Speaker: Dr. Mengyuan Gong, Department of Psychology (Cognition & Cognitive Neuroscience Program), Michigan State University
Time: 1:00pm - 2:00pm, Jan 28
Venue: Room 1115, Wang Kezhen Building
Abstract: Theories of neural information processing generally assume that sensory input is processed along hierarchical stages that start with analog representations and gradually transition to task-related, abstract representations. While the neural code of such abstract information remains unclear, neurophysiological findings suggest that a scalar code could be used to encode behavioral relevance. We tested this hypothesis in human fMRI studies, using data from five feature-based attention tasks, where we found robust bias within brain areas, consistent direction of such bias across areas for a given participant, and similar bias for multiple feature types (e.g. color, motion directions and objects). Using a receiver operating characteristics analysis to quantify the magnitude of the bias, we found stronger bias in frontoparietal areas than in visual areas, indicating more abstract representations in high-level areas. Our results thus suggest low-dimensional (possibly one-dimensional scalar) coding in association cortex that may facilitate the simple read-out of decision and control variables.
Bio: Mengyuan Gong is currently a Postdoctoral Research Fellow at Michigan State University. She obtained her B.S. degree in Psychology at Huazhong Normal University and received her Ph.D. in Cognitive Neuroscience at Peking University. Her research focuses on the neural mechanism of visual attention, reward learning and working memory, through the use of multimodal brain imaging techniques (EEG, TMS and fMRI) and state-of-the-art computational methods.