Machine behaviour.

Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.

Modelling animal network data in R using STRAND.

There have been recent calls for wider application of generative modelling approaches in applied social network analysis. At present, however, it remains difficult for typical end users-for example, field researchers-to implement generative network models, as there is a dearth of openly available software packages that make application of such models as simple as other, permutation-based approaches. Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of animal social network data that can be implemented using simple, base R syntax. To facilitate ease of use, we provide a tutorial demonstrating how STRAND can be used to model proportion, count or binary network data using stochastic block models, social relation models or a combination of the two modelling frameworks. STRAND facilitates the application of generative network models to a broad range of data found in the animal social networks literature.

Foraging and the importance of knowledge in Pemba, Tanzania: implications for childhood evolution.

Childhood is a period of life unique to humans. Childhood may have evolved through the need to acquire knowledge and subsistence skills. In an effort to understand the functional significance of childhood, previous research examined increases with age in returns to foraging across food resources. Such increases could be due to changes in knowledge, or other factors such as body size or strength. Here, we attempt to unpack these age-related changes. First, we estimate age-specific foraging returns for two resources. We then develop nonlinear structural equation models to evaluate the relative importance of ecological knowledge, grip strength and height in a population of part-time children foragers on Pemba island, Tanzania. We use anthropometric measures (height, strength, n = 250), estimates of ecological knowledge (n = 93) and behavioural observations for 63 individuals across 370 foraging trips. We find slower increases in foraging returns with age for trap hunting than for shellfish collection. We do not detect any effect of individual knowledge on foraging returns, potentially linked to information sharing within foraging parties. Producing accurate estimates of the distinct contribution of specific traits to an individual's foraging performance constitutes a key step in evaluating different hypotheses for the emergence of childhood.

The only thing that can stop bad causal inference is good causal inference.

In psychology, causal inference - both the transport from lab estimates to the real world and estimation on the basis of observational data - is often pursued in a casual manner. Underlying assumptions remain unarticulated; potential pitfalls are compiled in post-hoc lists of flaws. The field should move on to coherent frameworks of causal inference and generalizability that have been developed elsewhere.

Navigating cross-cultural research: methodological and ethical considerations.

The intensifying pace of research based on cross-cultural studies in the social sciences necessitates a discussion of the unique challenges of multi-sited research. Given an increasing demand for social scientists to expand their data collection beyond WEIRD (Western, educated, industrialized, rich and democratic) populations, there is an urgent need for transdisciplinary conversations on the logistical, scientific and ethical considerations inherent to this type of scholarship. As a group of social scientists engaged in cross-cultural research in psychology and anthropology, we hope to guide prospective cross-cultural researchers through some of the complex scientific and ethical challenges involved in such work: (a) study site selection, (b) community involvement and (c) culturally appropriate research methods. We aim to shed light on some of the difficult ethical quandaries of this type of research. Our recommendation emphasizes a community-centred approach, in which the desires of the community regarding research approach and methodology, community involvement, results communication and distribution, and data sharing are held in the highest regard by the researchers. We argue that such considerations are central to scientific rigour and the foundation of the study of human behaviour.

Conformity does not perpetuate suboptimal traditions in a wild population of songbirds.

Social learning is important to the life history of many animals, helping individuals to acquire new adaptive behavior. However despite long-running debate, it remains an open question whether a reliance on social learning can also lead to mismatched or maladaptive behavior. In a previous study, we experimentally induced traditions for opening a bidirectional door puzzle box in replicate subpopulations of the great tit Parus major Individuals were conformist social learners, resulting in stable cultural behaviors. Here, we vary the rewards gained by these techniques to ask to what extent established behaviors are flexible to changing conditions. When subpopulations with established foraging traditions for one technique were subjected to a reduced foraging payoff, 49% of birds switched their behavior to a higher-payoff foraging technique after only 14 days, with younger individuals showing a faster rate of change. We elucidated the decision-making process for each individual, using a mechanistic learning model to demonstrate that, perhaps surprisingly, this population-level change was achieved without significant asocial exploration and without any evidence for payoff-biased copying. Rather, by combining conformist social learning with payoff-sensitive individual reinforcement (updating of experience), individuals and populations could both acquire adaptive behavior and track environmental change.

