Multiplex model of mental lexicon reveals explosive learning in humans
This provides quantitative confirmation of conjectures about core structure in the mental lexicon, addressing a gap in psycholinguistic frameworks for researchers in cognitive science and linguistics.
The study tackled the problem of modeling multi-relational word similarities in the mental lexicon by proposing a multiplex network representation, revealing that a core cluster of words emerges around age 7 through an explosive transition linked to polysemy, which enhances lexical navigability and robustness.
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.