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Regional ambient temperature is associated with human personality

Nature Human Behaviour volume 1, pages 890–895 (2017)Cite this article

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An Author Correction to this article was published on 01 December 2017

This article has been updated

Abstract

Human personality traits differ across geographical regions1,2,3,4,5. However, it remains unclear what generates these geographical personality differences. Because humans constantly experience and react to ambient temperature, we propose that temperature is a crucial environmental factor that is associated with individuals’ habitual behavioural patterns and, therefore, with fundamental dimensions of personality. To test the relationship between ambient temperature and personality, we conducted two large-scale studies in two geographically large yet culturally distinct countries: China and the United States. Using data from 59 Chinese cities (N = 5,587), multilevel analyses and machine learning analyses revealed that compared with individuals who grew up in regions with less clement temperatures, individuals who grew up in regions with more clement temperatures (that is, closer to 22 °C) scored higher on personality factors related to socialization and stability (agreeableness, conscientiousness, and emotional stability) and personal growth and plasticity (extraversion and openness to experience). These relationships between temperature clemency and personality factors were replicated in a larger dataset of 12,499 ZIP-code level locations (the lowest geographical level feasible) in the United States (N = 1,660,638). Taken together, our findings provide a perspective on how and why personalities vary across geographical regions beyond past theories (subsistence style theory, selective migration theory and pathogen prevalence theory). As climate change continues across the world, we may also observe concomitant changes in human personality.

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Fig. 1: Temperature clemency and personality scores of the 59 Chinese continental cities.
Fig. 2: Standardized partial effect sizes of the predictor variables in the full multilevel models (calculated by t-to-r transformation).
Fig. 3: Variable importance plots of the predictor variables in machine-learning analyses.

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  • 01 December 2017

    In the Supplementary Information file originally published for this Letter, in three places ‘conscientiousness’ was mistakenly written ‘contentiousness’, and on page 11, ‘second’ should have read ‘secondary’, and ‘third’ should have read ‘tertiary’. These errors have been corrected.

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Acknowledgements

This research is partly supported by the National Science Foundation of China (NSFC) grant no. 91224008 and no. 91324201 and the Foundation of Beijing Key Laboratory of Behavior and Mental Health grant no. Z151100001615053 to L.W. We are grateful to V. Benet-Martínez, M. Morris, and E. Page-Gould for their valuable insights, and C. Chen, J. Chen, X. Di, Y. Huang, J. Jiang, M. Jiang, D. Li, M. Li, C. Liu, Y. Ma, J. Ma, L. Peng, L. Qiao, L. Ren, P. Wang, H. Yu, J. Zhang, M. Zhou and other graduate students working with L.W. for their help with data collection. We thank the China Meteorological Administration for providing the climate data and the Chinese Center for Disease Control and Prevention for providing the disease data. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Author notes

  1. Wenqi Wei and Jackson G. Lu contributed equally to this work.

Authors and Affiliations

  1. School of Psychological and Cognitive Sciences and Beijing Key Laboratory for Behavior and Mental Health, Peking University, Beijing, China

    Wenqi Wei, Han Wu, Wenjie Yuan, Wenjing Gui, Xiao-Yi Guo, Bingtan Li, Xiaojie Li, Yang-Mei Han, Meizhen Lv, Xiang-Qing Guo, Yera Choe, Qiyu Bai, Ying Han & Lei Wang

  2. Center for Biomedical Informatics, College of Medicine, Texas A&M University Health Science Center, Bryan, TX, USA

    Wenqi Wei

  3. Columbia Business School, Columbia University, New York, NY, USA

    Jackson G. Lu & Adam D. Galinsky

  4. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Samuel D. Gosling

  5. School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia

    Samuel D. Gosling

  6. Department of Psychology, University of Cambridge, Cambridge, UK

    Peter J. Rentfrow

  7. School of Psychology, Liaoning Normal University, Dalian, China

    Qi Zhang

  8. School of Psychology, Nanjing Normal University, Nanjing, China

    Yongyu Guo

  9. Department of Psychology, Soochow University, Suzhou, China

    Ming Zhang

  10. Atof Inc., Cambridge, MA, USA

    Jeff Potter

  11. Wisejoy.com, Beijing, China

    Jian Wang

  12. Department of Human Resource Management, Business School, Nankai University, Tianjin, China

    Weipeng Lin

  13. School of Labor and Human Resources, Renmin University of China, Beijing, China

    Kun Yu

  14. Guanghua School of Management, Peking University, Beijing, China

    Zhe Shang

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Contributions

L.W. conceived the core research idea. W.W., J.G.L., A.D.G. and L.W. designed the research. W.W., J.G.L., H.W., S.D.G., P.J.R., W.Y., Q.Z., Y.G., M.Z., W.G., X.Y.G., J.P., J.W., B.L., X.L., Y.M.H., M.L., X.Q.G., Y.C., W.L., K.Y., Q.B., Z.S., Y.H., and L.W. performed the research. W.W., J.G.L., H.W., W.Y. and L.W. analysed the data. W.W., J.G.L., A.D.G., S.D.G., P.J.R. and L.W. wrote the paper.

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Correspondence to Lei Wang.

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A correction to this article is available online at https://doi.org/10.1038/s41562-017-0275-2.

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Wei, W., Lu, J.G., Galinsky, A.D. et al. Regional ambient temperature is associated with human personality. Nat Hum Behav 1, 890–895 (2017). https://doi.org/10.1038/s41562-017-0240-0

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