Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo


Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

The researchers recorded brain activity as people played the game and found specific neural signatures for strategists. These people—who made up only about 10 percent of the group—showed greater activation in the dorsal prefrontal cortex, a region involved in cognitive control, as well as in the Brodman area 10, a region known to be involved in thinking about other people, says Montague. In addition, a region of the right temporal lobe “varied with expected payoffs in strategic deceivers but was not in other types of players,” he says.

Hans Breiter, a neuroscientist and psychiatrist at the Martinos Center for Biomedical Imaging, applauds the research for applying sophisticated mathematical techniques to decision-making. Rather than looking at simple statistical relationships, “they are looking for variables that will be seen across different stimuli and different populations,” says Breiter. The ideal variables, he explains, can be easily distinguished from noise and can also be seen at another level, like in the brain.

The ability to quantify these characteristics will be crucial in applying the techniques to psychiatry. Breiter and others hope that the approach, dubbed quantitative psychiatry, will help make diagnosis of mental illness more closely resemble that of infectious disease or high blood pressure. Both of these have multiple potential causes that will each respond best to specific types of treatment.

In a second paper, published last month in PLoS Computational Biology, Montague and colleagues demonstrated how this type of game could be applied to psychiatry. Researchers had pairs of people play a simple trust game in which one player, the “investor,” gave a certain amount of money to the investee. The amount was tripled in plain sight en route to the investee, who then chose how much to give back to the investor. In this case, one player was healthy while the second had one of several neuropsychiatric disorders: borderline personality disorder, major depression, or attention deficit hyperactivity disorder (ADHD.) Borderline personality disorder is characterized by difficulty with relationships and lack of trust, qualities that could be picked up in social interactions.

The pairs played multiple rounds of the game, in order to build a model of their partner. Scientists then created a mathematical model to track how the healthy participants played over time. They found that their characteristics segregated into four different clusters, roughly correlated to the type of psychiatric disorder their partner had. The researchers then showed they could automate the system, employing a computer model, created in a previous study, capable of playing the role of the healthy player. Scientists could again predict which cluster the human player fell into, using only the choices made by the computer model.

“What [Montague] has done is shown proof-of-principle to applying a more sophisticated approach to diagnosis that potentially could be automated,” says Breiter. “I suspect in combination with one or two other quantitative tests, this could be exquisitely sensitive.” For example, he notes, a major depressive disorder is likely made up of a number of different illnesses, “but we don’t have a quantitative metric to explicitly diagnose major depression or to subtype these illnesses.”

Montague, in collaboration with Fonagy, now plans to test this approach on a much greater number of people, with the intention of refining the ability to classify them as either healthy or suffering from a psychiatric disorder. “We hope to use these games to assign quantitative parameters that could be tracked during and post-therapy and suggest the neural systems that could be involved,” says Montague.

0 comments about this story. Start the discussion »

Credit: PNAS

Tagged: Biomedicine, autism, brain imaging, fMRI, neuroeconomics

Reprints and Permissions | Send feedback to the editor

From the Archives


Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me