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Physical topography is associated with human personality

Nature Human Behaviour volume 4, pages 1135–1144 (2020)Cite this article

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Abstract

Regional differences in personality are associated with a range of consequential outcomes. But which factors are responsible for these differences? Frontier settlement theory suggests that physical topography is a crucial factor shaping the psychological landscape of regions. Hence, we investigated whether topography is associated with regional variation in personality across the United States (n = 3,387,014). Consistent with frontier settlement theory, results from multilevel modelling revealed that mountainous areas were lower on agreeableness, extraversion, neuroticism and conscientiousness but higher on openness to experience. Conditional random forest algorithms confirmed mountainousness as a meaningful predictor of personality when tested against a conservative set of controls. East–west comparisons highlighted potential differences between ecological (driven by physical features) and sociocultural (driven by social norms) effects of mountainous terrain.

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Fig. 1: Illustration of mountainousness measure.
Fig. 2: Topographical map of the United States based on mountainousness measure.
Fig. 3: Variable importance value plots.
Fig. 4: Effects of mountainousness on personality.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request. The personality data from the Gosling–Potter Internet Personality Project are propriety data and may not currently be shared publicly. To enquire about access to these proprietary data, please contact S.D.G. (samg@austin.utexas.edu). The mountainousness measure (based on standard deviation in elevation across a 20-/50-mile radius from one’s ZIP code of living) was developed by the research team, extracting topographical information from satellite images and geocoordinates. As such, a dataset containing the three mountainousness measures for the United States, as well as corresponding code, are available on our project page on the OSF (https://osf.io/y2mdw/). The sociodemographic ZIP code-level data are freely available from the United States Census Bureau and can be publicly accessed (https://www.census.gov/programs-surveys/acs).

Code availability

The analysis scripts are available as R code and SPSS syntax files on our OSF project page (https://osf.io/y2mdw/).

References

  1. Borhani, N. E. Changes and geographic distribution of mortality from cerebrovascular disease. Am. J. Public Health 55, 673–681 (1965).

    CAS  Google Scholar 

  2. Levine, R. et al. Helping in 36 U.S. cities. J. Personal. Soc. Psychol. 67, 69–82 (1994).

    Google Scholar 

  3. McCann, S. J. H. Societal threat, authoritarianism, conservatism, and U.S. state death penalty sentencing (1977–2004). J. Personal. Soc. Psychol. 94, 913–923 (2008).

    Google Scholar 

  4. Oishi, S. Socioecological psychology. Annu. Rev. Psychol. 65, 581–609 (2014).

    Google Scholar 

  5. Rentfrow, P. J. & Jokela, M. Geographical psychology: the spatial organization of psychological phenomena. Curr. Dir. Psychol. Sci. 25, 393–398 (2016).

    Google Scholar 

  6. Camperio Ciani, A. S. & Capiluppi, C. Gene flow by selective emigration as a possible cause for personality differences between small islands and mainland populations. Eur. J. Personal. 25, 53–64 (2011).

    Google Scholar 

  7. Kitayama, S. et al. Voluntary settlement and the spirit of independence: evidence from Japan’s “northern frontier”. J. Personal. Soc. Psychol. 91, 369–384 (2006).

    Google Scholar 

  8. Rentfrow, P. J. et al. A theory of the emergence, persistence, and expression of geographic variation in psychological characteristics. Perspect. Psychol. Sci. 3, 339–369 (2008).

    Google Scholar 

  9. Rentfrow, P. J. et al. Regional personality differences in Great Britain. PLoS ONE 10, e0122245 (2015).

    Google Scholar 

  10. Harrington, J. R. & Gelfand, M. J. Tightness–looseness across the 50 United States. Proc. Natl Acad. Sci. USA 111, 7990–7995 (2014).

    CAS  Google Scholar 

  11. Obschonka, M. et al. In the shadow of coal: how large-scale industries contributed to present-day regional differences in personality and well-being. J. Personal. Soc. Psychol. 115, 903–927 (2018).

