Bibliographic record
Abstract
The ACC is known to play a crucial role in the fast adaptations of behavior based on immediate reward values. What is less certain is whether the ACC is also involved in long-term adaptations to situations with uncertain outcomes. To study this issue, we placed macaque monkeys in a probabilistic context in which the appropriate strategy to maximize reward was to identify the stimulus with the highest reward value (optimal stimulus). Only knowledge of the theoretical average reward value associated with this stimulus,-referred to as “the task value”-, was available. Remarkably, in each trial, ACC pre-reward activity correlated with the task value. Importantly, this neuronal activity was observed prior to the discovery of the optimal stimulus. We hypothesize that the received rewards and the task-value, constructed a priori through learning, are used to guide behavior and identify the optimal stimulus. We tested this hypothesis by muscimol deactivation of the ACC. As predicted, this inactivation impaired the search for the optimal stimulus. We propose that ACC participates in long-term adaptation of voluntary reward-based behaviors by encoding general task values and received rewards.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.748 | 0.527 |
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".