Thalamic-Prefrontal Cortical-Ventral Striatal Circuitry Mediates Dissociable Components of Strategy Set Shifting
Bibliographic record
Abstract
The mediodorsal nuclei of thalamus (MD), prefrontal cortex (PFC), and nucleus accumbens core (NAc) form an interconnected network that may work together to subserve certain forms of behavioral flexibility. The present study investigated the functional interactions between these regions during performance of a cross-maze-based strategy set-shifting task. In Experiment 1, reversible bilateral inactivation of the MD via infusions of bupivacaine did not impair simple discrimination learning, but did disrupt shifting from response to visual cue discrimination strategy, and vice versa. This impairment was due to an increase in perseverative errors. In Experiment 2, asymmetrical disconnection inactivations of the MD on one side of the brain and PFC on the other also caused a perseverative deficit when rats were required to shift from a response to a visual cue discrimination strategy, as did disconnections between the PFC and the NAc. However, inactivation of the MD on one side of the brain and the NAc contralaterally resulted in a selective increase in never-reinforced errors, suggesting this pathway is important for eliminating inappropriate strategies during set shifting. These data indicate that set shifting is mediated by a distributed neural circuit, with separate neural pathways contributing dissociable components to this type of behavioral flexibility.
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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".