Pub Date : 2025-10-08DOI: 10.1038/s44159-025-00497-z
Nature Reviews Psychology is encouraging authors to include a citation diversity statement to draw attention to citation imbalances and confirm that they made efforts to cite publications from a diverse group of researchers. Nature Reviews Psychology is encouraging authors to include a citation diversity statement to draw attention to citation imbalances and confirm that they made efforts to cite publications from a diverse group of researchers.
{"title":"Citation diversity statements","authors":"","doi":"10.1038/s44159-025-00497-z","DOIUrl":"10.1038/s44159-025-00497-z","url":null,"abstract":"Nature Reviews Psychology is encouraging authors to include a citation diversity statement to draw attention to citation imbalances and confirm that they made efforts to cite publications from a diverse group of researchers. Nature Reviews Psychology is encouraging authors to include a citation diversity statement to draw attention to citation imbalances and confirm that they made efforts to cite publications from a diverse group of researchers.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"4 10","pages":"617-617"},"PeriodicalIF":21.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44159-025-00497-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-02DOI: 10.1038/s44159-025-00493-3
Joyce C. He, Benjamin B. Keller, Sonia K. Kang
For decades, researchers, leaders and policymakers have worked to develop and implement interventions to increase the organizational representation of historically under-represented and marginalized groups, such as women in STEM and Black students attending prestigious universities. Despite substantial investments of time and resources, progress has stalled — and, worryingly, these efforts are facing growing backlash. In this Review, we examine diversity initiatives and policies grounded in psychological theory, particularly social cognition and person perception. We begin by outlining common organizational diversity strategies, identifying their psychological foundations and assessing their effectiveness. Although these approaches address an essential dimension of under-representation, they have limited effectiveness when applied alone because they primarily target individuals and intrapersonal processes (for example, stereotypes, prejudice and discrimination) while leaving systems that perpetuate inequality intact. We then consider adjacent literatures of choice architecture and judgement and decision-making, which offer complementary tools for advancing diversity by addressing both the systems in which people operate and the processes that shape individual behaviour. When combined with psychologically informed initiatives, these approaches offer a promising and sustainable path towards meaningful progress in organizational diversity. Despite widespread adoption of diversity initiatives grounded in psychological insights, evidence for their effectiveness is mixed. In this Review, He et al. present behavioural design as a promising avenue for bridging individual-level and system-level approaches to organizational diversity.
{"title":"Bridging individual-level and system-level approaches to advance psychology-based diversity initiatives","authors":"Joyce C. He, Benjamin B. Keller, Sonia K. Kang","doi":"10.1038/s44159-025-00493-3","DOIUrl":"10.1038/s44159-025-00493-3","url":null,"abstract":"For decades, researchers, leaders and policymakers have worked to develop and implement interventions to increase the organizational representation of historically under-represented and marginalized groups, such as women in STEM and Black students attending prestigious universities. Despite substantial investments of time and resources, progress has stalled — and, worryingly, these efforts are facing growing backlash. In this Review, we examine diversity initiatives and policies grounded in psychological theory, particularly social cognition and person perception. We begin by outlining common organizational diversity strategies, identifying their psychological foundations and assessing their effectiveness. Although these approaches address an essential dimension of under-representation, they have limited effectiveness when applied alone because they primarily target individuals and intrapersonal processes (for example, stereotypes, prejudice and discrimination) while leaving systems that perpetuate inequality intact. We then consider adjacent literatures of choice architecture and judgement and decision-making, which offer complementary tools for advancing diversity by addressing both the systems in which people operate and the processes that shape individual behaviour. When combined with psychologically informed initiatives, these approaches offer a promising and sustainable path towards meaningful progress in organizational diversity. Despite widespread adoption of diversity initiatives grounded in psychological insights, evidence for their effectiveness is mixed. In this Review, He et al. present behavioural design as a promising avenue for bridging individual-level and system-level approaches to organizational diversity.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"4 11","pages":"702-717"},"PeriodicalIF":21.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Self-harm has proven social contagion effects among young people. However, a comprehensive understanding of the complex dynamics that contribute to self-harm contagion in adolescents is lacking. In this Review, we synthesize evidence regarding the social contagion of self-harm in young people using a social ecological approach. At the individual level, psychological and neurobiological vulnerabilities increase young people’s susceptibility to social contagion. At the interpersonal level, social contagion of self-harm occurs through peer interactions and social media connections, as well as through family ties including parental, sibling and grandparental relationships. At the community level, social contagion is evident in high-risk clusters of young people in institutional settings (schools, universities, psychiatric hospitals and justice-involved youth institutes), on social media and in digital spaces, and in neighbourhoods, where socioeconomic disadvantage is a key structural constraint that amplifies self-harm contagion. At the societal level, media-regulation challenges, global pandemics and political context exacerbate the social contagion of self-harm by intensifying pre-existing risk factors across individual, interpersonal and community levels. We address these multilevel factors to bridge psychological and public health perspectives of social contagion dynamics and describe prevention and intervention efforts that might offer scalable, evidence-based solutions for mitigating self-harm among youth. Adolescents are particularly at risk for social contagion effects of self-harm. In this Review, Chen et al. specify factors that exacerbate self-harm social contagion at the individual, interpersonal, community and societal levels and discuss how to bridge psychological and public health perspectives to mitigate self-harm risk in this age group.
