Network Growth Modeling to Capture Individual Lexical Learning

Abstract

Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words play a larger role in predicting network growth as these young learners add new words to their lexicon. We also examine how the importance of these semantic and phonological representations changes during the course of development. We propose a novel and significant theoretical framework for network growth models of acquisition and test the ability of these models to predict what words a specific child is likely to learn approximately one month in the future. We find that which acquisition model best fits is influenced by the underlying network representation, the assumed process of growth, and the network centrality measure used to relate the cognitive underpinnings of acquisition to network growth. The joint importance of representation, process, and the contribution of individual words to the predictive accuracy of the network model highlights the complex and multifaceted nature of early acquisition, provides new tools, and suggests experimental hypotheses for studying lexical acquisition.

Evidence against a relation between bilingualism and creativity

“…Semantic networks have recently been used to explain individual differences in people's knowledge [29,30]. In these accounts, each person has a different semantic network, which is Funding: KVL was supported by the UW-Madison L&S Senior Honors Thesis Research Grant.…”

Investigating the Influence of Inverse Preferential Attachment on Network Development

“…Previous research has shown that the structure of the phonological lexicon has measurable influences on various language-related processes [ 7 , 8 , 9 ]. Research investigating the processes that facilitate the acquisition of the phonological form of a word indicate that phonological network growth may be driven by alternative network growth mechanisms other than the widely studied preferential attachment [ 10 , 11 , 12 ]. Central to the present study is a recent paper by Siew and Vitevitch [ 12 ], who conducted a longitudinal analysis of phonological networks of English and Dutch words and found that preferential attachment was a better predictor of acquisition than preferential acquisition.…”

Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning

“…Alternative growth models have also been proposed; for instance, preferential acquisition (Hills, Maouene, Maouene, Sheya, & Smith, 2009 ) predicts that new words are more likely to be learnt if they are themselves well connected to other words in the learning environment, regardless of whether they are linked to a known word with many preexisting connections. It is, however, beyond the scope of the current article to offer an exhaustive comparison of these growth models; interested readers may consult these excellent articles: Beckage and Colunga ( 2019 ), Hills et al ( 2009 ), and Fourtassi, Bian, and Frank ( 2019 ).…”