Hidden Markov Models Reveal Tactical Adjustment of Temporally Clustered Courtship Displays in Response to the Behaviors of a Robotic Female.

We present a statistical approach-a custom-built hidden Markov model (HMM)-that is broadly applicable to the analysis of temporally clustered display events, as found in many animals, including birds, orthopterans, and anurans. This HMM can simultaneously estimate both the expected lengths of each animal's display bouts and their within-bout display rates. We highlight the HMM's ability to estimate changes in animals' display effort over time and across different social contexts, using data from male greater sage grouse (Centrocercus urophasianus). Male display effort was modeled across three sites in two experimental treatments (robotic female simulating interested or uninterested behavior) and in the presence or absence of live females. Across contexts, we show that sage grouse males primarily adjust their bout lengths rather than their within-bout display rates. Males' responses to female behavior were correlated with male mating success: males with more matings showed high display persistence regardless of female behavior, while males with fewer matings tended to invest selectively in females that were already showing interest in mating. Additionally, males with higher mating success responded more to the presence of a female than males with fewer matings did. We conclude with suggestions for adapting our HMM approach for use in other animal systems.

Pay-off-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate.

The type and variety of learning strategies used by individuals to acquire behaviours in the wild are poorly understood, despite the presence of behavioural traditions in diverse taxa. Social learning strategies such as conformity can be broadly adaptive, but may also retard the spread of adaptive innovations. Strategies like pay-off-biased learning, by contrast, are effective at diffusing new behaviour but may perform poorly when adaptive behaviour is common. We present a field experiment in a wild primate, Cebus capucinus, that introduced a novel food item and documented the innovation and diffusion of successful extraction techniques. We develop a multilevel, Bayesian statistical analysis that allows us to quantify individual-level evidence for different social and individual learning strategies. We find that pay-off-biased and age-biased social learning are primarily responsible for the diffusion of new techniques. We find no evidence of conformity; instead rare techniques receive slightly increased attention. We also find substantial and important variation in individual learning strategies that is patterned by age, with younger individuals being more influenced by both social information and their own individual experience. The aggregate cultural dynamics in turn depend upon the variation in learning strategies and the age structure of the wild population.

Variants at serotonin transporter and 2A receptor genes predict cooperative behavior differentially according to presence of punishment.

Punishment of free-riding has been implicated in the evolution of cooperation in humans, and yet mechanisms for punishment avoidance remain largely uninvestigated. Individual variation in these mechanisms may stem from variation in the serotonergic system, which modulates processing of aversive stimuli. Functional serotonin gene variants have been associated with variation in the processing of aversive stimuli and widely studied as risk factors for psychiatric disorders. We show that variants at the serotonin transporter gene (SLC6A4) and serotonin 2A receptor gene (HTR2A) predict contributions to the public good in economic games, dependent upon whether contribution behavior can be punished. Participants with a variant at the serotonin transporter gene contribute more, leading to group-level differences in cooperation, but this effect dissipates in the presence of punishment. When contribution behavior can be punished, those with a variant at the serotonin 2A receptor gene contribute more than those without it. This variant also predicts a more stressful experience of the games. The diversity of institutions (including norms) that govern cooperation and punishment may create selective pressures for punishment avoidance that change rapidly across time and space. Variant-specific epigenetic regulation of these genes, as well as population-level variation in the frequencies of these variants, may facilitate adaptation to local norms of cooperation and punishment.

Ontogeny of prosocial behavior across diverse societies.

Humans are an exceptionally cooperative species, but there is substantial variation in the extent of cooperation across societies. Understanding the sources of this variability may provide insights about the forces that sustain cooperation. We examined the ontogeny of prosocial behavior by studying 326 children 3-14 y of age and 120 adults from six societies (age distributions varied across societies). These six societies span a wide range of extant human variation in culture, geography, and subsistence strategies, including foragers, herders, horticulturalists, and urban dwellers across the Americas, Oceania, and Africa. When delivering benefits to others was personally costly, rates of prosocial behavior dropped across all six societies as children approached middle childhood and then rates of prosociality diverged as children tracked toward the behavior of adults in their own societies. When prosocial acts did not require personal sacrifice, prosocial responses increased steadily as children matured with little variation in behavior across societies. Our results are consistent with theories emphasizing the importance of acquired cultural norms in shaping costly forms of cooperation and creating cross-cultural diversity.