{"title":"A social ecological approach to social contagion of self-harm among young people","authors":"Xue Wen, Shufang Sun, Danhua Lin, Weihua Yue, Runsen Chen","doi":"10.1038/s44159-025-00495-1","DOIUrl":"10.1038/s44159-025-00495-1","url":null,"abstract":"Self-harm has proven social contagion effects among young people. However, a comprehensive understanding of the complex dynamics that contribute to self-harm contagion in adolescents is lacking. In this Review, we synthesize evidence regarding the social contagion of self-harm in young people using a social ecological approach. At the individual level, psychological and neurobiological vulnerabilities increase young people’s susceptibility to social contagion. At the interpersonal level, social contagion of self-harm occurs through peer interactions and social media connections, as well as through family ties including parental, sibling and grandparental relationships. At the community level, social contagion is evident in high-risk clusters of young people in institutional settings (schools, universities, psychiatric hospitals and justice-involved youth institutes), on social media and in digital spaces, and in neighbourhoods, where socioeconomic disadvantage is a key structural constraint that amplifies self-harm contagion. At the societal level, media-regulation challenges, global pandemics and political context exacerbate the social contagion of self-harm by intensifying pre-existing risk factors across individual, interpersonal and community levels. We address these multilevel factors to bridge psychological and public health perspectives of social contagion dynamics and describe prevention and intervention efforts that might offer scalable, evidence-based solutions for mitigating self-harm among youth. Adolescents are particularly at risk for social contagion effects of self-harm. In this Review, Chen et al. specify factors that exacerbate self-harm social contagion at the individual, interpersonal, community and societal levels and discuss how to bridge psychological and public health perspectives to mitigate self-harm risk in this age group.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"4 11","pages":"718-736"},"PeriodicalIF":21.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1038/s44159-025-00491-5
Adela C. Timmons, Jacqueline B. Duong, Sierra N. Walters, Kayla E. Carta, Grace A. Jumonville, Alyssa S. Carrasco, Daniela N. Romero, Matthew W. Ahle, Jonathan S. Comer, Ishita P. Khurd, Theodora Chaspari
Artificial intelligence (AI) holds immense potential to provide scalable, personalized and accessible solutions to mental healthcare. However, biases in AI systems might exacerbate current mental healthcare disparities, particularly for minoritized populations. In this Perspective, we introduce a model for bias reduction and inclusion through dynamic generative equity (adaptive AI), which has been designed to prioritize equity throughout the development and implementation of AI systems in mental health interventions. This model integrates fair-aware machine learning with co-creation techniques, combining quantitative methodologies to detect bias in AI algorithms with qualitative input from community collaborators to ensure cultural relevance and practical application. We describe the model’s procedures and iterative feedback loops, which ensure that AI-based interventions are culturally responsive and evolve dynamically with real-time feedback. We also discuss the model’s potential applications, current limitations and areas for future research. Machine learning algorithms can increase the effectiveness of mental health interventions, but biased systems might exacerbate current disparities in case identification and treatment. In this Perspective, Timmons et al. propose a model that integrates artificial intelligence methods with co-creation techniques to reduce bias and increase inclusion in mental healthcare.
