A team of Johns Hopkins University neuroscientists has
discovered patterns of brain activity
that may underlie our remarkable ability to see and
understand the three-dimensional structure of
Computers can beat us at math and chess, but humans
are the experts at object vision. (That's
why some Web sites use object recognition tasks as part of
their authentication of human users.) It
seems trivial to us to describe a teapot as having a
C-shaped handle on one side, an S-shaped spout on
the other and a disk-shaped lid on top. But sifting this
three-dimensional information from the
constantly changing two-dimensional images coming in
through our eyes is one of the most difficult
tasks the brain performs. Even sophisticated computer
vision systems have never been able to
accomplish the same feat using two-dimensional camera
The Johns Hopkins research suggests that higher-level
visual regions of the brain represent
objects as spatial configurations of surface fragments,
something like a structural drawing. Individual
neurons are tuned to respond to surface fragment
substructures. For instance, one neuron from the
study responded to the combination of a forward-facing
ridge near the front and an upward-facing
concavity near the top. Multiple neurons with different
tuning sensitivities could combine like a three-
dimensional mosaic to encode the entire object surface. An
article describing these findings appears
in the November issue of Nature Neuroscience.
"Human beings are keenly aware of object structure,
and that may be due to this clear
structural representation in the brain," said Charles E.
Connor, an associate professor in the Zanvyl
Krieger Mind/Brain Institute at Johns Hopkins.
In the study, Connor and a postdoctoral fellow, Yukako
Yamane, trained two rhesus monkeys to
look at a computer monitor while 3-D pictures of objects
were flashed on the screen. At the same
time, the researchers recorded electrical responses of
individual neurons in higher-level visual regions
of the brain. A computer algorithm was used to guide the
experiment gradually toward object shapes
that evoked stronger responses.
This evolutionary stimulus strategy let the
experimenters pinpoint the exact 3-D shape
information that drove a given cell to respond.
These findings and other research on object coding in
the brain have implications for treating
patients with perceptual disorders. In addition, they could
inform new approaches to computer vision.
Connor said he also believes that understanding neural
codes could help explain why visual experience
feels the way it does, perhaps even why some things seem
beautiful and others displeasing.
"In a sense, artists are neuroscientists,
experimenting with shape and color, trying to evoke
unique, powerful responses from the visual brain," Connor
As a first step toward this neuro-aesthetic question,
the Connor laboratory plans to collaborate
with the Walters Art Museum in Baltimore to study human
responses to sculptural shape. Gary Vikan,
the Walters' director, is a strong believer in the power of
neuroscience to inform the interpretation
"My interest is in finding out what happens between a
visitor's brain and a work of art," Vikan
said. "Knowing what effect art has on patrons' brains will
contribute to techniques of display — lighting
and color and arrangement — that will enhance their
experiences when they come into the museum."
The plan is to let museum patrons view a series of
computer-generated 3-D shapes and rate
them aesthetically. The same computer algorithm will be
used to guide evolution of these shapes,
based, in this case, on aesthetic preference.
If this experiment can identify artistically powerful
structural motifs, the next step would be
to study how those motifs are represented at the neural
"Some researchers speculate that evolution determines
what kinds of shapes and such our
brains find pleasing," Vikan said. "In other words, perhaps
we are hard-wired to prefer certain things.
This collaboration with the Mind/Brain Institute at Johns
Hopkins could help us begin to understand
that in more depth."
This work was supported by the National Institutes of