The paper, “A common, high-dimensional model of the neural representational space in human ventral temporal cortex,” is in the October 20, 2011, issue of the journal, Neuron. The lead author of the paper is James Haxby, the Evans Family Distinguished Professor of Cognitive Neuroscience in the Department of Psychological and Brain Sciences. Haxby is also the Director of the Cognitive Neuroscience Center at Dartmouth and a professor in the Center for Mind/Brain Sciences at the University of Trento in Italy. Swaroop Guntupalli, a graduate student in Haxby’s laboratory, developed the software for the new methods and ran the tests of their validity.
Haxby developed a new method called hyperalignment to create this common code and the parameters that transform an individual’s brain activity patterns into the code.
The parameters are a set of numbers that act like a combination that unlocks that individual’s brain’s code, Haxby said, allowing activity patterns in that person’s brain to be decoded – specifying the visual images that evoked those patterns — by comparing them to patterns in other people’s brains.
“For example, patterns of brain activity evoked by viewing a movie can be decoded to identify precisely which part of the movie an individual was watching by comparing his or her brain activity to the brain activity of other people watching the same movie,” said Haxby.
When someone looks at the world, visual images are encoded into patterns of brain activity that capture all of the subtleties that make it possible to recognize an unlimited variety of objects, animals, and actions.
“Although the goal of this work was to find the common code, these methods can now be used to see how brain codes vary across individuals because of differences in visual experience due to training, such as that for air traffic controllers or radiologists, to cultural background, or to factors such as genetics and clinical disorders,” he said.
Because of variability in brain anatomy, brain decoding had required separate analysis of each individual. Although detailed analysis of an individual could break that person’s brain code, it didn’t say anything about the brain code for a different person. In the paper, Haxby shows that all individuals use a common code for visual recognition, making it possible to identify specific patterns of brain activity for a wide range of visual images that are the same in all brains. …
The image is not moving, by the way.