Nature Human Behaviour最新文献

{"title":"Becoming the ideal woman-of-colour academic for everyone but me","authors":"Yvonne Su","doi":"10.1038/s41562-024-02092-3","DOIUrl":"https://doi.org/10.1038/s41562-024-02092-3","url":null,"abstract":"Yvonne Su challenges the academy to stop tokenizing women of colour in academia. In this World View, she explains how embracing diversity must go beyond optics and calls for true transformation.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"59 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857712","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}

{"title":"Predicting replicability of COVID-19 social science preprints","authors":"","doi":"10.1038/s41562-024-01962-0","DOIUrl":"https://doi.org/10.1038/s41562-024-01962-0","url":null,"abstract":"This study assessed the replicability of COVID-19 social science preprints. Both beginners and experienced participants used a structured elicitation protocol to make better-than-chance predictions about the reliability of research claims under high uncertainty.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"41 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857711","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}

{"title":"Mutual inclusivity improves decision-making by smoothing out choice’s competitive edge","authors":"Xiamin Leng, Romy Frömer, Thomas Summe, Amitai Shenhav","doi":"10.1038/s41562-024-02064-7","DOIUrl":"https://doi.org/10.1038/s41562-024-02064-7","url":null,"abstract":"<p>Decisions form a central bottleneck to most tasks, one that people often experience as costly. Previous work proposes mitigating those costs by lowering one’s threshold for deciding. Here we test an alternative solution, one that targets the basis of most choice costs: the idea that choosing one option sacrifices others (mutual exclusivity). Across 6 studies (<i>N</i> = 565), we test whether this tension can be relieved by framing choices as inclusive (allowing selection of more than 1 option, as in buffets). We find that inclusivity makes choices more efficient by selectively reducing competition between potential responses as participants accumulate information for each of their options. Inclusivity also made participants feel less conflicted, especially when they could not decide which good option to keep or which bad option to get rid of. These inclusivity benefits were also distinguishable from the effects of manipulating decision threshold (increased urgency), which improved choices but not experiences thereof.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"147 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857714","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}

{"title":"Predicting the replicability of social and behavioural science claims in COVID-19 preprints","authors":"Alexandru Marcoci, David P. Wilkinson, Ans Vercammen, Bonnie C. Wintle, Anna Lou Abatayo, Ernest Baskin, Henk Berkman, Erin M. Buchanan, Sara Capitán, Tabaré Capitán, Ginny Chan, Kent Jason G. Cheng, Tom Coupé, Sarah Dryhurst, Jianhua Duan, John E. Edlund, Timothy M. Errington, Anna Fedor, Fiona Fidler, James G. Field, Nicholas Fox, Hannah Fraser, Alexandra L. J. Freeman, Anca Hanea, Felix Holzmeister, Sanghyun Hong, Raquel Huggins, Nick Huntington-Klein, Magnus Johannesson, Angela M. Jones, Hansika Kapoor, John Kerr, Melissa Kline Struhl, Marta Kołczyńska, Yang Liu, Zachary Loomas, Brianna Luis, Esteban Méndez, Olivia Miske, Fallon Mody, Carolin Nast, Brian A. Nosek, E. Simon Parsons, Thomas Pfeiffer, W. Robert Reed, Jon Roozenbeek, Alexa R. Schlyfestone, Claudia R. Schneider, Andrew Soh, Zhongchen Song, Anirudh Tagat, Melba Tutor, Andrew H. Tyner, Karolina Urbanska, Sander van der Linden","doi":"10.1038/s41562-024-01961-1","DOIUrl":"https://doi.org/10.1038/s41562-024-01961-1","url":null,"abstract":"<p>Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise (‘beginners’) updated their estimates and confidence in their judgements significantly more than groups with greater task expertise (‘experienced’). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners’ average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (<i>r</i>(98) = 0.48, <i>P</i> &lt; 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of ‘fast science’ under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"22 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857713","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}

{"title":"Challenges and promises of big team comparative cognition","authors":"Nicolás Alessandroni, Drew Altschul, Heidi A. Baumgartner, Marina Bazhydai, Sarah F. Brosnan, Krista Byers-Heinlein, Josep Call, Lars Chittka, Mahmoud Elsherif, Julia Espinosa, Marianne S. Freeman, Biljana Gjoneska, Onur Güntürkün, Ludwig Huber, Anastasia Krasheninnikova, Valeria Mazza, Rachael Miller, David Moreau, Christian Nawroth, Ekaterina Pronizius, Susana Ruiz-Fernández, Raoul Schwing, Vedrana Šlipogor, Ingmar Visser, Jennifer Vonk, Justin Yeager, Martin Zettersten, Laurent Prétôt","doi":"10.1038/s41562-024-02081-6","DOIUrl":"https://doi.org/10.1038/s41562-024-02081-6","url":null,"abstract":"Big team science has the potential to reshape comparative cognition research, but its implementation — especially in making fair comparisons between species, handling multisite variation and reaching researcher consensus — poses daunting challenges. Here, we propose solutions and discuss how big team science can transform the field.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"50 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840876","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}

