Exposure to automation explains religious declines.

The global decline of religiosity represents one of the most significant societal shifts in recent history. After millennia of near-universal religious identification, the world is experiencing a regionally uneven trend toward secularization. We propose an explanation of this decline, which claims that automation-the development of robots and artificial intelligence (AI)-can partly explain modern religious declines. We build four unique datasets composed of more than 3 million individuals which show that robotics and AI exposure is linked to 21st-century religious declines across nations, metropolitan regions, and individual people. Key results hold controlling for other technological developments (e.g., electricity grid access and telecommunications development), socioeconomic indicators (e.g., wealth, residential mobility, and demographics), and factors implicated in previous theories of religious decline (e.g., individual choice norms). An experiment also supports our hypotheses. Our findings partly explain contemporary trends in religious decline and foreshadow where religiosity may wane in the future.

The rise of COVID-19 cases is associated with support for world leaders.

COVID-19 has emerged as one of the deadliest and most disruptive events in recent human history. Drawing from political science and psychological theories, we examine the effects of daily confirmed cases in a country on citizens' support for the political leader through the first 120 d of 2020. Using three unique datasets which comprise daily approval ratings of head of government (n = 1,411,200) across 11 world leaders (Australia, Brazil, Canada, France, Germany, Hong Kong, India, Japan, Mexico, the United Kingdom, and the United States) and weekly approval ratings of governors across the 50 states in the United States (n = 912,048), we find a strong and significant positive association between new daily confirmed and total confirmed COVID-19 cases in the country and support for the heads of government. These analyses show that political leaders received a boost in approval in the early months of the COVID-19 pandemic. Moreover, these findings suggest that the previously documented "rally 'round the flag" effect applies beyond just intergroup conflict.

Perception of strong social norms during the COVID-19 pandemic is linked to positive psychological outcomes.

Social norms can coordinate individuals and groups during collective threats. Pandemic-related social norms (e.g., wearing masks, social distancing) emerged to curb the spread of COVID-19. However, little is known about the psychological consequences of the emerging norms. We conducted three experiments cross-culturally, during the early period of the COVID-19 pandemic in China (Study 1), the recovery period in China (Study 2), and the severe period in the United States and Canada (Study 3). Across the three studies, we first distinguished the opposite effects of social norms and risk perception on individuals' psychological characteristics during the COVID-19 pandemic and further revealed that individuals who perceived stronger pandemic norms reported a lower level of COVID-19 risk perception, which in turn would be associated with fewer negative emotions, lower pressure, more positive emotions, higher levels of trusts, and more confidence in fighting against COVID-19. Our findings show that perceived tighter social norms are linked to beneficial psychological outcomes. This research helps governments, institutions, and individuals understand the mechanism and benefits of social norms during the pandemic, thereby facilitating policy formulation and better responses to social crises.

A global analysis of cultural tightness in non-industrial societies.

Human groups have long faced ecological threats such as resource stress and warfare, and must also overcome strains on coordination and cooperation that are imposed by growing social complexity. Tightness-looseness (TL) theory suggests that societies react to these challenges by becoming culturally tighter, with stronger norms and harsher punishment of deviant behaviour. TL theory further predicts that tightening is associated with downstream effects on social, political and religious institutions. Here, we comprehensively test TL theory in a sample of non-industrial societies. Since previous studies of TL theory have sampled contemporary countries and American states, our analysis allows us to examine whether the theory generalizes to societies in the ethnographic record and also to explore new correlates of tightness that vary more in non-industrial societies. We find that tightness covaries across domains of social norms, such as socialization, law and gender. We also show that tightness correlates with several theorized antecedents (ecological threat, complexity, residential homogeneity) and several theorized consequences (intergroup contact, political authoritarianism, moralizing religious beliefs). We integrate these findings into a holistic model of tightness in non-industrial societies and provide metrics that can be used by future studies on cultural tightness in the ethnographic record.

Conflict Changes How People View God.

Religion shapes the nature of intergroup conflict, but conflict may also shape religion. Here, we report four multimethod studies that reveal the impact of conflict on religious belief: The threat of warfare and intergroup tensions increase the psychological need for order and obedience to rules, which leads people to view God as more punitive. Studies 1 (N = 372) and 2 (N = 911) showed that people's concern about conflict correlates with belief in a punitive God. Study 3 (N = 1,065) found that experimentally increasing the salience of conflict increases people's perceptions of the importance of a punitive God, and this effect is mediated by people's support for a tightly regulated society. Study 4 showed that the severity of warfare predicted and preceded worldwide fluctuations in punitive-God belief between 1800 CE and 2000 CE. Our findings illustrate how conflict can change the nature of religious belief and add to a growing literature showing how cultural ecologies shape psychology.

