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Political segregation is an important social problem, increasing polarization and impeding effective governance. Previous work has viewed the central driver of segregation to be political homophily, the tendency to associate with others who have similar views. Here we propose that, in addition to homophily, people's social tie decisions are driven by political acrophily, the tendency to associate with others who have more extreme political views (rather than more moderate). We examined this using a paradigm in which participants share emotions and attitudes on political policies, observe others' responses and choose which others to affiliate with. In four studies (N = 1,235), both liberal and conservative participants' social tie decisions reflected the presence of acrophily. We found that participants who viewed peers who expressed more extreme views as more prototypical of their political group also tended to engage in greater acrophily. These studies identify a previously overlooked tendency in tie formation.

There is widespread concern that rising affective polarization – particularly dislike for outpartisans – exacerbates Americans’ anti-democratic attitudes. Accordingly, scholars and practitioners alike have invested great effort in developing depolarization interventions that reduce affective polarization. Critically, however, it remains unclear whether these interventions reduce anti-democratic attitudes, or only change sentiments toward outpartisans. In this paper, we address this question with experimental tests (total n=8,385) of three previously established depolarization interventions: correcting misperceptions of outpartisans, priming interpartisan friendships, and observing warm cross-partisan interactions between political leaders. While these depolarization interventions reliably reduced affective polarization, we do not find compelling evidence that these interventions reduced support for undemocratic candidates, support for partisan violence, or prioritizing partisan ends over democratic means. Thus, future efforts to strengthen pro-democratic attitudes may do better if they target these outcomes directly. More broadly, these findings call into question the previously assumed causal effect of affective polarization on anti-democratic attitudes.

If explicitly, blatantly dehumanizing a group of people—overtly characterizing them as less than human—facilitates harming them, then reversing this process is paramount. Addressing dehumanization among American political partisans appears especially crucial, given that it has been linked to their anti-democratic hostility. Perhaps because of its overt nature, partisans recognize—and greatly exaggerate—the extent to which out-partisans explicitly, blatantly dehumanize them. Past research has found that when people perceive they are dehumanized by an outgroup (i.e., meta-dehumanization), they respond with reciprocal dehumanization. Therefore, we reasoned that partisans’ dehumanization could be reduced by correcting their exaggerated meta-dehumanization. Indeed, across three preregistered studies ( N = 4,154), an intervention correcting American partisans’ exaggerated meta-dehumanization reduced their own dehumanization of out-partisans. This decreased dehumanization persisted at a 1-week follow-up and predicted downstream reductions in partisans’ anti-democratic hostility, suggesting that correcting exaggerated meta-dehumanization can durably mitigate the dark specter of dehumanization.

We provide the first systematic investigation of trends in the incivility of American politicians on Twitter, a dominant platform for political communication in the United States. Applying a validated artificial intelligence classifier to all 1.3 million tweets made by members of Congress since 2009, we observe a 23% increase in incivility over a decade on Twitter. Further analyses suggest that the rise was partly driven by reinforcement learning in which politicians engaged in greater incivility following positive feedback. Uncivil tweets tended to receive more approval and attention, publicly indexed by large quantities of “likes” and “retweets” on the platform. Mediational and longitudinal analyses show that the greater this feedback for uncivil tweets, the more uncivil tweets were thereafter. We conclude by discussing how the structure of social media platforms might facilitate this incivility-reinforcing dynamic between politicians and their followers.

Overcoming vaccine hesitancy is critical to containing the COVID-19 pandemic in the United States. To increase vaccination rates, the State of Ohio launched a million dollar lottery in May 2021. Following a pre-registered analysis, we estimate the effects of Ohio’s lottery program Vax-a-Million on COVID-19 vaccination rates by comparing it to a “synthetic control” composed of eight other states. We find a statistically insignificant 1.3% decrease in the full vaccination rate in Ohio at the end of the lottery period. We investigate the robustness of our conclusion to model specifications through a multiverse analysis of 216 possible models, including longer time periods and alternative vaccination measures. The majority (88%) find small negative effects in line with the results of our pre-registered model. While our results are most consistent with a decrease in vaccination rate, they do not allow a firm conclusion on whether the lottery increased or decreased vaccine uptake.

