The research was conducted by computer scientist Tom Mitchell and cognitive neuroscientist Marcel Just of Carnegie Mellon University.
The boffins used functional magnetic resonance imaging (MRI) data to develop a sophisticated computational model that can predict brain activation patterns associated with specific words, or "concrete nouns".
Mitchell and Just first built a model that took the functional MRI activation patterns for 60 concrete nouns broken into 12 categories including 'animals', 'body parts', 'buildings', 'clothing', 'insects', 'vehicles' and 'vegetables'.
The model also analysed a "text corpus", or a set of texts that contained more than a trillion words, noting how each noun was used in relation to a set of 25 verbs associated with sensory or motor functions.
Combining the brain scan information with the analysis of the text corpus, the computer then predicted the brain activity pattern of thousands of other concrete nouns.
In cases where the actual activation patterns were known, the researchers found that the accuracy of the computer model's predictions was "significantly better than chance".
"We believe we have identified a number of the basic building blocks that the brain uses to represent meaning," said Mitchell.
"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."
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By Robert Jaques on Jun 4, 2008 1:57PM