Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Human languages with greater information density have higher communication speed but lower conversation breadth

Nature Human Behaviour volume 8, pages 644–656 (2024)Cite this article

Subjects

Abstract

Human languages vary widely in how they encode information within circumscribed semantic domains (for example, time, space, colour, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend towards faster communication but conceptually narrower conversations or expositions within which topics are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behaviour across levels of society.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Broad variation in the information density of human languages.
Fig. 2: Relation between information density and semantic density by knowledge domain.
Fig. 3: Association between information density and communicative speed.
Fig. 4: Effect of information density on conversational patterns and knowledge output.
Fig. 5: Antecedents and consequences of language information density.

Similar content being viewed by others

Data availability

The datasets analysed in the current study are available at the following links: for parallel corpora, https://opus.nlpl.eu/; for conversations, https://www.ldc.upenn.edu/; for audio duration, https://wordproject.org and https://faithcomesbyhearing.com; for language family and location, https://glottolog.org/; for Wikipedia articles, https://pypi.org/project/Wikipedia-API/; for language fusion and informativity, https://github.com/OlenaShcherbakova/Sociodemographic_factors_complexity/tree/v2.0/data; and for morphological complexity, https://github.com/mllewis/langLearnVar. The language information density, semantic density and communicative speed measures can be found at https://github.com/peteaceves/Language_Density_and_Communication.

Code availability

The code used to create the language information density and semantic density measures was written in Python v.3.7.2 and can be found at https://github.com/peteaceves/Language_Density_and_Communication. The statistical models were run in Stata v.17, and the code can be found in the Supplementary Information.

References

  1. de Saussure, F. Course in General Linguistics (Open Court, 1986).

  2. Bloomfield, L. Language (Holt, Rinehart & Winston, 1933).

  3. Sapir, E. Language: An Introduction to the Study of Speech (Harcourt, Brace, 1921).

  4. Levelt, W. J. M. Speaking: From Intention to Articulation (MIT Press, 1989).

  5. Thompson, B., Roberts, S. G. & Lupyan, G. Cultural influences on word meanings revealed through large-scale semantic alignment. Nat. Hum. Behav. 4, 1029–1038 (2020).

    PubMed  Google Scholar 

  6. Youn, H. et al. On the universal structure of human lexical semantics. Proc. Natl Acad. Sci. USA 113, 1766–1771 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Winawer, J. et al. Russian blues reveal effects of language on color discrimination. Proc. Natl Acad. Sci. USA 104, 7780–7785 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kay, P. & McDaniel, C. K. The linguistic significance of the meanings of basic color terms. Language 54, 610–646 (1978).

    Google Scholar 

  9. Davidoff, J., Davies, I. & Roberson, D. Colour categories in a stone-age tribe. Nature 398, 203–204 (1999).

    CAS  PubMed  Google Scholar 

  10. Kay, P., Berlin, B., Maffi, L. & Merrifield, W. in Color Categories in Language and Thought (eds Hardin, C. L. & Maffi, L.) 21–56 (Cambridge Univ. Press, 1997).

  11. Dolscheid, S., Shayan, S., Majid, A. & Casasanto, D. The thickness of musical pitch: psychophysical evidence for linguistic relativity. Psychol. Sci. 24, 613–621 (2013).

    PubMed  Google Scholar 

  12. Bock, K., Carreiras, M. & Meseguer, E. Number meaning and number grammar in English and Spanish. J. Mem. Lang. 66, 17–37 (2012).

    Google Scholar 

  13. Malt, B. C. et al. Talking about walking: biomechanics and the language of locomotion. Psychol. Sci. 19, 232–240 (2008).

    PubMed  Google Scholar 

  14. Malt, B. C. et al. Human locomotion in languages: constraints on moving and meaning. J. Mem. Lang. 74, 107–123 (2014).

    Google Scholar 

  15. Casasanto, D. & Boroditsky, L. Time in the mind: using space to think about time. Cognition 106, 579–593 (2008).

    PubMed  Google Scholar 

  16. Fuhrman, O. et al. How linguistic and cultural forces shape conceptions of time: English and Mandarin time in 3D. Cogn. Sci. 35, 1305–1328 (2011).

    PubMed  Google Scholar 

  17. Lai, V. T. & Boroditsky, L. The immediate and chronic influence of spatio-temporal metaphors on the mental representations of time in English, Mandarin, and Mandarin-English speakers. Front. Psychol. 4, 142 (2013).

