Literature DB >> 21564230

Network structure influences speech production.

Kit Ying Chan1, Michael S Vitevitch.   

Abstract

Network science provides a new way to look at old questions in cognitive science by examining the structure of a complex system, and how that structure might influence processing. In the context of psycholinguistics, clustering coefficient-a common measure in network science-refers to the extent to which phonological neighbors of a target word are also neighbors of each other. The influence of the clustering coefficient on spoken word production was examined in a corpus of speech errors and a picture-naming task. Speech errors tended to occur in words with many interconnected neighbors (i.e., higher clustering coefficient). Also, pictures representing words with many interconnected neighbors (i.e., high clustering coefficient) were named more slowly than pictures representing words with few interconnected neighbors (i.e., low clustering coefficient). These findings suggest that the structure of the lexicon influences the process of lexical access during spoken word production.
Copyright © 2010 Cognitive Science Society, Inc.

Entities:  

Year:  2010        PMID: 21564230     DOI: 10.1111/j.1551-6709.2010.01100.x

Source DB:  PubMed          Journal:  Cogn Sci        ISSN: 0364-0213


  32 in total

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Authors:  Nicholas Jarman; Chris Trengove; Erik Steur; Ivan Tyukin; Cees van Leeuwen
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2.  The Aging Neighborhood: Phonological Density in Naming.

Authors:  Jean K Gordon; Jake C Kurczek
Journal:  Lang Cogn Process       Date:  2014-01-01

3.  The influence of known-word-frequency on the acquisition of new neighbors in adults: evidence for exemplar representations in word-learning.

Authors:  Michael S Vitevitch; Holly L Storkel; Ana Clara Francisco; Katherine J Evans; Rutherford Goldstein
Journal:  Lang Cogn Neurosci       Date:  2014-12-01       Impact factor: 2.331

4.  Complex network structure influences processing in long-term and short-term memory.

Authors:  Michael S Vitevitch; Kit Ying Chan; Steven Roodenrys
Journal:  J Mem Lang       Date:  2012-07-01       Impact factor: 3.059

5.  The influence of 2-hop network density on spoken word recognition.

Authors:  Cynthia S Q Siew
Journal:  Psychon Bull Rev       Date:  2017-04

6.  How humans learn and represent networks.

Authors:  Christopher W Lynn; Danielle S Bassett
Journal:  Proc Natl Acad Sci U S A       Date:  2020-11-24       Impact factor: 11.205

7.  Phonological similarity influences word learning in adults learning Spanish as a foreign language.

Authors:  Melissa K Stamer; Michael S Vitevitch
Journal:  Biling (Camb Engl)       Date:  2012-07-01

8.  Individual differences in learning social and nonsocial network structures.

Authors:  Steven H Tompson; Ari E Kahn; Emily B Falk; Jean M Vettel; Danielle S Bassett
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2018-07-19       Impact factor: 3.051

9.  Breaking Down the Bilingual Cost in Speech Production.

Authors:  Jasmin Sadat; Clara D Martin; James S Magnuson; François-Xavier Alario; Albert Costa
Journal:  Cogn Sci       Date:  2015-10-25

10.  How children explore the phonological network in child-directed speech: A survival analysis of children's first word productions.

Authors:  Matthew T Carlson; Morgan Sonderegger; Max Bane
Journal:  J Mem Lang       Date:  2014-08       Impact factor: 3.059

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