Alcohol-impaired speed and accuracy of cognitive functions: A review of acute tolerance and recovery of cognitive performance.
Bibliographic record
Abstract
Much research on the effects of a dose of alcohol has shown that motor skills recover from impairment as blood alcohol concentrations (BACs) decline and that acute tolerance to alcohol impairment can develop during the course of the dose. Comparable alcohol research on cognitive performance is sparse but has increased with the development of computerized cognitive tasks. This article reviews the results of recent research using these tasks to test the development of acute tolerance in cognitive performance and recovery from impairment during declining BACs. Results show that speed and accuracy do not necessarily agree in detecting cognitive impairment, and this mismatch most frequently occurs during declining BACs. Speed of cognitive performance usually recovers from impairment to drug-free levels during declining BACs, whereas alcohol-increased errors fail to diminish. As a consequence, speed of cognitive processing tends to develop acute tolerance, but no such tendency is shown in accuracy. This "acute protracted error" phenomenon has not previously been documented. The findings pose a challenge to the theory of alcohol tolerance on the basis of physiological adaptation and raise new research questions concerning the independence of speed and accuracy of cognitive processes, as well as hemispheric lateralization of alcohol effects. The occurrence of alcohol-induced protracted cognitive errors long after speed returned to normal is identified as a potential threat to the safety of social drinkers that requires urgent investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".