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 = 0.35 and 0.27, respectively). However, we find no evidence for a statistically significant difference between these groups in their engagement in a behavioral tree planting task. This conceptual-behavioral polarization incongruence results from self-identified conservatives acting despite not believing, rather than self-identified liberals not acting on their beliefs. We also find three interventions (emphasizing effective collective actions, writing a letter to a future generation member, and writing a letter from the future self) boost climate beliefs and policy support across the ideological spectrum, and one intervention (emphasizing scientific consensus) stimulates the climate action of people identifying as liberal. None of the interventions tested show evidence for a statistically significant boost in climate action for self-identified conservatives. We discuss implications for practitioners deploying targeted climate interventions.

Addressing climate change with behavioral science: A global intervention tournament in 63 countries.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

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. According to this model, social identity goals can override accuracy goals, leading to belief alignment with party members rather than facts. We propose an extended version of this model that incorporates the role of informational context in misinformation belief and sharing. Partisanship involves cognitive and motivational aspects that shape party members' beliefs and actions. This includes whether they seek further evidence, where they seek that evidence, and which sources they trust. Understanding the interplay between social identity and accuracy is crucial in addressing misinformation.

A Network Approach to Investigate the Dynamics of Individual and Collective Beliefs: Advances and Applications of the BENDING Model.

Changing entrenched beliefs to alter people's behavior and increase societal welfare has been at the forefront of behavioral-science research, but with limited success. Here, we propose a new framework of characterizing beliefs as a multidimensional system of interdependent mental representations across three cognitive structures (e.g., beliefs, evidence, and perceived norms) that are dynamically influenced by complex informational landscapes: the BENDING (Beliefs, Evidence, Norms, Dynamic Information Networked Graphs) model. This account of individual and collective beliefs helps explain beliefs' resilience to interventions and suggests that a promising avenue for increasing the effectiveness of misinformation-reduction efforts might involve graph-based representations of communities' belief systems. This framework also opens new avenues for future research with meaningful implications for some of the most critical challenges facing modern society, from the climate crisis to pandemic preparedness.

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. We review research detailing how every phase of the COVID-19 pandemic has been polarized, including judgments of risk, spatial distancing, mask wearing, and vaccination. We describe the role of political ideology, partisan identity, leadership, misinformation, and mass communication in this public-health crisis. We then assess the overall impact of polarization on infections, illness, and mortality during the pandemic; offer a psychological analysis of key policy questions; and identify a set of future research questions for scholars and policy experts. Our analysis suggests that the catastrophic death toll in the United States was largely preventable and due, in large part, to the polarization of the pandemic. Finally, we discuss implications for public policy to help avoid the same deadly mistakes in future public-health crises.

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.

Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence.

When we look at someone's face, we rapidly and automatically form robust impressions of how trustworthy they appear. Yet while people's impressions of trustworthiness show a high degree of reliability and agreement with one another, evidence for the accuracy of these impressions is weak. How do such appearance-based biases survive in the face of weak evidence? We explored this question using an iterated learning paradigm, in which memories relating (perceived) facial and behavioral trustworthiness were passed through many generations of participants. Stimuli consisted of pairs of computer-generated people's faces and exact dollar amounts that those fictional people shared with partners in a trust game. Importantly, the faces were designed to vary considerably along a dimension of perceived facial trustworthiness. Each participant learned (and then reproduced from memory) some mapping between the faces and the dollar amounts shared (i.e., between perceived facial and behavioral trustworthiness). Much like in the game of 'telephone', their reproductions then became the training stimuli initially presented to the next participant, and so on for each transmission chain. Critically, the first participant in each chain observed some mapping between perceived facial and behavioral trustworthiness, including positive linear, negative linear, nonlinear, and completely random relationships. Strikingly, participants' reproductions of these relationships showed a pattern of convergence in which more trustworthy looks were associated with more trustworthy behavior - even when there was no relationship between looks and behavior at the start of the chain. These results demonstrate the power of facial stereotypes, and the ease with which they can be propagated to others, even in the absence of any reliable origin of these stereotypes.

