Pub Date : 2025-11-20DOI: 10.1038/s41562-025-02342-y
George Baffour Awuah
Economist George Baffour Awuah discusses how a quiet revolution is helping to turn a waste crisis to economic opportunity in the Global South. But for scale, a supportive ecosystem is required.
{"title":"A quiet revolution is turning the waste crisis into opportunity","authors":"George Baffour Awuah","doi":"10.1038/s41562-025-02342-y","DOIUrl":"10.1038/s41562-025-02342-y","url":null,"abstract":"Economist George Baffour Awuah discusses how a quiet revolution is helping to turn a waste crisis to economic opportunity in the Global South. But for scale, a supportive ecosystem is required.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"9 11","pages":"2225-2226"},"PeriodicalIF":15.9,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1038/s41562-025-02354-8
Maria Raquel Passos Lima
Maria Raquel Lima is based in Brazil, where communities suffer owing to waste colonialism. She explains why polluters must pay and affected communities must lead the solutions.
{"title":"To decolonize waste, we must make sure the polluter pays","authors":"Maria Raquel Passos Lima","doi":"10.1038/s41562-025-02354-8","DOIUrl":"10.1038/s41562-025-02354-8","url":null,"abstract":"Maria Raquel Lima is based in Brazil, where communities suffer owing to waste colonialism. She explains why polluters must pay and affected communities must lead the solutions.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"9 11","pages":"2221-2222"},"PeriodicalIF":15.9,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1038/s41562-025-02370-8
Alessandro T. Gifford, Maya A. Jastrzębowska, Johannes J. D. Singer, Radoslaw M. Cichy
{"title":"Publisher Correction: In silico discovery of representational relationships across visual cortex","authors":"Alessandro T. Gifford, Maya A. Jastrzębowska, Johannes J. D. Singer, Radoslaw M. Cichy","doi":"10.1038/s41562-025-02370-8","DOIUrl":"10.1038/s41562-025-02370-8","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"9 12","pages":"2671-2671"},"PeriodicalIF":15.9,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41562-025-02370-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02325-z
Annemarie Verkerk, Olena Shcherbakova, Hannah J. Haynie, Hedvig Skirgård, Christoph Rzymski, Quentin D. Atkinson, Simon J. Greenhill, Russell D. Gray
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. Despite their great diversity, human languages are shaped by recurring grammatical universals. Verkerk et al. show that about one-third of the proposed universals hold cross-linguistically through analyses of the Grambank database.
{"title":"Enduring constraints on grammar revealed by Bayesian spatiophylogenetic analyses","authors":"Annemarie Verkerk, Olena Shcherbakova, Hannah J. Haynie, Hedvig Skirgård, Christoph Rzymski, Quentin D. Atkinson, Simon J. Greenhill, Russell D. Gray","doi":"10.1038/s41562-025-02325-z","DOIUrl":"10.1038/s41562-025-02325-z","url":null,"abstract":"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. Despite their great diversity, human languages are shaped by recurring grammatical universals. Verkerk et al. show that about one-third of the proposed universals hold cross-linguistically through analyses of the Grambank database.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 1","pages":"126-136"},"PeriodicalIF":15.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41562-025-02325-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02339-7
Thomas S. Dee, Jaymes Pyne
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. In a quasi-experimental analysis of emergency calls in California communities, Dee and Pyne find that having mental health first responders accompany police on qualified calls reduces the number of individuals placed in involuntary psychiatric detentions.
{"title":"Emergency mental health co-responders reduce involuntary psychiatric detentions in the USA","authors":"Thomas S. Dee, Jaymes Pyne","doi":"10.1038/s41562-025-02339-7","DOIUrl":"10.1038/s41562-025-02339-7","url":null,"abstract":"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. In a quasi-experimental analysis of emergency calls in California communities, Dee and Pyne find that having mental health first responders accompany police on qualified calls reduces the number of individuals placed in involuntary psychiatric detentions.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 1","pages":"148-155"},"PeriodicalIF":15.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41562-025-02339-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02348-6
Payam Piray
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. Piray shows a problem of low statistical power in many studies that use Bayesian model selection in the context of computational modelling in psychology and human neuroscience.