    PubMed  PubMed Central  Google Scholar 

  18. Levinson, S. C. Space in Language and Cognition: Explorations in Cognitive Diversity (Cambridge Univ. Press, 2003).

  19. Levinson, S., Meira, S. & The Language and Cognition Group. ‘Natural concepts’ in the spatial topological domain—adpositional meanings in crosslinguistic perspective: an exercise in semantic typology. Language 79, 485–516 (2003).

  20. Majid, A., Bowerman, M., Kita, S., Haun, D. B. M. & Levinson, S. C. Can language restructure cognition? The case for space. Trends Cogn. Sci. 8, 108–114 (2004).

    PubMed  Google Scholar 

  21. Feist, M. I. Space between languages. Cogn. Sci. 32, 1177–1199 (2008).

    PubMed  Google Scholar 

  22. Majid, A., Boster, J. S. & Bowerman, M. The cross-linguistic categorization of everyday events: a study of cutting and breaking. Cognition 109, 235–250 (2008).

    PubMed  Google Scholar 

  23. Saji, N. et al. Word learning does not end at fast-mapping: evolution of verb meanings through reorganization of an entire semantic domain. Cognition 118, 45–61 (2011).

    PubMed  Google Scholar 

  24. Lewis, M. & Lupyan, G. Gender stereotypes are reflected in the distributional structure of 25 languages. Nat. Hum. Behav. 4, 1021–1028 (2020).

    PubMed  Google Scholar 

  25. Enfield, N. J., Majid, A. & van Staden, M. Cross-linguistic categorisation of the body: introduction. Lang. Sci. 28, 137–147 (2006).

    Google Scholar 

  26. Brown, C. H. Language and Living Things: Uniformities in Folk Classification and Naming (Rutgers Univ. Press, 1984).

  27. Berlin, B. Ethnobiological Classification: Principles of Categorization of Plants and Animals in Traditional Societies (Princeton Univ. Press, 2014).

  28. Kemp, C., Xu, Y. & Regier, T. Semantic typology and efficient communication. Annu. Rev. Linguist. 4, 109–128 (2018).

    Google Scholar 

  29. Enfield, N. J. Linguistic relativity from reference to agency. Annu. Rev. Anthropol. 44, 207–224 (2015).

    Google Scholar 

  30. Hofstadter, D. & Sander, E. Surfaces and Essences: Analogy as the Fuel and Fire of Thinking (Basic Books, 2013).

  31. Li, P. & Gleitman, L. Turning the tables: language and spatial reasoning. Cognition 83, 265–294 (2002).

    CAS  PubMed  Google Scholar 

  32. Gleitman, L. & Fisher, C. in The Cambridge Companion to Chomsky (ed. McGilvray, J. A.) 123–142 (Cambridge Univ. Press, 2005).

  33. Pinker, S. The Language Instinct (HarperCollins, 1994).

  34. Berlin, B. & Kay, P. Basic Color Terms: Their Universality and Evolution (Univ. California Press, 1969).

  35. Evans, N. & Levinson, S. C. The myth of language universals: language diversity and its importance for cognitive science. Behav. Brain Sci. 32, 429–448, discussion 448–494 (2009).

  36. Davidson, D. On the very idea of a conceptual scheme. Proc. Addresses Am. Phil. Assoc. 47, 5–20 (1973).

    Google Scholar 

  37. Lupyan, G. & Dale, R. Why are there different languages? The role of adaptation in linguistic diversity. Trends Cogn. Sci. 20, 649–660 (2016).

    PubMed  Google Scholar 

  38. Pellegrino, F., Coupé, C. & Marsico, E. Across-language perspective on speech information rate. Language 87, 539–558 (2011).

    Google Scholar 

  39. Coupé, C., Oh, Y. M., Dediu, D. & Pellegrino, F. Different languages, similar encoding efficiency: comparable information rates across the human communicative niche. Sci. Adv. 5, eaaw2594 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. Lewis, M., Cahill, A., Madnani, N. & Evans, J. Local similarity and global variability characterize the semantic space of human languages. Proc. Natl Acad. Sci. 120, e2300986120 (2023).

  41. Gibson, E. et al. How efficiency shapes human language. Trends Cogn. Sci. 23, 389–407 (2019).

    PubMed  Google Scholar 

  42. Bentz, C., Alikaniotis, D., Cysouw, M. & Ferrer-i-Cancho, R. The entropy of words—learnability and expressivity across more than 1000 languages. Entropy 19, 275 (2017).

