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Previous research has shown that the matching of rhythmic behaviour between individuals (synchrony) increases cooperation. Such synchrony is most noticeable in music, dance and collective rituals. As well as the matching of behaviour, such collective performances typically involve shared intentionality: performers actively collaborate to produce joint actions. Over three experiments we examined the importance of shared intentionality in promoting cooperation from group synchrony. Experiment 1 compared a condition in which group synchrony was produced through shared intentionality to conditions in which synchrony or asynchrony were created as a by-product of hearing the same or different rhythmic beats. We found that synchrony combined with shared intentionality produced the greatest level of cooperation. To examinef the importance of synchrony when shared intentionality is present, Experiment 2 compared a condition in which participants deliberately worked together to produce synchrony with a condition in which participants deliberately worked together to produce asynchrony. We found that synchrony combined with shared intentionality produced the greatest level of cooperation. Experiment 3 manipulated both the presence of synchrony and shared intentionality and found significantly greater cooperation with synchrony and shared intentionality combined. Path analysis supported a reinforcement of cooperation model according to which perceiving synchrony when there is a shared goal to produce synchrony provides immediate feedback for successful cooperation so reinforcing the group's cooperative tendencies. The reinforcement of cooperation model helps to explain the evolutionary conservation of traditional music and dance performances, and furthermore suggests that the collectivist values of such cultures may be an essential part of the mechanisms by which synchrony galvanises cooperative behaviours.

During the early stages of the COVID-19 pandemic, there was a spike in the reporting of hate crimes (Human Rights Watch, 2020). However, the extent to which the pandemic affected prejudice across a general population—not merely among those disposed to hate crimes—remains unclear. Also unclear is the extent to which prejudice was restricted to specific minority groups associated with the virus, or whether prejudice spilled over to other minority groups. To address these questions, we use panel data collected from participants in a large national longitudinal (panel) study of New Zealanders before and during the early COVID-19 pandemic and systematically quantified social warmth ratings across a broad range of minority-groups (The New Zealand Attitudes and Values Study, N = 30,327, years 2018–2020). We discover reduced warmth toward Chinese, Asians (broadly defined), immigrants, Muslims, refugees, Indians, and the mentally ill. In absolute terms, warmth towards Chinese decreased the most (0.11 SD). Notably, changes in warmth were not detected toward NZ Europeans, Māori, Pacific Islanders, the overweight, or the elderly. Overall, these findings suggest that in New Zealand, pandemic prejudice may spread beyond minority groups associated with the virus to other groups perceived as non-prototypical of national identity.

Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

The analysis of ‘moderation’, ‘interaction’, ‘mediation’ and ‘longitudinal growth’ is widespread in the human sciences, yet subject to confusion. To clarify these concepts, it is essential to state causal estimands, which requires the specification of counterfactual contrasts for a target population on an appropriate scale. Once causal estimands are defined, we must consider their identification. I employ causal directed acyclic graphs and single world intervention graphs to elucidate identification workflows. I show that when multiple treatments exist, common methods for statistical inference, such as multi-level regressions and statistical structural equation models, cannot typically recover the causal quantities we seek. By properly framing and addressing causal questions of interaction, mediation, and time-varying treatments, we can expose the limitations of popular methods and guide researchers to a clearer understanding of the causal questions that animate our interests.

The human sciences should seek generalisations wherever possible. For ethical and scientific reasons, it is desirable to sample more broadly than ‘Western, educated, industrialised, rich, and democratic’ (WEIRD) societies. However, restricting the target population is sometimes necessary; for example, young children should not be recruited for studies on elderly care. Under which conditions is unrestricted sampling desirable or undesirable? Here, we use causal diagrams to clarify the structural features of measurement error bias and target population restriction bias (or ‘selection restriction’), focusing on threats to valid causal inference that arise in comparative cultural research. We define any study exhibiting such biases, or confounding biases, as weird (wrongly estimated inferences owing to inappropriate restriction and distortion). We explain why statistical tests such as configural, metric and scalar invariance cannot address the structural biases of weird studies. Overall, we examine how the workflows for causal inference provide the necessary preflight checklists for ambitious, effective and safe comparative cultural research.

