Previous research in the evolutionary and psychological sciences has suggested that markers or tags of ethnic or group membership may help to solve cooperation and coordination problems. Cheating remains, however, a problem for these views, insofar as it is possible to fake the tag. While evolutionary psychologists have suggested that humans evolved the propensity to overcome this free rider problem, it is unclear how this module might manifest at the group level. In this study, we investigate the degree to which native and non-native speakers of accents – which are candidates for tags of group membership – spoken in the UK and Ireland can detect mimicry. We find that people are, overall, better than chance at detecting mimicry, and secondly we find substantial inter-group heterogeneity, suggesting that cultural evolutionary processes drive the manifestations of cheater detection. We discuss alternative explanations and suggest avenues of further inquiry.

Our limited knowledge of the climate prevailing over Europe during former glaciations is the main obstacle to reconstruct the past evolution of the ice coverage over the Alps by numerical modelling. To address this challenge, we perform a two-step modelling approach: First, a regional climate model is used to downscale the time slice simulations of a global earth system model in high resolution, leading to climate snapshots during the Last Glacial Maximum (LGM) and the Marine Isotope Stage 4 (MIS4). Second, we combine these snapshots and a climate signal proxy to build a transient climate over the last glacial period and force the Parallel Ice Sheet Model to simulate the dynamical evolution of glaciers in the Alps. The results show that the extent of modelled glaciers during the LGM agrees with several independent key geological imprints, including moraine-based maximal reconstructed glacial extents, known ice transfluences and trajectories of erratic boulders of known origin and deposition. Our results highlight the benefit of multiphysical coupled climate and glacier transient modelling over simpler approaches to help reconstruct paleo glacier fluctuations in agreement with traces they have left on the landscape.

Infection prevention program leaders report frequent use of criteria to distinguish recently recovered coronavirus disease 2019 (COVID-19) cases from actively infectious cases when incidentally positive asymptomatic patients were identified on routine severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing. Guidance on appropriate interpretation of high-sensitivity molecular tests can prevent harm from unnecessary precautions that delay admission and impede medical care.

In a survey of infection prevention programs, leaders reported frequent clinical and infection prevention practice modifications to avoid coronavirus disease 2019 (COVID-19) exposure that exceeded national guidance. Future pandemic responses should emphasize balanced approaches to precautions, prioritize educational campaigns to manage safety concerns, and generate an evidence-base that can guide appropriate infection prevention practices.

Psychological and cultural evolutionary accounts of human sociality propose that beliefs in punitive and monitoring gods that care about moral norms facilitate cooperation. While there is some evidence to suggest that belief in supernatural punishment and monitoring generally induce cooperative behaviour, the effect of a deity's explicitly postulated moral concerns on cooperation remains unclear. Here, we report a pre-registered set of analyses to assess whether perceiving a locally relevant deity as moralistic predicts cooperative play in two permutations of two economic games using data from up to 15 diverse field sites. Across games, results suggest that gods’ moral concerns do not play a direct, cross-culturally reliable role in motivating cooperative behaviour. The study contributes substantially to the current literature by testing a central hypothesis in the evolutionary and cognitive science of religion with a large and culturally diverse dataset using behavioural and ethnographically rich methods.

Auctions, normally considered as devices facilitating trade, also provide a way to probe mechanisms governing one’s valuation of some good or action. One of the most intriguing phenomena in auction behavior is the winner’s curse — the strong tendency of participants to bid more than rational agent theory prescribes, often at a significant loss. The prevailing explanation suggests that humans have limited cognitive abilities that make estimating the correct bid difficult, if not impossible. Using a series of auction structures, we found that bidding approaches rational agent predictions when participants compete against a computer. However, the winner’s curse appears when participants compete against other humans, even when cognitive demands for the correct bidding strategy are removed. These results suggest the humans assign significant future value to victories over human but not over computer opponents even though such victories may incur immediate losses, and that this valuation anomaly is the origin of apparently irrational behavior.

Many decisions in everyday life involve a choice between exploring options that are currently unknown and exploiting options that are already known to be rewarding. Previous work has suggested that humans solve such “explore-exploit” dilemmas using a mixture of two strategies: directed exploration, in which information seeking drives exploration by choice, and random exploration, in which behavioral variability drives exploration by chance. One limitation of this previous work was that, like most studies on explore-exploit decision making, it focused exclusively on the domain of gains, where the goal was to maximize reward. In many real-world decisions, however, the goal is to minimize losses and it is well known from Prospect Theory that behavior can be quite different in this domain. In this study, we compared explore-exploit behavior of human subjects under conditions of gain and loss. We found that people use both directed and random exploration regardless of whether they are exploring to maximize gains or minimize losses and that there is quantitative agreement between the exploration parameters across domains. Our results also revealed an overall bias towards the more uncertain option in the domain of losses. While this bias towards uncertainty was qualitatively consistent with the predictions of Prospect Theory, quantitatively we found that the bias was better described by a Bayesian account, in which subjects had a prior that was optimistic for losses and pessimistic for gains. Taken together, our results suggest that explore-exploit decisions are driven by three independent processes: directed and random exploration, and a baseline uncertainty seeking that is driven by a prior.

