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Tracking the stream of consciousness


Scientists at University College London (UCL) have developed a method of tracking someone’s stream of consciousness based on their brain activity alone. In a study published in the latest issue of Current Biology, the UCL team found that brain activity measured in volunteers who were viewing a visual illusion could be used to accurately track their subjective experience while it underwent many spontaneous changes

In the study, funded by the Wellcome Trust, John-Dylan Haynes and Geraint Rees presented volunteers with a visual illusion known as “binocular rivalry”. When very different images are presented separately to each eye, they compete for access to consciousness. Volunteers experience many spontaneous switches in their awareness, sometimes seeing one image and sometimes the other.

While volunteers experienced these spontaneous switches in awareness, the UCL team measured patterns of activity in their brains using functional MRI (fMRI) brain scanning. They found that brain activity could be used to blindly predict with high precision which of the two images a volunteer was perceiving, and how their conscious perception changed over several minutes of viewing. The study thus shows that it is possible to predict the changing stream of consciousness from brain activity alone.

Dr John-Dylan Haynes of the UCL Institute of Neurology says: “Previous research on visual perception has tended to focus on perception of static, unchanging scenes, ignoring the fact that our stream of consciousness is highly dynamic and our perception changes from second to second.

“Our study represents an important but very early stage step towards eventually building a machine that can track a person’s consciousness on a second-by-second basis. These findings could be used to help develop or improve devices that help paralyzed people, such as those with locked-in syndrome, communicate through measurements of their brain activity. But we are still a long way off from developing a universal mind-reading machine.”

Jenny Gimpel | alfa
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