Dopaminergic Circuitry and Risk/Reward Decision Making: Implications for Schizophrenia
Bibliographic record
Abstract
Abnormal reinforcement learning and representations of reward value are present in schizophrenia, and these impairments can manifest as deficits in risk/reward decision making. These abnormalities may be due in part to dopaminergic dysfunction within cortico-limbic-striatal circuitry. Evidence from studies with laboratory animal have revealed that normal DA activity within different nodes of these circuits is critical for mediating dissociable processes that can refine decision biases. Moreover, both phasic and tonic dopamine transmission appear to play separate yet complementary roles in these processes. Tonic dopamine release within the prefrontal cortex and nucleus accumbens, serves as a "running rate-meter" of reward and reflects contextual information such as reward uncertainty and overt choice behavior. On the other hand, manipulations of outcome-related phasic dopamine bursts and dips suggest these signals provide rapid feedback to allow for quick adjustments in choice as reward contingencies change. The lateral habenula is a key input to the DA system that phasic signals is necessary for expressing subjective decision biases; as suppression of activity within this nucleus leads to catastrophic impairments in decision making and random patterns of choice behavior. As schizophrenia is characterized by impairments in using positive and negative feedback to appropriately guide decision making, these findings suggest that these deficits in these processes may be mediated, at least in part, by abnormalities in both tonic and phasic dopamine transmission.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".