Neural biomarkers for assessing different types of imagery in pictorial health warning labels for cigarette packaging: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Countries around the world have increasingly adopted pictorial health warning labels (HWLs) for tobacco packages to warn consumers about smoking-related risks. Research on how pictorial HWLs work has primarily analysed self-reported responses to HWLs; studies at the neural level comparing the brain's response to different types of HWLs may provide an important complement to prior studies, especially if self-reported responses are systematically biased. In this study we characterise the brain's response to three types of pictorial HWLs for which prior self-report studies indicated different levels of efficacy. METHODS: Current smokers rated pictorial HWLs and then observed the same HWLs during functional MRI (fMRI) scanning. Fifty 18-50-year-old current adult smokers who were free from neurological disorders were recruited from the general population and participated in the study. Demographics, smoking-related behaviours and self-reported ratings of pictorial HWL stimuli were obtained prior to scanning. Brain responses to HWLs were assessed using fMRI, focusing on a priori regions of interest. RESULTS: Pictorial HWL stimuli elicited activation in a broad network of brain areas associated with visual processing and emotion. Participants who rated the stimuli as more emotionally arousing also showed greater neural responses at these sites. CONCLUSIONS: Self-reported ratings of pictorial HWLs are correlated with neural responses in brain areas associated with visual and emotional processing. Study results cross-validate self-reported ratings of pictorial HWLs and provide insights into how pictorial HWLs are processed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".