The Growth of Children's Semantic and Phonological Networks: Insight From 10 Languages

When is a word in good company for learning?

Although identifying the referents of single words is often cited as a key challenge for getting word learning off the ground, it overlooks the fact that young learners consistently encounter words in the context of other words. How does this company help or hinder word learning? Prior investigations into early word learning from children's real-world language input have yielded conflicting results, with some influential findings suggesting an advantage for words that keep a diverse company of other words, and others suggesting the opposite. Here, we sought to triangulate the source of this conflict, comparing different measures of diversity and approaches to controlling for correlated effects of word frequency across multiple languages. The results were striking: while different diversity measures on their own yielded conflicting results, once nonlinear relationships with word frequency were controlled, we found convergent evidence that contextual consistency supports early word learning. RESEARCH HIGHLIGHTS: The words children learn occur in a sea of other words. The company words keep ranges from highly variable to highly consistent and circumscribed. Prior findings conflict over whether variability versus consistency helps early word learning. Accounting for correlated effects of word frequency resolved the conflict across multiple languages. Results reveal convergent evidence that consistency helps early word learning.

Obsolescence effects in second language phonological networks

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 obsolescence effects (i.e., a word's finite timespan 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. First-language-given neighborhood densities are rarely reached even by the most advanced language learners. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to incorporate phonetically more distant phonological neighbors at earlier acquisition stages. Overall, the findings suggest an obsolescence effect in growth that favors younger words. Implications for the second-language lexicon include leveraged learning mechanisms and learning bouts focused on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks.

The role of translation equivalents in bilingual word learning

Bilingual environments present an important context for word learning. One feature of bilingual environments is the existence of translation equivalents (TEs)-words in different languages that share similar meanings. Documenting TE learning over development may give us insight into the mechanisms underlying word learning in young bilingual children. Prior studies of TE learning have often been confounded by the fact that increases in overall vocabulary size with age lead to greater opportunities for learning TEs. To address this confound, we employed an item-level analysis, which controls for the age trajectory of each item independently. We used Communicative Development Inventory data from four bilingual datasets (two English-Spanish and two English-French; total N = 419) for modeling. Results indicated that knowing a word's TE increased the likelihood of knowing that word for younger children and for TEs that are more similar phonologically. These effects were consistent across datasets, but varied across lexical categories. Thus, TEs may allow bilingual children to bootstrap their early word learning in one language using their knowledge of the other language. RESEARCH HIGHLIGHTS: Bilingual children must learn words that share a common meaning across both languages, that is, translation equivalents, like dog in English and perro in Spanish. Item-level models explored how translation equivalents affect word learning, in addition to child-level (e.g., exposure) and item-level (e.g., phonological similarity) factors. Knowing a word increased the probability of knowing its corresponding translation equivalent, particularly for younger children and for more phonologically-similar translation equivalents. These findings suggest that young bilingual children use their word knowledge in one language to bootstrap their learning of words in the other language.

How does linguistic context influence word learning?

While there are well-known demonstrations that children can use distributional information to acquire multiple components of language, the underpinnings of these achievements are unclear. In the current paper, we investigate the potential pre-requisites for a distributional learning model that can explain how children learn their first words. We review existing literature and then present the results of a series of computational simulations with Vector Space Models, a type of distributional semantic model used in Computational Linguistics, which we evaluate against vocabulary acquisition data from children. We focus on nouns and verbs, and we find that: (i) a model with flexibility to adjust for the frequency of events provides a better fit to the human data, (ii) the influence of context words is very local, especially for nouns, and (iii) words that share more contexts with other words are harder to learn.

Predicting Age of Acquisition for Children's Early Vocabulary in Five Languages Using Language Model Surprisal

What makes a word easy to learn? Early-learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child-directed speech, computed using n-gram and long-short-term-memory (LSTM) language models. Predictability derived from LSTMs was generally a better predictor than predictability derived from n-gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross-linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable.

Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure

In adults, nouns and verbs have varied and multilevel semantic interrelationships. In children, evidence suggests that nouns and verbs also have semantic interrelationships, though the timing of the emergence of these relationships and their precise impact on later noun and verb learning are not clear. In this work, we ask whether noun and verb semantic knowledge in 16-30-month-old children tend to be semantically isolated from one another or semantically interacting from the onset of vocabulary development. Early word learning patterns were quantified using network science. We measured the semantic network structure for nouns and verbs in 3,804 16-30-month-old children at several levels of granularity using a large, open dataset of vocabulary checklist data. In a cross-sectional approach in Experiment 1, early nouns and verbs exhibited stronger network relationships with other nouns and verbs than expected across multiple network levels. Using a longitudinal approach in Experiment 2, we examined patterns of normative vocabulary development over time. Initial noun and verb learning was supported by strong semantic connections to other nouns, whereas later-learned words exhibited strong connections to verbs. Overall, these two experiments suggest that nouns and verbs demonstrate early semantic interactions and that these interactions impact later word learning. Early verb and noun learning is affected by the emergence of noun and verb semantic networks during early lexical development.

Drivers of Lexical Processing and Implications for Early Learning

Understanding words in unfolding speech requires the coordination of many skills to support successful and rapid comprehension of word meanings. This multifaceted ability emerges before our first birthday, matures over a protracted period of development, varies widely between individuals, forecasts future learning outcomes, and is influenced by immediate context, prior knowledge, and lifetime experience. This article highlights drivers of early lexical processing abilities while exploring questions regarding how learners begin to acquire, represent, and activate meaning in language. The review additionally explores how lexical processing and representation are connected while reflecting on how network science approaches can support richly detailed insights into this connection in young learners. Future research avenues are considered that focus on addressing how language processing and other cognitive skills are connected.

Not just form, not just meaning: Words with consistent form-meaning mappings are learned earlier

By leveraging Phonology-to-Semantics Consistency (PSC), which quantifies form-meaning systematicity as the semantic similarity between a target word and its phonological nearest neighbours, we document a unique effect of systematicity on Age of Acquisition (AoA). This effect is also found after controlling for the effect of neighbourhood density measured for word forms and lexical semantics and several other standard predictors of AoA. Moreover, we show that the effect of systematicity is not reducible to iconicity. Finally, we extensively probe the reliability of this finding by testing different statistical models, analysing systematicity in phonology and orthography and implementing random baselines, reporting a robust, unique negative effect of systematicity on AoA, such that more systematic words tend to be learned earlier. We discuss the findings in the light of studies on non-arbitrary form-meaning mappings and their role in language learning, focusing on the analogical process at the interface of form and meaning upon which PSC is based and how it could help children infer the semantics of novel words when context is scarce or uninformative, ultimately speeding up word learning.

Exploring Patterns of Stability and Change in Caregivers' Word Usage Across Early Childhood

The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child-directed speech, extracting various patterns of word use/co-occurrence. Previous work using these models has not measured how these patterns may change throughout development, however. In this work, we leverage natural language processing methods-originally developed to study historical language change-to compare caregivers' use of words when talking to younger versus older children. Some words' usage changed more than others; this variability could be predicted based on the word's properties at both the individual and category levels. These findings suggest that caregivers' changing patterns of word use may play a role in scaffolding children's acquisition of conceptual structure in early development.

The map trap: Why and how word learning research should move beyond mapping

A pervasive goal in the study of how children learn word meanings is to explain how young children solve the mapping problem. The mapping problem asks how language learners connect a label to its referent. Mapping is one part of word learning, however, it does not reflect other critical components of word meaning construction, such as the encoding of lexico-semantic relations and socio-pragmatic context. In this paper, we argue that word learning researchers' overemphasis of mapping has constrained our experimental paradigms and hypotheses, leading to misconceived theories and policy interventions. We first explain how the mapping focus limits our ability to study the richness and complexity of what infants and children learn about, and do with, word meanings. Then, we describe how our focus on mapping has constrained theory development. Specifically, we show how it has led to (a) the misguided emphasis on referent selection and ostensive labeling, and (b) the undervaluing of diverse pathways to word knowledge, both within and across cultures. We also review the consequences of the mapping focus outside of the lab, including myopic language learning interventions. Last, we outline an alternative, more inclusive approach to experimental study and theory construction in word learning research. This article is categorized under: Psychology > Language Psychology > Theory and Methods Psychology > Learning.

