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Community structure in the phonological network.

Siew CS - Front Psychol (2013)

Bottom Line: Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities.Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935).The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

View Article: PubMed Central - PubMed

Affiliation: Spoken Language Laboratory, Department of Psychology, University of Kansas Lawrence, KS, USA.

ABSTRACT
Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

No MeSH data available.


Raw biphone counts of real and random community 15. The x-axis represents the different biphones found within these communities and the biphones (on both x-axes) were arranged based on their frequency of occurrence in the real community in descending order.
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Figure 3: Raw biphone counts of real and random community 15. The x-axis represents the different biphones found within these communities and the biphones (on both x-axes) were arranged based on their frequency of occurrence in the real community in descending order.

Mentions: Figures 2, 3 show the raw biphone counts from the real and random communities 1 and 15. In these figures the sequence of biphones on the x-axis is the same for both real and random communities, and arranged (in decreasing order) by their frequency in the real community. Two things are clear from the figures. One, random communities contain a large number of different biphones compared to the real communities. Second, the raw counts of biphones found in real communities are much larger than the same biphones in random communities. Taken together, this strongly suggests that communities in the phonological network consist of words with certain phonological segments.


Community structure in the phonological network.

Siew CS - Front Psychol (2013)

Raw biphone counts of real and random community 15. The x-axis represents the different biphones found within these communities and the biphones (on both x-axes) were arranged based on their frequency of occurrence in the real community in descending order.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753538&req=5

Figure 3: Raw biphone counts of real and random community 15. The x-axis represents the different biphones found within these communities and the biphones (on both x-axes) were arranged based on their frequency of occurrence in the real community in descending order.
Mentions: Figures 2, 3 show the raw biphone counts from the real and random communities 1 and 15. In these figures the sequence of biphones on the x-axis is the same for both real and random communities, and arranged (in decreasing order) by their frequency in the real community. Two things are clear from the figures. One, random communities contain a large number of different biphones compared to the real communities. Second, the raw counts of biphones found in real communities are much larger than the same biphones in random communities. Taken together, this strongly suggests that communities in the phonological network consist of words with certain phonological segments.

Bottom Line: Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities.Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935).The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

View Article: PubMed Central - PubMed

Affiliation: Spoken Language Laboratory, Department of Psychology, University of Kansas Lawrence, KS, USA.

ABSTRACT
Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

No MeSH data available.