Time: Friday, December 29, 2023, 14:00
Location: Lecture Hall 537, Building 3, Hainayuan, Zijingang Campus, Zhejiang University
Cracking the Code: Unveiling Hidden Patterns in Words, Speech, and Objects Through the Power of Implicit Statistical Learning
Speaker: Shelley Xiuli Tong, Ph.D.
Professor in the Faculty of Education at the University of Hong Kong
Director of the Speech, Language, and Reading Lab
Shelley Xiuli Tong, Ph.D., is Full Professor in the Faculty of Education at the University of Hong Kong and Director of the Speech, Language, and Reading Lab (https://sirlab.edu.hku.hk/). For the past 15 years, she has addressed educational and clinical issues on learning, language, and reading development in children with dyslexia and/or autism, including cognitive diagnostic assessment and preventive and remedial interventions, resulting in over 79 publications in internationally prestigious journals, such as Developmental Cognitive Neuroscience, Cognition, Child Development, Journal of Educational Psychology, and Educational Psychology Review. Her most significant research contribution posits six original theoretical models (i.e., SLR, TTRACE, SLOR, NCCP, PLSM, and PCH) that advance our understanding of the mechanisms underlying statistical learning, suprasegmental speech perception and biliteracy acquisition, and prosodic reading. Furthermore, she has translated her research into a patented product—an intelligent dyslexic interface design (I-DID)—that capitalizes on individual strengths of children with dyslexia and reflects her life-long commitment to transform scientific evidence into public policy and practice.
Abstract
Humans possess remarkable abilities to learn new words, acquire language, and recognize objects based on sparse and ambiguous inputs. These abilities are rooted in the robust and efficient learning mechanism of statistical learning, which enables individuals to automatically detect regularities and patterns in their environment through exposure to multiple stimuli. Despite decades of research demonstrating the involvement of statistical learning in the formation of memory and internal models of prediction, the cognitive and neural mechanisms underpinning statistical learning remain unclear.
In this talk, I will share my team's intensive research on statistical learning over the past decade and discuss a series of behavioral and neurophysiological experiments designed to address newly emerging questions that uncover how statistical learning functions in the human brain across various contexts. Specifically, I will address three fundamental questions: 1) Is statistical learning disrupted in individuals with neurodevelopmental disorders, especially dyslexia?; 2) How do different types of statistical learning change across ages and interact with other cognitive functions?; and 3) What cognitive and neural mechanisms support statistical learning?
In conclusion, I aim to demonstrate how new paradigms and theoretical frameworks are necessary to advance our understanding of how humans comprehend the world, the mind, and the increasingly complex relationships between people and machines.
