Shifting evaluative construal: Common and distinct neural components of moral, pragmatic, and hedonic evaluations.

People generate evaluations of different attitude objects based on their goals and aspects of the social context. Prior research suggests that people can shift between at least three types of evaluations to judge whether something is good or bad: (how costly or beneficial it is), (whether it is aligned with moral norms), and (whether it feels good; Van Bavel et al., 2012).

Political polarization and health.

In addition to social determinants of health, such as economic resources, education, access to care and various environmental factors, there is growing evidence that political polarization poses a substantial risk to individual and collective well-being. Here we review the impact of political polarization on public health. We describe the different forms of polarization and how they are connected to health outcomes, highlighting the COVID-19 pandemic as a case study of the health risks of polarization.

Morality in the anthropocene: The perversion of compassion and punishment in the online world.

Although much of human morality evolved in an environment of small group living, almost 6 billion people use the internet in the modern era. We argue that the technological transformation has created an entirely new ecosystem that is often mismatched with our evolved adaptations for social living. We discuss how evolved responses to moral transgressions, such as compassion for victims of transgressions and punishment of transgressors, are disrupted by two main features of the online context.

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems.

Preregistered Replication and Extension of "Moral Hypocrisy: Social Groups and the Flexibility of Virtue".

The tendency for people to consider themselves morally good while behaving selfishly is known as moral hypocrisy. Influential work by Valdesolo and DeSteno (2007) found evidence for intergroup moral hypocrisy such that people were more forgiving of transgressions when they were committed by an in-group member than an out-group member. We conducted two experiments to examine moral hypocrisy and group membership in an online paradigm with Prolific workers from the United States: a direct replication of the original work with minimal groups ( = 610; nationally representative) and a conceptual replication with political groups ( = 606; 50% Democrats and 50% Republicans).

Changing the incentive structure of social media may reduce online proxy failure and proliferation of negativity.

Social media takes advantage of people's predisposition to attend to threatening stimuli by promoting content in algorithms that capture attention. However, this content is often not what people expressly state they would like to see. We propose that social media companies should weigh users' expressed preferences more heavily in algorithms.

The differential impact of climate interventions along the political divide in 60 countries.

A major barrier to climate change mitigation is the political polarization of climate change beliefs. In a global experiment conducted in 60 countries (N = 51,224), we assess the differential impact of eleven climate interventions across the ideological divide. At baseline, we find political polarization of climate change beliefs and policy support globally, with people who reported being liberal believing and supporting climate policy more than those who reported being conservative (Cohen's d = 0.

On the Efficacy of Accuracy Prompts Across Partisan Lines: An Adversarial Collaboration.

The spread of misinformation is a pressing societal challenge. Prior work shows that shifting attention to accuracy increases the quality of people's news-sharing decisions. However, researchers disagree on whether accuracy-prompt interventions work for U.

Updating the identity-based model of belief: From false belief to the spread of misinformation.

The spread of misinformation threatens democratic societies, hampering informed decision-making. Partisan identity biases perceptions of reality, promoting false beliefs. The Identity-based Model of Political Belief explains how social identity shapes information processing and contributes to misinformation.

The count: an identity-based intervention to counter partisan misinformation sharing.

Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US ( = 1709) and UK ( = 804), we found that crowdsourcing accuracy judgements by adding a count (next to the count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition).

Social Media and Morality.

Nearly five billion people around the world now use social media, and this number continues to grow. One of the primary goals of social media platforms is to capture and monetize human attention. One means by which individuals and groups can capture attention and drive engagement on these platforms is by sharing morally and emotionally evocative content.

The Costs of Polarizing a Pandemic: Antecedents, Consequences, and Lessons.

Polarization has been rising in the United States of America for the past few decades and now poses a significant-and growing-public-health risk. One of the signature features of the American response to the COVID-19 pandemic has been the degree to which perceptions of risk and willingness to follow public-health recommendations have been politically polarized. Although COVID-19 has proven more lethal than any war or public-health crisis in American history, the deadly consequences of the pandemic were exacerbated by polarization.

People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content.

Recent studies have documented the type of content that is most likely to spread widely, or go "viral," on social media, yet little is known about people's perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.

The Best-Case Heuristic: Relative Optimism in Relationships, Politics, and a Global Health Pandemic.

In four experiments covering three different life domains, participants made future predictions in what they considered the most realistic scenario, an optimistic best-case scenario, or a pessimistic worst-case scenario ( = 2,900 Americans). Consistent with a , participants made "realistic" predictions that were much closer to their best-case scenario than to their worst-case scenario. We found the same best-case asymmetry in health-related predictions during the COVID-19 pandemic, for romantic relationships, and a future presidential election.

Individual-level solutions may support system-level change â if they are internalized as part of one's social identity.

System-level change is crucial for solving society's most pressing problems. However, individual-level interventions may be useful for creating behavioral change before system-level change is in place and for increasing necessary public support for system-level solutions. Participating in individual-level solutions may increase support for system-level solutions - especially if the individual-level solutions are internalized as part of one's social identity.

The role of political devotion in sharing partisan misinformation and resistance to fact-checking.

Online misinformation is disproportionality created and spread by people with extreme political attitudes, especially among the far-right. There is a debate in the literature about why people spread misinformation and what should be done about it. According to the purely cognitive account, people largely spread misinformation because they are lazy, not biased.

Negativity drives online news consumption.

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743).