Negative Affect, Affect Regulation, and Food Choice: A Value-Based Decision-Making Analysis
Bibliographic record
Abstract
Maladaptive eating is one of the greatest threats to health and well-being in the 21st century. Psychological factors that drive maladaptive eating are of interest as they may offer low-cost intervention targets. One such factor is negative affect. If negative affect does lead to maladaptive eating, interventions that reduce negative affect should lead to improved eating and food choice. One relevant class of techniques is affect regulation strategies. In the present research, we use survey data and a value-based decision-making task to demonstrate that negative affect is associated with maladaptive eating and food choice. We find that negative affect decreases the weight that participants place on the health benefits of food. We also show that teaching participants to use reappraisal to downregulate negative affect leads to healthier food choices. These findings indicate that reappraisal applied to incidental negative affect may be an effective method for improving eating and food choice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".