Dopamine-Glutamate Interactions in Reward-Related Incentive Learning
Bibliographic record
Abstract
Extensive evidence implicates the neurotransmitter dopamine (DA) in reward-related incentive learning (for reviews, see refs. , , , , , , , , , , , , ). DA projections to the nucleus accumbens (NAc; refs. , , , ), striatum (), amygdala (), and medial prefrontal cortex (mPFC; ref. () have been shown to be involved. In recent years, researchers have begun to focus on the neurochemical mechanisms underlying the role of DA in learning and significant advances have been made (, , ). Many data suggest that DA afferents interact with glutamatergic (Glu) afferents common to the same cell when reward-related learning occurs (see ref. ). Results further suggest that a number of signaling molecules activated by Glu and DA synaptic transmission interact to bring about short-term and long-term alterations that mediate the neurochemical and structural changes that form the basis of reward-related incentive learning (see ref. ). In this chapter, we will review some of the studies examining the role of DA and especially Glu neurotransmission in reward-related learning. This will be followed by a discussion of evidence that provides a basis for understanding the DA-Glu interactions and the signaling pathways that mediate the effects of reward on behavior. Finally, the role of Glu in reward-related learning will be considered from the point of view of this evidence.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".