Learning to generalise but not segment an artificial language at 17 months predicts children's language skills 3 years later.

We investigated whether learning an artificial language at 17 months was predictive of children's natural language vocabulary and grammar skills at 54 months. Children at 17 months listened to an artificial language containing non-adjacent dependencies, and were then tested on their learning to segment and to generalise the structure of the language. At 54 months, children were then tested on a range of standardised natural language tasks that assessed receptive and expressive vocabulary and grammar. A structural equation model demonstrated that learning the artificial language generalisation at 17 months predicted language abilities - a composite of vocabulary and grammar skills - at 54 months, whereas artificial language segmentation at 17 months did not predict language abilities at this age. Artificial language learning tasks - especially those that probe grammar learning - provide a valuable tool for uncovering the mechanisms driving children's early language development.

How executive functioning, sentence processing, and vocabulary are related at 3 years of age.

There is a wealth of evidence demonstrating that executive function (EF) abilities are positively associated with language development during the preschool years, such that children with good executive functions also have larger vocabularies. However, why this is the case remains to be discovered. In this study, we focused on the hypothesis that sentence processing abilities mediate the association between EF skills and receptive vocabulary knowledge, in that the speed of language acquisition is at least partially dependent on a child's processing ability, which is itself dependent on executive control. We tested this hypothesis in longitudinal data from a cohort of 3- and 4-year-old children at three age points (37, 43, and 49 months). We found evidence, consistent with previous research, for a significant association between three EF skills (cognitive flexibility, working memory [as measured by the Backward Digit Span], and inhibition) and receptive vocabulary knowledge across this age range. However, only one of the tested sentence processing abilities (the ability to maintain multiple possible referents in mind) significantly mediated this relationship and only for one of the tested EFs (inhibition). The results suggest that children who are better able to inhibit incorrect responses are also better able to maintain multiple possible referents in mind while a sentence unfolds, a sophisticated sentence processing ability that may facilitate vocabulary learning from complex input.

Non-adjacent dependency learning in infancy, and its link to language development.

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants' capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants'  statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants' vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants' segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

Does speed of processing or vocabulary size predict later language growth in toddlers?

It is becoming increasingly clear that the way that children acquire cognitive representations depends critically on how their processing system is developing. In particular, recent studies suggest that individual differences in language processing speed play an important role in explaining the speed with which children acquire language. Inconsistencies across studies, however, mean that it is not clear whether this relationship is causal or correlational, whether it is present right across development, or whether it extends beyond word learning to affect other aspects of language learning, like syntax acquisition. To address these issues, the current study used the looking-while-listening paradigm devised by Fernald, Swingley, and Pinto (2001) to test the speed with which a large longitudinal cohort of children (the Language 0-5 Project) processed language at 19, 25, and 31 months of age, and took multiple measures of vocabulary (UK-CDI, Lincoln CDI, CDI-III) and syntax (Lincoln CDI) between 8 and 37 months of age. Processing speed correlated with vocabulary size - though this relationship changed over time, and was observed only when there was variation in how well the items used in the looking-while-listening task were known. Fast processing speed was a positive predictor of subsequent vocabulary growth, but only for children with smaller vocabularies. Faster processing speed did, however, predict faster syntactic growth across the whole sample, even when controlling for concurrent vocabulary. The results indicate a relatively direct relationship between processing speed and syntactic development, but point to a more complex interaction between processing speed, vocabulary size and subsequent vocabulary growth.

Aligning Developmental and Processing Accounts of Implicit and Statistical Learning.

A long-standing question in child language research concerns how children achieve mature syntactic knowledge in the face of a complex linguistic environment. A widely accepted view is that this process involves extracting distributional regularities from the environment in a manner that is incidental and happens, for the most part, without the learner's awareness. In this way, the debate speaks to two associated but separate literatures in language acquisition: statistical learning and implicit learning. Both fields have explored this issue in some depth but, at present, neither the results from the infant studies used by the statistical learning literature nor the artificial grammar learning tasks studies from the implicit learning literature can be used to fully explain how children's syntax becomes adult-like. In this work, we consider an alternative explanation-that children use error-based learning to become mature syntax users. We discuss this proposal in the light of the behavioral findings from structural priming studies and the computational findings from Chang, Dell, and Bock's (2006) dual-path model, which incorporates properties from both statistical and implicit learning, and offers an explanation for syntax learning and structural priming using a common error-based learning mechanism. We then turn our attention to future directions for the field, here suggesting how structural priming might inform the statistical learning and implicit learning literature on the nature of the learning mechanism.