From anticipation to action, the role of dopamine in perceptual decision making: an fMRI-tyrosine depletion study
Bibliographic record
Abstract
During simple sensorimotor decision making, neurons in the parietal cortex extract evidence from sensory information provided by visual areas until a decision is reached. Contextual information can bias parietal activity during the task and change the decision-making parameters. One type of contextual information is the availability of reward for correct decisions. We tested the hypothesis that the frontal lobes and basal ganglia use contextual information to bias decision making to maximize reward. Human volunteers underwent functional MRI while making decisions about the motion of dots on a computer monitor. On rewarded trials, subjects responded more slowly by increasing the threshold to decision. Rewarded trials were associated with activation in the ventral striatum and prefrontal cortex in the period preceding coherent dot motion, and the degree of activation predicted the increased decision threshold. Decreasing dopamine transmission, using a tyrosine-depleting amino acid mixture, abolished the reward-related corticostriatal activation and eliminated the correlation between striatal activity and decision threshold. These observations provide direct evidence that some reward-related functional MRI signals in the striatum are the result of dopamine neuron activity and demonstrate that mesolimbic dopamine transmission can influence perceptual and decision-making neural processes engaged to maximize reward harvest.
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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 | 0.001 |
| 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".