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. 2014 Nov 18:5:1307.
doi: 10.3389/fpsyg.2014.01307. eCollection 2014.

The influence of clustering coefficient on word-learning: how groups of similar sounding words facilitate acquisition

Affiliations

The influence of clustering coefficient on word-learning: how groups of similar sounding words facilitate acquisition

Rutherford Goldstein et al. Front Psychol. .

Abstract

Clustering coefficient, C, measures the extent to which neighbors of a word are also neighbors of each other, and has been shown to influence speech production, speech perception, and several memory-related processes. In this study we examined how C influences word-learning. Participants were trained over three sessions at 1-week intervals, and tested with a picture-naming task on nonword-nonobject pairs. We found an advantage for novel words with high C (the neighbors of this novel word are likely to be neighbors with each other), but only after the 1-week retention period with no additional exposures to the stimuli. The results are consistent with the spreading-activation network-model of the lexicon proposed by Chan and Vitevitch (2009). The influence of C on various language-related processes suggests that characteristics of the individual word are not the only things that influence processing; rather, lexical processing may also be influenced by the relationships that exist among words in the lexicon.

Keywords: clustering coefficient; neighborhood density; network science; word-learning.

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Figures

Figure 1
Figure 1
The word BADGE has many connections within the neighborhood, thus a high clustering coefficient. The word LOG has few connections within the neighborhood, thus a low clustering coefficient.

References

    1. Arbesman S., Strogatz S. H., Vitevitch M. S. (2010). The structure of phonological networks across multiple languages. Int. J. Bifurcat. Chaos 20, 679–685 10.1142/S021812741002596X - DOI
    1. Beckage N., Hills T. T., Smith L. (2011). Small worlds and semantic network growth in typical and late talkers. PLoS ONE 6:e19348. 10.1371/journal.pone.0019348 - DOI - PMC - PubMed
    1. Boersma P., Weenink D. (2009). Praat: Doing Phonetics by Computer (Version 5.1.31) [Computer program]. Available online at: http://www.praat.org/ (Retrieved September, 2010).
    1. Carlson M. T., Bane M., Sonderegger M. (2011). Global properties of the phonological networks in child and child-directed speech, in Proceedings of the 35th Boston University Conference on Language Development, Vol. 1, eds Danis N., Mesh K., Sung H. (Somerville, MA: Cascadilla Press; ), 97–109.
    1. Carpenter G. A., Grossberg S. (1987). A massively parallel architecture for a self- organizing neural pattern recognition machine. Comput. Vis. Graph. Image Process 37, 54–115. 10.1016/S0734-189X(87)80014-2 - DOI - PubMed

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