What is the evolutionary function of cuteness? Research on cuteness originated with the study of juvenile morphology following Lorenz’s Kindchenschema proposal, which identified specific cues of juvenility that reliably elicit a distinctive affective response. Because juvenile cues activate protective responses typically associated with vulnerability and distress (e.g., compassion, harm avoidance), cuteness has often been interpreted as an emotion whose primary function is protection. However, this leaves a key pattern unexplained: children are perceived as cutest not when they are helpless, but when they are sociable, curious, and eager to interact. More surprisingly, we also perceive adults as cute when they display not vulnerability, but naïveté, enthusiasm, and teachability. To solve this issue, we propose a developmental investment theory of cuteness, according to which cuteness is not merely a protection-eliciting mechanism but a motivational system that evolved to support prosocial investment in the development of inexperienced individuals. Crucially, this account expands the range of cues that elicit cuteness beyond juvenile morphology to include behavioral and cognitive signals of immaturity and learning potential. Drawing on evolutionary anthropology, cognitive neuroscience, developmental psychology and social sciences, we argue that cuteness identifies motivated but inexperienced individuals, and elicits not rescue, but teaching, guidance, and social play. This theory provides a better account of which traits elicit cuteness, what responses they provoke, and how cuteness is strategically deployed. It explains why cuteness can be attractive in romantic interactions, why modern, learning-rich environments foster an explosion of cuteness and why women disproportionately perform cuteness in patriarchal societies. Finally, we suggest that, over evolutionary time, this system may have contributed to the domestication of other species, and to the self-domestication of our own.

Behaviour-change interventions unfold in social systems where people learn from others. We develop a stylised agent-based model to examine how four canonical social learning rules – conformist transmission, informational prestige-biased copying, payoff-biased copying and random copying – shape the impact of a simple seeding intervention. Two arms evolve under identical conditions and learning rules, differing only in initial adoption: both start with exactly 5% baseline adopters and the treatment arm additionally seeds 20% of the remaining non-adopters, yielding an exact 25% vs 5% contrast at $t = 0$. Across homogeneous populations, 70/30 mixed ecologies and sweeps over the share of payoff-biased learners, we track adoption trajectories and treatment–control lift; we also vary payoff parameters, prestige informativeness and conformist thresholds in robustness analyses. We find that the same seeding intervention can stall, drift or cascade depending on the learning ecology. In the baseline specification, conformist dynamics exhibit threshold effects that erase treatment gains, prestige-biased and random copying can preserve positive final lift when diffusion remains incomplete and payoff-biased copying mainly changes the diffusion regime rather than preserving large end-point gaps. Robustness checks show that negative payoff premia suppress diffusion, weak or noisy payoff signals can generate treatment advantages, prestige effects depend on how informative prestige is and conformist treatment effects are concentrated in narrow threshold-boundary regions. These results motivate policy heuristics that evaluate interventions relative to local diffusion potential, make successful outcomes visible when payoff cues matter and tailor seeding to the prevailing mix of learning rules.

Analogies are a fundamental part of our cognition and communication. Humans are also a social species with shared cultural repertoires learnt throughout our lifetimes. As such, we expect analogies to enable the learning of complex, novel information by communication that takes advantage of shared cultural information. Here, we demonstrate the plausibility of this proposal and clarify its scope through computational modelling. We first model the individual-level process of learning via analogy with access to a shared repertoire of cultural information, using NK landscapes to represent high-dimensional solution spaces for complex problems. We then analyse a model of population dynamics to consider the conditions under which analogical communication will evolve and be maintained when costly. Our analyses suggest that even when analogy use is costly in terms of both search time and memory constraints, it can nevertheless be advantageous by allowing learners to obtain high-quality solutions more efficiently and more often than those who do not learn via analogy.

