Using Virtual Reality to Induce and Assess Objective Correlates of Nicotine Craving: Paradigm Development Study
Bibliographic record
Abstract
BACKGROUND: Craving is a clinically important phenotype for the development and maintenance of nicotine addiction. Virtual reality (VR) paradigms are successful in eliciting cue-induced subjective craving and may even elicit stronger craving than traditional picture-cue methods. However, few studies have leveraged the advances of this technology to improve the assessment of craving. OBJECTIVE: This report details the development of a novel, translatable VR paradigm designed to both elicit nicotine craving and assess multiple eye-related characteristics as potential objective correlates of craving. METHODS: A VR paradigm was developed, which includes three Active scenes with nicotine and tobacco product (NTP) cues present, and three Neutral scenes devoid of NTP cues. A pilot sample (N=31) of NTP users underwent the paradigm and completed subjective measures of nicotine craving, sense of presence in the VR paradigm, and VR-related sickness. Eye-gaze fixation time ("attentional bias") and pupil diameter toward Active versus Neutral cues, as well as spontaneous blink rate during the Active and Neutral scenes, were recorded. RESULTS: The NTP Cue VR paradigm was found to elicit a moderate sense of presence (mean Igroup Presence Questionnaire score 60.05, SD 9.66) and low VR-related sickness (mean Virtual Reality Sickness Questionnaire score 16.25, SD 13.94). Scene-specific effects on attentional bias and pupil diameter were observed, with two of the three Active scenes eliciting greater NTP versus control cue attentional bias and pupil diameter (Cohen d=0.30-0.92). The spontaneous blink rate metrics did not differ across Active and Neutral scenes. CONCLUSIONS: This report outlines the development of the NTP Cue VR paradigm. Our results support the potential of this paradigm as an effective laboratory-based cue-exposure task and provide early evidence of the utility of attentional bias and pupillometry, as measured during VR, as useful markers for nicotine addiction.
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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 | 0.001 | 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 | 0.001 | 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".