Large language models (LLMs) such as ChatGPT and GPT-3/4, built on artificial intelligence, hold immense potential to support, augment, or even replace psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, potential applications of clinical LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Second, stages of integrating LLMs into psychotherapy (via assistive, collaborative, and fully autonomous LLM applications) are presented, analogous to the development of autonomous vehicle technology. Third, recommendations for the responsible development of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Fourth, recommendations are made for the critical evaluation of clinical LLMs, which psychologists are uniquely positioned to scope and guide. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

Deep partisan conflict in the mass public threatens the stability of American democracy. We conducted a megastudy (n=32,059) testing 25 interventions designed by academics and practitioners to reduce Americans’ partisan animosity and anti-democratic attitudes. We find nearly every intervention reduced partisan animosity, most strongly by highlighting sympathetic and relatable individuals with different political beliefs. We also identify several interventions that reduced support for undemocratic practices and partisan violence, most strongly by correcting misperceptions of outpartisans’ views – showing that anti-democratic attitudes, although difficult to move, are not intractable. Furthermore, both factor analysis and patterns of intervention effect sizes provide convergent evidence for limited overlap between these sets of outcomes, suggesting that, contrary to popular belief, different strategies are most effective for reducing partisan animosity versus anti-democratic attitudes. Taken together, our findings provide a toolkit of promising strategies for practitioners and shed new theoretical light on challenges facing American democracy.

Political segregation is a significant social problem and a contributor to intergroup conflict and prejudice. Recent work suggests that one driver of segregation is political acrophily, people’s tendency to affiliate with others of their own political group who have more extreme (rather than more moderate) political views. However, acrophily has only been examined in controlled settings, where participants were exposed to hypothetical peers. The goal of the current project was to examine the role of acrophily in natural interactions on social media. In Study 1 (N = 1,992), we validated that people preferred tweets produced by more politically extreme users compared to more moderate users of their political group. In Study 2, we analyzed a large network of users on Twitter (3,898,327 unique ties), and found that users tended to affiliate with peers who were more politically extreme than other available users. Findings provide important out-of-the-lab evidence for acrophily.

The past decade has witnessed a proliferation of Big Team Science (BTS), endeavours where a comparatively large number of researchers pool their intellectual and/or material resources in pursuit of a common goal. Despite this burgeoning interest, there exists little guidance on how to create, manage, and participate in these collaborations. In this paper, we integrate insights from a multidisciplinary set of BTS initiatives to provide a how-to guide for BTS. We first discuss initial considerations for launching a BTS project, such as building the team, identifying leadership, governance, tools, and open science approaches. We then turn to issues related to running and completing a BTS project, such as study design, ethical approvals, and issues related to data collection, management, and analysis. Finally, we address topics that present special challenges for BTS, including authorship decisions, collaborative writing, and team decision making.

The rise of social media provides citizens with direct access to information shared by political elites. Yet, more than ever before, citizens play a critical role in diffusing elite-generated content. What kinds of content spread on social media? Do conservative and liberal citizens differ in the elite content with which they engage? These questions relate to long-standing academic and popular debates about whether political behavior is symmetric or asymmetric with respect to political ideology. We analyze more than 13 million users’ retweets of messages by U.S. Members of Congress on Twitter from 2009 to 2019, leveraging estimates of users’ political ideology constructed from over 3.5 billion prior retweets. We find limited evidence for ideological symmetry where the strength of ideology predicts diffusion choices similarly, on the left and right. In contrast, we find robust support for ideological asymmetry. Consistent with theories of ideological asymmetry, liberals retweeted a more diverse set of policy topics than conservatives by 19.4%. They also engaged more with toxic content from in-group elites by 56%. Given the tendency for people to follow like-minded others on social media, these diffusion patterns imply that liberals are exposed to more politically diverse and toxic elite-generated content on social media, while conservatives receive more politically homogeneous and less toxic content. The demand and supply dynamics of political information suggest the existence of divergent polarized information bubbles such that liberals and conservatives reside in distinct information ecosystems.

The emergence of transformer models that leverage deep learning and web-scale corpora has made it possible for artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets in the US and globally. Here, we investigate whether the currently most powerful, openly-available AI model – GPT-3 – is capable of influencing the beliefs of humans, a social behavior recently seen as a unique purview of other humans. Across three preregistered experiments featuring diverse samples of Americans (total N=4,836), we find consistent evidence that messages generated by AI are persuasive across a number of policy issues, including an assault weapon ban, a carbon tax, and a paid parental-leave program. Further, AI-generated messages were as persuasive as messages crafted by lay humans. Compared to the human authors, participants rated the author of AI messages as being more factual and logical, but less angry, unique, and less likely to use story-telling. Our results show the current generation of large language models can persuade humans, even on polarized policy issues. This work raises important implications for regulating AI applications in political contexts, to counter its potential use in misinformation campaigns and other deceptive political activities.

