If this were a test, they would have gotten a D.

Ken Norman/Princeton

“Internal states are kind of ineffable,” says Turk-Browne, an associate professor of psychology at the Princeton Neuroscience Institute. “You may not know when you're in a good or bad state. We wanted to see: If we give people feedback before they make mistakes, can they learn to be more sensitive to their own internal states?”

It turns out they can, Turk-Browne says. The key is that, for some subjects, the pictures were controlled not by their own brains, but by someone else’s: meaningless jitter. Of the 16 subjects who got their own brain feedback, 11 said they felt they were making the pictures clearer by focusing, as opposed to four of 16 who watched the placebo feedback. What the scientists found is that only people whose own brains drove the images’ dimming improved their ability to focus. Paying attention, in other words, is like learning basketball or French: Good old-fashioned practice matters.

“I think what's exciting about this finding,” explains Turk-Browne, “is the idea that certain aspects of cognition like attention are only partly consciously accessible. So, if we can directly access people's mental states with real time fMRI, we can give them more information than they could get from their own mind.”

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Neuroscientists have been reading brain patterns with computer programs like this for just over a decade. Machine-learning algorithms, like the ones Google and Facebook use to recognize everything online, can hack the brain’s code, too: essentially software for reading brain scans. Given samples of neural patterns—your brain imagining faces, say, versus your brain picturing places—a decoder is trained to tell whether you are remembering a face (Jennifer Aniston, President Obama) or a location (the Hollywood sign, the White House). A prior study by researchers at the memory lab of professor Ken Norman, a co-developer of the attention tool, read out these categories from people’s brains as they freely recalled pictures they had studied earlier. Similar work has "decoded" what people see, attend to, learn, remember falsely, and dream. What’s new and remarkable now is how fast neural decoding is happening. Machines today can harness brain activity to drive what a person sees in real time.

“The idea that we could tell anything about a person's thoughts from a single brain snapshot was such a rush,” Norman recalls of the early days, over a decade ago. “Certainly the kinds of decoding we are doing now can be done much faster.”

Here is how Princeton's current scanner sees a human brain: First, it divides a brain image into around 40,000 cubes, called voxels, or 3-D pixels. This basic unit of fMRI is a 3 millimeter by 3 millimeter cube of brain. So, the neural pattern representing any mental state—from how you feel when you smell your wife’s perfume to suicidal despair—is represented by this matrix. The same neural code for, say, Scarlett Johansson, will represent her in your memory, or as you talk to her on the phone, or in your dreams. The decoding approach, first pioneered in 2001 by the neuroscientist James Haxby and colleagues at Princeton, is known technically as “multi-voxel pattern analysis,” or MVPA. This “decoding” is distinct from the more common, less sophisticated form of fMRI analysis that gets a lot of attention in the media, the kind that shows what parts of the brain “light up” when a person does a task, relative to a control. “Though fMRI is not very cheap to use, there may be a certain advantage of neurofeedback training, compared to pure behavioral training,” suggests Kazuhisa Shibata, an assistant professor at Brown University, “if this work is shown to generalize to other tasks or domains.”