The COVID-19 pandemic may be one of the greatest modern societal challenges that requires widespread collective action and cooperation. While a handful of actions can help reduce pathogen transmission, one critical behavior is to self-isolate. Public health messages often use persuasive language to change attitudes and behaviors, which can evoke a wide range of negative and positive emotional responses. In a U.S. representative sample ( N = 955), we presented two messages that leveraged either threatening or prosocial persuasive language, and measured self-reported emotional reactions and willingness to self-isolate. Although emotional responses to the interventions were highly heterogeneous, personality traits known to be linked with distinct emotional experiences (extraversion and neuroticism) explained significant variance in the arousal response. While results show that both types of appeals increased willingness to self-isolate (Cohen's d = 0.41), compared to the threat message, the efficacy of the prosocial message was more dependent on the magnitude of the evoked emotional response on both arousal and valence dimensions. Together, these results imply that prosocial appeals have the potential to be associated with greater compliance if they evoke highly positive emotional responses.
Faces impart exhaustive information about their bearers, and are widely used as stimuli in psychological research. Yet many extant facial stimulus sets have substantially less detail than faces encountered in real life. In this paper, we describe a new database of facial stimuli, the Multi-Racial Mega-Resolution database (MR2). The MR2 includes 74 extremely high resolution images of European, African, and East Asian faces. This database provides a high-quality, diverse, naturalistic, and well-controlled facial image set for use in research. The MR2 is available under a Creative Commons license, and may be accessed online.
The COVID-19 pandemic may be one of the greatest modern societal challenges that requires widespread collective action and cooperation. While a handful of actions can help reduce pathogen transmission, one critical behavior is to self-isolate. Public health messages often use persuasive language to change attitudes and behaviors, which can evoke a wide range of negative and positive emotional responses. In a U.S. representative sample (N = 955), we presented two messages that leveraged either threatening or prosocial persuasive language, and measured self-reported emotional reactions and willingness to self-isolate. Although emotional responses to the interventions were highly heterogeneous, personality traits known to be linked with distinct emotional experiences (extraversion and neuroticism) explained significant variance in the arousal response. While results show that both types of appeals increased willingness to self-isolate (Cohen’s d = .41), compared to the threat message, the efficacy of the prosocial message was more dependent on the magnitude of the evoked emotional response on both arousal and valence dimensions. Together, these results imply that prosocial appeals have the potential to be associated with greater compliance if they evoke highly positive emotional responses.
People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.