From calls to ban critical race theory to concerns about “woke culture,” American conservatives have mobilized in opposition to antiracist claims and movements. Here, we propose that this opposition has crystallized into a distinct racial ideology among white Americans, profoundly shaping contemporary racial politics. We explore opposition to antiracism (or anti-antiracism) across five studies (total N=6,076). We find anti-antiracism is prevalent among white Americans, particularly Republicans, is a powerful predictor of several policy positions, and is strongly associated with—though conceptually distinct from—various measures of anti-Black prejudice (Study 1). Indeed, among whites, anti-antiracism is more strongly related to support for Donald Trump than political ideology, socioeconomic variables, or several measures of racial prejudice, including scores on the Implicit Association Test. Next, we demonstrate in two pre-registered survey-experiments that anti-antiracism predicts whites’ responses to policy frames (Study 2a) and political candidates (Study 2b). Finally, however, we find whites high in anti-antiracism update their beliefs about the severity of anti-Black discrimination when presented with authoritative data (Quillian et al. 2017; Studies 3a-3b). These findings show opposition to antiracism is widespread and influential among white Americans, such that this racial ideology represents a critical axis of political polarization in the contemporary US.

While most Americans perceive current levels of inequality in the US as too high, economically progressive candidates who champion redistributive policies designed to reduce inequality rarely win elections. Here we propose that progressive candidates achieve greater support by framing their policy platforms in terms of values that resonate beyond their progressive base. In two experiments (total N = 4,138), including one pre-registered experiment conducted on a national probability sample, we found that a presidential candidate who framed his progressive economic platform using values consistent with the “binding” moral foundations – e.g., patriotism, family, and respect for tradition – as opposed to values consistent with the “individualizing” foundations – e.g., equality and social justice – received significantly stronger support. This effect was driven by increased support among conservatives and, unexpectedly, among moderates as well. By comparison, a manipulation of how progressive the candidate’s platform was had small and inconsistent effects. Despite the potential gains associated with binding framing, analyses using (a) text-based analysis of every word spoken in a presidential debate since 2000, and (b) machine learning-based measures of recent presidential candidates’ economic views, reveal that appeals to binding values are least common among economically progressive candidates. These findings show, however, that the alignment between values and economic policies is malleable, suggesting economically progressive candidates can build broader coalitions by reframing the values they associate with their platforms.

Social and behavioral science research proliferated during the COVID-19 pandemic, reflecting the substantial increase in influence of behavioral science in public health and public policy more broadly. This review presents a comprehensive assessment of 742 scientific articles on human behavior during COVID-19. Two independent teams evaluated 19 substantive policy recommendations (“claims”) on potentially critical aspects of behaviors during the pandemic drawn from the most widely cited behavioral science papers on COVID-19. Teams were made up of original authors and an independent team, all of whom were blinded to other team member reviews throughout. Both teams found evidence in support of 16 of the claims; for two claims, teams found only null evidence; and for no claims did the teams find evidence of effects in the opposite direction. One claim had no evidence available to assess. Seemingly due to the risks of the pandemic, most studies were limited to surveys, highlighting a need for more investment in field research and behavioral validation studies. The strongest findings indicate interventions that combat misinformation and polarization, and to utilize effective forms of messaging that engage trusted leaders and emphasize positive social norms.

In polarized political environments, partisans tend to deploy empathy parochially, furthering division. We propose that belief in the usefulness of cross-partisan empathy—striving to understand other people with whom one disagrees politically—promotes out-group empathy and has powerful ramifications for both intra- and interpersonal processes. Across four studies (total N = 4,748), we examined these predictions in online and college samples using surveys, social-network analysis, preregistered experiments, and natural-language processing. Believing that cross-partisan empathy is useful is associated with less partisan division and politically diverse friendship networks (Studies 1 and 2). When prompted to believe that empathy is a political resource—versus a political weakness—people become less affectively polarized (Study 3) and communicate in ways that decrease out-partisans’ animosity and attitudinal polarization (Study 4). These findings demonstrate that belief in cross-partisan empathy impacts not only individuals’ own attitudes and behaviors but also the attitudes of those they communicate with.