Onset Neighborhood Density Slows Lexical Access in High Vocabulary 30-Month Olds

There is consensus that the adult lexicon exhibits lexical competition. In particular, substantial evidence demonstrates that words with more phonologically similar neighbors are recognized less efficiently than words with fewer neighbors. How and when these effects emerge in the child's lexicon is less clear. In the current paper, we build on previous research by testing whether phonological onset density slows lexical access in a large sample of 100 English-acquiring 30-month-olds. The children participated in a visual world looking-while-listening task, in which their attention was directed to one of two objects on a computer screen while their eye movements were recorded. We found moderate evidence of inhibitory effects of onset neighborhood density on lexical access and clear evidence for an interaction between onset neighborhood density and vocabulary, with larger effects of onset neighborhood density for children with larger vocabularies. Results suggest the lexicons of 30-month-olds exhibit lexical-level competition, with competition increasing with vocabulary size.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes

Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.

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

Here, we view the mental lexicon as a semantic network where words are connected if they are semantically related. Steyvers and Tenenbaum (Cognitive Science, 29, 41-78, 2005) proposed that the growth of semantic networks follows preferential attachment, the observation that new nodes are more likely to connect to preexisting nodes that are more well connected (i.e., the rich get richer). If this is the case, well-connected known words should be better at acquiring new links than poorly connected words. We tested this prediction in three paired-associate learning (PAL) experiments in which participants memorized arbitrary cue-response word pairs. We manipulated the semantic connectivity of the cue words, indexed by the words' free associative degree centrality. Experiment 1 is a reanalysis of the PAL data from Qiu and Johns (Psychonomic Bulletin & Review, 27, 114-121, 2020), in which young adults remembered 40 cue-response word pairs (e.g., nature-chain) and completed a cued recall task. Experiment 2 is a preregistered replication of Qiu and Johns. Experiment 3 addressed some limitations in Qiu and Johns's design by using pseudowords as the response items (e.g., boot-arruity). The three experiments converged to show that cue words of higher degree centrality facilitated the recall/recognition of the response items, providing support for the notion that better-connected words have a greater ability to acquire new links (i.e., the rich do get richer). Importantly, while degree centrality consistently accounted for significant portions of variance in PAL accuracy, other psycholinguistic variables (e.g., concreteness, contextual diversity) did not, suggesting that degree centrality is a distinct variable that affects the ease of verbal associative learning.

Continuous developmental change explains discontinuities in word learning

Cognitive development is often characterized in terms of discontinuities, but these discontinuities can sometimes be apparent rather than actual and can arise from continuous developmental change. To explore this idea, we use as a case study the finding by Stager and Werker (1997) that children's early ability to distinguish similar sounds does not automatically translate into word learning skills. Early explanations proposed that children may not be able to encode subtle phonetic contrasts when learning novel word meanings, thus suggesting a discontinuous/stage-like pattern of development. However, later work has revealed (e.g., through using more precise testing methods) that children do encode such contrasts, thus favoring a continuous pattern of development. Here, we propose a probabilistic model that represents word knowledge in a graded fashion and characterizes developmental change as improvement in the precision of this graded knowledge. Our model explained previous findings in the literature and provided a new prediction - the referents' visual similarity modulates word learning accuracy. The models' predictions were corroborated by human data collected from both preschool children and adults. The broader impact of this work is to show that computational models, such as ours, can help us explore the extent to which episodes of cognitive development that are typically thought of as discontinuities may emerge from simpler, continuous mechanisms.