Food temptations spontaneously elicit compensatory beliefs in dieters
Bibliographic record
Abstract
Through various self-regulatory strategies, individuals attempt to strike a balance between the satisfaction of immediate desires and fulfillment of long term goals. One such strategy is described by the compensatory beliefs model, which suggests that individuals rationalize their surrender to an immediate desire or temptation. This model is indirectly supported by earlier studies where compensatory beliefs were induced by the experimental context. The current pilot study examines whether compensatory beliefs can be self-initiated i.e. are spontaneously generated as a response to temptation. We recruited ten female McGill students currently on a weight loss diet and assigned them randomly to a temptation and a control group. We presented all participants with a choice between two identical cookies, differently described for the temptation and control groups: for the temptation condition one cookie was labeled as high in fat and sugar and the other as low in fat and sugar while for the control condition both cookies were labeled as low in fat and sugar. Participants listed compensatory thoughts in both a closed and an open response format. Our pilot data show that dieters indeed spontaneously generate compensatory beliefs in response to temptation. Compensatory beliefs should be considered a factor in unsuccessful self-regulation and more specifically in failed dieting attempts.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".