One approach to studying how children acquire language is to simulate language acquisition through computational modelling. Computational models implement theories of language acquisition and simulation outcomes can then be tested against existing real-world data or in new empirical research. It is more than ten years ago that Journal of Child Language published a special issue on the topic, edited and introduced by Brian MacWhinney (MacWhinney, 2010). Now is thus a good time to take stock of recent developments by bringing together a collection of articles that explore recent research and insights from computational modelling of child language acquisition.

There is a current ‘theory crisis’ in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.

This review describes a computational approach for modeling the development of speech motor control in infants. We address the development of two levels of control: articulation of individual speech sounds (defined here as phonemes, syllables, or words for which there is an optimized motor program) and production of sound sequences such as phrases or sentences. We describe the DIVA model of speech motor control and its application to the problem of learning individual sounds in the infant’s native language. Then we describe the GODIVA model, an extension of DIVA, and how chunking of frequently produced phoneme sequences is implemented within it.

Verb-marking errors are a characteristic feature of the speech of typically-developing (TD) children and are particularly prevalent in the speech of children with Developmental Language Disorder (DLD). However, both the pattern of verb-marking error in TD children and the pattern of verb-marking deficit in DLD vary across languages and interact with the semantic and syntactic properties of the language being learned. In this paper, we review work using a computational model called MOSAIC. We show how this work allows us to understand several features of the cross-linguistic data that are otherwise difficult to explain. We also show how discrepancies between the developmental data and the quantitative predictions generated by MOSAIC can be used to identify weaknesses in our current understanding and lead to further theory development; and how the resulting model (MOSAIC+) helps us understand differences in the cross-linguistic patterning of verb-marking errors in TD children and children with DLD.

Computational cognitive modeling is a tool we can use to evaluate theories of syntactic acquisition. Here, I review several models implementing theories that integrate information from both linguistic and non-linguistic sources to learn different types of syntactic knowledge. Some of these models additionally consider the impact of factors coming from children’s developing non-linguistic cognition. I discuss some existing child behavioral work that can inspire future model-building, and conclude by considering more specifically how to build better models of syntactic acquisition.

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.

Computational models of reading have tended to focus on the cognitive requirements of mapping among written, spoken, and meaning representations of individual words in adult readers. Consequently, the alignment of these computational models with behavioural studies of reading development has to date been limited. Models of reading have provided us with insights into the architecture of the reading system, and these have recently been extended to investigate literacy development, and the early language skills that influence children’s reading. These models show us: how learning to read builds on early language skills, why various reading interventions might be more or less effective for different children, and how reading develops across different languages and writing systems. Though there is growing alignment between descriptive models of reading behaviour and computational models, there remains a gap, and I lay out the groundwork for how translation may become increasingly effective through future modelling work.

The present study aims at analysing the role of infinitival clauses (INFCs) in German child-adult dialogue. In German subject-less INFCs are a grammatical sentence pattern. Extensive corpora of spontaneous speech between 6 children aged 1;5 to 2;10 and adults were analysed applying structural and contextual analyses. We extended Freudenthal, Pine and Gobet’s (2010) model of lexically specific learning to include INFCs in adult input. Results show that frequencies of adult INFC and MOD+INF clauses are related to child INFCs. We interpret these results as reflecting shared verb vocabulary and, regarding INFCs, as an adaptation of adult CDS to child grammatical structure. While most child INFCs have modal meaning, some occur in non-modal contexts. The majority of child INFCs are subject-less clauses with final infinitives and therefore grammatical. Results are discussed in terms of the pragmatic function of child and adult INFCs and the role of adult INFCs in German CDS.

For shared book reading to be effective for language development, the adult and child need to be highly engaged. The current paper adopted a mixed-methods approach to investigate caregiver’s language-boosting behaviours and children’s engagement during shared book reading. The results revealed there were more instances of joint attention and caregiver’s use of prompts during moments of higher engagement. However, instances of most language-boosting behaviours were similar across episodes of higher and lower engagement. Qualitative analysis assessing the link between children’s engagement and caregiver’s use of speech acts, revealed that speech acts do seem to contribute to high engagement, in combination with other aspects of the interaction.

Variegation – the presence of more than one supraglottal consonant per word – is a key challenge for children as they increase their expressive vocabulary toward the end of the single-word period. Here we consider the prosodic structures of target words and child forms in English, Finnish, French, Japanese and Mandarin to determine whether children learning these languages respond similarly to the challenge or instead differ in ways related to the phonological structure of the adult language. Based on proportional occurrence of each structure, we find that the word forms of children learning Mandarin and Japanese show more variegation than do those of children learning the European languages, although their target words do not; proportions of reduplication, consonant harmony and single-consonant words also differ by language. We conclude that experience with the structure of the language – and thus representation, as well as immature articulatory skills – shapes children’s responses to variegation.

We examined whether and how the degree of meaning overlap between morphologically related words influences sentence plausibility judgment in children. In two separate studies with kindergarten and second-graders, English-speaking and French-speaking children judged the plausibility of sentences that included two paired target words. Some of these word pairs were morphologically related, across three conditions with differing levels of meaning overlap: low (wait-waiter), moderate (fold-folder) and high (farm-farmer). In another two conditions, word pairs were related only by phonology (rock-rocket) or semantics (car-automobile). Children in both ages and languages demonstrated higher plausibility scores as meaning overlap increased between morphologically related words. Further, kindergarten children rated sentences that included word pairs with phonological overlap as more plausible than second-grade children, while second-grade children rated those with high meaning overlap as more plausible than kindergarten children. We interpret these findings in light of current models of morphological development.