Prefrontal D2‐receptor stimulation mediates flexible adaptation of economic preference hierarchies
Bibliographic record
Abstract
Advantageous economic decision making requires flexible adaptation of gain-based and loss-based preference hierarchies. However, where the neuronal blueprints for economic preference hierarchies are kept and how they may be adapted remains largely unclear. Phasic cortical dopamine release likely mediates flexible adaptation of neuronal representations. In this PET study, cortical-binding potential (BP) for the D(2)-dopamine receptor ligand [(11)C]FLB 457 was examined in healthy participants during multiple sessions of a probabilistic four-choice financial decision-making task with two behavioral variants. In the changing-gains/constant-losses variant, the implicit gain-based preference hierarchy was unceasingly changing, whereas the implicit loss-based preference hierarchy was constant. In the constant-gains/changing-losses variant, it was the other way around. These variants served as paradigms, respectively, contrasting flexible adaptation versus maintenance of loss-based and gain-based preference hierarchies. We observed that in comparison with the constant-gains/changing-losses variant, the changing-gains/constant-losses variant was associated with a decreased D(2)-dopamine receptor-BP in the right lateral frontopolar cortex. In other words, lateral frontopolar D(2)-dopamine receptor stimulation was specifically increased during continuous adaptation of mental representations of gain-based preference hierarchies. This finding provides direct evidence for the existence of a neuronal blueprint of gain-based decision-making in the lateral frontopolar cortex and a crucial role of local dopamine in the flexible adaptation of mental concepts of future behavior.
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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 | 0.000 | 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".