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Session Type: Virtual Working Group Roundtable
Statistical learning, or the ability to involuntarily extract regularities from environmental input, plays a critical role in human language and cognition. Recent advances in educational neuroscience have led to a growing number of studies investigating (1) how the human brain deal with various types of (quasi-)regularities in establishing optimal representations of the probabilistic world; and (2) how individual and item characteristics may constraint what we are capable of learning. This roundtable session compiled six studies focusing on neurocognitive mechanisms of statistical learning involving neurodiverse populations across Eastern and Western cultures. The goal is to promote a synergized application of state-of-the-art brain science research and advanced language and reading practices to best support neurodiverse learners worldwide.
Electrophysiological Evidence of Children's Statistical Learning in Chinese Orthographic-Semantic Connections - Rujun Duan, The Education University of Hong Kong; Xiuhong Tong, The Education University of Hong Kong
Statistical Learning in Children With/Without Dyslexia: Impacts of Working Memory and Item Characteristics - Puyuan Zhang, University of Hong Kong; Shelley Xiuli Tong, University of Hong Kong
The Changing Role of Attention in Statistical Learning Over Development - Zhenghan Qi, Northeastern University; Anqi Hu, University of Delaware; Yi-Lun Weng, University of Delaware
Could Adults Learn Languages Like Children? Facilitating Statistical Language Learning by Cognitive Disruption - Riikka Möttönen, University of Helsinki; Eleonore Smalle, Ghent University
The Implicit Learning of Speech-Print Associations in Structured Language Streams - Manli Zhang, University of Hong Kong; Lars Riecke, Maastricht University; Milene Bonte, Maastricht University
Efficacy of Implicit and Explicit Statistical Learning: A Randomized Controlled Trial Intervention for Chinese Children With Dyslexia - Stephen Man Kit Lee, University of Hong Kong; Shelley Xiuli Tong, University of Hong Kong