This story is from the category The Brain
Date posted: 30/05/2008
Carnegie Mellon University researchers have built a computer model that can predict which specific words a user is thinking about based on brain scans.
The team started with the assumption that the brain processes words in terms of how they relate to movement and sensory information. Words such as 'hammer', for example, are known to cause movement-related areas of the brain to light up; on the other hand, the word 'castle' triggers activity in regions that process spatial information.
They also also knew that different nouns are associated more often with some verbs than with others ? the verb 'eat', for example, is more likely to be found in conjunction with 'celery' than with 'aeroplane'.
The researchers designed the model to try and use these semantic links to work out how the brain would react to particular nouns. They fed 25 such verbs into the model.
Volunteers were scanned with fMRI as they looked at nouns. After the model was trained, it could predict which pattern of brain activity was associated with two nouns (after being trained on 58 other nouns). Later, given a pattern of brain activity, it could also rank which nouns matched the pattern. Its performance was better than chance in both tests.
The only way in which this advance differs from other recent advances is that it studies word association rather than image association. Still, the ability, however basic, to understand the formation of language within the neurons of the brain is still a healthy step forwards for brain-machine interfaces.
See the full Story via external site: www.nature.com
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