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

Abstract

While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics.

The Effectiveness of Online Messages for Promoting Smoking Cessation Resources: Predicting Nationwide Campaign Effects From Neural Responses in the EX Campaign

“…However, it will be interesting to examine which specific audience characteristics and individual differences—demographic, psychological, and behavioral—must be matched between the testing samples and the campaign target audience. For instance, it has been shown that brain responses to health risk communication differed between individuals depending on their preexisting level of risk perception (Schmälzle et al, 2013 ) and that responses to anti-drug messages differed between individuals based on their drug use risk (Huskey et al, 2017 ). Thus, while it is common wisdom in the social sciences that samples need to be representative of the population to warrant robust inferences, questions remain whether certain responses to messages are obligatory across all receivers and which individual differences must be taken into account.…”

The limited capacity model of motivated mediated message processing: meta-analytically summarizing two decades of research

“…Psychophysiological measures include heart rate (Clayton, Lang, Leshner, & Quick, 2019;Keene, Clayton, Berke, Loof, & Bolls, 2017), skin conductance (Clayton, Keene, Leshner, Lang, & Bailey, 2020;Wang, Morey, & Srivastava, 2012), and facial electromyography at the corrugator supercilii muscle region (Leshner, Clayton, Bolls, & Bhandari, 2018;Rubenking & Lang, 2014), orbicularis oculi (Bailey, 2015(Bailey, , 2016 and zygomaticus major muscle region (Wang & Lang, 2012;Yegiyan & Bailey, 2016). A small handful of studies have investigated the neural basis of the LC4MP using electroencephalography (Stróżak & Francuz, 2016) or functional magnetic resonance imaging (Huskey, Mangus, Turner, & Weber, 2017;Seelig, et al, 2014); however, too few of these studies exist for conducting a meta-analysis, and therefore these approaches are excluded from further consideration in the present study.…”

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

“…And second, it misses out on the potential power of using neuroimaging to improve health message tailoring. For example, regarding the first shortcoming, research shows that high drug-risk individuals, characterized by high issue involvement, exhibit qualitatively different patterns of brain activation and functional connectivity (suggestive of counter-arguing against putatively highly persuasive messages) than low drug-risk individuals ( Weber et al, 2015a ; Huskey et al, 2017 ). In both cases, ignoring the individual difference dimension of drug-risk would have led to a hybrid pattern of results that likely did not occur in any of these two groups.…”

“…The last decade has seen substantial progress in our understanding of the persuasion network ( Falk et al, 2010 )—that is, the collection of brain regions that are activated while individuals are encountering persuasive messages (for a recent critical review, see Cacioppo et al, 2017 ). Research in this domain has led to a number of advances, including an increasingly resolved map of the network’s putative constituent regions ( Falk et al, 2010 ; Kaye et al, 2016 ), the factors to which they are sensitive ( Falk and Scholz, 2018 ), how they represent persuasive messages ( Pegors et al, 2017 ), their interconnections ( Ramsay et al, 2013 ; Huskey et al, 2017 ; Cooper et al, 2018 ), and their neural similarities in persuasive message processing across audience members ( Imhof et al, 2017 , 2020 ). In addition to shedding light on theoretical debates (e.g., Weber et al, 2015a ), these results in the neural domain have been shown to offer real-world utility by improving predictions of subsequent behavior above and beyond traditional measures (for a review, see Berkman and Falk, 2013 ).…”

“…We expose participants who are at either high- or low-risk for drug use to each of these messages. Importantly, drug-risk has been shown to interact with message characteristics, specifically the interaction between MSV and AS, and result in different behavioral ( Weber et al, 2013 ) and neural ( Weber et al, 2015a ; Huskey et al, 2017 ) responses. Moreover a large body of theoretical research investigating the Elaboration Likelihood Model demonstrates that the interaction between MSV and AS influences persuasive message processing ( Petty and Cacioppo, 1986 ).…”