Abstract
Human languages differ widely, yet they share systematic regularities in the underlying semantic representations being expressed. How such similarities and differences arise remains unclear, in part because semantic theories often lack a principled link to neurocognitive constraints. Drawing on neurocognitive accounts in which semantic knowledge is grounded in biologically salient information dimensions, we examine how environmental factors shape conceptual representations in language. Here we show that word meanings across languages are organized along shared neurocognitive dimensions, while systematic variation along these dimensions is associated with climate. Using word embeddings from 53 languages and behavioral ratings from speakers of 8 languages, we find converging evidence that climatic variables explain semantic variation beyond commonly considered sociocultural factors. Complementary exploratory brain data further suggest climate-related modulation of activity patterns in the right anterior temporal lobe. Together, these findings indicate that semantic representations in language reflect biologically grounded dimensions that are flexibly shaped by long-term environmental conditions.
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Data availability
The multilingual fastText embeddings used in Study 1 are publicly available at https://fasttext.cc. Additional distributional semantic vectors used in validation analyses were obtained from the subs2vec model (https://github.com/jvparidon/subs2vec). The NorthEuraLex word list and concept translations are available at http://northeuralex.org. Colexification data were obtained from the CLICS database (version 3.0; https://clics.clld.org). Climate variables were obtained from the WorldClim 2.0 database (https://www.worldclim.org). Cultural variables were obtained from the D-PLACE database (https://d-place.org). The behavioral rating data from Study 2 (13-dimensional ratings for 207 concepts in 8 languages), together with derived semantic matrices and environmental distance matrices, are available at OSF. The fMRI data re-analyzed in Study 3 were originally collected and shared by Malik-Moraleda et al.51 and are available at OSF. Our derived language-level neural representational dissimilarity matrices and analysis scripts are available at OSF. Source Data underlying the main figures and tables are provided with this paper. Source data are provided with this paper.
Code availability
All data analyses were conducted using Python (version 3.9.1) and R (version 4.5.2). Complete details of software packages and analysis code are available at OSF.
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Acknowledgements
We thank Haojie Wen, Xi Yu, Shuang Tian, Dingchen Zhang, and Shuyue Wang for help and comments on earlier drafts of the manuscript. We are grateful to Yongqin Liu for insightful discussions on climate-related analyses. This work was supported by the Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project 2021ZD0204100 (2021ZD0204104 awarded to Y.B.), and the National Natural Science Foundation of China (32595491 awarded to Y.B., 32171052 to X.W.).
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Y.B. conceived and supervised the research; Z.F. performed the research; Y.C., T.Z., and Y.L. collected rating data; Z.F. and Y.C. analyzed the data; X.W. contributed valuable discussions; and Y.B., Z.F., and X.W. wrote the paper.
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Fu, Z., Chu, Y., Zhang, T. et al. Semantic similarity across languages reflects neurocognitive dimensions shaped by climate. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70608-8
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DOI: https://doi.org/10.1038/s41467-026-70608-8