{"title":"Addressing low statistical power in computational modelling studies in psychology and neuroscience","authors":"Payam Piray","doi":"10.1038/s41562-025-02348-6","DOIUrl":"10.1038/s41562-025-02348-6","url":null,"abstract":"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. Piray shows a problem of low statistical power in many studies that use Bayesian model selection in the context of computational modelling in psychology and human neuroscience.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 2","pages":"347-356"},"PeriodicalIF":15.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41562-025-02348-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02340-0
Anne G. E. Collins
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. In this study, Collins proposes an alternative dual-process (working memory and habit) model of reinforcement learning in humans.
{"title":"A habit and working memory model as an alternative account of human reward-based learning","authors":"Anne G. E. Collins","doi":"10.1038/s41562-025-02340-0","DOIUrl":"10.1038/s41562-025-02340-0","url":null,"abstract":"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. In this study, Collins proposes an alternative dual-process (working memory and habit) model of reinforcement learning in humans.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 2","pages":"357-369"},"PeriodicalIF":15.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41562-025-02340-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1038/s41562-025-02335-x
We hypothesized that, if the olfactory system involves fine-grained sensorimotor feedback, similarly to what has been observed in other sensory systems, the brain might modulate sniffs in real time according to detailed perceptual features of odours. We analysed more than 13,000 sniffs in response to 160 distinct odours to show that sniff patterns reflect fine-grained perceptual information and are potentially modulated by the amygdala.
{"title":"Sniffing dynamics reflect fine differences in perception of odours","authors":"","doi":"10.1038/s41562-025-02335-x","DOIUrl":"10.1038/s41562-025-02335-x","url":null,"abstract":"We hypothesized that, if the olfactory system involves fine-grained sensorimotor feedback, similarly to what has been observed in other sensory systems, the brain might modulate sniffs in real time according to detailed perceptual features of odours. We analysed more than 13,000 sniffs in response to 160 distinct odours to show that sniff patterns reflect fine-grained perceptual information and are potentially modulated by the amygdala.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 1","pages":"14-15"},"PeriodicalIF":15.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1038/s41562-025-02338-8
Ili Ma, Mubashir Sultan, Anastasia Kozyreva, Wouter van den Bos
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. Adolescents are especially vulnerable to misinformation but also possess unique strengths. This Perspective outlines a forward-looking research agenda to understand these vulnerabilities and foster resilience through age-appropriate interventions.
{"title":"Understanding the impact of misinformation on adolescents","authors":"Ili Ma, Mubashir Sultan, Anastasia Kozyreva, Wouter van den Bos","doi":"10.1038/s41562-025-02338-8","DOIUrl":"10.1038/s41562-025-02338-8","url":null,"abstract":"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. Adolescents are especially vulnerable to misinformation but also possess unique strengths. This Perspective outlines a forward-looking research agenda to understand these vulnerabilities and foster resilience through age-appropriate interventions.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 1","pages":"18-28"},"PeriodicalIF":15.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1038/s41562-025-02326-y
Catastrophic forgetting is a common problem for artificial learning systems, but whether it occurs in humans is unclear. We revealed that both humans and neural networks show similar patterns of forgetting, which reflect a fundamental trade-off: reusing prior knowledge speeds up new learning but can corrupt old memories. Individuals differed in how they navigate this balance.
{"title":"Parallels between human and artificial minds when new learning erases old knowledge","authors":"","doi":"10.1038/s41562-025-02326-y","DOIUrl":"10.1038/s41562-025-02326-y","url":null,"abstract":"Catastrophic forgetting is a common problem for artificial learning systems, but whether it occurs in humans is unclear. We revealed that both humans and neural networks show similar patterns of forgetting, which reflect a fundamental trade-off: reusing prior knowledge speeds up new learning but can corrupt old memories. Individuals differed in how they navigate this balance.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 1","pages":"12-13"},"PeriodicalIF":15.9,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145498180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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