    Google Scholar 

  43. Bellos, D. Is That a Fish in Your Ear? Translation and the Meaning of Everything (Penguin Books, 2011).

  44. Huffman, D. A. A method for the construction of minimum-redundancy codes. Proc. IRE 40, 1098–1101 (1952).

    Google Scholar 

  45. Harris, Z. S. Distributional structure. Word World 10, 146–162 (1954).

    Google Scholar 

  46. Jurafsky, D. & Martin, J. H. Speech and Language Processing (Stanford Univ., 2018).

  47. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems Vol. 26 (MIT Press, 2013).

  48. Pennington, J., Socher, R. & Manning, C. Glove: global vectors for word representation. In Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1532–1543 (Association for Computational Linguistics, 2014).

  49. Kozlowski, A. C., Taddy, M. & Evans, J. A. The geometry of culture: analyzing the meanings of class through word embeddings. Am. Sociol. Rev. 84, 905–949 (2019).

    Google Scholar 

  50. Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 356, 183–186 (2017).

    CAS  PubMed  Google Scholar 

  51. Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V. & Kalai, A. T. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems 4349–4357 (MIT Press, 2016).

  52. Hamilton, W. L., Leskovec, J. & Jurafsky, D. Diachronic word embeddings reveal statistical laws of semantic change. Preprint at https://arxiv.org/abs/1605.09096v6 (2016).

  53. Arora, S., Li, Y., Liang, Y., Ma, T. & Risteski, A. Linear algebraic structure of word senses, with applications to polysemy. Trans. Assoc. Comput. Linguist. 6, 483–495 (2018).

    Google Scholar 

  54. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).

    Google Scholar 

  55. Mnih, A. & Hinton, G. Three new graphical models for statistical language modelling. In Proc. 24th International Conference on Machine Learning 641–648 (Association for Computing Machinery, 2007).

  56. Arora, S., Li, Y., Liang, Y., Ma, T. & Risteski, A. A latent variable model approach to PMI-based word embeddings. Trans. Assoc. Comput. Linguist. 4, 385–399 (2016).

    Google Scholar 

  57. Davelaar, E. J. & Raaijmakers, J. G. W. in Cognitive Search: Evolution, Algorithms, and the Brain (eds Todd, P. M. et al.) 177–194 (MIT Press, 2012).

  58. Romney, A. K., Brewer, D. D. & Batchelder, W. H. Predicting clustering from semantic structure. Psychol. Sci. 4, 28–34 (1993).

    Google Scholar 

  59. Howard, M. W., Jing, B., Addis, K. M. & Kahana, M. J. Semantic structure and episodic memory Ch. 7. in LSA: A Road Towards Meaning (eds McNamara, D. & Dennis, S.) (Erlbaum, 2007).

  60. Abbott, J. T., Austerweil, J. L. & Griffiths, T. L. Random walks on semantic networks can resemble optimal foraging. Psychol. Rev. 122, 558–569 (2015).

    PubMed  Google Scholar 

  61. Hills, T. T., Todd, P. M. & Jones, M. N. Foraging in semantic fields: how we search through memory. Top. Cogn. Sci. 7, 513–534 (2015).

    PubMed  Google Scholar 

  62. Charnov, E. L. Optimal foraging, the marginal value theorem. Theor. Popul. Biol. 9, 129–136 (1976).

    CAS  PubMed  Google Scholar 

  63. Pirolli, P. L. T. Information Foraging Theory: Adaptive Interaction with Information (Oxford Univ. Press, 2007).

  64. Harbison, J. I., Dougherty, M. R., Davelaar, E. J. & Fayyad, B. On the lawfulness of the decision to terminate memory search. Cognition 111, 416–421 (2009).

    PubMed  Google Scholar 

  65. Lewis, M. & Frank, M. C. Linguistic niches emerge from pressures at multiple timescales. In Proc. 38th Annual Conference of the Cognitive Science Society 1385–1390 (Cognitive Science Society, 2016).

  66. Lupyan, G. & Dale, R. Language structure is partly determined by social structure. PLoS ONE 5, e8559 (2010).

    PubMed  PubMed Central  Google Scholar 

  67. Shcherbakova, O. et al. Societies of strangers do not speak less complex languages. Sci. Adv. 9, eadf7704 (2023).

    PubMed  PubMed Central  Google Scholar 

  68. Pellegrino, F., Coupé, C. & Marsico, E. A cross-language perspective on speech information rate. Language 87, 539–558 (2011).

    Google Scholar 

  69. Schürmann, T. & Grassberger, P. Entropy estimation of symbol sequences. Chaos 6, 414–427 (1996).

    PubMed  Google Scholar 

  70. Shannon, C. E. Prediction and entropy of printed English. Bell Syst. Tech. J. 30, 50–64 (1951).

    Google Scholar 

  71. Kontoyiannis, I., Algoet, P. H., Suhov, Y. M. & Wyner, A. J. Nonparametric entropy estimation for stationary processes and random fields, with applications to English text. IEEE Trans. Inf. Theory 44, 1319–1327 (1998).

