Environmental stress effects on appetite: Changing desire for high- and low-energy foods depends on the nature of the perceived threat
Bibliographic record
Abstract
Abstract It is well-documented that harsh environmental conditions influence appetite and food choice. However, the experience of environmental harshness is complex and shaped by several underlying dimensions, notably threats to one's social support, economic prospects, and physical safety. Here, we examined the differential effects of these three dimensions of environmental harshness on desire for specific food items. We first showed 564 participants images of 30 food items. Next, they rated how much they desired each item. The participants were then randomly assigned to a condition where they read one of six scenario stories that described someone's current living conditions. Each scenario story emphasized one of the three dimensions (social support, economic prospects, physical safety), with two levels (safe, harsh). Following this, the participants once again rated how desirable each food item was. The results showed that exposure to cues of low social support and high physical threat reduce the desire to eat, whereas cues of economic harshness had little effect. Further analysis revealed a significant interaction between energy level of different foods and perceived threat to physical safety. These findings are important in helping to understand how current environmental conditions influence changes in appetite and desire for different kinds of food items.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".