    Google Scholar 

  12. Rentfrow, P. J. et al. Divided we stand: three psychological regions of the United States and their political, economic, social, and health correlates. J. Personal. Soc. Psychol. 105, 996–1012 (2013).

    Google Scholar 

  13. Mõttus, R. et al. Personality traits below facets: the consensual validity, longitudinal stability, heritability, and utility of personality nuances. J. Personal. Soc. Psychol. 112, 474–490 (2017).

    Google Scholar 

  14. McCrae, R. R. & John, O. P. An introduction to the Five-Factor Model and its applications. J. Pers. 60, 175–215 (1992).

    CAS  Google Scholar 

  15. John, O. P. & Srivastava, S. in Handbook of Personality: Theory and Research Vol. 2 (eds Pervin, L. A. & John, O. P.) 102–138 (Guilford Press, 1999).

  16. van de Vliert, E. Climato-economic habitats support patterns of human needs, stresses, and freedoms. Behav. Brain Sci. 36, 465–480 (2013).

    Google Scholar 

  17. Wei, W. et al. Regional ambient temperature is associated with human personality. Nat. Hum. Behav. 1, 890–895 (2017).

    Google Scholar 

  18. Uskul, A. K. et al. Ecocultural basis of cognition: farmers and fishermen are more holistic than herders. Proc. Natl Acad. Sci. USA 105, 8552–8556 (2008).

    CAS  Google Scholar 

  19. Talhelm, T. et al. Large-scale psychological differences within China explained by rice versus wheat agriculture. Science 344, 603–608 (2014).

    CAS  Google Scholar 

  20. Fincher, C. L. et al. Pathogen prevalence predicts human cross-cultural variability in individualism/collectivism. Proc. R. Soc. B 275, 1279–1285 (2008).

    Google Scholar 

  21. Jokela, M. Personality predicts migration within and between U.S. states. J. Res. Personal. 43, 79–83 (2009).

    Google Scholar 

  22. Kitayama, S. et al. Ethos of independence across regions in the United States: the production-adoption model of cultural change. Am. Psychol. 65, 559–574 (2010).

    Google Scholar 

  23. Conway, L. G. et al. in Geographical Psychology (ed. Rentfrow, P. J.) 31–50 (American Psychological Association, 2014).

  24. Olsson, O. & Paik, C. Long-run cultural divergence: evidence from the Neolithic Revolution. J. Dev. Econ. 122, 197–213 (2016).

    Google Scholar 

  25. Sng, O. et al. The behavioral ecology of cultural psychological variation. Psychol. Rev. 125, 714–743 (2018).

    Google Scholar 

  26. Plaut, V. C. et al. Place matters: consensual features and regional variation in American well-being and self. J. Personal. Soc. Psychol. 83, 160–184 (2002).

    Google Scholar 

  27. Varnum, M. E. W. & Kitayama, S. What’s in a name? Popular names are less common on frontiers. Psychol. Sci. 22, 176–183 (2011).

    Google Scholar 

  28. Feng, J. et al. Ongoing voluntary settlement and independent agency: evidence from China. Front. Psychol. 8, 1287 (2017).

  29. Ishii, K. Consequences of voluntary settlement: normative beliefs related to independence in Hokkaido. J. Cogn. Cult. 14, 159–169 (2014).

    Google Scholar 

  30. Vandello, J. A. & Cohen, D. Patterns of individualism and collectivism across the United States. J. Personal. Soc. Psychol. 77, 279–292 (1999).

    Google Scholar 

  31. Dollinger, S. J. et al. Individuality and relatedness of the self: an autophotographic study. J. Personal. Soc. Psychol. 71, 1268–1278 (1996).

    CAS  Google Scholar 

  32. Grimm, S. D. et al. Self-described traits, values, and moods associated with individualism and collectivism. J. Cross Cult. Psychol. 30, 466–500 (1999).

    Google Scholar 

  33. Realo, A. et al. The hierarchical structure of collectivism. J. Res. Personal. 31, 93–116 (1997).

    Google Scholar 

  34. Moorman, R. H. & Blakely, G. L. Individualism-collectivism as an individual difference predictor of organizational citizenship behavior. J. Organ. Behav. 16, 127–142 (1995).

