Exploring preferences for variable delays over fixed delays to high-value food rewards as a model of food-seeking behaviours in humans
Bibliographic record
Abstract
Foraging and operant models suggest that animals will tolerate uncertainty or risk to obtain food quickly. In modern food environments, sustained access to quick energy-dense foods can promote weight gain. Here, we used a discrete-choice procedure to examine peoples' decisions about when next to eat high-value, palatable food rewards, probabilistically delivered immediately or following longer delays. In Experiment 1, moderately hungry young females showed consistent preferences for a variable delay option that delivered food rewards immediately or following long delays over a fixed delay option that delivered the same rewards following intermediate delays. These preferences were stronger in females with higher BMIs compared with lower BMIs, suggesting that quick food can enhance the value of uncertain or 'risky' food-seeking strategies in individuals vulnerable to future weight gain. In Experiment 2, prior exposure to a subtle and not easily identifiable food aroma increased selections of the variable delay option following delayed food rewards in a mixed sample of male and female adults, providing preliminary evidence that food cues can sustain uncertain food-seeking strategies. These data highlight a working hypothesis that the rapid delivery and consumption of food rewards, and food cues, can increase risk-tolerance in the food-seeking behaviours of individuals who are vulnerable to weight gain. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".