Scientists provide important information to the public. Whether that information influences decision-making depends on trust. In the USA, gaps in trust in scientists have been stable for 50 years: women, Black people, rural residents, religious people, less educated people and people with lower economic status express less trust than their counterparts (who are more represented among scientists). Here we probe the factors that influence trust. We find that members of the less trusting groups exhibit greater trust in scientists who share their characteristics (for example, women trust women scientists more than men scientists). They view such scientists as having more benevolence and, in most cases, more integrity. In contrast, those from high-trusting groups appear mostly indifferent about scientists' characteristics. Our results highlight how increasing the presence of underrepresented groups among scientists can increase trust. This means expanding representation across several divides-not just gender and race/ethnicity but also rurality and economic status.

Although collision-like causes are fundamental in philosophical and psychological theories of causation, humans conceptualize many events as causes that lack direct contact. Here we argue that how people think and talk about different causes is deeply connected, and investigate how children learn this mapping. If Andy hits Suzy with his bike, Suzy falls into a fence and it breaks, Andy 'caused' the fence to break but Suzy 'broke' it. If Suzy forgets sunscreen and gets sunburned, the absence of sunscreen 'caused' Suzy's sunburn, but the sun 'burned' her skin. We tested 691 children and 270 adults. Four-year-old children mapped 'caused' to distal causes and 'broke' to proximal causes (Experiment 1). Although 4-year-old children did not map 'caused' to absences until later (Experiment 2), they already referred to absences when asked 'why' an outcome occurred (Experiment 3). Our findings highlight the role of semantics and pragmatics in developing these mappings.

Climate-related disasters such as wildfires and floods pose escalating risks to communities worldwide, yet motivating individuals to adopt protective measures remains a persistent challenge. In a pre-registered field experiment with 12,985 Australian homeowners in wildfire-prone areas, we demonstrate that a simple behavioural intervention-integrating proximal cues, such as participants' suburbs, into climate risk communications-significantly increases engagement. Participants who received localized messages were twice as likely to seek further information about wildfire preparedness compared with those who received generic communications (odds ratio of 2.03, 95% confidence interval of 1.33 to 3.16). This effect highlights the power of behavioural interventions in addressing barriers to climate adaptation, particularly by reducing psychological distance and fostering place attachment. By making abstract climate risks tangible and personally relevant, the intervention nudges individuals towards action. These findings suggest a scalable, low-cost approach for enhancing disaster preparedness, offering insights for leveraging behavioural science to mitigate the impact of climate-related disasters.

Computational modelling is a powerful tool for uncovering hidden processes in observed data, yet it faces underappreciated challenges. Among these, determining appropriate sample sizes for computational studies remains a critical but overlooked issue, particularly for model selection analyses. Here we introduce a power analysis framework for Bayesian model selection, a method widely used to choose the best model among alternatives. Our framework reveals that while power increases with sample size, it decreases as more models are considered. Using this framework, we empirically demonstrate that psychology and human neuroscience studies often suffer from low statistical power in model selection. A total of 41 of 52 studies reviewed had less than 80% probability of correctly identifying the true model. The field also heavily relies on fixed effects model selection, which we demonstrate has serious statistical issues, including high false positive rates and pronounced sensitivity to outliers.

Reinforcement learning (RL) algorithms have had tremendous success accounting for reward-based learning across species, including instrumental learning in contextual bandit tasks, and they capture variance in brain signals. However, reward-based learning in humans recruits multiple processes, including memory and choice perseveration; their contributions can easily be mistakenly attributed to RL computations. Here I investigate how much of reward-based learning behaviour is supported by RL computations in a context where other processes can be factored out. Reanalysis and computational modelling of 7 datasets (n = 594) in diverse samples show that in this instrumental context, reward-based learning is best explained by a combination of a fast working-memory-based process and a slower habit-like associative process, neither of which can be interpreted as a standard RL-like algorithm on its own. My results raise important questions for the interpretation of RL algorithms as capturing a meaningful process across brain and behaviour.

Humans accumulate extensive repertoires of culturally transmitted information, reaching breadths exceeding any individual's innovation capacity (culturally dependent repertoires). It is unclear whether other animals require social learning to acquire adult-like breadths of information in the wild, including by key developmental milestones, or whether animals are capable of constructing their knowledge repertoires primarily through independent exploration. We investigated whether social learning mediates orangutans' diet-repertoire development, by translating an extensive dataset describing wild orangutans' behaviour into an empirically validated agent-based model. In this model, diets reliably developed to adult-like breadths only when simulated immatures benefited from multiple forms of social learning. Moreover, social learning was required for diets to reach adult-like breadths by the age immatures become independent from their mothers. This implies that orangutan diets constitute culturally dependent repertoires, with social learning enhancing the rate and outcomes of diet development past individual potentials. We discuss prospective avenues for researching the building of cultural repertoires in hominids and other species.

