CAN ALCOHOL LEAD TO INHIBITION OR DISINHIBITION? APPLYING ALCOHOL MYOPIA TO ANIMAL EXPERIMENTATION
Bibliographic record
Abstract
AIMS: Animal experimentation often demonstrates that alcohol leads to disinhibited behaviour, such as increased aggression, increased social behaviour, or increased impulsivity. However, human experimentation demonstrates that alcohol can have either disinhibiting or inhibiting effects on behaviour, depending on salient environmental cues. Our aim was to illustrate how alcohol myopia theory could be applied to the literature assessing the effects of alcohol on behaviour in animals. METHODS: The effects of alcohol on animal behaviour were reviewed in several domains, including aggression, social behaviours, and impulsivity. Suggestions for testing alcohol myopia with animal research paradigms were provided. RESULTS: Current animal research paradigms are often designed in such a way that alcohol myopia cannot be tested. To test alcohol myopia, we recommend manipulating the salience of both impelling and inhibiting environmental cues. CONCLUSIONS: Disinhibition alone cannot explain alcohol's effects on behaviour. We contend that alcohol myopia theory helps to explain some contradictory findings in the human and animal literature. We encourage animal researchers to develop research paradigms to provide tests of alcohol myopia.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".