Δ9-Tetrahydrocannabinol decreases willingness to exert cognitive effort in male rats
Bibliographic record
Abstract
BACKGROUND: -Tetrahydrocannabinol (THC) is the main psychoactive ingredient in cannabis, yet other phytocannabinoids in the plant, such as cannabidiol (CBD), have unique properties. Given that CBD can modulate the undesirable effects of THC, therapeutic agents, such as nabiximols, contain higher CBD:THC ratios than illicit marijuana. We tested the hypothesis that THC impairs a relevant cognitive function for long-term success, namely willingness to exert cognitive effort for greater rewards, and that CBD could attenuate such decision-making impairments. METHODS: = 29) performing the rat cognitive effort task (rCET) received acute THC and CBD, independently and concurrently, in addition to other cannabinoids. Rats chose between 2 options differing in reward magnitude, but also in the cognitive effort (attentional load) required to obtain them. RESULTS: We found that THC decreased choice of hard trials without impairing the animals' ability to accurately complete them. Strikingly, this impairment was correlated with CB1 receptor density in the medial prefrontal cortex - an area previously implicated in effortful decision-making. In contrast, CBD did not affect choice. Coadministration of 1:1 CBD:THC matching that in nabiximols modestly attenuated the deleterious effects of THC in "slacker" rats. LIMITATIONS: Only male rats were investigated, and the THC/CBD coadministration experiment was carried out in a subset of individuals. CONCLUSION: These findings confirm that THC, but not CBD, selectively impairs decision-making involving cognitive effort costs. However, coadministration of CBD only partially ameliorates such THC-induced dysfunction.
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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) | 0.001 | 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 | 0.001 |
| 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".