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.
Highlights
Children do not learn words in isolation
Modeling learning of the single most likely word has been examined in a similar paradigm by Hills and colleagues using a mathematical formalism very similar to the model we propose here [2, 7]. βs are optimized across training data for each network representation, growth process, and centrality we compared and evaluated on unseen children and their vocabulary growth trajectory. δcdi(i,x) is computed the same way for each model and varies for individual children only based on renormalization of the Communicative Development Inventory (CDI) norms for that specific child x, as well as based on the words in the network representation used to compute δ(i,x)
Alignment between CDI words and network representations results in networks of different sizes ranging from 133 words to the full 677 words included in our longitudinal CDI assessment
Summary
Children do not learn words in isolation. Instead children must learn the meanings and relationships of words within a communicative context, and in the context of other words.ese same relationships, which make learning initial words challenging, likely o er sca olding and support that help children make sense of the world around them. e connections and relationships between words likely aid future learning of new words. We set forth to build a predictive network growth model of the words a child is likely to learn based on the emerging structure of the child’s current vocabulary. Our growth models assume that words enter the graph (become part of the child’s productive vocabulary) based on either the child’s current vocabulary knowledge or the structure of the global language environment as captured by the full network structure. With these assumptions, we have a systematic way of linking possible learning mechanisms and processes to the structure and connectivity of a child’s lexical network
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