Description

• Title:
  Using complex networks to understand the mental lexicon
• Creator:
  Vitevitch, Michael S.;
  Goldstein, Rutherford;
  Siew, Cynthia S.Q.;
  Castro, Nichol
• Description:
  Network science is an emerging discipline drawing from sociology, computer science, physics and a number of other fields to examine complex systems in economical, biological, social, and technological domains. To examine these complex systems, nodes are used to represent individual entities, and links are used to represent relationships between entities, forming a web-like structure, or network, of the entire system. The structure that emerges in these complex networks influences the dynamics of that system. We provide a short review of how this mathematical approach has been used to examine the structure found in the phonological lexicon, and of how subsequent psycholinguistic investigations demonstrate that several of the structural characteristics of the phonological network influence various language-related processes, including word retrieval during the recognition and production of spoken words, recovery from instances of failed lexical retrieval, and the acquisition of word-forms. This approach allows researchers to examine the lexicon at the micro-, meso-, and macro-levels, holding much promise for increasing our understanding of language-related processes and representations.    
• Object availability:
  free access
• Rights:
  Prawa autorskie (c) 2014 Michael S. Vitevitch*, Rutherford Goldstein, Cynthia S.Q. Siew, Nichol Castro
• Date:
  2014.11.19
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion;
  Peer-reviewed article
• Language:
  English
• Source:
  Yearbook of the Poznan Linguistic Meeting; Vol. 1 (2014); 119-138;
  Yearbook of the Poznań Linguistic Meeting; Tom 1 (2014); 119-138;
  2449-7525
• Format:
  application/pdf
• Publisher:
  Adam Mickiewicz University Poznan
• Relation: https://pressto.amu.edu.pl/index.php/yplm/article/view/21611/20750
• Subject:
  mental lexicon;
  network science;
  word recognition;
  word production
• Identifier: https://pressto.amu.edu.pl/index.php/yplm/article/view/21611
• Data provider:
  Platforma Otwartych Czasopism Naukowych UAM
• Can I use it?:
  seek permission
• Type:
  other