Social movements are critical agents of social change, but are rarely monolithic. Instead, movements are often made up of distinct factions with unique agendas and tactics, and there is little scientific consensus on when these factions may complement—or impede—one another’s influence. One central debate concerns whether radical flanks within a movement increase support for more moderate factions within the same movement by making the moderate faction seem more reasonable—or reduce support for moderate factions by making the entire movement seem unreasonable. Results of two online experiments conducted with diverse samples (N = 2,772), including a study of the animal rights movement and a preregistered study of the climate movement, show that the presence of a radical flank increases support for a moderate faction within the same movement. Further, it is the use of radical tactics, such as property destruction or violence, rather than a radical agenda, that drives this effect. Results indicate the effect owes to a contrast effect: Use of radical tactics by one flank led the more moderate faction to appear less radical, even though all characteristics of the moderate faction were held constant. This perception led participants to identify more with and, in turn, express greater support for the more moderate faction. These results suggest that activist groups that employ unpopular tactics can increase support for other groups within the same movement, pointing to a hidden way in which movement factions are complementary, despite pursuing divergent approaches to social change.

Scholars, policy makers, and the general public have expressed growing concern about the possibility of large-scale political violence in the United States. Prior research substantiates these worries, as studies reveal that many American partisans support the use of violence against rival partisans. Here, we propose that support for partisan violence is based in part on greatly exaggerated perceptions of rival partisans’ support for violence. We also predict that correcting these inaccurate “metaperceptions” can reduce partisans’ own support for partisan violence. We test these hypotheses in a series of preregistered, nationally representative, correlational, longitudinal, and experimental studies (total n = 4,741) collected both before and after the 2020 US presidential election and the 2021 US Capitol attack. In Studies 1 and 2, we found that both Democrats’ and Republicans’ perceptions of their rival partisans’ support for violence and willingness to engage in violence were very inaccurate, with estimates ranging from 245 to 442% higher than actual levels. Further, we found that a brief, informational correction of these misperceptions reduced support for violence by 34% (Study 3) and willingness to engage in violence by 44% (Study 4). In the latter study, a follow-up survey revealed that the correction continued to significantly reduce support for violence approximately 1 mo later. Together, these results suggest that support for partisan violence in the United States stems in part from systematic overestimations of rival partisans’ support for violence and that correcting these misperceptions can durably reduce support for partisan violence in the mass public.

Rising partisan animosity is associated with a reduction in support for democracy and an increase in support for political violence. Here we provide a multi-level review of interventions designed to reduce partisan animosity, which we define as negative thoughts, feelings and behaviours towards a political outgroup. We introduce the TRI framework to capture three levels of intervention-thoughts (correcting misconceptions and highlighting commonalities), relationships (building dialogue skills and fostering positive contact) and institutions (changing public discourse and transforming political structures)-and connect these levels by highlighting the importance of motivation and mobilization. Our review encompasses both interventions conducted as part of academic research projects and real-world interventions led by practitioners in non-profit organizations. We also explore the challenges of durability and scalability, examine self-fulfilling polarization and interventions that backfire, and discuss future directions for reducing partisan animosity.

Progress toward gender equality is thwarted by underrepresentation of women in political leadership, even as most Americans report they would vote for women candidates. Here we hypothesize that women candidates are often disadvantaged by pragmatic bias, a tendency to withhold support for members of groups for whom success is perceived to be difficult or impossible to achieve. Across six studies (total N=7,895), we test whether pragmatic bias impedes women’s access to a highly significant political leadership position – the U.S. presidency. Across two studies, 2020 Democratic primary voters perceived women candidates to be less electable, and these beliefs were correlated with lower intentions to vote for women candidates (Studies 1-2). Voters identified many reasons women would be less electable than men, including others’ unwillingness to vote for women, biased media coverage, and higher requirements to prove themselves. We next tested interventions to reduce pragmatic bias. Merely correcting misperceptions of Americans’ reported readiness for a woman president did not increase intentions to vote for a woman (Study 3). However, across three experiments (including one preregistered on a nationally representative sample) presenting evidence that women earn as much support as men in U.S. general elections increased Democratic primary voters’ intentions to vote for women presidential candidates, an effect driven by heightened perceptions of these candidates’ electability (Studies 4-6). These findings highlight that social change efforts can be thwarted by people’s sense of what is possible, but this may be overcome by credibly signaling others’ willingness to act collectively.