The role of melanin‐concentrating hormone in conditioned reward learning
Bibliographic record
Abstract
The orexigenic neuropeptide melanin-concentrating hormone (MCH) is well positioned to play a key role in connecting brain reward and homeostatic systems due to its synthesis in hypothalamic circuitry and receptor expression throughout the cortico-striatal reward circuit. Here we examined whether targeted-deletion of the MCH receptor (MCH-1R) in gene-targeted heterozygote and knockout mice (KO), or systemic treatment with pharmacological agents designed to antagonise MCH-1R in C57BL/6J mice would disrupt two putative consequences of reward learning that rely on different neural circuitries: conditioned reinforcement (CRf) and Pavlovian-instrumental transfer (PIT). Mice were trained to discriminate between presentations of a reward-paired cue (CS+) and an unpaired CS-. Following normal acquisition of the Pavlovian discrimination in all mice, we assessed the capacity for the CS+ to act as a reinforcer for new nose-poke learning (CRf). Pharmacological disruption in control mice and genetic deletion in KO mice impaired CRf test performance, suggesting MCH-1R is necessary for initiating and maintaining behaviors that are under the control of conditioned reinforcers. To examine a dissociable form of reward learning (PIT), a naïve group of mice were trained in separate Pavlovian and instrumental lever training sessions followed by the PIT test. For all mice the CS+ was capable of augmenting ongoing lever responding relative to CS- periods. These results suggest a role for MCH in guiding behavior based on the conditioned reinforcing value of a cue, but not on its incentive motivational value.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".