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美国宾夕法尼亚州立大学李平教授系列讲座第一讲

【来源: | 发布日期:2018-10-29 | 阅读次数:

目:Understanding the Second Language Learning Brain: Neurocognitive and Computational Approaches

主讲人:李平教授(美国宾夕法尼亚州立大学)

间:20181029(周一)下午2:00-4:00

点:北京语言大学主楼南侧210教室

主办单位:北京语言大学对外汉语研究中心


主讲人简介:

李平,北京大学中文语言学学士 (1983),普朗克研究所心理语言学及荷兰莱顿大学博士。毕业后在加州大学圣地亚哥分校的语言研究中心和McDonald Pew认知神经科学中心进行博士后研究。 1992年至1996年期间担任香港中文大学助理教授, 2004-2008年担任美国里士满大学心理学和认知科学教授。目前他是宾夕法尼亚州立大学心理学、语言学、信息科学与技术等专业终身教授,脑、行为、认知中心主任,计算科学研究院副主任。他还是美国白宫脑计划美国科学基金会科研课题首席科学家 (2015-2019), 中国长江学者讲座教授 (2015-2017)。目前担任Journal of Neurolinguistics的主编,Frontiers in Psychology:Language Sciences的副主编。他曾担任Bilingualism: Language andCognition 的主编 (2003-2013),Society for Computers in Psychology的主席(2012)及美国国家科学基金会的认知神经科学计划和感知、行为和认知计划的主任(2007-2009)。 李平在心理语言学、语言习得、计算机模型及双语的认知神经机制等领域出版了大量的研究专著及150余篇学术论文。有关李平教授研究的更多信息请点击http://blclab.org/


内容简介:

How does the learning of a second language (L2) impact the functional and neuroanatomical changes in the brain when the brain is already dedicated to one’s native language (L1)? How can we identify, predict, and promote rapid L2 learning-induced brain changes? Although the mainstream neuroscience of language has focused on the learning and representation of L1, in recent years there has been a surge of interest in the L2 mind and brain. In this talk, I ask how second language experience shapes functional and structural brain changes, and present evidence from our short-term training and long-term longitudinal studies of students who learn Chinese as their L2. Our studies help to identify (a) how neurocognitive changes occur as a function of learning contexts (traditional classroom vs. 3D virtual environment based learning), (b) how brain changes may capture learning success and effectiveness, and (c) whether these changes may be predicted based on individual learners’ neurocognitive profiles. Findings from our studies provide insights into the understanding of neuroplasticity (e.g., how learning leads to domain-specific vs. domain-general brain changes), individual differences (e.g., how cognitive capacity impacts and predicts learning success), and knowledge representation (e.g., how neurocognitive patterns reflect acquired knowledge in the L2).