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Aging effects in an evolving phonological network

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Luef, Eva Maria, 2022, Aging effects in an evolving phonological network, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), http://hdl.handle.net/11234/1-4793.
Date issued
2022
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1 files
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Phonological networks are representations of word forms and their phonological relationships with other words in a given language lexicon. A principle underlying the growth (or evolution) of those networks is preferential attachment, or the ‘rich-gets-richer’ mechanisms, according to which words with many phonological neighbors (or links) are the main beneficiaries of future growth opportunities. Due to their limited number of words, language lexica constitute node-constrained networks where growth cannot keep increasing in a linear way; hence, preferential attachment is likely mitigated by certain factors. The present study investigated aging effects (i.e., a word’s finite time span of being active in terms of growth) in an evolving phonological network of English as a second language. It was found that phonological neighborhoods are constructed by one large initial lexical spurt, followed by sublinear growth spurts that eventually lead to very limited growth in later lexical spurts during network evolution, all the while obeying the law of preferential attachment. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to attach more distant phonological neighbors in the lower proficiency levels, while phonologically more similar neighbors enter phonological neighborhoods at more advanced levels of English as a second language. Overall, the findings suggest an aging effect in growth that favors younger words. In addition, beginning learners seem to prefer the acquisition of phonological neighbors that are easier to discriminate. Implications for the second language lexicon include leveraged learning mechanisms, learning bouts focussed on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks.
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    The Excel sheet contains data for each of the investigated proficiency levels
    of L2 English (i.e., A1, A2, B1, B2, C1). Each separate profiiency data sheet
    is set up the same way:
    
    1. the first column contains the words that are added to a lexicon at the
    specific proficiency level. At the A1 level, these words undergo the maximal
    number of growth spurts as they age in the lexicon. At the C1 level, the newly
    learned words only undergo one growth spurt (to C2).
    
    2. Next, the raw number of phonological neighbors that exist within a given
    proficiency level are listed.
    
    3. The proportions of known words at a given proficiency level are calculated
    in relation to the maximal number of neighbors known at the last L2 proficiency
    level C2. So, C2 words/ neighbors represent 100%.
    
    4. The average weighted degrees of words at the different proficiency levels
    are listed in the next few columns.
    
    5. The Clearpond-ENGLISH data base was used to determine the L1-given maximal
    neighborhood density of each word.
    
    6. Lastly, the proportion of neighbors known at each proficiency level in
    relation to the L1 number of neighbors is calculated for each word. This feeds
    the saturation analysis. A saturation of 100% indicates that the L2 learners at
    a given proficiency level and for a given word have acquired all words that
    exist in the L1 lexicon for a given neighborhood.