Human Development Colloquium: Yi Ting Huang (HESP)
Learning Language, Fast and Slow: How to Overcome Sparse Data and Signal Degradation During Moment- To-Moment Comprehension
Abstract: Traditional approaches to language development focus on relationships between aggregate inputs (e.g., total words heard) and outcomes (e.g., vocabulary size), assuming that what parents say is what children learn. This, of course, ignores an obvious fact about acquisition – Children initially have no idea what parents are talking about. Instead, they must infer linguistic representations through iterative encounters with sentences. To better understand these processes, I will introduce three lines of research that describe developmental algorithms for sentence processing, and their variation with language experience and impairment status. First, we will examine how 4- to 6-year-olds determine who did what to whom in sentences, and show ways in which their strategies vary with properties of the communicative context. Second, we will take a closer look at SES language gaps, and show how systematic variation in language experiences preserve learning ability but alter sentence-processing strategies. Finally, we will turn to children with Developmental Language Disorder, who face profound difficulties producing, understanding, and learning from language in the school-aged years. We present preliminary findings from a randomized controlled trial to alter sentence processing and improve comprehension. We will close by considering causal pathways between chronometric and ontogenetic processes, and discuss their implications for how children recreate language from input, what a satisfying algorithmic-level description of language acquisition might look like, and why more cognitive scientists should be working at the intersection of basic and translational research.
Bio: Yi Ting Huang is an Associate Professor in the Department of Hearing and Speech Sciences (HESP). She received her doctorate in developmental psychology at Harvard University and trained as a postdoctoral fellow in cognitive psychology at the University of North Carolina at Chapel Hill. A researcher in language processing in adults and children, she has applied this expertise to multiple populations and societally important problems, including the relationship between language learning and poverty, cochlear implant users and children learning language in the more isolated setting of a pandemic. Dr. Huang has led several interdisciplinary projects and is the principal investigator of a UMD Grand Challenges team project on “Fostering Inclusivity through Technology” that aims to improve remote work experiences for autistic individuals. During the pandemic, she spearheaded KidTalk, a project that studied young children’s pandemic language experience, funded by the National Science Foundation (NSF). Dr. Huang is also a co-principal investigator on the NSF-funded project that established the Language Science Station at Planet Word Museum in Washington, D.C. She is currently the Director of the Maryland Language Science Center, a member of the Executive Committee for the Program in Neuroscience and Cognitive Science, and an Associate Editor of the journal Language Acquisition.
