Exposure to attachment figure cue reduces cigarette craving.
Bibliographic record
Abstract
Cue-induced craving among smokers is a well-documented phenomenon. Conditioning theory is used to explain how stimuli elicit cravings. Attachment theory may explain additional influences on cue-induced craving. This study tested the effects of cigarette cues and attachment figure cues on craving and affect. Cue-reactivity as a function of cue type was examined to explore the magnitude of craving effect sizes for each cue type. Thirty-six smokers (27 men) were exposed to cigarette cues (i.e., cigarette) and attachment figure cues (i.e., digital photos of people identified as attachment figures). Neutral to cue exposure differences in craving and affect were assessed. Sixty-one percent of participants reported increased craving to the cigarette cue (M = 61.1, SD = 29.4) compared to the neutral cue (M = 46.6, SD = 28.8), Mdiff = 20.7 (10.60, 30.73), Yt (23) = 4.24, p = .0003. When exposed to an attachment figure photo, 56% participants reported decreased craving (M = 38.2, SD = 31.6) compared to the neutral photo (M = 50.9, SD = 29.8), Mdiff = -15.73 (-30.83, -0.63), Yt (21) = -2.17, p = .042. There was a significant difference in the craving for cigarette cues (M = 14.5, SD = 22.8 (CI: 6.94, 22.39)), and attachment figure photos (M = -12.7, SD = 28.6 (CI: -22.38, -3.01); t(35) = 4.3, p < .001). The effect sizes for cigarette cues and attachment figure photos were d = .50, d = .42, respectively. Participants' responses to attachment cues were not influenced by their reactivity to cigarette cues or their attachment figure's smoking status. Findings support a plausible model in which an attachment figure photo decreases craving via affect regulation. Further research on cues that have an inhibitory effect on craving may inform new cessation strategies. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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) | 0.003 | 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".