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.

Humans exhibit considerable variation in how they value their own interest relative to the interests of others. Deciphering the neural codes representing potential rewards for self and others is crucial for understanding social decision-making. Here we integrate computational modeling with fMRI to investigate the neural representation of social value and the modulation by oxytocin, a nine-amino acid neuropeptide, in participants evaluating monetary allocations to self and other (self-other allocations). We found that an individual's preferred self-other allocation serves as a reference-point for computing the value of potential self-other allocations. In more-prosocial participants, amygdala activity encoded a social-value-distance signal, i.e. the value dissimilarity between potential and preferred allocations. Intranasal oxytocin administration amplified this amygdala representation and increased prosocial behavior in more-individualist participants but not in more-prosocial ones. Our results reveal a neurocomputational mechanism underlying social-value representations and suggest that oxytocin may promote prosociality by modulating social-value representations in the amygdala.

The spread of COVID-19 represents a global public health crisis, yet some nations were more effective than others at limiting the spread of the virus during the early stages of the pandemic. Here we show that institutional and cultural factors combine to partly explain these cross-cultural differences. Nations with tight cultures and efficient governments were the most effective at limiting COVID-19’s growth and mortality rates as of early April, and this interaction of cultural tightness and government efficiency is robust to controlling for underreporting of cases, economic development, inequality, median age, population density, climatological variation, and other dimensions of cross-cultural variation (collectivism, power distance, relational mobility). A formal evolutionary model explores the mechanism that may underlie our findings, suggesting that these cross-cultural trends may be associated with group variation in cooperation under conditions of high threat. These analyses shed light on why some nations contained COVID-19 more effectively than others.

Vascular bundles within maize (Zea mays L.) stalks play a key role in the mechanical support of plant architecture as well as in water and nutrient transportation. Convenient and accurate phenotyping of vascular bundles may help phenotypic identification of germplasm resources for breeding. Based on practical sample preparation procedures for maize stalks, we acquired serials of cross-sectional images using a micro-computed tomography (CT) imaging device. An image processing pipeline dedicated to the phenotyping of vascular bundles was also developed to automatically segment and validate vascular bundles from the cross-sectional images of maize stalks, from which phenotypic traits of vascular bundles, i.e. number, area, and spatial distribution, were calculated. More profound quantification of spatial distribution was given as area ratio of vascular bundles, which described the distribution of vascular bundles associated with the centroid of maize stalks. In addition, three-dimensional visualisation was performed to reveal the spatial configuration and distribution of vascular bundles. The proposed method significantly improves computation accuracy for the phenotypic traits of vascular bundles compared with previous methods, and is expected to be useful for illustrating relationships between phenotypic traits of vascular bundles and their function.

Humans interact with each other on a daily basis by developing and maintaining various social norms and it is critical to form a deeper understanding of how such norms develop, how they change, and how fast they change. In this work, we develop an evolutionary game-theoretic model based on research in cultural psychology that shows that humans in various cultures differ in their tendencies to conform with those around them. Using this model, we analyze the evolutionary relationships between the tendency to conform and how quickly a population reacts when conditions make a change in norm desirable. Our analysis identifies conditions when a tipping point is reached in a population, causing norms to change rapidly.