    Google Scholar 

  72. Levinson, S. C. Presumptive Meanings: The Theory of Generalized Conversational Implicature (MIT Press, 2000).

  73. Caplan, S., Kodner, J. & Yang, C. Miller’s monkey updated: communicative efficiency and the statistics of words in natural language. Cognition 205, 104466 (2020).

    PubMed  Google Scholar 

  74. Brochhagen, T. & Boleda, G. When do languages use the same word for different meanings? The Goldilocks principle in colexification. Cognition 226, 105179 (2022).

    PubMed  Google Scholar 

  75. Bentz, C., Dediu, D., Verkerk, A. & Jäger, G. The evolution of language families is shaped by the environment beyond neutral drift. Nat. Hum. Behav. 2, 816–821 (2018).

    PubMed  Google Scholar 

  76. Olson, J. A., Nahas, J., Chmoulevitch, D., Cropper, S. J. & Webb, M. E. Naming unrelated words predicts creativity. Proc. Natl Acad. Sci. USA 118, e2022340118 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N. & Malone, T. W. Evidence for a collective intelligence factor in the performance of human groups. Science 330, 686–688 (2010).

    CAS  PubMed  Google Scholar 

  78. McGrath, J. E. Groups: Interaction and Performance (Prentice-Hall, 1984).

  79. McMahan, P. & Evans, J. Ambiguity and engagement. Am. J. Sociol. 124, 860–912 (2018).

    Google Scholar 

  80. Murray, D. et al. Unsupervised embedding of trajectories captures the latent structure of mobility. Preprint at https://arxiv.org/abs/2012.02785 (2020).

  81. Lucy, J. A. Linguistic relativity. Annu. Rev. Anthropol. 26, 291–312 (1997).

    Google Scholar 

  82. Lucy, J. A. Language Diversity and Thought: A Reformulation of the Linguistic Relativity Hypothesis (Cambridge Univ. Press, 1992).

  83. Tiedemann, J. Parallel data, tools and interfaces in OPUS. In Proc. 8th International Conference on Language Resources and Evaluation (eds Calzolari, N. et al.) 2214–2218 (European Language Resources Association, 2012).

  84. Christodouloupoulos, C. & Steedman, M. A massively parallel corpus: the Bible in 100 languages. Lang. Resour. Eval. 49, 375–395 (2015).

    PubMed  Google Scholar 

  85. YouVersion https://www.bible.com/ (2017).

  86. ParaCrawl (NTT Communication Science Laboratories, accessed 1 June 2017); https://www.paracrawl.eu/

  87. OpenSubtitles https://www.opensubtitles.org/ (2017).

  88. Rafalovitch, A. & Dale, R. United Nations General Assembly resolutions: a six-language parallel corpus. In Proc. MT Summit XII (Association for Computational Linguistics, 2009).

  89. Juola, P. Measuring linguistic complexity: the morphological tier. J. Quant. Linguist. 5, 206–213 (1998).

    Google Scholar 

  90. Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. Preprint at https://arxiv.org/abs/1301.3781 (2013).

  91. Mikolov, T., Yih, W.-T. & Zweig, G. Linguistic regularities in continuous space word representations. In Proc. NAACL-HLT 2013 746–751 (Association for Computational Linguistics, 2013).

  92. Hammarström, H., Forkel, R., Haspelmath, M. & Bank, S. Glottolog v.4.4. Max Planck Institute for Evolutionary Anthropology https://glottolog.org (2021).

  93. Chen, X., Ender, P., Mitchell, M. & Wells, C. Regression with Stata. UCLA https://stats.oarc.ucla.edu/stata/webbooks/reg/ (2003).

  94. Huber, P. J. The behavior of maximum likelihood estimates under nonstandard conditions. In Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability Vol. 1 (Eds Le Cam, L. M. & Neyman, J.) 221–233 (Univ. California Press, 1967).