    Google Scholar 

  35. Oishi, S. et al. Personality and geography: introverts prefer mountains. J. Res. Personal. 58, 55–68 (2015).

    Google Scholar 

  36. Triandis, H. C. et al. Individualism and collectivism: cross-cultural perspectives on self-ingroup relationships. J. Personal. Soc. Psychol. 54, 323–338 (1988).

    Google Scholar 

  37. Arpaci, I. et al. The relationship among individual differences in individualism-collectivism, extraversion, and self-presentation. Pers. Individ. Dif. 121, 89–92 (2018).

    Google Scholar 

  38. Conway, L. G. et al. Ecological origins of freedom: pathogens, heat stress, and frontier topography predict more vertical but less horizontal governmental restriction. Personal. Soc. Psychol. Bull. 43, 1378–1398 (2017).

    Google Scholar 

  39. Conway, L. G. et al. Socio-ecological influences on political ideology. Curr. Opin. Psychol. 32, 76–80 (2020).

    Google Scholar 

  40. Rentfrow, P. J. Statewide differences in personality: toward a psychological geography of the United States. Am. Psychol. 65, 548–558 (2010).

    Google Scholar 

  41. Nodregio. Mountain Areas in Europe: Analysis of Mountain Areas in EU Member States, Acceding and Other European Countries. European Commission contract No 2002.CE.16.0.AT.136 (2004).

  42. Rapino, M. A. & Fields, A. K. Mega Commuters in the U.S.: Time and Distance in Defining the Long Commute Using the American Community Survey. Working Paper 2013-03 (United States Census Bureau, 2013).

  43. Nezlek, J. in Cross-cultural Research Methods in Psychology (eds Matsumoto, D. & Van de Vijver, F.) 299–345 (Cambridge Univ. Press, 2010).

  44. Fulmer, C. A. et al. On “feeling right” in cultural contexts. Psychol. Sci. 21, 1563–1569 (2010).

    Google Scholar 

  45. Götz, F. M. et al. Regional cultures and the psychological geography of Switzerland: person–environment–fit in personality predicts subjective wellbeing. Front. Psychol. https://doi.org/10.3389/fpsyg.2018.00517 (2018).

  46. Bleidorn, W. et al. To live among like-minded others: exploring the links between person-city personality fit and self-esteem. Psychol. Sci. 27, 419–427 (2016).

    Google Scholar 

  47. Hemphill, J. F. Interpreting the magnitudes of correlation coefficients. Am. Psychol. 58, 78–79 (2003).

    Google Scholar 

  48. Strobl, C. et al. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 323–348 (2009).

    Google Scholar 

  49. Ebert, T., Götz, F. M., Gladstone, J. J., Müller, S. R., & Matz, S. C. Spending reflects not only who we are but also who we are around: The joint effects of individual and geographic personality on consumption. J. Personal. Soc. Psychol. https://doi.org/10.1037/pspp0000344 (2020).

  50. IJzerman, H. et al. The human penguin project: climate, social integration, and core body temperature. Collabra Psychol. 4, 37 (2018).

  51. Steiger, J. H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).

    Google Scholar 

  52. Rentfrow, P. J. & Jokela, M. in The Praeger Handbook of Personality Across Cultures (ed. Church, T.) 225–250 (Praeger Publishing, 2017).

  53. DeYoung, C. G. et al. Between facets and domains: 10 aspects of the Big Five. J. Personal. Soc. Psychol. 93, 880–896 (2007).

    Google Scholar 

  54. Hotard, S. R. et al. Interactive effects of extraversion, neuroticism, and social relationships on subjective well-being. J. Personal. Soc. Psychol. 57, 321–331 (1989).

    Google Scholar 

  55. Bogg, T. & Roberts, B. W. Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality. Psychol. Bull. 130, 887–919 (2004).

