Research

We test both bottom-up and top-down approaches in learning the phonemic status of the sounds of English and Japanese. We used large corpora of spontaneous speech to provide the learner with an input that models both the linguistic properties and statistical regularities of each language. We found both approaches to help discriminate between allophonic and phonemic contrasts with a high degree of accuracy, although top-down cues proved to be effective only on an interesting subset of the data

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 NLP methods that were originally developed to study historical language change to compare caregivers’ use of words when talking to younger vs. 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 level. 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.

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.

Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child’s early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external learning environment, instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains across 10 languages. We show that phonological and semantic connectivity in the learning environment drives growth in both production- and comprehension-based vocabularies, even controlling for word frequency and length. This pattern of findings suggests a word learning process where children harness their statistical learning abilities to detect and learn highly connected words in the learning environment.

Early verbal dialogue emerges during the second year of life, despite children’s still-developing linguistic, pragmatic, and cognitive abilities. This raises a puzzle: how can young children produce contingent responses under the real-time demands of turn-taking? In this study, we articulate a unified scaffolding account and test whether three caregiver-side mechanisms help explain this early emergence: lexical specificity in initiating utterances, interactive routines that constrain next-intent selection, and short caregiver realizations that can serve as models for children’s own intent expression. Using recently developed automatic annotation tools for communicative intents and response contingency, we investigated this question in large-scale naturalistic corpora of child–caregiver dialogue, spanning 40 corpora and involving more than 600 children across more than 2,500 transcripts. Across corpora, each mechanism was associated with higher child contingency, and all three remained predictive in a joint model controlling for age, child utterance length, and frequency-based factors. These findings are consistent with the view that early dialogue participation is supported not only by children’s emerging capacities, but also by caregivers’ structuring of interaction in ways that reduce processing demands at multiple steps of response formation. More broadly, the results provide quantitative support for a scaffolding-based account of early dialogue emergence and help connect developmental evidence to broader theories of real-time communication.

Many debates in the language acquisition literature have revolved around the role of negative evidence (e.g., clarification requests) for the acquisition of grammar. However, the scientific study of this question has not been settled with traditional research methods, given that it requires handling children’s natural social interaction while controlling for the specific role of error-contingent feedback, independent of other types of input. Here, we compared language models trained on large corpora of child-directed language to the same models that were additionally fine-tuned through reinforcement learning. To this end, a reward model was trained on child-caregiver conversations to provide parent-like feedback. The goal of this comparison is to test whether there are learning gains in grammar induced by caregivers’ feedback above and beyond learning from input alone. Focusing on clarification requests, we found that the fine-tuned models produced more grammatical utterances compared to baseline models. However, performance on challenging benchmarks of grammar knowledge evaluation (i.e. Zorro and Blimp) did not improve. The broad impact of the current work is introducing a methodological framework which enables scientists to test many types of feedback, including signals beyond the verbal modality, leading to a more comprehensive evaluation of natural caregiver feedback in language development.