  95. White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–838 (1980).

    Google Scholar 

  96. Bills, A. et al. IARPA Babel Amharic Language Pack IARPA-babel307b-v1.0b (Linguistic Data Consortium, 2019); https://doi.org/10.35111/ehfb-ka57

  97. Bills, A. et al. IARPA Babel Bengali Language Pack IARPA-babel103b-v0.4b (Linguistic Data Consortium, 2016); https://doi.org/10.35111/5jdb-wp44

  98. Andresen, L. et al. IARPA Babel Cebuano Language Pack IARPA-babel301b-v2.0b (Linguistic Data Consortium, 2018); https://doi.org/10.35111/f0b3-5398

  99. Bills, A. et al. IARPA Babel Georgian Language Pack IARPA-babel404b-v1.0a (Linguistic Data Consortium, 2016); https://doi.org/10.35111/dcr5-ga44

  100. Andresen, L. et al. IARPA Babel Guarani Language Pack IARPA-babel305b-v1.0c (Linguistic Data Consortium, 2019); https://doi.org/10.35111/qdg9-7a64

  101. Adams, N. et al. IARPA Babel Igbo Language Pack IARPA-babel306b-v2.0c (Linguistic Data Consortium, 2019); https://doi.org/10.35111/7988-wd73

  102. Bills, A. et al. IARPA Babel Kazakh Language Pack IARPA-babel302b-v1.0a (Linguistic Data Consortium, 2018); https://doi.org/10.35111/rwmc-nm96

  103. Benowitz, D. et al. IARPA Babel Lithuanian Language Pack IARPA-babel304b-v1.0b (Linguistic Data Consortium, 2019); https://doi.org/10.35111/m5qd-dk93

  104. Conners, T. et al. IARPA Babel Tagalog Language Pack IARPA-babel106-v0.2g (Linguistic Data Consortium, 2016); https://doi.org/10.35111/mp23-rd11

  105. Bills, A. et al. IARPA Babel Tamil Language Pack IARPA-babel204b-v1.1b (Linguistic Data Consortium, 2017); https://doi.org/10.35111/3j2w-kb06

  106. Bills, A. et al. IARPA Babel Telugu Language Pack IARPA-babel303b-v1.0a (Linguistic Data Consortium, 2018); https://doi.org/10.35111/vm6x-za86

  107. Andresen, J. et al. IARPA Babel Turkish Language Pack IARPA-babel105b-v0.5 (Linguistic Data Consortium, 2016); https://doi.org/10.35111/mb8z-6p26

  108. Andrus, T. et al. IARPA Babel Vietnamese Language Pack IARPA-babel107b-v0.7 (Linguistic Data Consortium, 2017); https://doi.org/10.35111/yrqp-r555

  109. Adams, N. et al. IARPA Babel Zulu Language Pack IARPA-babel206b-v0.1e LDC2017S19 (Linguistic Data Consortium, 2017); https://doi.org/10.35111/te29-8988

  110. Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. Enriching word vectors with subword information. In Transactions of the Association for Computational Linguistics Vol. 5 (Association for Computational Linguistics, 2017).

  111. Grave, E., Bojanowski, P., Gupta, P., Joulin, A. & Mikolov, T. Learning word vectors for 157 languages. In Proc. International Conference on Language Resources and Evaluation (Eds Calzolari, N. et. al.) (European Language Resources Association, 2018).

  112. Gordon, R. G. Ethnologue, Languages of the World (SIL International, accessed 1 October 2017); https://www.ethnologue.com

  113. Hofstede, G. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations (SAGE, 2001).

  114. Hofstede, G. Culture’s Consequences: International Differences in Work-Related Values (Sage, 1984).

Download references

Acknowledgements

We thank Z. Chen (Stanford University) for her excellent research assistance and are grateful for comments from C. Chambers (Johns Hopkins University), M. Lewis (Meta), D. Casasanto (Cornell University), G. Lupyan (University of Wisconsin-Madison), J. L. Martin (University of Chicago), A. Sharkey (Arizona State University), J. Murphy (RAND Corporation) and J. Chu (Massachusetts Institute of Technology). P.A. also thanks the judges of the 2017 INFORMS/Organization Science Dissertation Proposal Competition for their feedback and acknowledges support from the National Science Foundation Doctoral Dissertation Research Improvement Grant (no. 1702788). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Management and Organization, Carey Business School, Johns Hopkins University, Baltimore, MD, USA

    Pedro Aceves

  2. Department of Sociology & Knowledge Lab, University of Chicago, Chicago, IL, USA

    James A. Evans

  3. Santa Fe Institute, Santa Fe, NM, USA

    James A. Evans

Authors
  1. Pedro Aceves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James A. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.A. and J.A.E. designed the research. P.A. analysed the data. P.A. and J.A.E. wrote the paper.

Corresponding author

Correspondence to Pedro Aceves.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks N. Enfield and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10, Tables 1–37 and 41–43, and Text.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aceves, P., Evans, J.A. Human languages with greater information density have higher communication speed but lower conversation breadth. Nat Hum Behav 8, 644–656 (2024). https://doi.org/10.1038/s41562-024-01815-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-024-01815-w

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing