Prefrontal Cortical Inactivations Decrease Willingness to Expend Cognitive Effort on a Rodent Cost/Benefit Decision-Making Task
Bibliographic record
Abstract
Personal success often necessitates expending greater effort for greater reward but, equally important, also requires judicious use of our limited cognitive resources (e.g., attention). Previous animal models have shown that the prelimbic (PL) and infralimbic (IL) regions of the prefrontal cortex (PFC) are not involved in (physical) effort-based choice, whereas human studies have demonstrated PFC contributions to (mental) effort. Here, we utilize the rat Cognitive Effort Task (rCET) to probe PFC's role in effort-based decision making. In the rCET, animals can choose either an easy trial, where the attentional demand is low but the reward (sugar) is small or a difficult trial on which both the attentional demand and reward are greater. Temporary inactivation of PL and IL decreased all animals' willingness to expend mental effort and increased animals' distractibility; PL inactivations more substantially affected performance (i.e., attention), whereas IL inactivations increased motor impulsivity. These data imply that the PFC contributes to attentional resources, and when these resources are diminished, animals shift their choice (via other brain regions) accordingly. Thus, one novel therapeutic approach to deficits in effort expenditure may be to focus on the resources that such decision making requires, rather than the decision-making process per se.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".