Cracking the Code: Unveiling Hidden Patterns in Words, Speech, and Objects Through Implicit Statistical Learning

Professor Shelley Xiuli Tong, Director of Speech, Language and Reading Lab  (https://slrlab.edu.hku.hk/) Faculty of Education – The University of Hong Kong

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 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 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 encoding 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 probabilistic world, the mind, and the increasingly complex relationships between people and machines.