{"title":"How human–AI feedback loops alter human perceptual, emotional and social judgements","authors":"Moshe Glickman, Tali Sharot","doi":"10.1038/s41562-024-02077-2","DOIUrl":"https://doi.org/10.1038/s41562-024-02077-2","url":null,"abstract":"<p>Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (<i>n</i> = 1,401 participants), we reveal a feedback loop where human–AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI’s influence, rendering them more susceptible to it. These findings uncover a mechanism wherein AI systems amplify biases, which are further internalized by humans, triggering a snowball effect where small errors in judgement escalate into much larger ones.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"47 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840878","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}

{"title":"The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation","authors":"Tabea Schoeler, Jean-Baptiste Pingault, Zoltán Kutalik","doi":"10.1038/s41562-024-02061-w","DOIUrl":"https://doi.org/10.1038/s41562-024-02061-w","url":null,"abstract":"<p>Although the use of short self-report measures is common practice in biobank initiatives, such a phenotyping strategy is inherently prone to reporting errors. To explore challenges related to self-report errors, we first derived a reporting error score in the UK Biobank (UKBB; <i>n</i> = 73,127), capturing inconsistent self-reporting in time-invariant phenotypes across multiple measurement occasions. We then performed genome-wide scans on the reporting error score, applied downstream analyses (linkage disequilibrium score regression and Mendelian randomization) and compared its properties to the UKBB participation propensity. Finally, we improved phenotype resolution for 24 measures and inspected the changes in genomic findings. We found that reporting error was present across all 33 assessed self-report measures, with repeatability levels as low as 47% (childhood body size). Reporting error was not independent from UKBB participation, evidenced by the negative genetic correlation between the two outcomes (<i>r</i>_g = −0.77), their shared causes (for example, education) and the loss in self-report accuracy following participation bias correction. Across all analyses, the impact of reporting error ranged from reduced power (for example, for gene discovery) to biased estimates (for example, if present in the exposure variable) and attenuation of genome-wide quantities (for example, 21% relative attenuation in SNP heritability for childhood height). Our findings highlight that both self-report accuracy and selective participation are competing biases and sources of poor reproducibility for biobank-scale research.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"89 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840879","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}

{"title":"Report uncertainty information to improve trust in science","authors":"Raul Cruz-Cano, David B. Allison","doi":"10.1038/s41562-024-02084-3","DOIUrl":"https://doi.org/10.1038/s41562-024-02084-3","url":null,"abstract":"The results of scientific studies should be accompanied by information that individuals can use to make uncertainty judgements. By including this information, we might increase trust in the scientific process. We advocate for scientists to use quantitative uncertainty informative data to provide this information when reporting results.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"49 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804536","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}

{"title":"Neuropsychiatric polygenic scores are weak predictors of professional categories","authors":"Georgios Voloudakis, Karen Therrien, Simone Tomasi, Veera M. Rajagopal, Shing Wan Choi, Ditte Demontis, John F. Fullard, Anders D. Børglum, Paul F. O’Reilly, Gabriel E. Hoffman, Panos Roussos","doi":"10.1038/s41562-024-02074-5","DOIUrl":"https://doi.org/10.1038/s41562-024-02074-5","url":null,"abstract":"<p>Polygenic scores (PGS) enable the exploration of pleiotropic effects and genomic dissection of complex traits. Here, in 421,889 individuals with European ancestry from the Million Veteran Program and UK Biobank, we examine how PGS of 17 neuropsychiatric traits are related to membership in 22 broad professional categories. Overall, we find statistically significant but weak (the highest odds ratio is 1.1 per PGS standard deviation) associations between most professional categories and genetic predisposition for at least one neuropsychiatric trait. Secondary analyses in UK Biobank revealed independence of these associations from observed fluid intelligence and sex-specific effects. By leveraging aggregate population trends, we identified patterns in the public interest, such as the mediating effect of education attainment on the association of attention-deficit/hyperactivity disorder PGS with multiple professional categories. However, at the individual level, PGS explained less than 0.5% of the variance of professional membership, and almost none after we adjusted for education and socio-economic status.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"82 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797014","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}

{"title":"How trait impressions of faces shape subsequent mental state inferences","authors":"Chujun Lin, Umit Keles, Mark A. Thornton, Ralph Adolphs","doi":"10.1038/s41562-024-02059-4","DOIUrl":"https://doi.org/10.1038/s41562-024-02059-4","url":null,"abstract":"<p>People form impressions of one another in a split second from faces. However, people also infer others’ momentary mental states on the basis of context—for example, one might infer that somebody feels encouraged from the fact that they are receiving constructive feedback. How do trait judgements of faces influence these context-based mental state inferences? In this Registered Report, we asked participants to infer the mental states of unfamiliar people, identified by their neutral faces, under specific contexts. To increase generalizability, we representatively sampled all stimuli from inclusive sets using computational methods. We tested four hypotheses: that trait impressions of faces (1) are correlated with subsequent mental state inferences in a range of contexts, (2) alter the dimensional space that underlies mental state inferences, (3) are associated with specific mental state dimensions in this space and (4) causally influence mental state inferences. We found evidence in support of all hypotheses.</p>","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"79 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758223","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}