Abstract
Word similarities affect language acquisition and use in a multi-relational way barely accounted for in the literature. We propose a multiplex network representation of this mental lexicon of word similarities as a natural framework for investigating large-scale cognitive patterns. Our representation accounts for semantic, taxonomic, and phonological interactions and it identifies a cluster of words which are used with greater frequency, are identified, memorised, and learned more easily, and have more meanings than expected at random. This cluster emerges around age 7 through an explosive transition not reproduced by null models. We relate this explosive emergence to polysemy – redundancy in word meanings. Results indicate that the word cluster acts as a core for the lexicon, increasing both lexical navigability and robustness to linguistic degradation. Our findings provide quantitative confirmation of existing conjectures about core structure in the mental lexicon and the importance of integrating multi-relational word-word interactions in psycholinguistic frameworks.
Highlights
Investigating relationships between words offers insights into both the structure of language and the influence of cognition on linguistic tasks[1,2]
Compared to previous work on multiplex modelling of language development[32], our multiplex representation is enriched with node-level attributes related to cognition and language: (i) age of acquisition ratings[42], (ii) concreteness ratings[43], (iii) identification times in lexical decision tasks[51], (iv) frequency of word occurrence in Open Subtitles[52], (v) polysemy scores, i.e. the number of definitions of a word in WordNet, used to approximate polysemy in computational linguistics[9,17]
It has been conjectured that the mental lexicon has a core set of concepts[6,22,45,46]; we show here how various cognitive metrics can be correlated with the largest viable cluster (LVC), suggesting that future work may benefit from considering the LVC as a quantification of lexical core structure
Summary
Investigating relationships between words offers insights into both the structure of language and the influence of cognition on linguistic tasks[1,2]. The structural organisation of mental pathways among words was analysed in several large-scale investigations, considering similarity of words in terms of their semantic meaning[3,17,18], their phonology[8,12,19,20,21], or their taxonomy[14,22,23] All these networks, based on different definitions of relationships between words, were found to be highly navigable: words were found to be clustered with each other and separated by small network distances (sometimes called small-world networks[24]). Multilayer networks simultaneously encode multiple types of interaction among units of a complex networked system They can be used to extract information about linguistic structures beyond www.nature.com/scientificreports/. The usefulness of multiplex representations has recently been shown for diverse applications including the human brain[33,34], social network analysis[35,36,37], transportation[38,39] and ecology[40,41]
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