Neural circuits mediating food cue-reactivity: toward a new model shaping the interplay of internal and external factors
Bibliographic record
Abstract
In the current opinion paper, we suggest a new perspective on the neuroimaging studies investigating the neural bases of food-cue regulation. Stemming from the evidence that different factors can modulate the neural response to drug cues (Jasinska et al. 2014, Neurosc. Biobeh. Rev.), we addressed the role of the major internal (e.g., biological, psychological) and external (e.g., environmental, cue-specific) factors that influence the neural reactivity to food-related cues, highlighting the brain circuits affected by the simple and interactive effects across different factors. The proposed model will be useful to frame new research ideas in which different contextual factors are modeled according to a factorial design, allowing to explore higher-order interactions at the neurofunctional level. Elucidating such interactions will not only lead to a better understanding of the neurocognitive bases of the normal and pathological eating behavior, but it will also pave the way to more effective, ecological, and tailor-made (behavioral or brain-centered) interventions, where internal and external contextual factors are incorporated in the treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".