{"title":"Bridging fair-aware artificial intelligence and co-creation for equitable mental healthcare","authors":"Adela C. Timmons, Jacqueline B. Duong, Sierra N. Walters, Kayla E. Carta, Grace A. Jumonville, Alyssa S. Carrasco, Daniela N. Romero, Matthew W. Ahle, Jonathan S. Comer, Ishita P. Khurd, Theodora Chaspari","doi":"10.1038/s44159-025-00491-5","DOIUrl":"10.1038/s44159-025-00491-5","url":null,"abstract":"Artificial intelligence (AI) holds immense potential to provide scalable, personalized and accessible solutions to mental healthcare. However, biases in AI systems might exacerbate current mental healthcare disparities, particularly for minoritized populations. In this Perspective, we introduce a model for bias reduction and inclusion through dynamic generative equity (adaptive AI), which has been designed to prioritize equity throughout the development and implementation of AI systems in mental health interventions. This model integrates fair-aware machine learning with co-creation techniques, combining quantitative methodologies to detect bias in AI algorithms with qualitative input from community collaborators to ensure cultural relevance and practical application. We describe the model’s procedures and iterative feedback loops, which ensure that AI-based interventions are culturally responsive and evolve dynamically with real-time feedback. We also discuss the model’s potential applications, current limitations and areas for future research. Machine learning algorithms can increase the effectiveness of mental health interventions, but biased systems might exacerbate current disparities in case identification and treatment. In this Perspective, Timmons et al. propose a model that integrates artificial intelligence methods with co-creation techniques to reduce bias and increase inclusion in mental healthcare.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"4 12","pages":"793-807"},"PeriodicalIF":21.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145675702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1038/s44159-025-00489-z
Bei Xiao, Chenxi Liao
Humans visually assess materials to estimate their physical properties — such as judging the ripeness of fruits — and to plan actions — such as planning steps on an icy road. Despite the complexity and diversity of materials in the world, humans can infer a wide range of material properties from a brief glance. Much previous research has focused on the visual mechanisms that underlie material perception, and it is often described as a task of mid-level vision. In this Review, we discuss the broader importance of material perception in cognition and action planning. We begin by describing the visual inference of material properties and then discuss its relationships with recognition, language and intuitive physics. We conclude that material perception should be considered part of the perception–action loop and highlight the multisensory nature of material perception in guiding actions. We end by discussing how deep learning facilitates the discovery of meaningful representations and generates testable models of material perception. By framing material perception as a process that dynamically integrates perception, cognition and action, we may improve our understanding of how humans interpret and interact with the physical world. Humans can infer a range of material properties from a brief glance. In this Review, Xiao and Liao discuss the varied facets of visual material perception, including its relationship to action planning and broader cognition.
{"title":"Material perception connects vision, cognition and action","authors":"Bei Xiao, Chenxi Liao","doi":"10.1038/s44159-025-00489-z","DOIUrl":"10.1038/s44159-025-00489-z","url":null,"abstract":"Humans visually assess materials to estimate their physical properties — such as judging the ripeness of fruits — and to plan actions — such as planning steps on an icy road. Despite the complexity and diversity of materials in the world, humans can infer a wide range of material properties from a brief glance. Much previous research has focused on the visual mechanisms that underlie material perception, and it is often described as a task of mid-level vision. In this Review, we discuss the broader importance of material perception in cognition and action planning. We begin by describing the visual inference of material properties and then discuss its relationships with recognition, language and intuitive physics. We conclude that material perception should be considered part of the perception–action loop and highlight the multisensory nature of material perception in guiding actions. We end by discussing how deep learning facilitates the discovery of meaningful representations and generates testable models of material perception. By framing material perception as a process that dynamically integrates perception, cognition and action, we may improve our understanding of how humans interpret and interact with the physical world. Humans can infer a range of material properties from a brief glance. In this Review, Xiao and Liao discuss the varied facets of visual material perception, including its relationship to action planning and broader cognition.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"4 11","pages":"687-701"},"PeriodicalIF":21.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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