Most research on social media considers them as supports for information transmission, explaining online success (and pathologies) by focusing on consumers’ biases and interests. This article takes a different perspective, applying ideas from an ecological approach to culture. Success online depends both on the intrinsic appeal of content to receivers and on how well content serves producers’ strategic goals within the constraints and affordances of specific platforms. These goals include reputation management, coalition building and identity management, and coordination or participation in shared activities. Transmission is often a by-product of these motivations, and replication fidelity plays a limited role compared with transformations that adapt content to local incentives. Finally, the article suggests that platforms and communities can be understood as distinct niches, each characterised by different audience structures, affordances, metrics, and algorithmic pressures. This perspective reframes persistent debates on social media dynamics, including misinformation, radicalisation and polarisation, and the reasons behind online success.

Current debates concerning the use of digital technology often focus on privacy, yet privacy attitudes and behaviour are remarkably under-theorized, and relatively little empirical research has investigated privacy beyond the realm of digital communications. Building on evolutionary scholarship on information exchange, we outline a theoretical model in which cultural concepts of privacy reflect the workings of evolved psychological mechanisms that aim to regulate others’ access to fitness-relevant information towards adaptive ends. Results of two initial U.S. vignette studies distributed via Prolific (n = 425, 120) support the core predictions of this model, suggesting that people may have implicit and unstated assumptions regarding how information spreads in social environments. Specifically, participants’ privacy evaluations were predicted by whether information was intentionally acquired, the extent to which information was transmitted, and an individual’s position in an information transfer event. Importantly, how information was acquired and the nature of its transmission constituted independent but interacting influences on privacy perceptions. Additionally, results suggest the location within shared social networks of the individual to whom information is transmitted is used as a proxy for the potential costs of dissemination.

In this paper, we chart an emerging academic terrain: cultural evolution of the arts, which is a theory-driven exploration of artistic dynamics, often done with large datasets of music, literature, movies, paintings, or games. This field has grown at the intersection of cultural evolution theory and several academic fields: computational humanities, anthropology, network science, and others, and poses interesting challenges for each of them. What constitutes artistic transmission in the first place? Is it possible to find recurring patterns in artistic history – and how much data is needed for that? What makes the evolution of the arts different from the evolution of other forms of knowledge? We discuss all these problems in this paper. Additionally, we perform a bibliometric analysis of this field and explore a co-citation network of the works on artistic evolution. Finally, we highlight major challenges for this field in the future, as the arts are rapidly evolving in the digital age.

Individual social identities indicate group affiliations and are typically associated with group-typical preferences, signals that indicate group membership, and the propensity to condition actions on the social signals of others, resulting in group-differentiated interaction norms. Past work modelling identity signalling and co-ordination has typically assumed that individuals belong to one of a discrete set of groups. Yet individuals can simultaneously belong to multiple groups, which may be nested within larger groupings. Here, we introduce the generalized Bach or Stravinsky game, a co-ordination game with ordered preferences, which allows us to construct a model that captures the overlapping and hierarchical nature of social identity. Our model unifies several prior results into a single framework, including results related to co-ordination, minority disadvantage, and cross-cultural competence. Our model also allows agents to express complex social identities through multidimensional signalling, which we use to explore a variety of complex group structures. Our consideration of intersectional identities exposes flaws in naive measures of group structure, illustrating how empirical studies may overlook some social identities if they do not consider the behaviours that those identities function to afford.

Human handedness results from the interplay of genetic and cultural influences. A gene-culture co-evolutionary model for handedness was introduced by Laland et al. (1995), and this study generalizes that model and the related analysis. We address ambiguities in the original methodology, particularly regarding maximum-likelihood estimation, and incorporate sex differences in cultural transmission. By fitting this extended framework to existing familial and twin datasets, we demonstrate that accounting for criterion shifts significantly improves model fit and parameter estimation accuracy. We find stronger maternal than paternal effects on handedness, with daughters exhibiting greater sensitivity to these effects than sons. We provide an open-source Python implementation of the model, which is a robust platform for comparing gene-culture models and applying them to diverse datasets.

