Pub Date : 2025-12-23DOI: 10.1038/s41562-025-02331-1
Dawei Wang, Difang Huang, Haipeng Shen, Brian Uzzi
Human–machine partnerships are increasingly used to address grand societal challenges, yet knowledge of the comparative strengths of humans and machines to innovate is nascent. Here we compare the ability of humans (N = 9,198) and large language models (LLMs, N = 215,542 observations) to generate novel ideas in an established creativity task. We present three key results. First, human creativity on average is slightly higher than that of LLMs. Second, creativity differences are pronounced at the extremes of the distribution, with humans exhibiting greater variability and higher levels of creativity in the right-hand tail of the distribution. Third, attempts to increase the creativity of LLMs through instructing LLMs to take on genius personas or different demographic roles lifted performance up to a threshold beyond which the output became opposite real-life patterns, whereas strategic prompt-engineering efforts yielded mixed to negative results. We discuss the implications of our findings for human–machine collaboration and problem solving.
人机合作越来越多地用于解决重大的社会挑战，但关于人类和机器在创新方面的比较优势的知识还处于萌芽阶段。在这里，我们比较了人类（N = 9198）和大型语言模型（llm, N = 215,542个观察结果）在既定创造力任务中产生新想法的能力。我们提出了三个关键结果。首先，人类的创造力平均略高于法学硕士。其次，创造力差异在分布的极端是明显的，在分布的右尾部，人类表现出更大的可变性和更高水平的创造力。第三，试图通过指导法学硕士扮演天才角色或不同的人口角色来提高法学硕士的创造力，将绩效提升到一个阈值，超过这个阈值，产出就会变成与现实生活相反的模式，而战略性的即时工程努力产生了好坏参半的结果。我们讨论了我们的发现对人机协作和问题解决的影响。
{"title":"A large-scale comparison of divergent creativity in humans and large language models","authors":"Dawei Wang, Difang Huang, Haipeng Shen, Brian Uzzi","doi":"10.1038/s41562-025-02331-1","DOIUrl":"https://doi.org/10.1038/s41562-025-02331-1","url":null,"abstract":"Human–machine partnerships are increasingly used to address grand societal challenges, yet knowledge of the comparative strengths of humans and machines to innovate is nascent. Here we compare the ability of humans (N = 9,198) and large language models (LLMs, N = 215,542 observations) to generate novel ideas in an established creativity task. We present three key results. First, human creativity on average is slightly higher than that of LLMs. Second, creativity differences are pronounced at the extremes of the distribution, with humans exhibiting greater variability and higher levels of creativity in the right-hand tail of the distribution. Third, attempts to increase the creativity of LLMs through instructing LLMs to take on genius personas or different demographic roles lifted performance up to a threshold beyond which the output became opposite real-life patterns, whereas strategic prompt-engineering efforts yielded mixed to negative results. We discuss the implications of our findings for human–machine collaboration and problem solving.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"371 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808145","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 : 2025-12-23DOI: 10.1038/s41562-025-02361-9
Erik Lamontagne, Vincent Leroy, Sean Howell, Sylvie Boyer, Bruno Ventelou
Here we explore the well-being of sexual and gender diverse (LGBTQ+) people using three socioecological dimensions of homophobia, family, community and national and their socioeconomic status via a convenience sample of 82,324 participants. Participants from the Middle East and North Africa reported the lowest subjective well-being (mean 4.78, s.d. of 2.70), followed by Eastern Europe and Central Asia (mean 5.22, s.d. of 2.13). The Structural Homophobic Climate Index (β = −1.68, 95% confidence interval (CI) −2.38 to −0.99) and family-level homophobia (β = −0.84, 95% CI −0.87 to −0.81) were negatively related to LGBTQ+ well-being. Economic precarity significantly interacted with the negative association between homophobia and participants’ well-being. The weight of a country’s homophobic climate on well-being was nearly halved for economically secure participants compared with those economically deprived. Participants unaware of their human immunodeficiency virus status reported the lowest well-being (β = −0.20, 95% CI −0.23 to −0.16) controlling for homophobia. Public health measures should address homophobic stigma and discrimination, focusing on the lowest socioeconomic strata.