Revenge: A Multilevel Review and Synthesis.

Why do people take revenge? This question can be difficult to answer. Vengeance seems interpersonally destructive and antithetical to many of the most basic human instincts. However, an emerging body of social scientific research has begun to illustrate a logic to revenge, demonstrating why revenge evolved in humans and when and how people take revenge. We review this evidence and suggest that future studies on revenge would benefit from a multilevel perspective in which individual acts of revenge exist within higher-level cultural systems, with the potential to instigate change in these systems over time. With this framework, we can better understand the interplay between revenge's psychological properties and its role in cultural evolution.

Worldwide divergence of values.

Social scientists have long debated the nature of cultural change in a modernizing and globalizing world. Some scholars predicted that national cultures would converge by adopting social values typical of Western democracies. Others predicted that cultural differences in values would persist or even increase over time. We test these competing predictions by analyzing survey data from 1981 to 2022 (n = 406,185) from 76 national cultures. We find evidence of global value divergence. Values emphasizing tolerance and self-expression have diverged most sharply, especially between high-income Western countries and the rest of the world. We also find that countries with similar per-capita GDP levels have held similar values over the last 40 years. Over time, however, geographic proximity has emerged as an increasingly strong correlate of value similarity, indicating that values have diverged globally but converged regionally.

Algorithm-mediated social learning in online social networks.

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

Valence-dependent mutation in lexical evolution.

A central goal of linguistics is to understand how words evolve. Past research has found that macro-level factors such as frequency of word usage and population size explain the pace of lexical evolution. Here we focus on cognitive and affective factors, testing whether valence (positivity-negativity) explains lexical evolution rates. Using estimates of cognate replacement rates for 200 concepts on an Indo-European language tree spanning six to ten millennia, we find that negative valence correlates with faster cognate replacement. This association holds when controlling for frequency of use, and follow-up analyses show that it is most robust for adjectives ('dirty' versus 'clean'; 'bad' versus 'good'); it does not consistently reach statistical significance for verbs, and never reaches significance for nouns. We also present experiments showing that individuals are more likely to replace words for negative versus positive concepts. Our findings suggest that emotional valence affects micro-level guided variation, which drives macro-level valence-dependent mutation in adjectives.

Exposure to robot preachers undermines religious commitment.

Over the last decade, robots continue to infiltrate the workforce, permeating occupations that once seemed immune to automation. This process seems to be inevitable because robots have ever-expanding capabilities. However, drawing from theories of cultural evolution and social learning, we propose that robots may have limited influence in domains that require high degrees of "credibility"; here we focus on the automation of religious preachers as one such domain. Using a natural experiment in a recently automated Buddhist temple (Study 1) and a fully randomized experiment in a Taoist temple (Study 2), we consistently show that religious adherents perceive robot preachers-and the institutions which employ them-as less credible than human preachers. This lack of credibility explains reductions in religious commitment after people listen to robot (vs. human) preachers deliver sermons. Study 3 conceptually replicates this finding in an online experiment and suggests that religious elites require perceived minds (agency and patiency) to be credible, which is partly why robot preachers inspire less credibility than humans. Our studies support cultural evolutionary theories of religion and suggest that escalating religious automation may induce religious decline. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence.

Robots are transforming the nature of human work. Although human-robot collaborations can create new jobs and increase productivity, pundits often warn about how robots might replace humans at work and create mass unemployment. Despite these warnings, relatively little research has directly assessed how laypeople react to robots in the workplace. Drawing from cognitive appraisal theory of stress, we suggest that employees exposed to robots (either physically or psychologically) would report greater job insecurity. Six studies-including two pilot studies, an archival study across 185 U.S. metropolitan areas (Study 1), a preregistered experiment conducted in Singapore (Study 2), an experience-sampling study among engineers conducted in India (Study 3), and an online experiment (Study 4)-find that increased exposure to robots leads to increased job insecurity. Study 3 also reveals that this robot-related job insecurity is in turn positively associated with burnout and workplace incivility. Study 4 reveals that self-affirmation is a psychological intervention that might buffer the negative effects of robot-related job insecurity. Our findings hold across different cultures and industries, including industries not threatened by robots. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Supernatural explanations across 114 societies are more common for natural than social phenomena.

