Examination of the effects of cannabinoid ligands on decision making in a rat gambling task
Bibliographic record
Abstract
Although exposure to delta-9-tetrahydrocannabinol (THC) is perceived to be relatively harmless, mounting evidence has begun to show that it is associated with a variety of cognitive deficits, including poor decision making. THC-induced impairments in decision making are thought to be the result of cannabinoid CB1 receptor activation, and although clinical literature suggests that chronic activation via THC contributes to perturbations in decision making, acute CB1 receptor modulation has yielded mixed results. Using an animal model to examine how CB1-specific ligands impact choice biases would provide significant insight as to how recruitment of the endocannabinoid system may influence decision making. Here, we used the rat gambling task (rGT), a validated analogue of the human Iowa Gambling Task, to assess baseline decision making preferences in male Wistar rats. After acquisition rGT performance was measured. Animals were challenged with the CB1 receptor antagonist rimonabant, the partial agonist THC, and the synthetic agonist WIN55,212-2. Animals were also treated acutely with the fatty acid amide hydrolase (FAAH) inhibitor URB597 to selectively upregulate the endocannabinoid anandamide. Blockade of the CB1 receptor produced a trend improvement in decision making in animals who preferred the advantageous task options, yet left choice unaffected in risk-prone rats. Neither CB1 receptor agonist had strong effects on decision making, but a high dose THC decreased premature responses, whereas WIN55,212-2 did the opposite. URB597 did not affect task performance. These results indicate that although chronic CB1 receptor activation may be associated with impaired decision making, acute modulation has modest effects on choice and instead may play a substantive role in regulating impulsive responding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".