Evolutionary Human Sciences最新文献

The mate guarding theory of conservative clothing posits that veiling reduces women's physical allure and sexual attractiveness, thereby diminishing men's attraction towards them and deterring potential rivals for a woman's partner. This theory argues that the importance of veiling is influenced by ecological factors in a way that it is of higher importance to control women's sexuality in harsher environments to secure paternal investment. A prediction of this theory is that the importance of veiling should be influenced by community size, where individuals' reputations, specifically men's, might have different weightings, and their perceived sense of controlling a partner's activity may differ. Using pre-existing data from seven countries encompassing over 9000 individuals, the current study explored the association of town size and importance of veiling for women. Results showed a U-shaped relationship where in small towns and large cities, individuals, specifically men, give more importance to the veiling of women. This finding not only has multiple implications in terms of the effect of community size on male policing behaviours of women and sexual restrictions, but it also might point to a wider relationship regarding the association of community size and moral values.

According to the Trivers-Willard hypothesis (TWH), when the mother's condition around conception influences the future reproductive success of male and female offspring differently, the adjustment of offspring sex ratio (SR) to maternal condition will increase the parents' fitness. The TWH has been tested in several taxa, including humans where socioeconomic status as an index of condition has been widely used. The results are inconsistent, possibly because the preconditions of the TWH are not always met. To investigate the preconditions and prediction of the TWH in the contemporary Hungarian population, we collected data by an online questionnaire on self-perceived childhood living standard, the number of children and the sex of the respondents' siblings. We found no sex-specific relationship between reproductive success and childhood living standards, thus the precondition of the TWH was not met. We found no relationship between socioeconomic status and offspring SR when data from the whole country was used, but there was a tendency in the predicted direction when we used data from Budapest and considered the SR of only those family members who were born under similar conditions. Similar approaches should be preferred in the future to avoid noise caused by changing status during the reproductive lifespan.

People willingly follow norms and values, often incurring material costs. This behaviour supposedly stems from evolved norm psychology, contributing to large-scale cooperation among humans. It has been argued that cooperation is influenced by two types of norms: injunctive and descriptive. This study theoretically explores the socialisation of humans under these norms. Our agent-based model simulates scenarios where diverse agents with heterogeneous norm psychologies engage in collective action to maximise their utility functions that capture three motives: gaining material payoff, following injunctive and descriptive norms. Multilevel selective pressure drives the evolution of norm psychology that affects the utility function. Further, we develop a model with exapted conformity, assuming selective advantage for descriptive norm psychology. We show that norm psychology can evolve via cultural group selection. We then identify two normative conditions that favour the evolution of norm psychology, and therefore cooperation: injunctive norms promoting punitive behaviour and descriptive norms. Furthermore, we delineate different characteristics of cooperative societies under these two conditions and explore the potential for a macro transition between them. Together, our results validate the emergence of large-scale cooperative societies through social norms and suggest complementary roles that conformity and punishment play in human prosociality.

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.