Adults are skilled at using language to construct/negotiate identity and to signal affiliation with others, but little is known about how these abilities develop in children. Clearly, children mirror statistical patterns in their local environment (e.g., Canadian children using "zed" instead of "zee"), but do they flexibly…

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary…

People learn language variation through exposure to linguistic interactions. The way we take part in these interactions is shaped by our lexical representations, the mechanisms of language processing, and the social context. Existing work has looked at how we learn and store variation in the ambient language. How this is mediated by the social…

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition ("AoA") while controlling for frequency,…

Prior research indicates that humans adapt their language depending on context. This linguistic sensitivity has been suggested to indicate a natural pedagogy shared by all humans. This sensitivity has, however, only been demonstrated with English-speaking samples thus far. In two studies, we followed the experimental procedure of the original…

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant…

The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…