Preferential Involvement by Nucleus Accumbens Shell in Mediating Probabilistic Learning and Reversal Shifts
Bibliographic record
Abstract
Different subregions of nucleus accumbens (NAc) have been implicated in reward seeking, promoting flexible approach responses, suppressing nonrewarded actions, and facilitating shifts between different discrimination strategies. Interestingly, the NAc does not appear to mediate shifting between stimulus-reward associations (i.e., reversal learning) when reinforcement is predictable. How these nuclei may facilitate flexible response strategies when reward delivery is uncertain remains unclear. We investigated the effects of inactivation of the NAc shell and core on probabilistic reversal learning using an operant task wherein a "correct" response delivered reward on 80% of trials, and an "incorrect" response was reinforced on 20% of trials. Reinforcement contingencies were reversed repeatedly within a session. In well-trained rats, shell inactivation reduced the number of reversals completed and selectively reduced win-stay behavior. This impairment was apparent during the first discrimination, indicating a more general deficit in the use of probabilistic reward feedback to guide action selection. Shell inactivation also impaired reversal performance on a similar task where correct/incorrect choices always/never delivered reward. However, this impairment only emerged after both levers had been associated with reward. Inactivation of NAc core did not impair reversal performance but increased latencies to approach the response levers. These results suggest the NAc shell and core facilitate reward seeking in a distinct yet complementary manner when the relationship between specific actions and reward is uncertain or ambiguous and cognitive flexibility is required. The core promotes approach toward reward-associated stimuli, whereas the shell refines response selection to those specific actions more likely to yield reward.
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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) | 0.001 | 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".