ArticleScientific reports2018
The morphospace of language networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Article
- Evolution of Brains and Computers: The Roads Not Taken.Entropy (Basel, Switzerland) · 2022Article
- Article
- Article
- Article
- Fate of Duplicated Neural Structures.Entropy (Basel, Switzerland) · 2020Review
- Ageing, computation and the evolution of neural regeneration processes.Journal of the Royal Society, Interface · 2020Article
- Evolving complexity: how tinkering shapes cells, software and ecological networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- Criticality in Pareto Optimal Grammars?Entropy (Basel, Switzerland) · 2020Article
- A World Unto Itself: Human Communication as Active Inference.Frontiers in psychology · 2020Article
- Evolutionary aspects of reservoir computing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Recurrence Networks in Natural Languages.Entropy (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
What is the nature of language? How has it evolved in different species? Are there qualitative, well-defined classes of languages? Most studies of language evolution deal in a way or another with such theoretical contraption and explore the outcome of diverse forms of selection on the communication matrix that somewhat optimizes communication. This framework naturally introduces networks mediating the communicating agents, but no systematic analysis of the underlying landscape of possible language graphs has been developed. Here we present a detailed analysis of network properties on a generic model of a communication code, which reveals a rather complex and heterogeneous morphospace of language graphs. Additionally, we use curated data of English words to locate and evaluate real languages within this morphospace. Our findings indicate a surprisingly simple structure in human language unless particles with the ability of naming any other concept are introduced in the vocabulary. These results refine and for the first time complement with empirical data a lasting theoretical tradition around the framework of least effort language.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.