{"title":"Homophobia, economic precarity and the well-being of sexual and gender diverse people in a 153-country survey","authors":"Erik Lamontagne, Vincent Leroy, Sean Howell, Sylvie Boyer, Bruno Ventelou","doi":"10.1038/s41562-025-02361-9","DOIUrl":"https://doi.org/10.1038/s41562-025-02361-9","url":null,"abstract":"Here we explore the well-being of sexual and gender diverse (LGBTQ+) people using three socioecological dimensions of homophobia, family, community and national and their socioeconomic status via a convenience sample of 82,324 participants. Participants from the Middle East and North Africa reported the lowest subjective well-being (mean 4.78, s.d. of 2.70), followed by Eastern Europe and Central Asia (mean 5.22, s.d. of 2.13). The Structural Homophobic Climate Index (β = −1.68, 95% confidence interval (CI) −2.38 to −0.99) and family-level homophobia (β = −0.84, 95% CI −0.87 to −0.81) were negatively related to LGBTQ+ well-being. Economic precarity significantly interacted with the negative association between homophobia and participants’ well-being. The weight of a country’s homophobic climate on well-being was nearly halved for economically secure participants compared with those economically deprived. Participants unaware of their human immunodeficiency virus status reported the lowest well-being (β = −0.20, 95% CI −0.23 to −0.16) controlling for homophobia. Public health measures should address homophobic stigma and discrimination, focusing on the lowest socioeconomic strata.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"47 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808177","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 : 2025-12-23DOI: 10.1038/s41562-025-02366-4
Jianan Huang, Cong Cao, Hong Liu
China’s national academies have long served as barometers of academic development and scientific prestige. We use publicly available information to develop a dataset comprising 3,534 academy member profiles spanning 1905 to 2023. Using this dataset, we examine the evolving composition of China’s academic elite. Here we show that despite increasing globalization, the proportion of foreign-educated academy members has declined, while scholars from underrepresented regions—Western China and developing countries (or the Global South)—have benefited from preferential inclusive policies. Some elite-level returnee academics experience research underperformance upon returning. These trends reflect a broader shift towards academic indigenization and have wider implications for meritocracy, mobility and the sustainability of China’s talent strategies. This study examines complex reasons behind the above developments.
{"title":"Indigenization and inclusion in Chinese academia","authors":"Jianan Huang, Cong Cao, Hong Liu","doi":"10.1038/s41562-025-02366-4","DOIUrl":"https://doi.org/10.1038/s41562-025-02366-4","url":null,"abstract":"China’s national academies have long served as barometers of academic development and scientific prestige. We use publicly available information to develop a dataset comprising 3,534 academy member profiles spanning 1905 to 2023. Using this dataset, we examine the evolving composition of China’s academic elite. Here we show that despite increasing globalization, the proportion of foreign-educated academy members has declined, while scholars from underrepresented regions—Western China and developing countries (or the Global South)—have benefited from preferential inclusive policies. Some elite-level returnee academics experience research underperformance upon returning. These trends reflect a broader shift towards academic indigenization and have wider implications for meritocracy, mobility and the sustainability of China’s talent strategies. This study examines complex reasons behind the above developments.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"27 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808175","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 : 2025-12-23DOI: 10.1038/s41562-025-02359-3
Jacques Pesnot Lerousseau, Christopher Summerfield
Do humans learn like transformers? We trained both humans (n = 530) and transformer networks on a rule learning task where they had to respond to a query in a sequence. At test, we measured ‘in-context’ learning (generalize the rule to novel queries) and ‘in-weights’ learning (recall past experiences from memory). Manipulating the diversity and redundancy of examples in the training distribution, we found that humans and transformer networks respond in very similar ways. In both types of learner, redundancy and diversity trade off in driving in-weights and in-context learning, respectively, whereas a composite distribution with a balanced mix of redundancy and diversity allows the two strategies to be used in tandem. However, we also found that while humans benefit from dynamic training schedules that emphasize diverse examples early, transformers do not. So, while the same data-distributional properties promote learning in humans and transformer networks, only people benefit from curricula.
{"title":"Shared sensitivity to data distribution during learning in humans and transformer networks","authors":"Jacques Pesnot Lerousseau, Christopher Summerfield","doi":"10.1038/s41562-025-02359-3","DOIUrl":"https://doi.org/10.1038/s41562-025-02359-3","url":null,"abstract":"Do humans learn like transformers? We trained both humans (n = 530) and transformer networks on a rule learning task where they had to respond to a query in a sequence. At test, we measured ‘in-context’ learning (generalize the rule to novel queries) and ‘in-weights’ learning (recall past experiences from memory). Manipulating the diversity and redundancy of examples in the training distribution, we found that humans and transformer networks respond in very similar ways. In both types of learner, redundancy and diversity trade off in driving in-weights and in-context learning, respectively, whereas a composite distribution with a balanced mix of redundancy and diversity allows the two strategies to be used in tandem. However, we also found that while humans benefit from dynamic training schedules that emphasize diverse examples early, transformers do not. So, while the same data-distributional properties promote learning in humans and transformer networks, only people benefit from curricula.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"24 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808189","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 : 2025-12-22DOI: 10.1038/s41562-025-02362-8
Ayberk Ozkirli, Andrey Chetverikov, David Pascucci
For over a century, research has shown that human perceptual decisions are systematically influenced by prior perceptual experiences, a phenomenon known as serial dependence. It has recently been suggested that serial dependence can improve perceptual decision-making by mitigating uncertainty and reducing variability in perceptual estimates—leading to a superiority effect. However, this claim remains largely untested. Here we present a large-scale analysis, compiling the most extensive dataset of serial dependence studies from the past decade. Contrary to the proposed superiority effect, our findings indicate that serial dependence deteriorates rather than improves perceptual decision-making. These results challenge prevailing models and emphasize the need to rethink serial dependence and its role in human perception, cognition and behaviour.
