High-THC <i>Cannabis</i> smoke impairs working memory capacity in spontaneous tests of novelty preference for objects and odors in rats
Bibliographic record
Abstract
Abstract Working memory (WM) is an executive function that orchestrates the use of a limited amount of information, referred to as working memory capacity (WMC), in cognitive functions. In humans, Cannabis exposure impairs WM; however, it is unclear if Cannabis facilitates or impairs rodent WM. Existing literature also fails to address the effects of Cannabis exposure on rodent WMC using exposure paradigms that closely mirror patterns of human use. In the present study, WMC of rats was inferred by novelty preference after a short delay in spontaneous recognition-based tests. Either object or odor-based stimuli were used in different variations of the tests that present identical (IOT) and different (DOT) sets of stimuli (3 or 6) for low-and high-cognitive loads, respectively. Additionally, we present a human-machine hybrid (HYB) behavioral quantification approach which supplements stopwatch-based scoring with supervised machine learning (SML)-based classification, enabling behavioral data to be made publicly available. After validating the spontaneous tests, 6-item IOT and DOT tests with the HYB method were used to evaluate the impact of acute exposure to high-THC or high-CBD Cannabis smoke on novelty preference. Under control conditions, rats showed novelty preference in all test variations. We found that high-THC, but not high-CBD, Cannabis smoke exposure impaired novelty preference for objects under a high-cognitive load. Odor-based recognition deficits were seen under both low-, and high-cognitive loads only following high-THC smoke exposure. Ultimately, these data show that Cannabis smoke exposure impacts novelty preference in a load-dependent, and stimuli-specific manner. Significance Statement Working memory (WM) capacity is the limited amount of information that can be utilized by WM to orchestrate processes like learning and memory. Using object-and odor-based spontaneous recognition tests, the impact of high-THC or high-CBD Cannabis smoke on novelty preference was evaluated. Behavioral measurements were generated using a combination of open-source analysis software and traditional stopwatch scoring to form a human-machine hybrid (HYB) scoring method. We show novelty preference deficits under high-cognitive loads in object-based tests, while impacting novelty preference under both high-and low-cognitive loads in the odor-based tests. Ultimately, we show that Cannabis smoke exposure affects cognitive functions that underly WM in rats, which has broad implications for human use.
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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 | 0.001 |
| 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 | 0.002 |
| 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".