Brain-based Prediction of Message Effectiveness (BB-PRIME)
Integrating data from a large subject sample, we are exploring the causal relationships between message features, message sharing, and behavior change.
In this study, we aim to deliver insight about causal relationships between message features, message sharing and health behavior change, and explore the impact of cross-cultural factors by integrating data from a broad subject sample at Penn, speaheaded by research director Dr. Rebecca Martin and at the University of Amsterdam, where the project is being spearheaded by Dr. Christin Scholz (former CNLab graduate student) and joint postdoc Dr. Hang-Yee Chan.
We will conduct brain imaging experiments manipulating the self and social relevance of health news stories and stories related to climate change, in order to causally map brain activity to behavior change, beliefs, and intentions. We will evaluate whether manipulating framing (e.g, by giving instructions to think about one’s self vs. think about others) before reading each news article snippet affects individuals’ rated self-relevance ratings, sharing intentions, and brain activity. Building on a large mega analysis of neural predictors of message effectiveness, we are refining computational models relating brain activity across studies in multiple domains to message self-relevance and effectiveness, and we will test the predictive abilities of these models in this new sample of brain data, which contains a cross-cultural sample of participants.
- Cosme, D., Scholz, C., Chan, H. Y., Doré, B., Pandey, P., Carreras-Tartak, J., Cooper, N., Paul, A., Burns, S., & Falk, E. B. (2021, May). Message self and social relevance is positively associated with sharing intentions: Correlational evidence from four studies. Annual conference of the International Communication Association, virtual.
- Victoria Romero (CACI International)
- David Koelle (Charles River Analytics)
- Zoe Fischer (CACI International
Phase II STTR 12.A, Topic No. A12A-T009 (SMART II)
Charles Rivers Analytics / Falk (Penn PI)
Award Number 140D0419C0093
Photo Credit (top image): Omaya Torres / insta @omi_art_dump