Brains, Meaning and Corpus Statistics

Posted in Conferences, Companies, Science on April 02, 2009

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 M. Mitchell is the E. Fredkin Professor and head of the Machine Learning Department at Carnegie Mellon University. Mitchell is a past President of the American Association of Artificial Intelligence (AAAI), and a Fellow of the AAAS and of the AAAI. His general research interests lie in machine learning, artificial intelligence, and cognitive neuroscience. Mitchell believes the field of machine learning will be the fastest growing branch of computer science during the 21st century.

Mitchell's web home page is

Tom M. Mitchell

E. Fredkin Professor and Department Head
Machine Learning Department
Carnegie Mellon University

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