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Representation of attentional priority in human cortex

首都师范大学  学术报告

 

题      目:Representation of attentional priority in human cortex

 

主    人:Taosheng Liu   Associate Professor  Michigan State University

      间:201736日(周),下午1:30

      首师大北二区二层大会议室

主 办 单 位:首都师范大学信息工程学院

主讲人介绍:

Taosheng Liu received his PhD in Cognitive Psychology from Columbia University and postdoctoral training at the Johns Hopkins University and New York University.  He is now an Associate Professor in the Department of Psychology at Michigan State University.  Taosheng Liu’s research interests are in the cognitive neuroscience of visual perception and attention, working memory, and decision making.  His main experimental techniques include using psychophysics and eyetracking to measure behavior and using functional magnetic resonance imaging (fMRI) to measure human brain activity.  Using a convergence of behavioral, neuroimaging, and computational methods, his lab currently focuses on the representation of feature- and object-based attentional priority in the brain, how attention affects perception, and the neural mechanism of decision making.

 

 

内 容 介 绍:

Humans can flexibly select certain aspects of the sensory information for prioritized processing. How such selection is achieved in the brain remains a major topic in cognitive neuroscience. In this talk, I will examine the neural mechanisms underlying both spatial and non-spatial selection. I will review evidence that space-based selection is controlled by dorsal frontoparietal areas that encode spatial priority in topographic maps, whereas feature- and object-based selection also rely on the same brain areas. These areas modulate neural activity in early visual areas to enhance the representation of task-relevant information. Furthermore, a recent study from our group found that spatial and feature-based priority forms a hierarchical structure in frontoparietal areas such that similar selection demands recruit similar neural activity patterns. These results suggest that the representation of attentional priority utilizes a computationally efficient organization to support flexible top-down control.