Evolutionary Human Sciences最新文献

Multiple proposals suggest that xenophobia increases when infectious disease threats are salient. The current longitudinal study tested this hypothesis by examining whether and how anti-immigrant sentiments varied in the Netherlands across four time points during the COVID-19 pandemic (May 2020, February 2021, October 2021 and June 2022 through Flycatcher.eu). The results revealed that (1) anti-immigrant sentiments were no higher in early assessments, when COVID-19 hospitalizations and deaths were high, than in later assessments, when COVID-19 hospitalizations were low, and (2) within-person changes in explicit disease concerns and disgust sensitivity did not relate to anti-immigrant sentiments, although stable individual differences in disgust sensitivity did. These findings suggest that anecdotal accounts of increased xenophobia during the pandemic did not generalize to the population sampled from here. They also suggest that not all increases in ecological pathogen threats and disease salience increase xenophobia.

The expensive-tissue hypothesis (ETH) posited a brain-gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct 'nutritional investment in brain tissues' (MRI-derived brain volumes) on the construct 'nutritional investment in lean body tissues' (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (-0.41, 95% CI, -1.13, 0.23) and brain and fat (-0.56, 95% CI, -2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

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.

Theorists of human evolution are interested in understanding major shifts in human behavioural capacities (e.g. the creation of a novel technological industry, such as the Acheulean). This task faces empirical challenges arising both from the complexity of these events and the time-depths involved. However, we also confront issues of a more philosophical nature, such as how to best think about causation and explanation. This article considers such fundamental questions from the perspective of a prominent theory of causation in the philosophy of science literature, namely, the interventionist theory of causation. A signature feature of this framework is its recognition of a family of distinct types of causes. We set out several of these causal notions and show how they can contribute to explaining transitions in human behavioural complexity. We do so, first, in a preliminary way, and then in a more detailed way, taking the origins of behavioural modernity as our extended case study. We conclude by suggesting some ways in which the approach developed here might be elaborated and extended.

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.

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 frequency of left-handedness in humans is ~10% worldwide and slightly higher in males than females. Twin and family studies estimate the heritability of human handedness at around 25%. The low but substantial frequency of left-handedness has been suggested to imply negative frequency-dependent selection, e.g. owing to a 'surprise' advantage of left-handers in combat against opponents more used to fighting right-handers. Because such game-theoretic hypotheses involve social interaction, here we perform an analysis of the evolution of handedness based on kin-selection, which is understood to play a major role in the evolution of social behaviour generally. We show that: (1) relatedness modulates the balance of right-handedness vs. left-handedness, according to whether left-handedness is marginally selfish vs. marginally altruistic; (2) sex differences in relatedness to social partners may drive sex differences in handedness; (3) differential relatedness of parents and offspring may generate parent-offspring conflict and sexual conflict leading to the evolution of maternal and paternal genetic effects in relation to handedness; and (4) differential relatedness of maternal-origin vs. paternal-origin genes may generate intragenomic conflict leading to the evolution of parent-of-origin-specific gene effects - such as 'genomic imprinting' - and associated maladaptation.

The prevalence of divorce in both parental and grandparental generations has led to a rise in the number of children who now have families that include both biological and step-grandparents. Despite the thorough examination of biological grandparents' contributions in the recent literature, there remains a scarcity of studies focusing on the investment of step-grandparents. Using population-based data from a sample of 2494 parents in Germany, we assessed grandparental investment through financial support and assistance with childcare of grandparents (N = 4238) and step-grandparents (N = 486). The study revealed that step-grandparents provided lower levels of investment in their grandchildren compared with biological grandparents. Furthermore, the study identified that a longer duration of co-residence between step-grandparents and parents earlier in life did not correspond to an increase or decrease in step-grandparental investment. However, investment by separated biological grandparents increased with the increasing length of co-residence with parents. In line with the scarce literature on step-grandparental investment, these findings indicate that mating effort may be the most important motivation for step-grandparental investment.

An increasingly common phenomenon in modern work and school settings is individuals taking on too many tasks and spending effort without commensurate rewards. Such an imbalance of efforts and rewards leads to myriad negative consequences, such as burnout, anxiety and disease. Here, we develop a model to explain how such effort-reward imbalances can come about as a result of biased social learning dynamics. Our model is based on a phenomenon that on some US college campuses is called 'the floating duck syndrome'. This phrase refers to the social pressure on individuals to advertise their successes but hide the struggles and the effort put in to achieve them. We show that a bias against revealing the true effort results in social learning dynamics that lead others to underestimate the difficulty of the world. This in turn leads individuals to both invest too much total effort and spread this effort over too many activities, reducing the success rate from each activity and creating effort-reward imbalances. We also consider potential ways to counteract the floating duck effect: we find that solutions other than addressing the root cause, biased observation of effort, are unlikely to work.