The Imbalance of Wanting and Liking Contributes to a Bias of Internal Attention Towards Positive Consequences of Tobacco Smoking
Bibliographic record
Abstract
Abstract Previous studies have shown that addiction is associated with an attentional bias towards external stimuli. However, it is currently unclear whether this bias extends to internal attention. The aim of the present study was to address this question within the Incentive Sensitization theory framework. To this end, structural equation models delineating the relationships between nicotine dependence, the imbalance of wanting and liking (WmL), personal relevance of smoking consequences, and antismoking intention were tested using online survey data of 826 tobacco users. Consistent with previous findings, WmL was disrupted with increasing nicotine dependence. The key finding was that a moderate positive correlation was observed between WmL and personal relevance of positive consequences, which suggests that dependence-related attentional bias might not only relate to the processing of external stimuli but also to what an individual considers important, which is linked to the distribution of internal attention. However, such attentional bias might not apply to all smokers to the same extent, based on the comparison of latent profiles of smokers. The findings indicate that the bias of internal attention may play a significant role in both the initiation of smoking cessation, as well as in the likelihood of relapse. This suggests that including a more diverse array of topics in health communication could be beneficial, given the varying emphasis on smoking consequences among different profiles.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".