Affective Priming of Alcohol Schema in Coping and Enhancement Motivated Drinkers
Bibliographic record
Abstract
This study investigated whether Coping Motivated and Enhancement Motivated drinkers differ in their degree of semantic activation of alcohol concepts on a primed Stroop colour-naming task following exposure to negative and positive affect primes, respectively. Participants were 48 undergraduates (not necessarily problem drinkers) who had elevated scores on the Coping Motivated or Enhancement Motivated subscale of the Drinking Motives Questionnaire - Revised. The Stroop task involved negative, positive and neutral primes that preceded alcohol and no-alcohol target words. As hypothesized, Coping Motivated drinkers showed semantic activation of alcohol concepts following exposure to negative but not neutral primes. Also as hypothesized, Enhancement Motivated drinkers showed semantic activation of alcohol concepts following exposure to positive but not negative primes. Unexpectedly, semantic activation of alcohol concepts was also observed among Coping Motivated drinkers following positive primes and among Enhancement Motivated drinkers following neutral primes. Theoretical implications are discussed, as are implications for improving cognitive behavioral interventions for problem drinkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".