Learning in any domain depends on how the data for learning are represented. In the domain of language acquisition, children's representations of the speech they hear determine what generalizations they can draw about their target grammar. But these input representations change over development as a function of children's developing linguistic…

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…

Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this…

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism--backward transitional probability--is able to predict children's speech productions with stable accuracy throughout the first few years of development. We…

Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different…

The ability to extract patterns from sensory input across time and space is thought to underlie the development and acquisition of language and literacy skills, particularly the subdomains marked by the learning of probabilistic knowledge. Thus, impairments in procedural learning are hypothesized to underlie neurodevelopmental disorders, such as…

Negation is a cornerstone of human language and one of the few universals found in all languages. Without negation, neither categorization nor efficient communication would be possible. Languages, however, differ remarkably in how they express negation. It is yet widely unknown how the way negation is marked influences the acquisition process of…

Becoming productive with grammatical categories is a gradual process in children's language development. Here, we investigated this transition process by focusing on Turkish causatives. Previous research examining spontaneous and elicited production of Turkish causatives with familiar verbs attested the onset and early stages of productivity at…

Children with developmental language disorder (DLD) have significant deficits in language ability that cannot be attributed to neurological damage, hearing impairment, or intellectual disability. The symptoms displayed by children with DLD differ across languages. In English, DLD is often marked by severe difficulties acquiring verb inflection.…

Across languages, lexical items specific to infant-directed speech (i.e., 'baby-talk words') are characterized by a preponderance of onomatopoeia (or highly iconic words), diminutives, and reduplication. These lexical characteristics may help infants discover the referential nature of words, identify word referents, and segment fluent speech into…

This study aims to disentangle the often-confounded effects of input frequency and morphophonological complexity in the acquisition of inflection, by focusing on simple and complex verb forms in Japanese. Study 1 tested 28 children aged 3;3-4;3 on stative (complex) and simple past forms, and Study 2 tested 30 children aged 3;5-5;3 on completive…

The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount…

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of…

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given…

To acquire one's native phonological system, language-specific phonological categories and relationships must be extracted from the input. The acquisition of the categories and relationships has each in its own right been the focus of intense research. However, it is remarkable that research on the acquisition of categories and the relations…