The University of Southampton
University of Southampton Institutional Repository

Multiplex model of mental lexicon reveals explosive learning in humans

Multiplex model of mental lexicon reveals explosive learning in humans
Multiplex model of mental lexicon reveals explosive learning in humans
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.
2045-2322
1-11
Stella, Massimo
37822c93-2522-4bc0-b840-ca32c75efbd7
Beckage, Nicole M.
d3db62dc-1f96-4e0b-8acb-f48d31181673
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
De Domenico, Manlio
d3995e47-15fb-4d81-bf1d-208a8248ed27
Stella, Massimo
37822c93-2522-4bc0-b840-ca32c75efbd7
Beckage, Nicole M.
d3db62dc-1f96-4e0b-8acb-f48d31181673
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
De Domenico, Manlio
d3995e47-15fb-4d81-bf1d-208a8248ed27

Stella, Massimo, Beckage, Nicole M., Brede, Markus and De Domenico, Manlio (2018) Multiplex model of mental lexicon reveals explosive learning in humans. Scientific Reports, 8, 1-11, [2259]. (doi:10.1038/s41598-018-20730-5).

Record type: Article

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.

Text
SciRep_multiplex_language_core(2018) - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (3MB)
Text
s41598-018-20730-5 - Version of Record
Available under License Creative Commons Attribution.
Download (3MB)

More information

Accepted/In Press date: 19 January 2018
e-pub ahead of print date: 2 February 2018
Published date: 5 February 2018

Identifiers

Local EPrints ID: 417584
URI: http://eprints.soton.ac.uk/id/eprint/417584
ISSN: 2045-2322
PURE UUID: aac955a2-ea9d-4266-9edf-ff8157d260ec

Catalogue record

Date deposited: 05 Feb 2018 17:30
Last modified: 06 Oct 2020 19:53

Export record

Altmetrics

Contributors

Author: Massimo Stella
Author: Nicole M. Beckage
Author: Markus Brede
Author: Manlio De Domenico

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×