Food as Fuel: Performance Goals Increase the Consumption of High-Calorie Foods at the Expense of Good Nutrition
Bibliographic record
Abstract
Abstract At work, at school, at the gym club, or even at home, consumers often face challenging situations in which they are motivated to perform their best. This research demonstrates that activating performance goals, whether in cognitive or physical domains, leads to an increase in the consumption of high-calorie foods at the expense of good nutrition. This effect derives from beliefs that the function of food is to provide energy for the body (food as fuel) coupled with poor nutrition literacy, leading consumers to overgeneralize the instrumental role of calories for performance. Indeed, nutrition experts choose very different foods (lower in calorie, higher in nutritional value) than lay consumers in response to performance goals. Also, performance goals no longer increase calorie intake when emphasizing the hedonic function of food (food for pleasure). Hence, while consumer research often interprets the overconsumption of pleasurable and unhealthy high-calorie foods as a consequence of hedonic goals and self-control failures, our research suggests that this overconsumption may also be explained by a maladaptive motivation to manage energy intake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".