A team of researchers at Princeton University has published an article on Nature Neuroscience to present the results of a new brain-scanning technology aimed at explaining how human focus works. From the abstract:
Lapses of attention can have negative consequences, including accidents and lost productivity. Here we used closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses. During a sustained attention task, the focus of attention was monitored in real time with multivariate pattern analysis of whole-brain neuroimaging data. When indicators of an attentional lapse were detected in the brain, we gave human participants feedback by making the task more difficult. Behavioral performance improved after one training session, relative to control participants who received feedback from other participants’ brains. This improvement was largest when feedback carried information from a frontoparietal attention network. A neural consequence of training was that the basal ganglia and ventral temporal cortex came to represent attentional states more distinctively. These findings suggest that attentional failures do not reflect an upper limit on cognitive potential and that attention can be trained with appropriate feedback about neural signals.
According to Taylor Beck (The Atlantic):
The scientists who invented this attention machine, led by professor Nick Turk-Browne, are calling it a “mind booster.” It could, they say, change the way we think about paying attention—and even introduce new ways of treating illnesses like depression. Here’s how the brain decoder works: You lie down in an a functional magnetic resonance imaging machine (fMRI)—similar to the MRI machines used to diagnose diseases—which lets scientists track brain activity. Once you’re in the scanner, you watch a series of pictures and press a button when you see certain targets. The task is like a video game—the dullest video game in the world, really, which is the point. You see a face, overlaid atop an image of a landscape. Your job is to press a button if the face is female, as it is 90 percent of the time, but not if it’s male. And ignore the landscape. (There’s also a reverse task, in which you’re asked to judge whether the scene is outside or inside, and ignore the faces.) […]
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 […] 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.”
Read the original paper on Nature Neuroscience. Find out more on The Atlantic.