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College of Information Sciences and Technology
Managing Information, Powering Intelligence
Distinguished Lecture Series
Presents
Stephen José Hanson
Brain Reading in the Human Visual
Pathways: Why there is no FACE area in the brain
In this talk, I will present some basics of fMRI methods, and provide a tutorial context for the acquisition of the MR
signal which will allow us to critically examine the evidence for a FACE area in the brain. Using the new methods for
"brain reading" developed jointly with the Haxby Lab (MPVA/PD), we will show that the so called FFA is not sensitive
to faces, per se; Nor is it homogenous in its stimulus response properties (even at very high spatial resolution); Nor is
it independent in its function relative to the other areas of the brain that comprise an identifiable network which
appears to do something other than "FACE" identification. In fact, we will show that the highly distributed nature of the
computation in temporal lobe and throughout cortex is unlikely to support anything like a local module for a specific
concept or category. Nonetheless, once the distributed encoding is properly decoded, basic categorical distinctions
in the temporal cortex, such as Animacy, can be revealed.
Stephen José Hanson is a Full Professor of Psychology at Rutgers University and the
Director of Rutgers Brain Imaging Center (RUBIC). Hanson has been the Department Chair
of Psychology and the Department Head of Learning Systems Department at SIEMENS
Corporate Research, and a research scientist in the Cognitive Science Laboratory at
Princeton University. He has held positions in the AI and Information Sciences Department at
BELLCORE, AT&T Bell Laboratories, SIEMENS Research, Indiana University and Princeton
University. Hanson has done modeling in a number of diverse areas including animal
learning theory (conditioning theory), human-computer interaction, behavioral genetics,
complex skills learning, and neural network learning algorithms. He has more recently
focused his research in model-based or computational neuroimaging.
Hanson specializes in the learning sciences and computational neuroimaging, and has
extensively studied learning in humans, animals, and machines. He has edited books and
published numerous papers and book chapters on these topics. He was the General Chair
(1992) for the Neural Information Processing Conference and elected to the NIPS foundation
board in 1993, where he serves on the Advisory Board. Hanson was a founding member of the McDonnell-Pew Cognitive
Neuroscience Advisory Board, which for over a decade helped launch the fields of Cognitive Neuroscience and Computational
Neuroimaging.
This lecture is co-sponsored by the College of Information Sciences and Technology, the Center for Big Data Analytics
and Discovery Informatics in the Institute for Cyberscience, the Social, Life, and Engineering Sciences Imaging Center
in the Social Science Research Institute, and the Center for Brain, Behavior and Cognition.
Friday, April 10, 2015
1:00 p.m. - 2:00 p.m. 113 IST Building (Cybertorium)