The prevalence of religious beliefs and practices is puzzling from an evolutionary perspective, but previous research has suggested that religious traditions may provide cooperative benefits and improve well-being. Seemingly in contrast to this claim are worldwide secularization trends in which people disaffiliate from religions and abandon belief in God. Theorists have suggested that diminished pressures on cooperation and well-being no longer motivate individuals to seek religious benefits and pay the associated participation costs. We investigate this claim using the National Study of Youth and Religion dataset, which tracks the development of religiosity among US Christians from adolescence to young adulthood (n = 3,370). Using a lagged panel design, we found that material security in Wave 1 (early adolescence) predicts a decrease in belief in God in Wave 4 (young adulthood), although this association is rather small. This result provides some support for the hypothesis that participation in religious traditions is associated with living in an insecure socio-ecology, where religious systems may still confer benefits on their members; yet it is not the only driver of secularization. We conclude with a call for further research using more nuanced measures and larger sample sizes to provide deeper insights into the potentially adaptive nature of cultural systems.

Archaeologists in North America often think of the bow and arrow as appearing more or less instantaneously, a conception baked into many culture-historical schemes. However, this specialized technology likely has a more complex history. From a single Old World origin, it is thought to have spread throughout North America from the Arctic after about 5000 cal BP. From there, it seems to have moved from north to south, but the specific timing of the arrival of this important technology is not known in great detail throughout most of California. Rather than using typological or culture-historical categories to discern this technological replacement, this study plots salient artifact attributes from a large sample of projectile points from central and northern California through continuous time to provide more detail on the timing of the spread of this important prehistoric technology. Results suggest the bow and arrow entered northeastern California before 2000 cal BP and moved southward, arriving at the Sacramento–San Joaquin Delta as much as 1,000 years later. The changepoint analysis method introduced here should be broadly applicable to a wide variety of similar archaeological patterns.

This article introduces a strategy for the large-scale corpus analysis of music audio recordings, aimed at identifying long-term trends and testing hypotheses regarding the repertoire represented in a given corpus. Our approach centers on computing evolution curves (ECs), which map style-relevant features, such as musical complexity, onto historical timelines. Unlike traditional approaches that rely on sheet music, we use audio recordings, leveraging their widespread availability and the performance nuances they capture. We also emphasize the benefits of pitch-class features based on deep learning, which improve the robustness and accuracy of tonal complexity measures compared to traditional signal processing methods. Addressing the frequent lack of exact work dates (year of composition) in historical corpora, we propose a heuristic method that aligns works with timelines using composers’ life dates. This method effectively preserves historical trends with minimal deviation compared to using actual work dates, as validated against available metadata from the Carus Audio Corpus, which spans 450 years of choral and sacred music and contains 5,729 tracks with detailed metadata. We demonstrate the utility of our strategy through case studies of this corpus, showing how ECs provide insights into stylistic developments that confirm expectations from musicology, thus highlighting the potential of computational studies in this field. For example, we observe a steady increase in tonal complexity from the Renaissance through the Baroque period, stable complexity levels in the 19th and 20th centuries, and consistently higher complexity in minor-key works compared to major-key works. Our visualizations also reveal that vocal music was more complex than instrumental music in the 18th century, but less complex in the 20th century. Finally, we conduct comparative analyses of individual composers, exploring how historical and biographical contexts may have influenced their works. Our findings highlight the potential of this strategy for computational corpus studies in musicological research.

Culture consists of practices – behaviour patterns – shared by members of a group. Some attempts to demonstrate evolution of cultural practices in the laboratory have shown evolution of material products, such as paper aeroplanes. Some attempts have shown evolution of actual group behaviour. The present experiments demonstrated evolution of group coordination across generations in punishing defection in a public-goods game. Cost of punishing defection varied across replicates that consisted of series of groups (generations) of 10 undergraduates each. Each generation played the game anonymously for 10 rounds and could write messages to the other participants and punish defection every round. The effectiveness of punishment depended on the number of participants choosing to punish. In Experiment 1, cultural transmission from generation to generation consisted of written advice from one generation read aloud to the next generation. In Experiment 2, transmission from generation to generation consisted of having some participants return from the previous group. The cost of punishing varied across replicates: zero, one, two or five cents. In both experiments, the evolution of altruistic punishing was strongly dependent on the cost of punishing. The results add to plausibility of studying evolution of complex behaviour patterns like cooperation in the laboratory.