The power of social influence over food intake: examining the effects of attentional bias and impulsivity
Bibliographic record
Abstract
Numerous studies have shown that people adjust their intake directly to that of their eating companions. A potential explanation for this modelling effect is that the eating behaviour of others operates as an external eating cue that stimulates food intake. The present study explored whether this cue-reactive mechanism can account for modelling effects on intake. It was investigated whether attentional bias towards dynamic eating cues and impulsivity would influence the degree of modelling. Participants completed one individual session and one session in which an experimental confederate accompanied them. In the first session, eye movements were recorded as an index of attentional bias to dynamic eating cues. In addition, self-reported impulsivity and response inhibition were assessed. The second session employed a between-participants design with three experimental conditions in which participants were exposed to a same-sex confederate instructed to eat nothing, a low or a large amount of M&Ms. A total of eighty-five young women participated. The participants' self-reported impulsivity determined the occurrence of modelling; only low-impulsive women adjusted their intake to that of their eating companion. Attention towards eating cues and response inhibition, however, did not moderate modelling of food intake. The present study suggests that cue-reactive mechanisms may not underlie modelling of food intake. Instead, the results emphasise the importance of social norms in explaining modelling effects, whereas it is suggested that the degree of impulsivity may play a role in whether or not women adhere to the intake norms set by their eating companion.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".