake. Participants (119 women) ate a pasta lunch at two separate sessions, one week apart. At the second session, participants were randomly assigned to receive either a regular portion of pasta (the same portion as the first session) or a large portion of pasta (a portion that was twice the size), and to receive false feedback about their food intake indicating that they had either eaten about the same as or substantially more than they had at the previous session. Participants were then asked to indicate the extent to which the amount of food served influenced how much they ate at that second session. Compared to participants who were informed that they had eaten the same amount across the two sessions, those who were informed that they ate more at the second session reported a stronger influence of the amount of food served if they also received a large portion of pasta, but not if they received a regular portion of pasta. These findings suggest that the willingness to implicate external influences (e.g., portion size) on one's food intake may be driven by a self-serving bias, providing an "excuse" for overeating. However, the external cue must be salient enough to be a plausible explanation for one's behavior.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".