Investigating serotonergic contributions to cognitive effort allocation, attention, and impulsive action in female rats
Bibliographic record
Abstract
Background: Individuals must frequently evaluate whether it is worth allocating cognitive effort for desired outcomes. Motivational deficits are a common feature of psychiatric illness such as major depression. Selective serotonin reuptake inhibitors are commonly used to treat this disorder, yet some data suggest these compounds are ineffective at treating amotivation, and may even exacerbate it. Aims: Here we used the rodent Cognitive Effort Task (rCET) to assess serotonergic (5-hydroxytryptamine, 5-HT) contributions to decision-making with cognitive effort costs. Methods: The rCET is a modified version of the 5-choice serial reaction time task, a well-validated test of visuospatial attention and impulse control. At the start of each rCET trial, rats chose one of two levers, which set the difficulty of an attentional challenge, namely the localization of a visual stimulus illuminated for 0.2 or 1 s on hard versus easy trials. Successful completion of hard trials was rewarded with double the sugar pellets. Twenty-four female Long–Evans rats were trained on the rCET and systemically administered the 5-HT 1A agonist 8-OH-DPAT, the 5-HT 2A antagonist M100907, the 5-HT 2C agonist Ro-60-0175, as well as the 5-HT 2C antagonist SB 242, 084. Results: 5-HT 2A antagonism dose-dependently reduced premature responding, while 5-HT 2C antagonism had the opposite effect. 8-OH-DPAT impaired accuracy of target detection at higher doses, while Ro-60-0175 dose-dependently improved accuracy on difficult trials. However, none of the drugs affected the rats’ choice of the harder option. Conclusion: When considered with existing work evaluating decision-making with physical effort costs, it appears that serotonergic signalling plays a minor role in guiding effort allocation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".