Saturday science subject: Mind reading

— 12:37 PM on March 15, 2008

Illusionists often draw applause by pretending to read minds and identify a card or object that only participants have seen. However, as the Scientific American reports, a team of Berkeley scientists have developed a technique that can do the same thing without resorting to any tricks. The concept isn't entirely new, but previous methods required researchers to know beforehand which images triggered what brain activity. This new technique doesn't:

"The advance brought forward here," [John-Dylan Haynes, a professor at the Bernstein Center for Computational Neuroscience Berlin and the Max Planck Institute for Human Cognitive and Brain Sciences that was not affiliated with the new work] continues, "is that they have set up a mathematical model that captures the properties of the visual part of the brain," which can then be applied to previously unseen objects.

Researchers used functional magnetic resonance images (fMRIs) to record activity in the visual cortices of a pair of volunteers (two of the study's co-authors) while they viewed a series of images. They examined the brain by dividing the regions into voxels (volumetric units, or 3-D pixels) and noting the part of the picture to which each section responded. For instance, one voxel, or slice, might respond in a certain pattern to, say, colors in the upper left-hand corner of the photo, whereas another voxel would be set off by something in a different portion of the picture.

Haynes says the team could "go back and infer what the image was that a person was seeing" by monitoring the activity in each brain section and deciphering what sort of information would most likely be found in the corresponding part of the visual field, or photograph.

Out of a set of 120 images that depicted "everything from people to houses to animals to fruit and other objects," the algorithm successfully identified what subjects were seeing 92% of the time. Accuracy went down as the number of images went up, though: increasing the image set to 1,000 images reduced accuracy to 80%, the researchers found.

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