Brains, Meaning and Corpus 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 the image of their fMRI brain activity. A more recent line involves developing a generative computational model capable of predicting 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 tera-word 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 Machine Learning Department Head at Carnegie Mellon University. Mitchell is a past President of the American Association of Artificial Intelligence (AAAI), past Chair of the American Association for the Advancement of Science (AAAS) section on Information, Computing and Communication, and a recent member of the US National Research Council's Computer Science and Telecommunications Board. 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.