Predicting Human Brain Activity Associated with the Meanings of Nouns
Tom M. Mitchell,1*
Svetlana V. Shinkareva,2
Andrew Carlson,1
Kai-Min Chang,3,4
Vicente L. Malave,5
Robert A. Mason,3
Marcel Adam Just3
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 with 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.
1 Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
2 Department of Psychology, University of South Carolina, Columbia, SC 29208, USA.
3 Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
4 Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
5 Cognitive Science Department, University of California, San Diego, La Jolla, CA 92093, USA.
* To whom correspondence should be addressed. E-mail: Tom.Mitchell{at}cs.cmu.edu
