In the usage-based approach to children’s language learning, language is seen as emerging from children’s preverbal communicative and cognitive skills. Children construct more abstract linguistic representations only gradually, and show uneven development in all aspects of their language learning. I will present results that show the relationship between children’s emerging linguistic structures and patterns in the speech addressed to them, and demonstrate the effects played by the consistency of markers, the complexity of the construction in question, and relative type and token frequencies within and across constructions. I highlight the contribution made by research that employs naturalistic, experimental, and modelling methodologies, and that is applied to a range of languages and to variability in the errors that children make. Finally, I will outline the outstanding issues for this approach, and how we might address them.

Grammatical constructions are typically partially but not fully productive, which leads to a conundrum for the learner. When can a construction be extended for use with new words and when can it not? The solution suggested here relies on two complementary processes. The first is dynamic categorization: as learners record the statistics of their language, they implicitly categorize the input on the basis of form and function. On the basis of this categorization process, general semantic and phonological constraints on productivity emerge, and productivity is to a large extent determined by the degree to which the category is well attested by similar exemplars. Occasionally, a semantically sensical and phonologically well-formed instance of a well-attested construction is simply not fully acceptable. It is suggested that a process of statistical preemption is at work in these cases: learners avoid using a construction if an alternative formulation has been systematically witnessed instead. The mechanism proposed for statistical preemption is competition-driven learning: when two competitors are activated but one reliably wins, the loser becomes less accessible over time. In this way, the paradox of partial productivity can be resolved.

This paper discusses how patterns of construction usage, implicitly learned over lifelong experience, tune the language processing system for fluent interactive lexical, syntactic, and semantic access. It reports on three experiments that investigate online processing of Verb–Argument Constructions (VACs) and the degree to which this is effected by (i) verb frequency in the language, (ii) verb frequency in the VAC, (iii) VAC-verb contingency, and (iv) verb prototypicality in terms of centrality within the VAC semantic network. Experiment 1 tested lexical decision of VAC exemplars presented as successive verb–preposition pairs. Experiment 2 tested lexical decision of VAC exemplars presented as arbitrarily interrupted verb–adverb–preposition pairs. Experiment 3 had participants judge whether two-word utterances were meaningful or not. All of the experiments show effects of Verb Frequency and Verb-VAC frequency: learners have rich implicit statistical knowledge of verb-VAC type–token frequency that guides processing. Lexical decision is additionally driven by semantic prototypicality (but not VAC-verb contingency ΔPcw), whereas meaning judgment is affected by VAC-verb contingency ΔPcw (but not semantic prototypicality). These findings, I argue, index the spreading activation of unconscious meaning representation in lexical decision in comparison to the election of a unitary interpretation in conscious comprehension. I conclude that speeded automatic VAC processing involves rich associations, tuned by verb type and token frequencies, their contingencies of usage, and their histories of prototypical and specific interpretations which interface syntax, lexis, and semantics.

‘Special reduction’ refers to instances of extreme phonetic reduction which is restricted to particular words or phrases, usually grammaticalizing constructions (going to > [gə̃ɾ̃ə̃]), greetings (hi from how are you), discourse markers (Spanish o sea > sa), or other sequences that are often used together. On the basis of data from English, Brazilian Portuguese, and Colombian Spanish, we argue that special reduction is based on the general phonetic tendencies in the language, but that these tendencies are carried to an extreme where word sequences are used with high frequency and become chunked, allowing formerly stressed syllables to lose stress and reduce. The data also show that special reduction takes place gradually over time, and reflects general patterns of change seen in the history of the language. In fact, in some examples, special reduction presages more general sound changes that occur later. We argue that the gradual phonetic changes that accumulate for particular words or phrases, eventually changing them dramatically, requires an exemplar model for the phonological representation of words and phrases, which is updated continually as sequences are used and affected by reductive phonetic processes.

Our research on statistical language learning shows that infants, young children, and adults can compute, online and with remarkable speed, how consistently sounds co-occur, how frequently words occur in similar contexts, and the like, and can utilize these statistics to find candidate words in a speech stream, discover grammatical categories, and acquire simple syntactic structure in miniature languages. However, statistical learning is not merely learning the patterns presented in the input. When their input is inconsistent, children sharpen these statistics and produce a more systematic language than the one to which they are exposed. When input languages inconsistently violate tendencies that are widespread in human languages, learners shift these languages to be more aligned with language universals, and children do so much more than adults. These processes explain why children acquire language (and other patterns) more effectively than adults, and also may explain how systematic language structures emerge in communities where usages are varied and inconsistent. Most especially, they suggest that usage-based learning approaches must account for differences between adults and children in how usage properties are acquired, and must also account for substantial changes made by adult and child learners in how input usage properties are represented during learning.