Host: Prof. Xing Tian, NYU Shanghai
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Abstract
Despite having limitless access to knowledge during this information explosion era, we all acknowledge that big data is not knowledge. Indeed, the underlying relational structure that links fragmented data is the key to knowledge acquisition. Humans excel at reasoning and inferring hidden relationships from outside events, an essential index of intelligence, yet the cognitive and neural mechanisms remain elusive. In this talk, I will present my lab's recent works aiming to understand how abstract structure knowledge, from simple sequences to hierarchy and networks, is represented and learned in the human brain. Our findings support dissociated structure-content neural representations and the essential role of relational structure in mediating memory and learning. We also demonstrate that brief pre-learning of certain paths that capture core structures could facilitate knowledge network learning.
Biography
Dr. Huan Luo is a tenured associate professor at the School of Psychological and Cognitive Sciences and a PI of IDG/McGovern Institute for Brain Research, Peking University. Her research primarily focuses on the brain mechanisms of perception, attention, and working memory in humans, particularly from a dynamic perspective. She is Chang Jiang Young Scholar and is supported by NSFC Key Program and Excellence Young Scientists Funds. Dr. Luo received her Ph.D. from the University of Maryland College Park, first worked at Chinese Academy of Sciences and joined Peking University since 2015. Dr. Luo currently serves as the associate editor for Progress in Neurobiology, and editorial members of PLoS Biology and eLife. As one of six labs in the world, her lab recently participated in a high-impact international collaborative project COGITATE supported by Templeton World Charity Foundation.