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Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by investigating whether statistical learning is involved in acquiring a morpho-syntactic structure from input that resembles natural languages more closely. We exposed 50 kindergarteners (M = 5 years, 5 months) to a miniature language in which they had to learn a grammatical marker that expressed number, and which could only be acquired on the basis of the distributional properties in the input. Half of the children performed an attention check during the experiment. Results show that young children are able to learn this meaning. We found no clear evidence that facilitating attention to the input increases learning performance.

Research consistently shows that adults engaged in tutored acquisition benefit from explicit instruction in several linguistic domains. For preschool children, it is often assumed that such explicit instruction does not make a difference. In the present study, we investigated whether explicit instruction affected young learners in acquiring a morpho-syntactic element. A total of 103 Dutch-speaking kindergartners (M = 5;7) received training in a miniature language to learn a meaningful agreement marker. Results from a picture matching task, during which eye movements were recorded, provided no evidence that explicit instruction led to higher accuracy rates, but suggest that it did lead to earlier predictive eye movements. These data seem incompatible with the idea that explicit instruction does not make a difference when kindergartners learn a grammatical element, and tentatively suggest that explicit instruction has a different effect on explicit knowledge than on implicit knowledge in this age group.

Many studies suggest that detecting statistical regularities in linguistic input plays a key role in language acquisition. Although statistical learning is not necessarily implicit in nature, it is often defined as learning that happens without awareness. This article investigates whether statistical learning in young children is indeed implicit, as often assumed. We trained 63 kindergarteners on a miniature language and assessed learning using a picture-matching task. We used an opt-out task to measure whether the kindergarteners possessed awareness of an acquired meaningful grammatical marker. In the opt-out paradigm, participants demonstrate awareness by expressing uncertainty through a nonverbal response: opting out. Our results are compatible with an earlier study of which the present study is a partial replication, suggesting that kindergarteners can acquire this marker from distributional properties in the input. Furthermore, although none of the children could verbalize knowledge of the structure during exit interviews, their behavior during the opt-out task indicated that they developed awareness of it.

We analyze argument structure of whole-entity and handling classifier predicates in four sign languages (Russian Sign Language, Sign Language of the Netherlands, German Sign Language, and Kata Kolok) using parallel datasets (retellings of the Canary Row cartoons). We find that all four languages display a systematic, or canonical, mapping between classifier type and argument structure, as previously established for several sign languages: whole-entity classifier predicates are mostly used intransitively, while handling classifier predicates are used transitively. However, our data sets also reveal several non-canonical mappings which we address in turn. First, it appears that whole-entity classifier predicates can be used unergatively, rather than unaccusatively, contrary to expectations. Second, our data contain some transitive uses of whole-entity classifier predicates. Finally, we find that handling classifier predicates can express various complex event structures. We discuss what these findings imply for existing theories of classifier predicates in sign languages.