Science News - October 8, 2011

Baby rationale Babies can think in probabilistic ways. After seeing two researchers both succeed and fail in using a sound-making toy, a baby who then fails with the same toy is likely to think the toy is faulty and tends to go for another (below, left). But when one researcher fails and a second succeeds, the baby more often takes the blame and asks for help (right).

SOURCE: H. GWEON AND L. SCHULZ/ SCIENCE 2011

Faulty toy scenario

100

Percent of children

80

60

40

20

0

Faulty user scenario

100

Percent of children

80

60

40

20

0

Child asks
for help
Child asks
for new toy
Child asks
for help
Child asks
for new toy

the button, but fritzed out the next time. This created the semblance of a faulty toy. In other cases, the toy worked well for one experimenter but never worked for another, suggesting that the toy was fine but the second experimenter was a poor operator.

When the babies were handed the toy that seemed like it was faulty, they quickly reached for a different toy. But when the babies thought they themselves might be to blame (when they witnessed the second experimenter fail with the toy and then they failed themselves), they handed the toy to a nearby parent in a plea for help.

By assessing others’ toy travails and
applying that knowledge to their current
problem, babies displayed very sophisti-
cated reasoning, Schulz says. “As early as
we can test, babies are using things that
are consistent with probabilistic mod-
els,” she says. “Babies are sensitive to the
statistics of the environment.”
Instead of looking for signs of probabi-
listic reasoning in young humans, some
scientists are looking for signs in other
species. A recent study in owls suggests
that aspects of their brains also follow
Bayesian rules.

BLUEORANGE STUDIO/SHUTTERSTOCK

Though owls are admirable hunters, they typically don’t hear sounds that come from areas in the periphery as well as sounds coming from the front. To explain this deficit, Brian Fischer of École Normale Supérieure in Paris and José Luis Peña of the Albert Einstein College of Medicine in the Bronx, N. Y., turned to Bayesian math.

The team devised a statistical model

of auditory processes with the assumption that owls may have evolved to assign less importance to signals coming from the periphery because hunting something at their backs might be too costly. A turning motion might scare prey away, for instance. In tests, Bayesian models closely predicted this actual owl behavior, the researchers reported in the August Nature Neuroscience.

In the owl’s auditory system, this bias toward hearing objects right in front may come preinstalled. Likewise, babies may be hardwired to quickly infer whether they are to blame for a nonworking toy.

Where, when and how these pieces of prior knowledge get filed away in the brain is still a mystery. Some scientists think priors—and the ability to use them— were built into brains over the course of evolution.

“Biological systems are not accidental,” Simoncelli says. “We believe that evolution shaped them, and shaped them to be good at what they do. And we have a lot of evidence that that’s true.”

‘Prior’engineering

Whether or not evolution designed Bayesian brains, some of those very brains are now intent on passing their Bayesian abilities on. Trained as an engineer, Simoncelli says that the same principles at work in the brain could be incredibly useful elsewhere. “My belief is that when we finally figure out how some of these circuits operate in brains in order to accomplish these feats, we’re going to change engineering,” he says.

“We’re going to revolutionize the way
we think about designing systems.”
Many of today’s robots, for example,
excel at precise tasks but are totally
inflexible. Robots that install windshields
on new cars perform the job flawlessly
each and every time. “We can make that
robot be fantastically good at putting that
windshield on,” Simoncelli says. “They’re
beautifully engineered systems.” But
those paragons of windshield installa-
tion would have a complete meltdown
if they were handed a glass sheet of the
wrong size. A similar robot based on the
human brain, though, might easily adapt
to changing circumstances and even file
away some priors of its own.

Nerve cells exhibit enormous flexibility. Constantly readjusting to input, interacting with neighbors and changing firing rates can lead to incredible adaptability, a prerequisite for Bayesian learning, Simoncelli says. The more scientists understand about nerve cell function, “the more we find they’re not fixed, dedicated devices that operate the same way throughout your lifetime,” he says.

Cracking the brain’s Bayesian operating system might lead to a new set of engineering principles. “We don’t know how to engineer systems that are more flexible, and we don’t know how the brain works. And we’re going to figure both those things out,” Simoncelli says. “And I believe that we’re going to do it at the same time.” s