Literate and preliterate children show different learning patterns in an artificial language learning task

Cultural evolution creates the statistical structure of language

Human language is unique in its structure: language is made up of parts that can be recombined in a productive way. The parts are not given but have to be discovered by learners exposed to unsegmented wholes. Across languages, the frequency distribution of those parts follows a power law. Both statistical properties-having parts and having them follow a particular distribution-facilitate learning, yet their origin is still poorly understood. Where do the parts come from and why do they follow a particular frequency distribution? Here, we show how these two core properties emerge from the process of cultural evolution with whole-to-part learning. We use an experimental analog of cultural transmission in which participants copy sets of non-linguistic sequences produced by a previous participant: This design allows us to ask if parts will emerge purely under pressure for the system to be learnable, even without meanings to convey. We show that parts emerge from initially unsegmented sequences, that their distribution becomes closer to a power law over generations, and, importantly, that these properties make the sets of sequences more learnable. We argue that these two core statistical properties of language emerge culturally both as a cause and effect of greater learnability.

The Starting Big approach to language learning

The study of language acquisition has a long and contentious history: researchers disagree on what drives this process, the relevant data, and the interesting questions. Here, I outline the Starting Big approach to language learning, which emphasizes the role of multiword units in language, and of coarse-to-fine processes in learning. I outline core predictions and supporting evidence. In short, the approach argues that multiword units are integral building blocks in language; that such units can facilitate mastery of semantically opaque relations between words; and that adults rely on them less than children, which can explain (some of) their difficulty in learning a second language. The Starting Big approach is a theory of how children learn language, how language is represented, and how to explain differences between first and second language learning. I discuss the learning and processing models at the heart of the approach and their cross-linguistic implications.

Starting Big: The Effect of Unit Size on Language Learning in Children and Adults

Multiword units play an important role in language learning and use. It was proposed that learning from such units can facilitate mastery of certain grammatical relations, and that children and adults differ in their use of multiword units during learning, contributing to their varying language-learning trajectories. Accordingly, adults learn gender agreement better when encouraged to learn from multiword units. Previous work has not examined two core predictions of this proposal: (1) that children also benefit from initial exposure to multiword units, and (2) that their learning patterns reflect a greater reliance on multiword units compared to adults. We test both predictions using an artificial-language. As predicted, both children and adults benefit from early exposure to multiword units. In addition, when exposed to unsegmented input - adults show better learning of nouns compared to article-noun pairings, but children do not, a pattern consistent with adults' predicted tendency to focus less on multiword units.