Clustering coefficients of lexical neighborhoods

Does neighborhood structure matter in spoken word recognition?

Nicholas Altieri, Thomas Gruenenfelder, David Pisoni

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) - a graph theoretic variable measuring the degree to which a word's neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalMental Lexicon
Volume5
Issue number1
DOIs
StatePublished - 2010

Fingerprint

Cluster Analysis
experiment
activation
Reaction Time
Lexical Neighborhood
Spoken Word Recognition
Neighbors
Experiment

Keywords

  • Clustering coefficient
  • Complex networks
  • Graph theory
  • Mental lexicon

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Cognitive Neuroscience

Cite this

Clustering coefficients of lexical neighborhoods : Does neighborhood structure matter in spoken word recognition? / Altieri, Nicholas; Gruenenfelder, Thomas; Pisoni, David.

In: Mental Lexicon, Vol. 5, No. 1, 2010, p. 1-21.

Research output: Contribution to journalArticle

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