Investigates anti-Muslim prejudice using a large national sample of non-Muslim New Zealanders who responded to the 2013 New Zealand Attitudes and Values Study. Evaluates theories that argue that prejudice and acceptance have a basis in inter-religious conflict. Considers whether the religious commitments of non-Muslims promote prejudice or acceptance. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

Confounding bias arises when a treatment and outcome share a common cause. In randomised controlled experiments (trials), treatment assignment is random, ostensibly eliminating confounding bias. Here, we use causal directed acyclic graphs to unveil eight structural sources of bias that nevertheless persist in these trials. This analysis highlights the crucial role of causal inference methods in the design and analysis of experiments, ensuring the validity of conclusions drawn from experimental data.

Examines how a natural disaster affects deeply held commitments and global ratings of personal health, depending on earthquake exposure. Investigates whether the earthquake-affected were more likely to believe in God. Examines whether religious affiliation was associated with differences in subjective ratings of personal health. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

Significance Here we show that the spatial prevalence of human societies that believe in moralizing high gods can be predicted with a high level of accuracy (91%) from historical, social, and ecological data. Using high-resolution datasets, we systematically estimate the relative effects of resource abundance, ecological risk, cultural diffusion, shared ancestry, and political complexity on the global distribution of beliefs in moralizing high gods. The methods presented in this paper provide a blueprint for how to leverage the increasing wealth of ecological, linguistic, and historical data to understand the forces that have shaped the behavior of our own species. Although ecological forces are known to shape the expression of sociality across a broad range of biological taxa, their role in shaping human behavior is currently disputed. Both comparative and experimental evidence indicate that beliefs in moralizing high gods promote cooperation among humans, a behavioral attribute known to correlate with environmental harshness in nonhuman animals. Here we combine fine-grained bioclimatic data with the latest statistical tools from ecology and the social sciences to evaluate the potential effects of environmental forces, language history, and culture on the global distribution of belief in moralizing high gods ( n = 583 societies). After simultaneously accounting for potential nonindependence among societies because of shared ancestry and cultural diffusion, we find that these beliefs are more prevalent among societies that inhabit poorer environments and are more prone to ecological duress. In addition, we find that these beliefs are more likely in politically complex societies that recognize rights to movable property. Overall, our multimodel inference approach predicts the global distribution of beliefs in moralizing high gods with an accuracy of 91%, and estimates the relative importance of different potential mechanisms by which this spatial pattern may have arisen. The emerging picture is neither one of pure cultural transmission nor of simple ecological determinism, but rather a complex mixture of social, cultural, and environmental influences. Our methods and findings provide a blueprint for how the increasing wealth of ecological, linguistic, and historical data can be leveraged to understand the forces that have shaped the behavior of our own species.

Collective rituals are present in all known societies, but their function is a matter of long-standing debates. Field observations suggest that they may enhance social cohesion and that their effects are not limited to those actively performing but affect the audience as well. Here we show physiological effects of synchronized arousal in a Spanish fire-walking ritual, between active participants and related spectators, but not participants and other members of the audience. We assessed arousal by heart rate dynamics and applied nonlinear mathematical analysis to heart rate data obtained from 38 participants. We compared synchronized arousal between fire-walkers and spectators. For this comparison, we used recurrence quantification analysis on individual data and cross-recurrence quantification analysis on pairs of participants' data. These methods identified fine-grained commonalities of arousal during the 30-min ritual between fire-walkers and related spectators but not unrelated spectators. This indicates that the mediating mechanism may be informational, because participants and related observers had very different bodily behavior. This study demonstrates that a collective ritual may evoke synchronized arousal over time between active participants and bystanders. It links field observations to a physiological basis and offers a unique approach for the quantification of social effects on human physiology during real-world interactions.