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Predicting Human Brain Activity Associated with the Meanings of Nouns

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At this session of the NLIP Reading Group we’ll be discussing the following paper:

Tom M. Mitchell, Svetlana V. Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L. Malave, Robert A. Mason and Marcel Adam Just. 2008. Predicting Human Brain Activity Associated with the Meanings of Nouns. Science 320: 1191-1195. (supplemental material is available here)

Abstract: The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained via a combination of data from a trillion-word text corpus, and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.

This talk is part of the Natural Language Processing Reading Group series.

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