Neural basis of egalitarian behavior.

Individuals are willing to sacrifice their own resources to promote equality in groups. These costly choices promote equality and are associated with behavior that supports cooperation in humans, but little is known about the brain processes involved. We use functional MRI to study egalitarian preferences based on behavior observed in the "random income game." In this game, subjects decide whether to pay a cost to alter group members' randomly allocated incomes. We specifically examine whether egalitarian behavior is associated with neural activity in the ventromedial prefrontal cortex and the insular cortex, two regions that have been shown to be related to social preferences. Consistent with previous studies, we find significant activation in both regions; however, only the insular cortex activations are significantly associated with measures of revealed and expressed egalitarian preferences elicited outside the scanner. These results are consistent with the notion that brain mechanisms involved in experiencing the emotional states of others underlie egalitarian behavior in humans.

Increased costs of cooperation help cooperators in the long run.

It has long been proposed that cooperation should increase in harsh environments, but this claim still lacks theoretical underpinnings. We modeled a scenario in which benefiting from altruistic behavior was essential to survival and reproduction. We used a spatial agent-based model to represent mutual cooperation enforced by environmental adversity. We studied two factors, the cost of unreciprocated cooperation and the environmental cost of living, which highlight a conflict between the short- and long-term rewards of cooperation. In the long run, cooperation is favored because only groups with a sufficient number of cooperators will survive. In the short run, however, harsh environmental costs increase the advantage of defectors in cooperator-defector interactions because the loss of resources leads to death. Our analysis sheds new light on the evolution of cooperation via interdependence and illustrates how selfish groups can incur short-term benefits at the cost of their eventual demise. We demonstrate how harsh environments select for cooperative phenotypes and suggest an explanation for the adoption of cooperative breeding strategies in human evolution. We also highlight the importance of variable population size and the role of socio-spatial organization in harsh environments.

Egalitarian motives in humans.

Participants in laboratory games are often willing to alter others' incomes at a cost to themselves, and this behaviour has the effect of promoting cooperation. What motivates this action is unclear: punishment and reward aimed at promoting cooperation cannot be distinguished from attempts to produce equality. To understand costly taking and costly giving, we create an experimental game that isolates egalitarian motives. The results show that subjects reduce and augment others' incomes, at a personal cost, even when there is no cooperative behaviour to be reinforced. Furthermore, the size and frequency of income alterations are strongly influenced by inequality. Emotions towards top earners become increasingly negative as inequality increases, and those who express these emotions spend more to reduce above-average earners' incomes and to increase below-average earners' incomes. The results suggest that egalitarian motives affect income-altering behaviours, and may therefore be an important factor underlying the evolution of strong reciprocity and, hence, cooperation in humans.

Evolution of animal personalities.

Wolf et al. propose a model to explain the existence of animal personalities, consistent with behavioural differences among individuals in various contexts--their explanation is counter-intuitive and cogent. However, all models have their limits, and the particular life-history requirements of this one may be unclear. Here we analyse their model and clarify its organismal scope.

Correction to: 'The natural selection of bad science' (2016) by Paul E. Smaldino and Richard McElreath.

[This corrects the article DOI: 10.1098/rsos.160384.][This corrects the article DOI: 10.1098/rsos.160384.].

Replication of the natural selection of bad science.

This study reports an independent replication of the findings presented by Smaldino and McElreath (Smaldino, McElreath 2016 R. Soc. Open Sci. 3, 160384 (doi:10.1098/rsos.160384)). The replication was successful with one exception. We find that selection acting on scientist's propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors' original conclusions. We call for more replication studies for simulations as unique contributions to scientific quality assurance.

Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND.

Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions.

Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale - whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry - whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.

Machine culture.

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of 'machine culture', culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission and selection. Recommender algorithms are altering social learning dynamics. Chatbots are forming a new mode of cultural transmission, serving as cultural models. Furthermore, intelligent machines are evolving as contributors in generating cultural traits-from game strategies and visual art to scientific results. We provide a conceptual framework for studying the present and anticipated future impact of machines on cultural evolution, and present a research agenda for the study of machine culture.