Adjacent and Non-Adjacent Word Contexts Both Predict Age of Acquisition of English Words: A Distributional Corpus Analysis of Child-Directed Speech

The Words Children Hear and See: Lexical Diversity Across-Modalities and Its Impact on Lexical Development

Early vocabulary development benefits from diverse lexical exposures within children's language environment. However, the influence of lexical diversity on children as they enter middle childhood and are exposed to multimodal language inputs remains unclear. This study evaluates global and local aspects of lexical diversity in three 1.6-million-word child-directed corpora, representing average Chinese children's speech, print, and media language environments. Additionally, pseudo-multimodal samples were compiled from the three corpora to compare with the unimodal environments on lexical diversity. We then investigated the associations between lexical diversity and the acquisition of 361 words spanning early-to-middle childhood. The findings show that print and pseudo-multimodal language provided the most diverse lexical environments, whereas speech exhibited the least diversity. However, speech diversity most strongly predicted lexical development, particularly before the onset of middle childhood. Exploratory analysis revealed that lexical diversity of other modalities emerged as stronger predictors thereafter. Early lexical development was best predicted by words' variations in connectivity with other words within an immediate context, whereas in middle childhood, variations in words' occurrences in larger context windows became the primary predictor, implicating children's growing ability to attend to linguistic contexts of increasing sizes. Importantly, higher diversity was consistently associated with earlier word acquisition across measures and developmental phases. These findings underscore the critical role of varied lexical experiences in children's language development.

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.