{"title":"Large-scale mega-analysis indicates that serial dependence deteriorates perceptual decision-making","authors":"Ayberk Ozkirli, Andrey Chetverikov, David Pascucci","doi":"10.1038/s41562-025-02362-8","DOIUrl":"https://doi.org/10.1038/s41562-025-02362-8","url":null,"abstract":"For over a century, research has shown that human perceptual decisions are systematically influenced by prior perceptual experiences, a phenomenon known as serial dependence. It has recently been suggested that serial dependence can improve perceptual decision-making by mitigating uncertainty and reducing variability in perceptual estimates—leading to a superiority effect. However, this claim remains largely untested. Here we present a large-scale analysis, compiling the most extensive dataset of serial dependence studies from the past decade. Contrary to the proposed superiority effect, our findings indicate that serial dependence deteriorates rather than improves perceptual decision-making. These results challenge prevailing models and emphasize the need to rethink serial dependence and its role in human perception, cognition and behaviour.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"5 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801579","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 : 2025-12-17DOI: 10.1038/s41562-025-02373-5
Federico Battiston, Valerio Capraro, Fariba Karimi, Sune Lehmann, Andrea Bamberg Migliano, Onkar Sadekar, Angel Sánchez, Matjaž Perc
Traditional social network models focus on pairwise interactions, overlooking the complexity of group-level dynamics that shape collective human behaviour. Here we outline how the framework of higher-order social networks—using mathematical representations beyond simple graphs—can more accurately represent interactions involving multiple individuals. Drawing from empirical data including scientific collaborations and contact networks, we demonstrate how higher-order structures reveal mechanisms of group formation, social contagion, cooperation and moral behaviour that are invisible in dyadic models. By moving beyond dyads, this approach offers a transformative lens for understanding the relational architecture of human societies, opening new directions for behavioural experiments, cultural dynamics, team science and group behaviour as well as new cross-disciplinary research. Battiston et al. discuss the emerging paradigm of higher-order network science and its applications to social systems and human dynamics.
{"title":"Higher-order interactions shape collective human behaviour","authors":"Federico Battiston, Valerio Capraro, Fariba Karimi, Sune Lehmann, Andrea Bamberg Migliano, Onkar Sadekar, Angel Sánchez, Matjaž Perc","doi":"10.1038/s41562-025-02373-5","DOIUrl":"10.1038/s41562-025-02373-5","url":null,"abstract":"Traditional social network models focus on pairwise interactions, overlooking the complexity of group-level dynamics that shape collective human behaviour. Here we outline how the framework of higher-order social networks—using mathematical representations beyond simple graphs—can more accurately represent interactions involving multiple individuals. Drawing from empirical data including scientific collaborations and contact networks, we demonstrate how higher-order structures reveal mechanisms of group formation, social contagion, cooperation and moral behaviour that are invisible in dyadic models. By moving beyond dyads, this approach offers a transformative lens for understanding the relational architecture of human societies, opening new directions for behavioural experiments, cultural dynamics, team science and group behaviour as well as new cross-disciplinary research. Battiston et al. discuss the emerging paradigm of higher-order network science and its applications to social systems and human dynamics.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"9 12","pages":"2441-2457"},"PeriodicalIF":15.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770629","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 : 2025-12-15DOI: 10.1038/s41562-025-02357-5
Haoyang Chen, Bo Liu, Shuyue Wang, Xiaosha Wang, Wenjuan Han, Xiaochun Wang, Yixin Zhu, Yanchao Bi
Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision–language DNN models, such as contrastive language–image pretraining (CLIP), with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited owing to the ‘black box’ nature of DNNs. Here we combine model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC to address this. Across four diverse datasets, CLIP consistently captured unique variance in VOTC neural representations, relative to both label-supervised (ResNet) and unsupervised (MoCo) models. This advantage tended to be left-lateralized at the group level, aligning with the human language network. Analyses of 33 patients who experienced a stroke revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP–brain correspondence and increased MoCo–brain correspondence, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging the manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models.
