Literature DB >> 28578131

Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

Teresa K Pegors1, Steven Tompson2, Matthew Brook O'Donnell3, Emily B Falk4.   

Abstract

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  FMRI; Health behavior; Motivation; Multivariate analyses; RSA; Smoking

Mesh:

Year:  2017        PMID: 28578131      PMCID: PMC5821423          DOI: 10.1016/j.neuroimage.2017.05.063

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  6 in total

1.  Associations between coherent neural activity in the brain's value system during antismoking messages and reductions in smoking.

Authors:  Nicole Cooper; Steven Tompson; Matthew B O'Donnell; Jean M Vettel; Danielle S Bassett; Emily B Falk
Journal:  Health Psychol       Date:  2018-02-15       Impact factor: 4.267

2.  The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.

Authors:  Richard Huskey; J Michael Mangus; Benjamin O Turner; René Weber
Journal:  Soc Cogn Affect Neurosci       Date:  2017-12-01       Impact factor: 3.436

3.  A Guide to Representational Similarity Analysis for Social Neuroscience.

Authors:  Haroon Popal; Yin Wang; Ingrid R Olson
Journal:  Soc Cogn Affect Neurosci       Date:  2019-11-01       Impact factor: 3.436

4.  Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns.

Authors:  Richard Huskey; Benjamin O Turner; René Weber
Journal:  Front Hum Neurosci       Date:  2020-12-03       Impact factor: 3.169

5.  Exploring physiologic reactions to persuasive information.

Authors:  Hanne A A Spelt; Luisa Asta; Els T Kersten-van Dijk; Jaap Ham; Wijnand A IJsselsteijn; Joyce H D M Westerink
Journal:  Psychophysiology       Date:  2022-01-23       Impact factor: 4.348

Review 6.  Neuroimaging, neuromodulation, and population health: the neuroscience of chronic disease prevention.

Authors:  Peter A Hall; Warren K Bickel; Kirk I Erickson; Dylan D Wagner
Journal:  Ann N Y Acad Sci       Date:  2018-06-04       Impact factor: 5.691

  6 in total

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