Prefrontal Dopamine Levels Determine the Balance between Cognitive Stability and Flexibility
Bibliographic record
Abstract
A key mechanism by which the prefrontal cortex (PFC) supports goal-oriented behaviors is attentional set formation: the formation and maintenance of an attentional bias toward relevant features. It has previously been proposed that a common single nucleotide polymorphism (val158met) in the gene that codes for the catechol O-methyltransferase (COMT) enzyme may affect an individual's ability to form and maintain an attentional set by modulating PFC dopamine (DA) levels. Here, we present data from a functional magnetic resonance imaging study that investigated the effect of this polymorphism on the tendency for older adults to display set-like behavior, and we compare these results to preexisting data from Parkinson's Disease (PD) patients. Our results demonstrate that putatively different levels of PFC DA predict both attentional set formation and right dorsolateral PFC (DLPFC) activation. More specifically, while for PD patients, val homozygotes showed heightened DLPFC activation and increased set-like behavior, for healthy older adults, the opposite pattern of results was observed. This interaction between COMT genotype and PD accords well with previous studies that have shown an excess of DA in the PFC in early PD patients and, furthermore, supports the hypothesis that there is an inverted-U shaped functional relationship between PFC DA levels and attentional set formation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".