Niche diversity can explain cross-cultural differences in personality structure

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The covariance structure of personality traits derived from statistical models (for example, Big Five) is often assumed to be a human universal. Cross-cultural studies have challenged this view, finding that less-complex societies exhibit stronger covariation among behavioural characteristics, resulting in fewer derived personality factors. To explain these results, we propose the niche diversity hypothesis, in which a greater diversity of social and ecological niches elicits a broader range of multivariate behavioural profiles and, hence, lower trait covariance in a population. We formalize this as a computational model, which reproduces empirical results from recent cross-cultural studies and also yields an additional prediction for which we find empirical support. This work provides a general explanation for population differences in personality structure in both humans and other animals and suggests a substantial reimagining of personality research: instead of reifying statistical descriptions of manifest personality structures, research should focus more on modelling their underlying causes.

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Fig. 1: Illustration of the model dynamics.
Fig. 2: Lower inter-trait correlations with more niches.
Fig. 3: Mean trait variance as a function of niche diversity and plasticity.
Fig. 4: Trait variance increases with socioecological complexity.
Fig. 5: More niches, more factors.

Data availability

Our empirical analysis was performed on data previously published, which can be retrieved from Table 5 in Schmitt et al.15 and Supplement S2 in Lukaszewski et al.35.

Code availability

The Java code for the agent-based model, the SAS scripts used to perform the EFA and the R scripts used to generate our plots are all available on the Open Science Framework repository (


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We thank audiences at UC Merced and MPI-EVA for discussion of these ideas, and B. Beheim, J. Bunce, J. Chung, E. Fried, A. Kandler, D. Lukas, J. Rohrer, B. Roberts and M. Turner for helpful comments on earlier drafts of this manuscript. The authors received no specific funding for this work.

Author information

P.E.S., A.L., C.v.R. and M.G. designed the research plan. P.E.S. built and analysed the computational model. M.G. analysed the empirical data and performed the EFA on the simulated data. P.E.S., A.L., C.v.R. and M.G. wrote the manuscript.

Correspondence to Paul E. Smaldino or Michael Gurven.

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Supplementary Results, Supplementary Discussion, Supplementary Figs. 1–5 and Supplementary Tables 1–3.

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