For the first time, scientists have shown that they can
predict when people will make risky decisions based on brain activity patterns.
Could this lead to a world where we consult brain scans to predict whether we’re making a risky choice or not?
Our lives revolve around choices, where we must sometimes choose
between a “safe” option and a “risky” one. We typically
know what the outcome of the safe option will be, but the risky option often
has multiple — and sometimes unknown — outcomes.
For example, if you get drunk at a bar, you could choose to
drive home or take a cab. If you take a cab, you can reasonably assume that
you’ll get home with little complication. If you drive, you may make it home in
one piece and save on the cab fare, but you could also get into an accident or
get pulled over by a police officer. But what’s going on in our brains to make
us choose the risky option over the safe one? Perhaps not surprisingly, the brain scans suggest that risk-taking is linked to poor impulse control.
“There has been quite a lot of research done on the
neural correlates of risk, and on how the brain responds to different types of
risk,” explained Sarah Helfinstein, a neurobiologist at the University of
Texas at Austin. Specifically, researchers have determined that there’s a large
network of brain regions that are “sensitive” to risk, including the
striatum, thalamus and insula. And the stronger the rewards an option presents,
the more likely we will choose it. “But what hasn’t been known is how all
of that stuff determines what kind of decisions people make,” Helfinstein
told io9.
If people are faced with a decision that’s associated with
a given amount of risk, what happens in their brain to make them choose the
risky option over the safe option? And could that brain activity be used to
actually predict people’s choices? Helfinstein
and her colleagues decided to find out.
Bursting Balloons
For the study, the researchers had 108 participants perform
a naturalistic risk-taking task called the Balloon Analog Risk Task, which
requires them to pump up a virtual balloon. Participants receive points for
each pump, which they can then “cash out” at any time, ending the
balloon round. But if the balloon pops before they cash out, they lose all of
their accumulated points. To further increase the stakes, the balloon is set to
randomly explode between the 1st and 12th (final) pump, so each pump carries a risk of exploding and losing points.
The participants each spent 9 minutes on the balloon
experiments while inside of an fMRI scanner, which recorded their brain
activity. The researchers fed a subset of the brain scan data from some of
participants into a “machine classification” algorithm. “This works by
giving the computer the data, and saying, ‘These samples here came from
trials where subjects made a risky choice, and these are from trials where
subjects made a safe choice,'” Helfinstein said. “‘Now look at data
from brain activity patterns and try to discriminate between the
decisions.'”
The team made sure to only give the algorithm fMRI data up
to the pump trials that immediately preceded the trials where the participants
made their choices. “We didn’t want the classifier to be able to tell the
difference between the choices based on things that weren’t relevant,” Helfinstein
explained. For example, if the computer had the brain maps from the decision
trials, it could possibly discriminate between trials based on motor or reward
effects in the brain, rather than the cognitive processes that lead up to the
risky or safe decision.
After teaching the algorithm, the researchers gave it the
rest of the brain scan maps. “We asked it, ‘From looking at this data, can
you tell us if the subjects are going to make risky or safe decision?'” Helfinstein
said. Amazingly, the algorithm accurately predicted participant choices about
72 percent of the time.
In addition to using a whole-brain classifier, Helfinstein
and her colleagues conducted a “searchlight analysis,” in which they
gave the computer data from only a tiny chunk of the brain at a time. This
process allowed them to see which brain regions are most involved in making
risky and safe choices. The regions identified in the searchlight analysis,
they found, were those that are involved in cognitive control — areas involved
with controlling your behavior and choices. Interestingly, these regions were
more active when people made safe choices than when they made risky choices,
suggesting that risky decisions may arise when the control systems fail to
initiate the safe choice.
Helfinstein doesn’t see any direct, practical applications
of the research. After all, people don’t spend their lives in fMRI scanners, so
it’s not as if we can tell when people are going to make a risky decision in
their day-to-day activities. However, the research may someday help habitual risk-takers
make safer decisions. “Maybe we can develop ways of helping people
cultivate control, as a way of helping them make safer decisions,” she said.
Check out the full
study over in the journal PNAS.
Top image via Jason Weaver/Flickr. Inset images via Helfinstein et al./PNAS.