Political and nonpolitical belief change elicits behavioral change.

Beliefs have long been theorized to predict behaviors and thus have been the target of many interventions aimed at changing false beliefs in the population. But does changing beliefs translate into predictable changes in behaviors? Here, we investigated the impact of belief change on behavioral change across two experiments (N = 576). Participants rated the accuracy of a set of health-related statements and chose corresponding campaigns to which they could donate funds in an incentivized-choice task. They were then provided with relevant evidence in favor of the correct statements and against the incorrect statements. Finally, they rated the accuracy of the initial set of statements again and were given a chance to change their donation choices. We found that evidence changed beliefs and this, in turn, led to behavioral change. In a preregistered follow-up experiment, we replicated these findings with politically charged topics and found a partisan asymmetry in the effect, such that belief change triggered behavioral change only for Democrats on Democratic topics, but not for Democrats on Republican topics or for Republicans on either topic. We discuss the implications of this work in the context of interventions aimed at stimulating climate action or preventative health behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Propagation of societal gender inequality by internet search algorithms.

Humans increasingly rely on artificial intelligence (AI) for efficient and objective decision-making, yet there is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded. As a consequence, their use by human decision makers may result in the propagation, rather than reduction, of existing disparities. To assess this hypothesis empirically, we tested the relation between societal gender inequality and algorithmic search output and then examined the effect of this output on human decision-making. First, in two multinational samples (n = 37, 52 countries), we found that greater nation-level gender inequality was associated with more male-dominated Google image search results for the gender-neutral keyword "person" (in a nation's dominant language), revealing a link between societal-level disparities and algorithmic output. Next, in a series of experiments with human participants (n = 395), we demonstrated that the gender disparity associated with high- vs. low-inequality algorithmic outputs guided the formation of gender-biased prototypes and influenced hiring decisions in novel scenarios. These findings support the hypothesis that societal-level gender inequality is recapitulated in internet search algorithms, which in turn can influence human decision makers to act in ways that reinforce these disparities.

The impact of information sources on COVID-19 knowledge accumulation and vaccination intention.

During a global health crisis, people are exposed to vast amounts of information from a variety of sources. Here, we assessed which information source could increase knowledge about COVID-19 (Study 1) and COVID-19 vaccines (Study 2). In Study 1, a US census matched sample of 1060 participants rated the accuracy of a set of statements and then were randomly assigned to one of 10 between-subjects conditions of varying sources providing belief-relevant information: a political leader (Trump/Biden), a health authority (Fauci/CDC), an anecdote (Democrat/Republican), a large group of prior participants (Democrats/Republicans/Generic), or no source (Control). Finally, they rated the accuracy of the initial set of statements again. Study 2 involved a replication with a sample of 1876 participants and focused on the COVID-19 vaccine. We found that knowledge increased most when the source of information was a generic group of people, irrespective of participants' political affiliation. We also found that while expert communications were most successful at increasing Democrats' vaccination intentions, no source was successful at increasing Republicans' vaccination intention. We discuss these findings in the context of the current misinformation epidemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s41060-021-00307-8.

The impact of social norms on health-related belief update.

People are constantly bombarded with information they could use to adjust their beliefs. Here, we are interested in exploring the impact of social norms on health-related belief update. To investigate, we recruited a sample of 200 Princeton University students, who first rated the accuracy of a set of health statements (pre-test). They were then provided with relevant evidence either in favor or against the initial statements, and were asked to rate how convincing each piece of evidence was. The evidence was randomly assigned to appear as normative or non-normative, and anecdotal or scientific. Finally, participants rated the accuracy of the initial set of statements again (post-test). The results show that participants rationally updated their beliefs more when the evidence was scientific compared to when it was anecdotal. More importantly to our primary inquiry, the results show that participants changed their beliefs more in line with the evidence when the evidence was portrayed as normative compared to when the evidence was portrayed as non-normative, pointing to the impactful influence social norms have on health beliefs. Both effects were mediated by participants' subjective evaluation of the convincingness of the evidence, indicating the mechanism by which evidence is selectively incorporated into belief systems.

