Predicting human brain activity associated with the meanings of nouns.
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.
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Tom.Mitchell@cs.cmu.edu
Tom M
TM
Mitchell
Svetlana V
SV
Shinkareva
Andrew
A
Carlson
Kai-Min
KM
Chang
Vicente L
VL
Malave
Robert A
RA
Mason
Marcel Adam
MA
Just
D000293:Adolescent; D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D019295:Computational Biology; D005260:Female; D006801:Humans; D007802:Language; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D012660:Semantics; D013067:Speech Perception / Q000502:physiology*