    Google Scholar 

  56. Schoen, H. & Steinbrecher, M. Beyond total effects: exploring the interplay of personality and attitudes in affecting turnout in the 2009 German federal election. Polit. Psychol. 34, 533–552 (2013).

    Google Scholar 

  57. Uchida, Y. et al. Farming cultivates a community-level shared culture through collective activities: examining contextual effects with multilevel analyses. J. Personal. Soc. Psychol. 116, 1–14 (2019).

    Google Scholar 

  58. Obschonka, M. et al. Did strategic bombing in the second world war lead to “German Angst”: a large-scale empirical test across 89 German cities. Eur. J. Personal. 31, 234–257 (2017).

    Google Scholar 

  59. Funder, D. C. & Ozer, D. J. Evaluating effect size in psychological research: sense and nonsense. Adv. Methods Pract. Psychol. Sci. 2, 156–168 (2019).

    Google Scholar 

  60. Gladstone, J. J. et al. Can psychological traits be inferred from spending? Evidence from transaction data. Psychol. Sci. 30, 1087–1096 (2019).

    Google Scholar 

  61. Cialdini, R. B. We have to break up. Perspect. Psychol. Sci. 4, 5–6 (2009).

    Google Scholar 

  62. Maner, J. K. Into the wild: field research can increase both replicability and real-world impact. J. Exp. Soc. Psychol. 66, 100–106 (2016).

    Google Scholar 

  63. Oishi, S. & Graham, J. Social ecology: lost and found in psychological science. Perspect. Psychol. Sci. 5, 356–377 (2010).

    Google Scholar 

  64. Mackay, T. F. C. et al. The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577 (2009).

    CAS  Google Scholar 

  65. Plomin, R. Genetics and general cognitive ability. Nature 402, 25–29 (1999).

    Google Scholar 

  66. Plomin, R. et al. The genetic basis of complex human behaviors. Science 264, 1733–1739 (1994).

    CAS  Google Scholar 

  67. Smith-Woolley, E. et al. Polygenic score for educational attainment captures DNA variants shared between personality traits and educational achievement. J. Personal. Soc. Psychol. 117, 1145–1163 (2019).

    Google Scholar 

  68. Okbay, A. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  Google Scholar 

  69. Adjerid, I. & Kelley, K. Big data in psychology: a framework for research advancement. Am. Psychol. 73, 899–917 (2018).

    Google Scholar 

  70. Chen, E. E. & Wojcik, S. P. A practical guide to big data research in psychology. Psychol. Methods 21, 458–474 (2016).

    Google Scholar 

  71. Kosinski, M. et al. Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines. Am. Psychol. 70, 543–556 (2015).

    Google Scholar 

  72. Miller, D. I. When do growth mindset interventions work? Trends Cogn. Sci. 23, 910–912 (2019).

    Google Scholar 

  73. Abelson, R. P. A variance explanation paradox: when a little is a lot. Psychol. Bull. 97, 129–133 (1985).

    Google Scholar 

  74. Bond, R. M. et al. A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298 (2012).

    CAS  Google Scholar 

  75. Matz, S. C. et al. In a world of big data, small effects can still matter: a reply to Boyce, Daly, Hounkpatin, and Wood (2017). Psychol. Sci. 28, 547–550 (2017).

    Google Scholar 

  76. Noftle, E. E. & Robins, R. W. Personality predictors of academic outcomes: big five correlates of GPA and SAT scores. J. Personal. Soc. Psychol. 93, 116–130 (2007).

    Google Scholar 

  77. Prentice, D. A. & Miller, D. T. When small effects are impressive. Psychol. Bull. 112, 160–164 (1992).

    Google Scholar 

  78. Ozer, D. J. & Benet-Martínez, V. Personality and the prediction of consequential outcomes. Annu. Rev. Psychol. 57, 401–421 (2006).

    Google Scholar 

  79. Roberts, B. W. et al. The power of personality: the comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2, 313–345 (2007).

    Google Scholar 

  80. Soto, C. J. How replicable are links between personality traits and consequential life outcomes? The life outcomes of personality replication project. Psychol. Sci. 30, 711–727 (2019).

