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Paper 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.
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