Impulsivity, Impulsive and Reflective Processes and the Development of Alcohol Use and Misuse in Adolescents and Young Adults
Bibliographic record
Abstract
This paper contrasts dual-process and personality approaches in the prediction of addictive behaviors and related risk behaviors. In dual-process models, behavior is described as the joint outcome of qualitatively different "impulsive" (or associative) and "reflective" processes. There are important individual differences regarding both types of processes, and the relative strength of both in a specific situation is influenced by prior behavior and state variables (e.g., fatigue, alcohol use). From this perspective, a specific behavior (e.g., alcohol misuse) can be predicted by the combined indices of the behavior-related impulsive processes (e.g., associations with alcohol), and reflective processes, including the ability to refrain from a motivationally salient action. Personality approaches have reported that general traits such as impulsivity predict addictive behaviors. Here we contrast these two approaches, with supplementary analyses on four datasets. We hypothesized that trait impulsivity can predict specific risky behaviors, but that its predictive power disappears once specific behavior-related associations, indicators of executive functioning, and their interaction are entered into the equation. In all four studies the observed interaction between specific associations and executive control (EC) was robust: trait impulsivity did not diminish the prediction of alcohol use by the interaction. Trait impulsivity was not always related to alcohol use, and when it was, the predictive power disappeared after entering the interaction between behavior-specific associations and EC in one study, but not in the other. These findings are interpreted in relation to the validity of the measurements used, which leads to a more refined hypothesis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".