Differential effects on effort discounting induced by inactivations of the nucleus accumbens core or shell.
Bibliographic record
Abstract
The authors investigated the contribution of the nucleus accumbens (NAc) core and shell to effort-based decision making using a discounting procedure. Selection of 1 lever delivered a smaller, 2-pellet reward immediately, whereas the other lever delivered a 4-pellet reward after a fixed ratio of presses (2, 5, 10, or 20) that increased over 4 blocks of 10 discrete choice trials. Subsequent testing employed an equivalent delays procedure, whereby the relative delay to reward delivery after selection of either option was equalized. In well-trained rats, inactivation of the core, but not the shell, via infusion of GABA A/B agonists muscimol/baclofen reduced preference for the high-effort option under standard conditions and also when rats were tested using an equivalent delays procedure. However, inactivation of the core did not alter preference for 4-pellet versus 2-pellet rewards when the relative costs of each option were the same (1 press). Thus, the NAc core, but not the shell, appears to be part of a neural circuit that biases choice toward larger rewards associated with a greater effort cost. Furthermore, the contributions by the NAc core to this form of decision making can be dissociated from its role in delay discounting.
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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) | 0.002 | 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".