The development of predictive processing: EEG studies of spoken language comprehension in rich naturalistic contexts

Understanding spoken language requires one to recognize individual words, bundle those words into sentences and make sense of what the sentences mean. These steps happen within a fraction of a second, as one word ends and the next one begins. Adults do this, in part, by using linguistic context to predict what they are likely to hear next. Adults predict not only the meaning of the next word but also the sounds that make up that word. This project explores how this ability to predict upcoming words and their sounds develops in 3- to 5-year-old children using non-invasive measurement of brain activity. The project uses a new child-friendly paradigm for measuring brain activity during a natural listening task. Children listen to novel or familiar stories while their brain activity is tracked by measuring electrical potentials on the scalp. This method allows for the collection of a large amount of data in a short time in a task that approximates children’s experience with spoken language outside the lab. Researchers construct statistical models to test whether the predictability of a word and familiarity with the story is related to the neural responses. This project advances understanding of the development of ability to predict upcoming words and their sounds, a core aspect of language learning and human intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2521560 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jesse Snedeker | Institution: Harvard University, CAMBRIDGE, MA | Award Amount: $772,299 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2521560 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2521560.html