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Record W3092736262 · doi:10.1093/scan/nsaa129

Health news sharing is reflected in distributed reward-related brain activity

2020· article· en· W3092736262 on OpenAlexaff
Bruce Doré, Christin Scholz, Elisa C Baek, Emily B. Falk

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill University
FundersNational Cancer InstituteAdvanced Research Projects AgencyDefense Advanced Research Projects AgencyCenter for Tobacco ProductsNational Institutes of Health
KeywordsNeuroimagingPsychologyVentromedial prefrontal cortexNeurosciencePopulationVentral striatumFunctional neuroimagingBrain activity and meditationPrefrontal cortexBrain mappingStriatumCognitive psychologyElectroencephalographyCognitionDopamineMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.342
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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