The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

Interdependence theory presents a logical analysis of the structure of interpersonal situations, offering a conceptual framework in which interdependence situations can be analyzed in terms of six dimensions. Specific situations present specific problems and opportunities, logically implying the relevance of specific motives and permitting their expression. Via the concept of transformation, the theory explains how interaction is shaped by broader considerations such as long-term goals and concern for a partner's welfare. The theory illuminates our understanding of social-cognitive processes that are of longstanding interest to psychologists such as cognition and affect, attribution, and self-presentation. The theory also explains adaptation to repeatedly encountered interdependence patterns, as well as the embodiment of such adaptations in interpersonal dispositions, relationship-specific motives, and social norms.

We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.

How effective are rewards (for cooperation) and punishment (for noncooperation) as tools to promote cooperation in social dilemmas or situations when immediate self-interest and longer term collective interest conflict? What variables can promote the impact of these incentives? Although such questions have been examined, social and behavioral scientists provide different answers. To date, there is no theoretical and/or quantitative review of rewards and punishments as incentives for cooperation in social dilemmas. Using a novel interdependence-theoretic framework, we propose that rewards and punishments should both promote cooperation, and we identify 2 variables—cost of incentives and source of incentives—that are predicted to magnify the effectiveness of these incentives in promoting cooperation.A meta-analysis involving 187 effect sizes revealed that rewards and punishments exhibited a statistically equivalent positive effect on cooperation (d =0.51 and 0.70, respectively). The effectiveness of incentives was stronger when the incentives were costly to administer, compared to free. Centralization of incentives did not moderate the effect size. Punishments were also more effective during iterated dilemmas when participants continued to interact in the same group, compared to both (a) iterated dilemmas with reassignment to a new group after each trial and (b) one-shot dilemmas. We also examine several other potential moderators, such as iterations, partner matching, group size, country, and participant payment. We discuss broad conclusions, consider implications for theory, and suggest directions for future research on rewards and punishment in social dilemmas.

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. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.