Gut-brain reinforcement: Insights from investigations in humans and in rodents
Bibliographic record
Abstract
Ingestive behavior is orchestrated by a complex interplay of neural circuits that sense and integrate nutritional information from the external environment and the internal milieu. A dynamic interaction between these circuits unfolds over time and allows organisms to learn to associate the nutritional benefits of foods and drinks realized in the body with antecedent actions (e.g., reaching, chewing) and sensory experiences in the environment (e.g., sight, smell) and oral cavity (e.g., flavor). This process of sequential gut-to-brain association learning optimizes behavior and metabolism by enabling the value of foods to be learned and updated based on their physiological consequences. As a result, when cues are encountered in the future, they can support predictive coding, such as the simulation of potential future states to guide adaptive food decisions, and they can acquire the capacity to elicit conditioned responses in the body to optimize metabolism. Here, we review recent advances in gut-brain reinforcement learning and highlight outstanding questions and controversies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".