Maintaining Intentional Control of Behavior Under Alcohol
Bibliographic record
Abstract
BACKGROUND: This research used a process dissociation paradigm to measure the influence of controlled and automatic processes on a word-stem completion task when correct performance under alcohol was positively reinforced, or had no particular consequence. It was predicted that the impairing effect of alcohol on controlled processes that govern intentional control of behavior would be resisted when drinkers were reinforced for performing well. METHODS: Four groups of eight male drug-free social drinkers initially studied a list of words. Two of the groups then received 0.56 g/kg alcohol (A) and two received a placebo (P) before the stem-completion task was performed. During the task, the correct responses of one pair of A and P groups were reinforced (money and verbal approval) whereas no reinforcement was provided to the other pair. RESULTS: As predicted, under alcohol, the influence of controlled processes that govern intentional responses was greater when reinforcement was provided than when it was absent (p = 0.005). Without reinforcement, controlled processes in the A group were lower than the P control group (p = 0.01). In contrast, the A and P groups that received reinforcement did not differ (p = 0.142). Controlled processes in the P groups were not affected by reinforcement (p = 0.65). In addition, the influence of automatic processes was not affected by alcohol or by reinforcement (p > 0.781). CONCLUSIONS: Positive reinforcement for behavior under alcohol increases the influence of controlled processes. These results suggest that the degree to which intentional control is retained under alcohol depends on the consequence of behavior in the situation. It seems that controlled processes enable drinkers to intentionally display the behavior that is rewarded.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".