    Google Scholar 

  81. Lu, J. G. et al. Polluted morality: air pollution predicts criminal activity and unethical behavior. Psychol. Sci. 29, 340–355 (2018).

    Google Scholar 

  82. Rogers, K. H. & Wood, D. Accuracy of United States regional personality stereotypes. J. Res. Personal. 44, 704–713 (2010).

    Google Scholar 

  83. Costa, P. T. Jr. & McCrae, R. R. Domains and facets: hierarchical personality assessment using the revised NEO personality inventory. J. Personal. Assess. 64, 21–50 (1995).

    Google Scholar 

  84. Soto, C. J. & John, O. P. The next Big Five Inventory (BFI-2): developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. J. Personal. Soc. Psychol. 113, 117–143 (2017).

    Google Scholar 

  85. Gosling, S. D. et al. Should we trust web-based studies? A comparative analysis of six preconceptions about Internet questionnaires. Am. Psychol. 59, 93–104 (2004).

    Google Scholar 

  86. United States Census Bureau. Quick Facts United States https://www.census.gov/quickfacts/fact/table/US/PST045218 (2018).

  87. United States Census Bureau. American Community Survey (2006–2010) https://www.census.gov/programs-surveys/acs/news/data-releases.2010.html (2011).

  88. United States Census Bureau. 2010 Census Data Products: United States https://www.census.gov/population/www/cen2010/glance/ (2011).

  89. Ebner-Priemer, U. W. et al. Analytic strategies for understanding affective (in)stability and other dynamic processes in psychopathology. J. Abnorm. Psychol. 118, 195–202 (2009).

    Google Scholar 

  90. Jahng, S. et al. Analysis of affective instability in ecological momentary assessment: indices using successive difference and group comparison via multilevel modeling. Psychol. Methods 13, 354–375 (2008).

    Google Scholar 

  91. United States Census Bureau. Median Center of Population for the United States: 1880 to 2010 https://www2.census.gov/geo/pdfs/reference/cenpop2010/centerpop_median2010.pdf (2010).

  92. van de Vliert, E. & Van Lange, P. A. M. Latitudinal psychology: an ecological perspective on creativity, aggression, happiness, and beyond. Perspect. Psychol. Sci. 14, 860–884 (2019).

    Google Scholar 

  93. Janitza, S. et al. Random forest for ordinal responses: prediction and variable selection. Comput. Stat. Data Anal. 96, 57–73 (2016).

    Google Scholar 

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Acknowledgements

The present research was supported by doctoral scholarships of the Economic and Social Research Council and the Cambridge Trust to F.M.G. We thank S. Volsa (Karl-Landsteiner University of Health Sciences, Austria) for assistance with visualization of our mountainousness measure, and A. Gvirtz (University of Cambridge, UK) for aesthetic and moral support. We also thank A. Nehrlich (University of Koblenz-Landau, Germany) for kindly allowing us to run some of the more computationally heavy analyses remotely. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

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

    Friedrich M. Götz & Peter J. Rentfrow

  2. Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria

    Stefan Stieger

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

    Samuel D. Gosling

  4. Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia

    Samuel D. Gosling

  5. Atof Inc., Cambridge, MA, USA

    Jeff Potter

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Contributions

F.M.G. and S.S. conceived the core research idea and designed the study. S.D.G. and J.P. collected and preprocessed the data from the Gosling–Potter Internet Personality Project. S.S. developed the mountainousness measure and collected the corresponding topographical information. F.M.G. analysed the data. F.M.G., S.D.G. and P.J.R. wrote the manuscript. S.S. contributed to interpretation of the results and provided critical revisions. All authors approved the final version of this manuscript.

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Correspondence to Friedrich M. Götz or Peter J. Rentfrow.

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Götz, F.M., Stieger, S., Gosling, S.D. et al. Physical topography is associated with human personality. Nat Hum Behav 4, 1135–1144 (2020). https://doi.org/10.1038/s41562-020-0930-x

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