Carnegie Mellon computer model reveals how brain represents meaning

Predicts brain activation patterns for thousands of concrete nouns

PITTSBURGH—Scientists at Carnegie Mellon University have taken an important step toward understanding how the human brain codes the meanings of words by creating the first computational model that can predict the unique brain activation patterns associated with names for things that you can see, hear, feel, taste or smell.

Researchers previously have shown that they can use functional magnetic resonance imaging (fMRI) to detect which areas of the brain are activated when a person thinks about a specific word. A Carnegie Mellon team has taken the next step by predicting these activation patterns for concrete nouns — things that are experienced through the senses — for which fMRI data does not yet exist.

The work could eventually lead to the use of brain scans to identify thoughts and could have applications in the study of autism, disorders of thought such as paranoid schizophrenia, and semantic dementias such as Pick’s disease.

The team, led by computer scientist Tom M. Mitchell and cognitive neuroscientist Marcel Just, constructed the computational model by using fMRI activation patterns for 60 concrete nouns and by statistically analyzing a set of texts totaling more than a trillion words, called a text corpus. The computer model combines this information about how words are used in text to predict the activation patterns for thousands of concrete nouns contained in the text corpus with accuracies significantly greater than chance.

The findings are being published in the May 30 issue of the journal Science.

“We believe we have identified a number of the basic building blocks that the brain uses to represent meaning,” said Mitchell, who heads the School of Computer Science’s Machine Learning Department. “Coupled with computational methods that capture the meaning of a word by how it is used in text files, these building blocks can be assembled to predict neural activation patterns for any concrete noun. And we have found that these predictions are quite accurate for words where fMRI data is available to test them.”