Changes in the Relative Balance of Approach and Avoidance Inclinations to Use Alcohol Following Cue Exposure Vary in Low and High Risk Drinkers
Bibliographic record
Abstract
According to the ambivalence model of craving, alcohol craving involves the dynamic interplay of separate approach and avoidance inclinations. Cue-elicited increases in approach inclinations are posited to be more likely to result in alcohol consumption and risky drinking behaviours only if unimpeded by restraint inclinations. Current study aims were 1) to test if changes in the net balance between approach and avoidance inclinations following alcohol cue exposure differentiate between low and high risk drinkers, and 2) if this balance is associated with alcohol consumption on a subsequent taste test. In two experiments (N = 60; N = 79), low and high risk social drinkers were exposed to alcohol cues, and pre and post approach and avoidance inclinations measured. An ad libitum alcohol consumption paradigm and a non-alcohol exposure condition were also included in Study 2. Cue-elicited craving was characterised by a predominant approach inclination only in the high risk drinkers. Conversely, approach inclinations were adaptively balanced by equally strong avoidance inclinations when cue-elicited craving was induced in low risk drinkers. For these low risk drinkers with the balanced craving profile, neither approach or avoidance inclinations predicted subsequent alcohol consumption levels during the taste test. Conversely, high risk drinkers, where the approach inclination predominated, each inclination synergistically predicted subsequent drinking levels during the taste test. In conclusion, results support the importance of assessing both approach and avoidance inclinations, and their relative balance following alcohol cue exposure. Specifically, this more comprehensive assessment reveals changes in craving profiles that are not apparent from examining changes in approach inclinations alone, and it is this shift in the net balance that distinguishes high from low risk drinkers.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".