“Brains, Meaning and Text Statistics”
How does the human brain represent meanings of words and pictures in terms of the underlying neural activity? This talk will present our research using machine learning methods together with fMRI brain imaging to study this question. One line of our research has involved training classifiers that identify which word a person is thinking about, based on their neural activity observed using fMRI. A more recent line involves developing a computational model that predicts the neural activity associated with arbitrary English words, including words for which we do not yet have brain image data. This computational model is trained using a combination of fMRI data associated with several dozen concrete nouns, together with statistics gathered from a trillion-word text corpus. Once trained, the model predicts fMRI activation for any other concrete noun appearing in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
Tom Mitchell, Ph.D. is Professor of Computer Science at Carnegie Mellon University, and Director of the Center for Automated Learning and Discovery.
Mitchell’s current work focuses on research topics including “how does the human brain represent word meanings?” and “can computes learn to extract information from the web?”
He earned his B.S. degree from MIT and his Ph.D. degree from Stanford University (1979). He received the IJCAI Computers and Thought award in recognition of his research in machine learning, and has been a Fellow of the American Association for Artificial Intelligence since 1990. He a member of the National Research Council Computer Science and Telecommunications Board, co-founder of the journal Machine Learning, and co-founder of the annual International Conference on Machine Learning, now in its 14th year.
Mitchell completed a textbook entitled “Machine Learning,” McGraw Hill, 1997, which provides an in depth introduction to the major algorithms used in data mining applications.