Neurophysiological functioning of occasional and heavy cannabis users during THC intoxication
Bibliographic record
Abstract
RATIONALE: Experienced cannabis users demonstrate tolerance to some of the impairing acute effects of cannabis. OBJECTIVES: The present study investigates whether event-related potentials (ERPs) differ between occasional and heavy cannabis users after acute Δ9-tetrahydrocannabinol (THC) administration, as a result of tolerance. METHODS: Twelve occasional and 12 heavy cannabis users participated in a double-blind, placebo-controlled, crossover study. On two separate days, they smoked a joint containing 0 or 500 μg/kg body weight THC. ERPs were measured while subjects performed a divided attention task (DAT) and stop signal task (SST). RESULTS: In the DAT, THC significantly decreased P100 amplitude in occasional but not in heavy cannabis users. P300 amplitude in the DAT was significantly decreased by THC in both groups. The N200 peak in the SST was not affected by treatment in neither of the groups. Performance in the SST was impaired in both groups after THC treatment, whereas performance in the DAT was impaired by THC only in the occasional users group. CONCLUSIONS: The present study confirms that heavy cannabis users develop tolerance to some of the impairing behavioral effects of cannabis. This tolerance was also evident in the underlying ERPs, suggesting that tolerance demonstrated on performance level is not (completely) due to behavioral compensation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".