Does Dorsal Striatum Mediate Stimulus-Response Learning or Decision Making? (P6.172)
Bibliographic record
Abstract
Objective/Background: We tested the prevalent contention that dorsal striatum (DS) mediates later-stage stimulus-response learning when responses become automatic or habitual. Using functional magnetic resonance imaging, we examined striatal brain activity during later-stage stimulus-response learning, once performance accuracy was greater than 90[percnt]. Design/Methods: Seven males and seven females (mean age 22) learned to associate abstract images with left or right button-presses during an initial study phase. These pairings were reinforced by feedback in Session 1 and practiced without feedback in Session 2. Session 3 measured whether stimulus-response associations had achieved habit status. Results: DS activity correlated with stimulus-response events only during Blocks 1-3 of Session 1 when response times and accuracy suggested a level of deliberation in responding. No significant DS activity occurred for Blocks 4-12 or in Session 2 when responses times and accuracy had reached plateau, though stimulus-response associations had not reached automaticity in Session 3. Conclusions: We conclude that DS underlies decision-making that continues to require deliberation and not stimulus-response learning or habit formation. DS activation ceased to occur in late stages of Session 1 and in Session 2, when responding was fast and highly accurate but before relations became automatic based on performance in Session 3. In Parkinson's disease, DS is significantly dopamine depleted. Increasingly, DS is shown to mediate cognitive functions. Elucidating DS-mediated cognition improves our understanding of cognitive dysfunction in Parkinson's disease. These results clarify the cognitive profile in Parkinson's disease and guide dopaminergic therapy. Indeed, learning seems spared but decision-making is impaired, reviewing cognitive studies in Parkinson's Disease.
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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) | 0.006 | 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".