Science News - October 8, 2011

“We’re going to continue to try to understand these processes,” says Eero Simoncelli, a computational neuroscientist at New York University. “It’s a long road. It’s going to be many decades until all of this gets worked out. But the progress is steady.”

Seeing and believing

When Pouget started studying the brain’s computations two decades ago, nobody thought that humans deal in probabilities, he says. Back then, researchers thought that if you want to catch a baseball, your brain computes the trajectory and spits out an exact answer, telling your body where to move the glove, he says. “Today, we say, ‘No, if you have a baseball flying at you, you compute the probability of where it might be and then you place your hand to maximize the probability that you’re going to catch it.’ ” This shift — from studying certitude to probabilities — is largely based on the work of Thomas Bayes, an 18th century English clergyman. A claim is more reliable if initial beliefs are also included in the assessment, Bayes proposed. And these initial beliefs, known as “priors” today, can be updated as more information comes in, narrowing the range of good solutions. At its heart, the concept is simple: Learning from experience leads to better predictions.

Take a doctor faced with a medical
mystery. A young boy comes into the
office with a slight fever, a headache
and joint pain, symptoms that could be
caused by a garden-variety cold or the
more nefarious Lyme disease. With no
additional information, the doctor might
as well flip a coin. But armed with key
pieces of information — medical school
tidbits and knowledge of whether the
boy played in tick-teeming woods, for
instance — the physician can come up
with a solid diagnosis.

Scientists don’t yet know what physi-
cal hardware in the brain might be per-
forming such Bayesian reasoning, but
simulations suggest variations in nerve
cell behavior might be responsible for
these seemingly complex calculations. “It
seems like sophisticated math,” Girshick
says. “But it could be quite simple.”
Some nerve cells respond strongly to
horizontal or vertical lines, while oth-
ers don’t give those orientations special
attention. “You get this Bayesian-like
behavior simply by the fact that you
have this nonuniformity in the brain,”
Girshick says.

Bomb amid batteries

As any airport security screener knows, spotting a bomb among a steady stream of computer batteries, alarm clocks and blow-dryers is notoriously difficult. But in the case of this visual challenge, called a visual search, the Bayesian brain appears to perform surprisingly well.

Given the incomplete information that humans get from their retinas, people’s visual search skills are remarkable, Pouget says.

“Visual search happens absolutely all the time,” he says. “We thought this is exactly the kind of task where a probabilistic approach would be great.” In a recent study, he and his team had participants watch a computer screen for a quick flash of a target — a previously seen line tilted at a particular angle. On the screen, this line was surrounded by distracting objects. Participants reported whether the target was there or not, and how confident they were in the answer.

When the target blended in with the background and the distracters were nice and bright, people grew worse at

Bayesian-based brains though they don’t yet have a clear idea of how the brain does the calculations needed to compute probabilities based on built-in assumptions, scientists do have some sense of the steps involved in encoding and decoding an environmental stimulus.