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

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.

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

“…Consistent with the view that finding value in messages is one key ingredient to success, building on insights from self‐affirmation theory, participants who were first randomized to reflect on their most important core values (versus participants who reflected on unimportant values to them) went on to show greater activity in the vmPFC during health messages, which, in turn, produced greater message‐consistent behavior change . Likewise, participants whose vmPFC activity more strongly represented the negative (risk) consequences of smoking during exposure to graphic warning labels were also more likely to reduce their smoking behavior . Taking a different approach, Chua and colleagues demonstrated that tailoring message content to smokers increased activity in the mPFC, which also predicted message‐consistent behavior change .…”

“…20,49 Likewise, participants whose vmPFC activity more strongly represented the negative (risk) consequences of smoking during exposure to graphic warning labels were also more likely to reduce their smoking behavior. 20,49,66 Taking a different approach, Chua and colleagues demonstrated that tailoring message content to smokers increased activity in the mPFC, which also predicted message-consistent behavior change. 48 All of these studies are consistent with the idea that finding personal relevance in messages may be one key factor in determining message value, and subsequent behavior change.…”

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

“…This suggests that careful work is needed to identify and tailor ( Noar et al , 2007 ) messages for specific and narrowly defined target audiences. This point is particularly important given that recent evidence demonstrates that individual differences in message encoding and social network structure predict persuasive message outcomes ( Pegors et al , 2017 ). In the past, traditional message channels (e.g.…”

A Guide To Representational Similarity Analysis for Social Neuroscience

“…The correlations from the neural RDM and each of the model RDMs were inputted into a regression analysis to predict change in frequency of smoking. It was found that the more health information was represented in the MPFC, the more likely participants were to reduce smoking (as indexed by self-report, ∼38 days later) (Pegors et al, 2017). Future extensions of this work could look to see if neural data predict behavior at longer time frames, which is more relevant for public health, and also use direct measures of smoking cessation to improve model prediction.…”