Richard McElreath’s research while affiliated with Max Planck Institute for Evolutionary Anthropology and other places

Animals often balance asocial and social information strategically, adjusting when and from whom they copy based on context. Yet the cognition driving this dynamic—and its broader implications—remains poorly understood. Here, we show that nest-building zebra finches follow a copy-if-dissatisfied strategy, but only when social information aligns with recent experience: after observing a conspecific build a nest, males were more likely to choose the demonstrated 'social' material—particularly on their first choice—if they had previously used low-quality material. Using Bayesian cognitive modelling, we estimated how latent learning mechanisms underpinned males' material choices, identifying two asocial and two social governing parameters. These parameters provide the first formal evidence for the cognitive basis of birds' nest building. Forward simulations informed—but not prescripted—by these parameters approximated observed material-choice trajectories, supporting their causal role. Additional simulations targeting simplified real-world contexts showed that payoff structure—not (dis)satisfaction—drove social material choice, though higher rewards did not proportionally boost its use, offering preliminary insights into the mechanisms underlying material-use variation more broadly. Our study illustrates how computational modelling can robustly link behaviour to underlying learning mechanisms and probe the generalisability of animal cognition—a rarity in this field.

The arcuate fascicle (AF) is the main fibre tract in the brain for human language. It connects frontal and temporal language areas in the superior and middle temporal gyrus (MTG). The AF’s connection to the MTG was considered unique to humans and has influenced theories of the evolution of language. Here, using high-resolution diffusion MRI of post-mortem brains, we demonstrate that both wild and captive chimpanzees have a direct AF connection into the MTG, albeit weaker than in humans. This finding challenges the notion of a strictly human-specific AF morphology and suggests that language-related neural specialisation in humans likely evolved through gradual evolutionary strengthening of a pre-existing connection, rather than arising de novo. It is likely that this neural architecture supporting complex communication was already present in the last common ancestor of hominins and chimpanzees 7 million years ago, enabling the evolution of language processes in the human lineage.

Across the social and biological sciences, researchers study social networks that are often longitudinal in nature, featuring the same set of individuals interacting with one another over long periods of time. Such network data may be collected via panel studies in human communities, or via long-form behavioral observation programs in animal communities. Important research questions often hinge on how dyadic ties or flows in the network at one time-step are associated with dyadic ties or flows at subsequent time-steps. Similar questions focus on the relationship between nodal characteristics across time-steps: for example, is an individual with a high out-degree at one time-step more likely to have a high in-degree in the next? These questions are effectively addressed by longitudinal extensionsof generative social network models like the Social Relations Model (SRM). Here, we present a novel longitudinal extension of the SRM, provide an implementation of the model in the STRAND R package, and provide tutorials teaching end-users how to fit the model to their own data. We provide worked examples of data analysis, parameter visualization, and results interpretation, using data-sets from both social science and animal behavior. The software package allows end-users to deploy complex longitudinal network analysis models using only simple, base-R model syntax.

During childhood, human speech utterances increase steadily in complexity, length and diversity. In contrast, the vocal repertoire of non-human primates has long been considered fixed from birth. Recent studies showing the acquisition of vocal sequences during ontogeny in chimpanzees and marmosets challenge this view. Here we further explore the potential flexibility of non-human primate vocal production by comparing the vocal sequence repertoire across age groups in sooty mangabeys, a species with a rich sequence repertoire for a catarrhine monkey. We recorded 1844 utterances from 75 individuals from two wild groups in Taï National Park, Ivory Coast. We used custom-made Bayesian models specifically designed to estimate the individual repertoire size of vocal sequences while accounting for under-sampling of certain vocalisations in certain individuals. We hereby provide a tool to estimate vocal repertoire size applicable to other taxa. We found no relevant ontogenetic changes in vocal repertoire size and utterance length. Ontogenetic vocal sequence expansion is therefore not universal among primates that routinely use vocal sequences to communicate. Rather, this feature may have evolved independently in distantly-related taxa due to social features thought to promote vocal complexity, such as the complex social organisation of chimpanzees and the cooperative breeding systems of marmosets.

In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials. Participants were exposed to various theories about the system, ranging from accurate to misleading. Our findings indicate that even under conditions conducive to exploration, the transmission of cultural knowledge canalizes learners’ focus, limiting their consideration of alternative solutions. This effect was observed in both the theories produced and the solutions attempted by participants, irrespective of the accuracy of the provided theories. These results challenge the notion that arbitrary solutions persist only when they are efficient or intuitive and underscore the significant role of cultural transmission in shaping human knowledge and technologies.

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Investments in housing influence migration and landscape construction, making them a key component of human–environment interactions. However, the strategic decision-making that builds residential landscapes is an underdeveloped area of research in evolutionary approaches to human behaviour. Our contribution to this literature is a theoretical model and an empirical test of this model using data from Ulaanbaatar, Mongolia. We develop a model of strategic housing decisions using stochastic dynamic programming (SDP) to explore the trade-offs between building, moving and saving over time, finding different trade-offs depending on optimization scenarios and housing costs. Household strategies are then estimated using data on 825 households that settled in the Ger districts of Ulaanbaatar between 1942 and 2020. The Ger districts are areas of self-built housing that feature both mobile dwellings (gers) and immobile houses (bashins). Using approximate Bayesian computation (ABC), we find the parameters of our dynamic programming model that best fit the empirical data. The model is able to capture the time horizon of housing changes and their bi-directionality, showing that moving from a fixed to mobile dwelling can also be an optimal strategy. However, the model underpredicts household persistence in dwelling types. We discuss deviations from model predictions and identify a more detailed exploration of risk and population mixes of strategies as key steps for future research.

Social learning is common in nature, yet cumulative culture (where knowledge and technology increase in complexity and diversity over time) appears restricted to humans. To understand why, we organized a computer tournament in which programmed entries specified when to learn new knowledge and when to refine (i.e. improve) existing knowledge. The tournament revealed a ‘refinement paradox’: refined behavior afforded higher payoffs as individuals converged on a small number of successful behavioral variants, but refining did not generally pay. Paradoxically, entries that refined only in certain conditions did best during behavioral improvement, while simple copying entries thrived when refinement levels were high. Cumulative cultural evolution may be rare in part because sophisticated strategies for improving knowledge and technology are initially advantageous, yet complex culture, once achieved, favors conformity, blind imitation and hyper-credulity.

A bstract A major goal of behavioural ecology is to explain how phenotypic and ecological factors shape the networks of social relationships that animals form with one another. This inferential task is notoriously challenging. The social networks of interest are generally not observed, but must be approximated from behavioural samples. Moreover, these data are highly dependent: the observed network edges correlate with one another, due to biological and sampling processes. Failing to account for the resulting uncertainty and biases can lead to dysfunctional statistical procedures, and thus to incorrect results. Here, we argue that these problems should be understood—and addressed—as problems of causal inference. For this purpose, we introduce a Bayesian causal modelling framework that explicitly defines the links between the target interaction network, its causes, and the data. We illustrate the mechanics of our framework with simulation studies and an empirical example. First, we encode causal effects of individual-, dyad-, and group-level features on social interactions using Directed Acyclic Graphs and Structural Causal Models. These quantities are the objects of inquiry, our estimands . Second, we develop estimators for these effects—namely, Bayesian multilevel extensions of the Social Relations Model. Third, we recover the structural parameters of interest, map statistical estimates to the underlying causal structures, and compute causal estimates from the joint posterior distribution. Throughout the manuscript, we develop models layer by layer, thereby illustrating an iterative workflow for causal inference in social networks. We conclude by summarising this workflow as a set of seven steps, and provide practical recommendations.