Human Language: Info Density Grants Speed But Less Breadth

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Article - Published: 16 February 2024

Human languages with greater information density

have higher communication speed but lower 

conversation breadth

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Pedro Aceves &     
James A. Evans 

Nature Human Behaviour (2024)Cite this article

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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.

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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.

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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.