The invention of agriculture is widely thought to have spurred the emergence of large-scale human societies. It has since been argued that only intensive agriculture can provide enough surplus for emerging states. Others have proposed it was the taxation potential of cereal grains that enabled the formation of states, making writing a critical development for recording those taxes. Here we test these hypotheses by mapping trait data from 868 cultures worldwide onto a language tree representing the relationships between cultures globally. Bayesian phylogenetic analyses indicate that intensive agriculture was as likely the result of state formation as its cause. By contrast, grain cultivation most likely preceded state formation. Grain cultivation also predicted the subsequent emergence of taxation. Writing, although not lost once states were formed, more likely emerged in tax-raising societies, consistent with the proposal that it was adopted to record those taxes. Although consistent with theory, a causal interpretation of the associations we identify is limited by the assumptions of our phylogenetic model, and several of the results are less reliable owing to the small sample size of some of the cross-cultural data we use.

Human language has a distinct systematic structure, where utterances break into individually meaningful words that are combined to form phrases. Here we show that natural-language-like systematicity arises in codes that are constrained by a statistical measure of complexity called predictive information, also known as excess entropy. Predictive information is the mutual information between the past and future of a stochastic process. In simulations, we find that codes that minimize predictive information break messages into groups of approximately independent features that are expressed systematically and locally, corresponding to words and phrases. Next, drawing on cross-linguistic text corpora, we find that actual human languages are structured in a way that yields low predictive information compared with baselines at the levels of phonology, morphology, syntax and lexical semantics. Our results establish a link between the statistical and algebraic structure of language and reinforce the idea that these structures are shaped by communication under general cognitive constraints.

Human languages show astonishing variety, yet their diversity is constrained by recurring patterns. Linguists have long argued over the extent and causes of these grammatical 'universals'. Using Grambank-a comprehensive database of grammatical features across the world's languages-we tested 191 proposed universals with Bayesian analyses that account for both genealogical descent and geographical proximity. We find statistical support for about a third of the proposed linguistic universals. The majority of these concern word order and hierarchical universals: two types that have featured prominently in earlier work. Evolutionary analyses show that languages tend to change in ways that converge on these preferred patterns. This suggests that, despite the vast design space of possible grammars, languages do not evolve entirely at random. Shared cognitive and communicative pressures repeatedly push languages towards similar solutions.

Human behaviour has been identified as a key dynamic in sustainable waste management and circular economy research. Drawing on recent behaviour publications from both fields, this Perspective highlights three issues relating to how they frame people. First, reference to 'consumers' in circular economy research contrasts with 'people' in some sustainable waste management papers. This represents an artificial separation of approaches to activities that are interwoven; furthermore, it implicitly defines a business agenda for the circular economy. Second, research into behaviour needs a broader methodological approach to identify variable needs and address underlying contextual and structural issues. Third, attention is needed to ongoing inequalities within and between countries that limit the effectiveness of circular economy implementation. Future research in these fields should prioritize human-centred approaches, including critical realism and qualitative methods, to uncover the socio-political constraints on behaviour and guide sustainability strategies that address the needs of people.

Historical efforts to deinstitutionalize those experiencing mental illness in the USA have inadvertently positioned police officers as the typical first responders to emergency calls involving mental health crises and empower them to initiate involuntary psychiatric detentions. Although potentially lifesaving, such detentions are controversial and costly, and they may be medically inappropriate for some of those detained. Here we present evidence from two quasi-experimental designs on the causal effects of a 'co-responder' programme that pairs mental health professionals with police officers as first responders on qualified emergency calls. The results indicate that a co-responder programme reduced the frequency of involuntary psychiatric detentions by 16.5% (that is, 370 fewer detentions over 2 years; b = -0.180, 95% confidence interval -0.325 to -0.034) but had no detectable effect on programme-related calls for service, criminal offences or arrests. Complementary results based on incident-level data suggest this reduction reflects both a co-responder's influence on the disposition of an individual incident and a reduction in future mental health emergencies.

There is an urgent need for targeted, evidence-based interventions to build resilience to misinformation among social media's most avid users: adolescents. Research on misinformation susceptibility is mostly focused on adults. However, adolescents encounter different types of (mis)information and undergo rapid social, emotional and cognitive changes. These changes can increase vulnerability to misinformation through social influence, emotional manipulation and cognitive biases, while also offering unique opportunities for resilience. Taking a developmental perspective, we outline how adolescents' susceptibility to misinformation differs from that of adults, propose a research agenda to systematically study these processes and introduce a Bayesian framework of belief updating tailored to social media contexts. Finally, we highlight how these insights inform age-appropriate interventions to promote resilience. This Perspective underscores the vital role that social sciences have in understanding and combating the harmful influence of misinformation on youth's beliefs and behaviours, while leveraging their strengths.