Food categorization determines whether healthier food is inferred to be tastier or less tasty
Bibliographic record
Abstract
Abstract Despite evidence that people believe that the unhealthier the food, the tastier it is, some studies also suggest the opposing belief—the healthier the food, the tastier it is. A framework is proposed to reconcile this contradiction, and four studies demonstrate that the discrete categorization of foods as healthful versus unhealthful determines which intuition consumers use. When stereotypically unhealthy foods (e.g., candies, ice cream, hot dogs) are encountered, they are automatically categorized as unhealthful and the properties associated with that category (e.g., sweetness, saltiness, fat content) become accessible. Inferences about taste are then based on these properties and the unhealthier the encountered products are (i.e., the higher the sugar and fat content they have), the tastier they are perceived to be (unhealthy = tasty belief). Conversely, when stereotypically healthful foods (e.g., fruits) are encountered, other properties (e.g., freshness, vitamins) become salient, and tastiness is mainly inferred based on these properties, leading to the inference that the healthier these foods are (i.e., the more freshness and vitamins they have), the tastier they are perceived to be (healthy = tasty belief). Marketers and policymakers can leverage these findings to understand better when emphasizing healthiness benefits or hurts taste perceptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".