The Effect of Prediction Error on Belief Update Across the Political Spectrum.

Making predictions is an adaptive feature of the cognitive system, as prediction errors are used to adjust the knowledge they stemmed from. Here, we investigated the effect of prediction errors on belief update in an ideological context. In Study 1, 704 Cloud Research participants first evaluated a set of beliefs and then either made predictions about evidence associated with the beliefs and received feedback or were just presented with the evidence. Finally, they reevaluated the initial beliefs. Study 2, which involved a U.S. Census-matched sample of 1,073 Cloud Research participants, was a replication of Study 1. We found that the size of prediction errors linearly predicts belief update and that making large errors leads to more belief update than does not engaging in prediction. Importantly, the effects held for both Democrats and Republicans across all belief types (Democratic, Republican, neutral). We discuss these findings in the context of the misinformation epidemic.

The synchronization of collective beliefs: From dyadic interactions to network convergence.

Systems of beliefs organized around religion, politics, and health constitute the building blocks of human communities. One central feature of these collectively held beliefs is their dynamic nature. Here, we study the dynamics of belief endorsement in lab-created 12-member networks using a 2-phase communication model. Individuals first evaluate the believability of a set of beliefs, after which, in Phase 1, some networks listen to a public speaker mentioning a subset of the previously evaluated beliefs while other networks complete a distracter task. In Phase 2, all participants engage in conversations within their network to discuss the initially evaluated beliefs. Believability is then measured both post conversation and after one week. We find that the public speaker impacts the community's beliefs by altering their mnemonic accessibility. This influence is long-lasting and amplified by subsequent conversations, resulting in community-wide belief synchronization. These findings point to optimal sociocognitive strategies for combating misinformation in social networks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Mnemonic accessibility affects statement believability: The effect of listening to others selectively practicing beliefs.

Belief endorsement is rarely a fully deliberative process. Oftentimes, one's beliefs are influenced by superficial characteristics of the belief evaluation experience. Here, we show that by manipulating the mnemonic accessibility of particular beliefs we can alter their believability. We use a well-established socio-cognitive paradigm (i.e., the social version of the selective practice paradigm) to increase the mnemonic accessibility of some beliefs and induce forgetting in others. We find that listening to a speaker selectively practicing beliefs results in changes in believability. Beliefs that are mentioned become mnemonically accessible and exhibit an increase in believability, while beliefs that are related to those mentioned exrience mnemonic suppression, which results in decreased believability. Importantly, the latter effect occurs regardless of whether the belief is scientifically accurate or inaccurate. Furthermore, beliefs that are endorsed with moderate-strength are particularly susceptible to mnemonically-induced believability changes. These findings, we argue, have the potential to guide interventions aimed at correcting misinformation in vulnerable communities.

Suppressing my memories by listening to yours: The effect of socially triggered context-based prediction error on memory.

The mind is a prediction machine. In most situations, it has expectations as to what might happen. But when predictions are invalidated by experience (i.e., prediction errors), the memories that generate these predictions are suppressed. Here, we explore the effect of prediction error on listeners' memories following social interaction. We find that listening to a speaker recounting experiences similar to one's own triggers prediction errors on the part of the listener that lead to the suppression of her memories. This effect, we show, is sensitive to a perspective-taking manipulation, such that individuals who are instructed to take the perspective of the speaker experience memory suppression, whereas individuals who undergo a low-perspective-taking manipulation fail to show a mnemonic suppression effect. We discuss the relevance of these findings for our understanding of the bidirectional influences between cognition and social contexts, as well as for the real-world situations that involve memory-based predictions.