OBJECTIVES/GOALS: To assemble publicly-available, proprietary, and geocoded datasets about social, environmental, behavioral, and psychological exposures experienced by children with asthma, to provide a technical overview of data aggregation, management, and integration processes utilized, and to build predictive models using sociome and clinical data. METHODS/STUDY POPULATION: Publicly-available data involving census information, crime, green space, building permits, vacant and abandoned buildings, traffic (City of Chicago data portal), pollution and weather (National Oceanic and Atmospheric Administration), and noise (Array of Things project) were assembled. We placed a local instance of the Pelias geocoder on the UChicago Center for Research Informatics HIPAA-compliant infrastructure. The UChicago Clinical Research Data Warehouse will be leveraged to obtain clinical information for children diagnosed with asthma at UChicago Medicine between 2007 and 2021. The address of each child will be subjected to geocoding, and this information will be aligned with imported sociome data. A model will be built to account for each sociome elements contribution to asthma outcomes. RESULTS/ANTICIPATED RESULTS: Here we are creating sustainable and scalable ways for collecting, standardizing, and sharing real-world sociome data, simultaneously linking those data back to patient information. With this work, we aim to demonstrate feasibility of a data-commons-as-a-service for clinical and sociome data and to provide technical specifications and descriptions of processes employed. Creating generalizable and scalable infrastructure to support research of social and environmental impacts on clinical outcomes is critical, and our work will provide a framework to be used in other disease states. Further, this infrastructure will facilitate the application of advanced analytical tools and visualization platforms to accelerate the study of diseases and lead to new insights into factors influencing outcomes. DISCUSSION/SIGNIFICANCE: Beyond focusing on and treating biological mechanisms of disease, advancing health also requires addressing adverse consequences of sociome factors on clinical outcomes. We describe an innovative process to comprehensively codify and quantify such information in a way suitable for large scale co-analysis with biological and clinical data.

Wetland sediments are valuable archives of environmental change but can be challenging to date. Terrestrial macrofossils are often sparse, resulting in radiocarbon (14C) dating of less desirable organic fractions. An alternative approach for capturing changes in atmospheric 14C is the use of terrestrial microfossils. We 14C date pollen microfossils from two Australian wetland sediment sequences and compare these to ages from other sediment fractions (n = 56). For the Holocene Lake Werri Berri record, pollen 14C ages are consistent with 14C ages on bulk sediment and humic acids (n = 14), whilst Stable Polycyclic Aromatic Carbon (SPAC) 14C ages (n = 4) are significantly younger. For Welsby Lagoon, pollen concentrate 14C ages (n = 21) provide a stratigraphically coherent sequence back to 50 ka BP. 14C ages from humic acid and >100 µm fractions (n = 13) are inconsistent, and often substantially younger than pollen ages. Our comparison of Bayesian age-depth models, developed in Oxcal, Bacon and Undatable, highlight the strengths and weaknesses of the different programs for straightforward and more complex chrono-stratigraphic records. All models display broad similarities but differences in modeled age-uncertainty, particularly when age constraints are sparse. Intensive dating of wetland sequences improves the identification of outliers and generation of robust age models, regardless of program used.

Central Africa is experiencing rapid urbanisation, and this situation comes along with changes in food habits and an increased prevalence of obesity and associated health risks. Factors influencing dietary intake among the diverse African populations are not well understood. Our objective was to characterise the dietary intake and their determinants in the two main ethnic groups experiencing nutrition transition in Cameroon, the Bamiléké and the Béti. We sampled Bamiléké (381) and Béti (347) adults living in both rural and urban, collected socio-demographic variables, assessed dietary patterns by using a food portion photographs book to administrate a FFQ and a 24-h dietary recall technique and derived their BMI from measured weight and height. The dietary patterns of Bamiléké people were composed of more energy-dense foods than the Béti people, regardless of the living area. The energy intake (13·8 (sd 4·6)–15·4 (sd 4·8) MJ v. 9·7 (sd 3·5)–11·2 (sd 3·9 MJ) and the obesity (15–29 % v. 5–8 %) were therefore higher in Bamiléké than in Béti, respectively. Multivariable linear regression analyses showed strong associations of both ethnicities (4·02 MJ; P < 0·001), living area (0·21 MJ; P < 0·001) and education (0·59 MJ; P < 0·048) with energy intake, independently of each other and other socio-demographic factors. The ethnicity factor has been characterised as the more important determinant of diet. Our findings provide new insights and perspectives highlighting the importance of anthropological factors when building prevention campaigns against obesity in Central Africa.

The timing and duration of the coldest period in the last glacial stage, often referred to as the last glacial maximum (LGM), has been observed to vary spatially and temporally. In Australia, this period is characterised by colder, and in some places more arid, climates than today. We applied Monte-Carlo change point analysis to all available continuous proxy records covering this period, primarily pollen records, from across Australia (n = 37) to assess this change. We find a significant change point occurred (within uncertainty) at 28.6 ± 2.8 ka in 25 records. We interpret this change as a shift to cooler climates, associated with a widespread decline in biological productivity. An additional change point occurred at 17.7 ± 2.2 ka in 24 records. We interpret this change as a shift towards warmer climates, associated with increased biological productivity. We broadly characterise the period between 28.6 (± 2.8) – 17.7 (± 2.2) ka as an extended period of maximum cooling, with low productivity vegetation that may have occurred as a combined response to reduced temperatures, lower moisture availability and atmospheric CO2. These results have implications for how the spatial and temporal coherence of climate change, in this case during the LGM, can be best interrogated and interpreted.