Humans across the globe use supernatural beliefs to explain the world around them. This article explores whether cultural groups invoke the supernatural more to explain natural phenomena (for example, storms, disease outbreaks) or social phenomena (for example, murder, warfare). Quantitative analysis of ethnographic text across 114 geographically and culturally diverse societies found that supernatural explanations are more prevalent for natural than for social phenomena, consistent with theories that ground the origin of religious belief in a human tendency to perceive intent and agency in the natural world. Despite the dominance of supernatural explanations of natural phenomena, supernatural explanations of social phenomena were especially prevalent in urbanized societies with more socially complex and anonymous groups. Our results show how people use supernatural beliefs as explanatory tools in non-industrial societies, and how these applications vary across small-scale communities versus large and urbanized groups.

Generalized morality culturally evolves as an adaptive heuristic in large social networks.

Why do people assume that a generous person should also be honest? Why do we even use words like "moral" and "immoral"? We explore these questions with a new model of how people perceive moral character. We propose that people vary in the extent to which they perceive moral character as "localized" (varying along many contextually embedded dimensions) versus "generalized" (varying along a single dimension from morally bad to morally good). This variation might be partly the product of cultural evolutionary adaptations to different kinds of social networks. As networks grow larger, perceptions of generalized morality are increasingly valuable for predicting cooperation during partner selection, especially in novel contexts. Our studies show that social network size correlates with perceptions of generalized morality in United States and international samples (Study 1) and that East African hunter-gatherers with greater exposure outside their local region perceive morality as more generalized compared to those who have remained in their local region (Study 2). We support the adaptive value of generalized morality in large and unfamiliar social networks with an agent-based model (Study 3), and in experiments where we manipulate partner unfamiliarity (Study 4). Our final study shows that perceptions of morality have become more generalized over the last 200 years of English-language history, which suggests that it may be coevolving with rising social complexity and anonymity in the English-speaking world (Study 5). We discuss the implications of this theory for the cultural evolution of political systems, religion, and taxonomical theories of morality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Catalyzing social change: Does concentration encourage action?

Countless social problems demand solutions, from climate change and gun control to poverty and systemic racism. But while some of these problems inspire action (e.g., "Black Lives Matter" and "Me Too" movements), most fail to gain traction or inspire new policy. Why do some problems garner more attention and response? We suggest that the relative timing of related events may play an important role. Specifically, action may be more likely when related events are concentrated in time. A multi-method investigation tests this possibility. Study 1 borrows a modeling strategy from the economics and marketing literatures to examine a particularly important domain: gun control. Analysis of over 40 years of gun control legislation finds that, even after controlling for the frequency of mass shootings, bills are more likely to be proposed (and passed) when shootings are concentrated in time. Study 2 further tests concentration's causal impact and demonstrates that concentration increases support against sexual assault. These findings illustrate how a modeling approach commonly used to study advertising goodwill can be applied to a broader set of situations, suggest why some social problems are more likely to catalyze action, and shed light on drivers of social movements and collective action.

From Text to Thought: How Analyzing Language Can Advance Psychological Science.

Humans have been using language for millennia but have only just begun to scratch the surface of what natural language can reveal about the mind. Here we propose that language offers a unique window into psychology. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis-natural-language processing and comparative linguistics-are contributing to how we understand topics as diverse as emotion, creativity, and religion and overcoming obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods and highlight the best way to combine language analysis with more traditional psychological paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

The new trinity of religious moral character: the Cooperator, the Crusader, and the Complicit.

Does religion make people good or bad? We suggest that there are at least three distinct profiles of religious morality: the Cooperator, the Crusader, and the Complicit. Cooperators forego selfishness to benefit others, crusaders harm outgroups to bolster their own religious community, and the complicit use religion to justify selfish behavior and reduce blame. Different aspects of religion motivate each character: religious reverence makes people cooperators, religious tribalism makes people crusaders, and religious absolution makes people complicit. This framework makes sense of previous research by explaining when and how religion can make people more or less moral.

The new science of religious change.

Humans have believed in gods and spirits since the earliest days of the Holocene, and many people still believe in them today. Although the existence of religious belief has been a human constant, the nature and prevalence of religion has changed dramatically throughout human history. Here we describe the emerging science of religious change. We first outline a multilevel framework for studying religious change drawn from theories of socioecological psychology and cultural evolution. We illustrate this framework with four case studies featuring two ancient religious changes (the rise of punitive religions and doctrinal rituals) and two modern religious changes (the rise of atheism and nontraditional religions). We then review useful methods for examining religious change, including ethnographic coding, agent-based modeling, and time-series analysis. Next, we explore future directions, highlighting the need for predictive forecasts, nonlinear models, and non-Western samples. We also outline ten key questions that need to be answered for a fuller understanding of religious change. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Tight cultures and vengeful gods: How culture shapes religious belief.

