General Departmental Seminar Series
Using fMRI and Machine Learning to Study Cognitive Processes
Tom M. Mitchell, Ph.D.
Fredkin Professor of Computer Science,
Center for Automated Learning and Discovery,
Carnegie Mellon University
4 - 5 p.m., 1221 Computer Sciences, 1210 West Dayton St.
Recent research on computational cognitive models (e.g., ACT-R, 4CAPS) has begun to develop computational models of cognitive processes that directly predict features of fMRI activity in different brain regions. We will examine ways in which machine learning methods might help in developing more accurate and precise models of this type, by automatically discovering the spatial-temporal patterns of fMRI activation associated with specific cognitive subprocesses. We first describe our recent successes training statistical machine learning classifiers to distinguish cognitive subprocesses such as reading a sentence versus viewing a picture, or reading words about tools versus words about buildings, based on the observed fMRI brain activation of the subject. We then describe our recent research to learn more complex models describing sequences of multiple cognitive processes operating in parallel. The goal of this research is to build models of interacting cognitive subprocesses that overlap in time and space.
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