Joint Effects of Impulsive and Self-Regulatory Process on Gambling Frequency
Bibliographic record
Abstract
Dual process models propose that behaviour is influenced by the interactive effect of impulsive (i.e., automatic or implicit) and self-regulatory (i.e., controlled or explicit) processes. Recently, evidence from the alcohol literature demonstrates that the impulse to engage in risky behaviour is mitigated by a high capacity to self-regulate. The current study aimed to extend this model to behavioural addictions, namely frequent gambling behaviour. It was hypothesized that impulsive processes favouring gambling (positive implicit gambling cognition) would predict frequent gambling, but only if the capacity to self-regulate was low. A treatment-seeking sample of 57 adults with problem gambling (Mage = 45.20 years, 54% men) completed two Single Category Implicit Association Tests, one reflecting tension-reduction, and the other enhancement, implicit gambling cognition. Participants also completed self-report measures of past week gambling frequency and the Gambling Abstinence Self-Efficacy Scale, which provided a measure of the self-regulatory capacity to abstain from gambling when emotionally aroused. Controlling for age and gender, consistent with hypotheses, implicit tension reduction gambling cognition positively predicted gambling frequency at low (p = .046) but not at high (p =.191) self-efficacy for gambling abstinence when feeling emotionally bad. However, self-efficacy for gambling abstinence when feeling emotionally good was not supported as a moderator of the effect of implicit enhancement gambling cognition on gambling frequency. Results suggest that the cognitions inherent in the impulsive process leading to frequent gambling are tension reduction or escape-related. Furthermore, emotionally relevant nuances to the ability to self-regulate gambling do exist; these nuances may contribute to both risk model specificity and interventions.RésuméLes modèles à doubles processus indiquent que le comportement est influencé par l’effet interactif de processus impulsifs (c’est-à-dire, automatiques ou implicites) et autorégulateurs (c’est-à-dire contrôlés ou explicites). Récemment, des preuves tirées de la littérature sur l’alcool démontrent que l’impulsion à s’engager dans des comportements à risque est contrebalancée par une forte capacité d’autorégulation. La présente étude visait à étendre ce modèle aux dépendances comportementales, à savoir le comportement de jeu fréquent. On a émis l’hypothèse que des processus impulsifs favorisant le jeu (cognition de jeu implicite positive) pourraient laisser présager un jeu fréquent, mais seulement si la capacité d’autorégulation était faible. Un échantillon de 57 adultes aux prises avec problème de jeu compulsif (moyenne = 45,20 ans, 54 % d’hommes) a effectué deux tests d’association implicites à catégorie unique, l’un reflétant la réduction de tension et l’autre, la cognition de jeu implicite. Les participants ont également rempli des une auto-évaluation de la fréquence de jeu de la semaine qui précédait et l’échelle d’auto-évaluation de l’abstinence (Gambling Abstinence Self-Efficacy Scale), qui mesure la capacité de s’abstenir de jouer lorsque l’émotion est forte. En contrôlant l’âge et le sexe, et à la lumière des hypothèses, la cognition de jeu implicite prédit positivement une faible auto-efficacité liée à la fréquence de jeu (p = 0,046) et non pas une haute auto-efficacité (p = 0,191) pour ce qui est de l’abstinence au jeu en situation d’émotion négative. Cependant, l’auto-régulation pour s’abstenir de jouer lorsqu’on se sent émotionnellement bien n’était pas considérée comme un modérateur de l’effet de la cognition de jeu implicite sur la fréquence de jeu. Les résultats suggèrent que les cognitions inhérentes au processus impulsif conduisant à un jeu fréquent sont la réduction de tension ou l’évasion. De plus, il existe des nuances pertinentes sur le plan émotionnel à la capacité d’auto-réglementer le jeu; celles-ci peuvent contribuer à la fois à la spécificité du modèle de risque et aux interventions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".