Glutamate‐Dopamine Cotransmission and Reward Processing in Addiction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While Dale's principle of "one neuron, one neurotransmitter" has undergone revisions to incorporate evidence of the corelease of atypical neurotransmitters such as neuropeptides, the corelease of classical neurotransmitters has only recently been realized. Surprisingly, numerous studies now indicate that the corelease of neurotransmitters in the mammalian central nervous system is not an obscure and rare phenomenon but is widespread and involves most classical neurotransmitters systems. However, the suggestion that glutamate can be coreleased with dopamine (DA) has remained controversial. Furthermore, glutamate-DA cotransmission has not yet been seriously considered in the context of the neurocircuitry of addiction. If glutamate is in fact coreleased with DA as some evidence now suggests, this may have significant implications for advancing our understanding of the interactive role that these 2 neurotransmitters play in cognitive and reward processes. In this commentary, we review the evidence for and against glutamate as a cotransmitter and discuss the potential role of glutamate-DA corelease in addiction. In particular, we describe a recently proposed model in which coreleased glutamate transmits a temporally precise prediction error signal of reward described by Schultz et al., whereas the function of coreleased DA is to exert prolonged modulatory influences on neuronal activity. In addition, we suggest that as alcohol consumption transitions from recreational use to addiction, there is a corresponding transition in the reward valence signal from better than predicted to worse than predicted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it