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

When: Monday, January 13rd, 11h 

Where: Salle des Voûtes, Campus Marseille St Charles.

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.

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Shelley Xiuli Tong, Ph.D., is a Full Professor at the University of Hong Kong’s Faculty of Education where she directs the Speech, Language, and Reading Lab. Recognized as an RGC Research Fellow and Fulbright Senior Scholar, her research, which has been funded by the U.S. National Academy of Education and the Hong Kong Research Grants Council, focuses on utilizing cognitive-behavioural, neurophysiological, and machine learning approaches to investigate the cognitive and neural mechanisms underlying statistical learning in children with dyslexia; the roles of prosodic reading in bilingual reading comprehension difficulties; and optimal solutions for classifying dyslexia, autism, and hearing-impairment. She has developed 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. Her work has resulted in over 80 publications in journals such as Child Development, Cognition, Developmental Cognitive Neuroscience, Journal of Educational Psychology, Learning and Instruction, and Educational Psychology Review.