{"title":"Combined evidence from artificial neural networks and human brain-lesion models reveals that language modulates vision in human perception","authors":"Haoyang Chen, Bo Liu, Shuyue Wang, Xiaosha Wang, Wenjuan Han, Xiaochun Wang, Yixin Zhu, Yanchao Bi","doi":"10.1038/s41562-025-02357-5","DOIUrl":"https://doi.org/10.1038/s41562-025-02357-5","url":null,"abstract":"Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision–language DNN models, such as contrastive language–image pretraining (CLIP), with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited owing to the ‘black box’ nature of DNNs. Here we combine model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC to address this. Across four diverse datasets, CLIP consistently captured unique variance in VOTC neural representations, relative to both label-supervised (ResNet) and unsupervised (MoCo) models. This advantage tended to be left-lateralized at the group level, aligning with the human language network. Analyses of 33 patients who experienced a stroke revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP–brain correspondence and increased MoCo–brain correspondence, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging the manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"158 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759565","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 : 2025-12-15DOI: 10.1038/s41562-025-02378-0
Shin Ling Wu
{"title":"Implications of Australia’s under-16 social media ban","authors":"Shin Ling Wu","doi":"10.1038/s41562-025-02378-0","DOIUrl":"10.1038/s41562-025-02378-0","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 2","pages":"204-205"},"PeriodicalIF":15.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760087","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 : 2025-12-15DOI: 10.1038/s41562-025-02360-w
Thomas Davidson
Multimodal large language models (MLLMs) could enhance the accuracy of automated content moderation by integrating contextual information. This study examines how MLLMs evaluate hate speech through a series of conjoint experiments. Models are provided with a hate speech policy and shown simulated social media posts that systematically vary in slur usage, user demographics and other attributes. The decisions from MLLMs are benchmarked against judgements by human participants (n = 1,854). The results demonstrate that larger, more advanced models can make context-sensitive evaluations that are closely aligned with human judgement. However, pervasive demographic and lexical biases remain, particularly among smaller models. Further analyses show that context sensitivity can be amplified via prompting but not eliminated, and that some models are especially responsive to visual identity cues. These findings highlight the benefits and risks of using MLLMs for content moderation and demonstrate the utility of conjoint experiments for auditing artificial intelligence in complex, context-dependent applications.
{"title":"Multimodal large language models can make context-sensitive hate speech evaluations aligned with human judgement","authors":"Thomas Davidson","doi":"10.1038/s41562-025-02360-w","DOIUrl":"https://doi.org/10.1038/s41562-025-02360-w","url":null,"abstract":"Multimodal large language models (MLLMs) could enhance the accuracy of automated content moderation by integrating contextual information. This study examines how MLLMs evaluate hate speech through a series of conjoint experiments. Models are provided with a hate speech policy and shown simulated social media posts that systematically vary in slur usage, user demographics and other attributes. The decisions from MLLMs are benchmarked against judgements by human participants (n = 1,854). The results demonstrate that larger, more advanced models can make context-sensitive evaluations that are closely aligned with human judgement. However, pervasive demographic and lexical biases remain, particularly among smaller models. Further analyses show that context sensitivity can be amplified via prompting but not eliminated, and that some models are especially responsive to visual identity cues. These findings highlight the benefits and risks of using MLLMs for content moderation and demonstrate the utility of conjoint experiments for auditing artificial intelligence in complex, context-dependent applications.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"1 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759566","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 : 2025-12-08DOI: 10.1038/s41562-025-02377-1
Cason D. Schmit, Gogoal Falia, Philip Sanusi
Threats to democracy — such as voter suppression, misinformation and gerrymandering — interfere with voters’ ability to advance policies that serve their health interests. The result is a dangerous feedback cycle: barriers to democracy increase health vulnerability, which then compounds existing democratic barriers.
{"title":"Threats to democracy are threats to health","authors":"Cason D. Schmit, Gogoal Falia, Philip Sanusi","doi":"10.1038/s41562-025-02377-1","DOIUrl":"10.1038/s41562-025-02377-1","url":null,"abstract":"Threats to democracy — such as voter suppression, misinformation and gerrymandering — interfere with voters’ ability to advance policies that serve their health interests. The result is a dangerous feedback cycle: barriers to democracy increase health vulnerability, which then compounds existing democratic barriers.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"10 2","pages":"222-224"},"PeriodicalIF":15.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704513","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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