Midbrain dopamine neurons during appetitive and aversive states
Bibliographic record
Abstract
A key role ascribed to midbrain dopamine (DA) neurons rests with learning about rewarding events by reflecting reward prediction errors (RPE). Research has shown that during reward learning a positive prediction error (e.g. surprising reward), leads to phasic excitation, while a negative prediction error (e.g. omission of an expected reward) leads to phasic inhibition in DA neurons. It remains unclear, however, how DA regulates learning about aversive events. Using behavioral electrophysiology we recorded from DA neurons in the ventral tegmental area (VTA) during a Pavlovian task in which auditory cues were trained as predictors of either an appetitive sucrose reward or aversive footshock. Our analyses confirmed a role for VTA DA neurons in tracking reward prediction error (RPE), that is, elevation in firing rate (FR) to the reward predictor and depression in FR at time of reward omission in a correlated fashion. Further, our goal was to determine whether DA firing would represent reward and aversion in line with a valence-based prediction error signal. We found that cue related phasic DA activity to both reward and aversion predicting cues contained both information about stimulus identity, as well as valence. Additionally, outcome omission was represented as state of opposite valence. These results support the hypothesis that midbrain DA neurons support learning by signaling valence-based prediction errors.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".