Health news sharing is reflected in distributed reward-related brain activity
Bibliographic record
Abstract
Neuroimaging has identified individual brain regions, but not yet whole-brain patterns, that correlate with the population impact of health messaging. We used neuroimaging to measure whole-brain responses to health news articles across two studies. Beyond activity in core reward value-related regions (ventral striatum, ventromedial prefrontal cortex), our approach leveraged whole-brain responses to each article, quantifying expression of a distributed pattern meta-analytically associated with reward valuation. The results indicated that expression of this whole-brain pattern was associated with population-level sharing of these articles beyond previously identified brain regions and self-report variables. Further, the efficacy of the meta-analytic pattern was not reducible to patterns within core reward value-related regions but rather depended on larger-scale patterns. Overall, this work shows that a reward-related pattern of whole-brain activity is related to health information sharing, advancing neuroscience models of the mechanisms underlying the spread of health information through a population.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".