Eating Right: Linking Food‐Related Decision‐Making Concepts From Neuroscience, Psychology, and Education
Bibliographic record
Abstract
ABSTRACT This literature review uses four dimensions to classify and compare how food‐related decision‐making is conceptualized and experimentally assessed in neuroscience and other disciplines: (1) food‐related decision‐making other than the decision of what to eat that is part of each eating episode, (2) decision complexes other than the eating episode itself, (3) the evolution of food‐related decision‐making over time, and (4) the nature of food related decisions. In neuroscience in particular, food‐related decision‐making research has been dominated by studies exploring the influence of a wide range of factors on the final outcome, the type and amount of foods eaten. In comparison, the steps that are leading up to this outcome have only rarely been discussed. Neuroscientists should broaden their historically narrow conceptualization of food‐related decision‐making. Then neuroscience research could help group the numerous hypothesized influences for each of the decision complexes into meaningful clusters that rely on the same or similar brain mechanisms and that thus function in similar ways. This strategy could help researchers improve existing broad models of human food‐related decision‐making from other disciplines. The integration of neuroscientific and behavioral science approaches can lead to a better model of food‐related decision‐making grounded in the brain and relevant to the design of more effective school and nonschool lifestyle interventions to prevent and treat obesity in children, adolescents, and adults.
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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) | 0.000 | 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) | 0.000 | 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".