Billions of people from around the world believe in vengeful gods who punish immoral behavior. These punitive religious beliefs may foster prosociality and contribute to large-scale cooperation, but little is known about how these beliefs emerge and why people adopt them in the first place. We present a cultural-psychological model suggesting that cultural tightness-the strictness of cultural norms and normative punishment-helps to catalyze punitive religious beliefs by increasing people's motivation to punish norm violators. Our model also suggests that tightness mediates the impact of ecological threat on punitive belief, explaining why punitive religious beliefs are most common in regions with high levels of ecological threat. Five multimethod studies support these predictions. Studies 1-3 focus on the effect of cultural tightness on punitive religious beliefs. Historical increases in cultural tightness precede and predict historical increases in punitive beliefs (Study 1), and both manipulating people's support for tightness (Study 2) and placing people in a simulated tight society (Study 3) increase punitive religious beliefs via the personal motivation to punish norm violators. Studies 4-5 focus on whether cultural tightness mediates the link between ecological threat and punitive religious beliefs. Cultural tightness helps explain why U.S. states with high ecological threat (e.g., natural hazards, scarcity) have the highest levels of punitive religious beliefs (Study 4) and why experimental manipulations of threat increase punitive religious beliefs (Study 5). Past research has shown how religion impacts culture, but our studies show how culture can shape religion. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The relationship between cultural tightness-looseness and COVID-19 cases and deaths: a global analysis.

BACKGROUND: The COVID-19 pandemic is a global health crisis, yet certain countries have had far more success in limiting COVID-19 cases and deaths. We suggest that collective threats require a tremendous amount of coordination, and that strict adherence to social norms is a key mechanism that enables groups to do so. Here we examine how the strength of social norms-or cultural tightness-looseness-was associated with countries' success in limiting cases and deaths by October, 2020. We expected that tight cultures, which have strict norms and punishments for deviance, would have fewer cases and deaths per million as compared with loose cultures, which have weaker norms and are more permissive. METHODS: We estimated the relationship between cultural tightness-looseness and COVID-19 case and mortality rates as of Oct 16, 2020, using ordinary least squares regression. We fit a series of stepwise models to capture whether cultural tightness-looseness explained variation in case and death rates controlling for under-reporting, demographics, geopolitical factors, other cultural dimensions, and climate. FINDINGS: The results indicated that, compared with nations with high levels of cultural tightness, nations with high levels of cultural looseness are estimated to have had 4·99 times the number of cases (7132 per million vs 1428 per million, respectively) and 8·71 times the number of deaths (183 per million vs 21 per million, respectively), taking into account a number of controls. A formal evolutionary game theoretic model suggested that tight groups cooperate much faster under threat and have higher survival rates than loose groups. The results suggest that tightening social norms might confer an evolutionary advantage in times of collective threat. INTERPRETATION: Nations that are tight and abide by strict norms have had more success than those that are looser as of the October, 2020. New interventions are needed to help countries tighten social norms as they continue to battle COVID-19 and other collective threats. FUNDING: Office of Naval Research, US Navy.

Could a rising robot workforce make humans less prejudiced?

Automation is becoming ever more prevalent, with robot workers replacing many human employees. Many perspectives have examined the economic impact of a robot workforce, but here we consider its social impact: How will the rise of robot workers affect intergroup relations? Whereas some past research has suggested that more robots will lead to more intergroup prejudice, we suggest that robots could also reduce prejudice by highlighting commonalities between all humans. As robot workers become more salient, intergroup differences-including racial and religious differences-may seem less important, fostering a perception of common human identity (i.e., panhumanism). Six studies (ΣN = 3,312) support this hypothesis. Anxiety about the rising robot workforce predicts less anxiety about human outgroups (Study 1), and priming the salience of a robot workforce reduces prejudice toward outgroups (Study 2), makes people more accepting of outgroup members as leaders and family members (Study 3), and increases wage equality across ingroup and outgroup members in an economic simulation (Study 4). This effect is mediated by panhumanism (Studies 5-6), suggesting that the perception of a common human ingroup explains why robot salience reduces prejudice. We discuss why automation may sometimes exacerbate intergroup tensions and other times reduce them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).