A Psycho-Genetic Study of Hedonic Responsiveness in Relation to “Food Addiction”
Bibliographic record
Abstract
While food addiction has no formally-recognized definition, it is typically operationalized according to the diagnostic principles established by the Yale Food Addiction Scale-an inventory based on the symptom criteria for substance dependence in the DSM-IV. Currently, there is little biologically-based research investigating the risk factors for food addiction. What does exist has focused almost exclusively on dopaminergic reward pathways in the brain. While brain opioid signaling has also been strongly implicated in the control of food intake, there is no research examining this neural circuitry in the association with food addiction. The purpose of the study was therefore to test a model predicting that a stronger activation potential of opioid circuitry-as indicated by the functional A118G marker of the mu-opioid receptor gene-would serve as an indirect risk factor for food addiction via a heightened hedonic responsiveness to palatable food. Results confirmed these relationships. In addition, our findings that the food-addiction group had significantly higher levels of hedonic responsiveness to food suggests that this bio-behavioral trait may foster a proneness to overeating, to episodes of binge eating, and ultimately to a compulsive and addictive pattern of food intake.
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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".