Reactivity to Smoking Cues in a Social Context: Virtual Reality Experiment
Bibliographic record
Abstract
Background: Social contextual factors influence the onset and maintenance of substance abuse. Virtual reality (VR) provides a standardized method to present social stimuli and is increasingly used in addiction research. Objective: This study examines the influence of a smoking versus a nonsmoking agent in VR on craving in nicotine-dependent male participants. Our primary hypothesis was that the interaction with a smoking agent is associated with increased craving compared to a nonsmoking agent. We expected higher craving in the presence of an agent regardless of the agent's smoking status. Methods: Using a head-mounted display (Oculus Rift), 50 nicotine-dependent smokers were exposed to four VR conditions on a virtual marketplace: first without an agent, second and third with an agent who either smoked or did not smoke in randomized order, and fourth without an agent as a follow-up condition. Before the follow-up condition, participants smoked a cigarette. Craving was assessed with the Questionnaire of Smoking Urges and a visual analog scale within VR and after each session. We also examined anxiety and agitation (visual analog scale), immersion and presence with the igroup Presence Questionnaire, and salivary cortisol levels. Results: Results showed no significant difference in the participants' craving, anxiety, or agitation between the smoking and nonsmoking agent conditions. However, craving, anxiety, and agitation increased from the marketplace without an interacting agent to the conditions with an interacting agent, and decreased after smoking a cigarette. Immersion was low in all conditions and decreased over time. Salivary cortisol levels were highest at baseline and decreased over the course of the experiment. Conclusions: These findings suggest that the presence of an agent (as a contextual factor) may override the specific influence of proximal stimuli (burning cigarette). The low immersion highlights the challenges in developing effective VR environments for cue exposure.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".