TraitAnxiety, Neuroticism, and the Brain Basis of Vulnerability to Affective Disorder
Bibliographic record
Abstract
This chapter provides an overview of neural mechanisms involved in reward learning, concentrating largely on corticobasal ganglia circuits. It explains how neural circuits contribute to computing value signals for both natural and more abstract social rewards and how these value signals contribute to learning. Given its heterogeneity in terms of connectivity and functionality, the basal ganglia and associated projections are a key component of a putative reward circuit and are the focus of the research described in the chapter. The chapter also talks about the human striatum using neuroimaging techniques. Early studies of reward processing in humans paralleled animal studies, suggesting that activity in the striatum correlated with value signals during reward processing. Processing of reward-related information is highly dependent on components of corticobasal ganglia circuits such as the striatum, orbitofrontal cortex (OFC), and accumbens (ACC), along with modulation by dopaminergic input.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".