
The Origins of Grammatical Productivity
Description
When do children learn to use the combinatorial elements of language structure, and how do they learn which generalizations are in the adult language? In this talk, I will discuss two studies investigating the origins of grammatical productivity in early first language acquisition among English-learning children. In the first study, I will show how a simplified probabilistic generative language model can be used to measure the strength of grammatical generalization implicit in the way children use the words "a" and "the" with various nouns. When this model is applied to large, longitudinal developmental corpora of transcribed speech it yields a developmental trajectory that differs from both of the prevailing theories of early grammatical productivity. The second study investigates children’s knowledge of the English regular plural, systematically testing both what children say as well as what they understand from the speech of others in a lab-based experiment. We find that children between 2 and 3 use the plural—including forming novel plurals—in their own speech, while failing to understand it when used by adults. Together, these two studies suggest that grammatical generalizations are themselves an integral part of the language learning process rather than its end product.
Link to Zoom Webinar: https://mit.zoom.us/j/93389937796
Speaker Bio
Stephan Meylan is a postdoctoral fellow in the Computational Psycholinguistics Lab at MIT Brain and Cognitive Sciences. He also holds a joint postdoctoral appointment in the Bergelson Lab in the Department of Psychology and Neuroscience at Duke University, where he was based 2018-2019. Stephan completed his PhD in 2018 at University of California, Berkeley, advised by Dr. Tom Griffiths. Stephan's research focuses on the role of generalization in language acquisition and language processing, and includes modeling, experimental, and corpus-based methods.
Additional Info
Upcoming Cog Lunch Talks:
August 11, 2020 - OPEN
August 18, 2020 - Martin Schrimpf