Pharmacological evidence of a cholinergic contribution to elevated impulsivity and risky decision-making caused by adding win-paired cues to a rat gambling task
Bibliographic record
Abstract
BACKGROUND: Pairing rewards with sensory stimulation, in the form of auditory and visual cues, increases risky decision-making in both rats and humans. Understanding the neurobiological basis of this effect could help explain why electronic gambling machines are so addictive, and inform treatment development for compulsive gambling and gaming. Numerous studies implicate the dopamine system in mediating the motivational influence of reward-paired cues; recent data suggest the cholinergic system also plays a critical role. Previous work also indicates that cholinergic drugs alter decision-making under uncertainty. AIMS: We investigated whether the addition of reward-concurrent cues to the rat gambling task (crGT) altered the effects of peripherally administered cholinergic compounds. METHODS: Muscarinic and nicotinic agonists and antagonists were administered to 16 male, Long-Evans rats trained on the crGT. Measures of optimal/risky decision-making and motor impulsivity were the main dependent variables of interest. RESULTS: The muscarinic receptor antagonist scopolamine improved decision-making overall, decreasing selection of one of the risky options while increasing choice of the more advantageous options. The muscarinic agonist oxotremorine increased choice latency but did not significantly affect option preference. Neither the nicotinic antagonist mecamylamine nor the agonist nicotine affected choice patterns, but mecamylamine decreased premature responding, an index of motor impulsivity. CONCLUSIONS: These results contrast sharply from those obtained previously using the uncued rGT, and suggest that the deleterious effects of win-paired cues on decision-making and impulse control may result from elevated cholinergic tone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.002 |
| 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".