Orbitofrontal and Thalamic Influences on Striatal Involvement in Human Reversal Learning
Bibliographic record
Abstract
ABSTRACT Cognitive flexibility is crucial for adaptation and is disrupted in neuropsychiatric disorders and psychopathology. Human studies of flexibility using reversal learning tasks typically contrast error trials before and after reversal, which provides little information about the mechanisms that support learning and expressing a new response. However, animal studies suggest a specific role in this latter process for the connections between the dorsal striatum and the centromedian parafascicular (CM-Pf) thalamus, a system which may recruit the striatal cholinergic interneurons, but which is not well understood in humans. This study investigated the role of this system in human probabilistic reversal learning, specifically with respect to learning a new response strategy, contrasting its function to that of the better understood orbitoftontal-striatal systems. Using psychophysiological interaction (PPI) analysis of functional magnetic resonance imaging (fMRI) data we show that connectivity between the striatum and both the lateral orbitofrontal cortex (lOFC) and CM-Pf pathways increased during reversal, but not initial learning. However, while the strength of lOFC-striatal connectivity was associated with the speed of the reversal, the strength of CM-Pf-striatal connectivity was associated specifically with the quality of the reversal (reduced regressive errors). These findings expand our understanding of flexibility mechanisms in the human brain, bridging the gap with animal studies of this system.
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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) | 0.001 | 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".