题目：

Predicting Individual Differences in Language Learning Across Populations

不同人群语言学习的个体差异预测

主讲人：黄俊文教授

时间：2022年3月30日11:00-12:30

地点：腾讯会议

会议号码：258-110-815

会议链接：

https://meeting.tencent.com/dm/eVkujh24KCAC

主办单位：华体会体 语言学系

主讲人简介：

Bio:

Patrick C.M. Wong is Professor of Linguistics and Cognitive Neuroscience and Founding Director of the Brain and Mind Institute at The Chinese University of Hong Kong (CUHK). Before moving to Hong Kong, he served on the faculty of Northwestern University for close to a decade. Wong’s research covers a wide range of basic and translational issues concerning the neural basis and disorders of language and music. Findings from this research have appeared in a broad array of interdisciplinary scholarly venues such as Nature Neuroscience, PNAS, and Science Advances. In 2021, he was named a Guggenheim Fellow for Humanities. Wong’s research has received public attention from media outlets such as The New York Times and the British Broadcasting Corporation/Public Radio International. A versatile and effective teacher, research mentor, and clinical educator, Wong is a three-time recipient of the Faculty Outstanding Teaching Award at CUHK.

黄俊文教授现任香港中文大学何鸿燊认知神经科学讲座教授及语言学教授、大脑与认知研究所所长，是一位认知神经科学家、语言学学者及言语治疗师。在加入中大之前，黄教授是美国西北大学的终身教授。黄教授的研究涉及语言习得、神经语言学、语音处理、沟通障碍、语言和遗传学、听觉神经科学及音乐认知，其相关研究成果发表于多个国际顶级跨学科刊物，如Nature Neuroscience, PNAS, Science Advances等。黄教授的研究也受到了纽约时报和英国广播公司/国际公共广播电台等媒体的公众关注。2021年，黄教授获得古根汉基金奖殊荣。（这个享负盛名的学术奖项旨在嘉许在文学及科学学科取得瞩目成就的杰出学者，支持及鼓励他们在各自领域持续不懈探索。自1925年成立至今，获颁古根汉基金奖的学者超过1万8千人，当中包括超过125名诺贝尔奖得主，以及众多普立兹奖、图灵奖及其他国际奖项的得主。）作为一名多才多艺且卓有成效的教师、研究生导师和临床教育家，他曾三次获颁香港中文大学文学院杰出教学奖。

黄俊文教授

主讲简介：

Abstract:

Children’s rank-order language developmental stability provides an opportunity to predict future language development with data collected in earlier years of life. In our research, we capitalize on this opportunity to evaluate hypotheses concerning language development across different typical and atypical populations. Direct biological measurements from young children as well as their health and family information are used to construct predictive models for individual-child predictions. These models provide the basis to address different questions about neural processing and language. For example, in typically developing children, we ask whether cortical and subcortical development interacts with native and non-native speech processing in infancy, and whether this interaction provides a basis for prediction of future development. In children who are hearing impaired, we examine whether brain regions that are most resilient to reduced auditory/spoken language input, measured via MRI before cochlear implantation (CI), enable a compensatory pathway to support better language development after CI. In both typical and atypical populations, we are in the process of testing whether individual-child predictions can inform the design and prescription of different types of early intervention and enhancement strategies in order to optimize language development for all children.

儿童语言发展的等级顺序稳定性为利用早年收集的数据来预测未来语言发展提供了机会。在我们的研究中，我们利用这个机会来评估有关典型和非典型人群语言发展的不同假设。我们用来自幼儿的直接生物学测量以及他们的健康和家庭信息构建针对不同个体的预测模型。这些模型为解决有关神经处理和语言的不同问题提供了基础。例如，在正常发育的儿童中，我们探索了皮层和皮层下发育是否与婴儿期的母语和非母语语音处理有关，以及这种关系是否为预测未来发展提供了基础。在听力受损的儿童中，我们通过MRI的测量锁定了在人工耳蜗植入(CI)之前对听觉和口语输入的减少最具复原性的大脑区域，并检测了这些区域是否能够提供某种补偿通道来支持CI之后的语言发展。目前我们正在研究典型和非典型儿童的个体预测是否能为设计不同类型的早期干预和增强策略提供帮助，以优化所有儿童的语言发展。