[img_inline align=”right” src=”http://padnws01.mcmaster.ca/images/inversion3.jpg” caption=”Faces”]

Notice anything different about
these two pictures? It's the same person, with one small difference.
One image has been altered, but most people won't see how
until they're viewed upright.

This is one example of the so-called the inversion effect
 it's harder for the brain to process upside-down objects
than upright objects, and the inversion effect is especially strong
for the perception of faces.

For most people, it's easy to recognize a range of
faces, even under various lighting conditions and from different
views. But when those faces are turned upside-down, we experience
problems, says Allison Sekuler, professor of psychology and
Canada Research Chair in Cognitive Neuroscience at McMaster University.

Sekuler says human faces consist of two eyes, a nose, and a mouth,
organized in just about the same way for every face. For decades,
people thought the face inversion effect meant that the brain uses
the information in faces in very different ways to recognize upright
and upside-down faces.

Traditionally, recognition of upright faces was thought to hinge
on the organization of features across the whole face, whereas recognition
of upside-down faces relied much more on identifying local features.

Sekuler and her team set out to test that idea directly. Their
results, which will appear in the journal Current Biology
on Tuesday, provide an entirely new picture of what goes on when
our brains picture faces. To obtain a clear view of how the brain
processes information about faces, the researchers actually added
visual noise (resembling snow on a de-tuned television)
to face images. By keeping track of how that noise
affected perception, the researchers were able to tell what parts
of the faces were most important for recognition. Surprisingly,
all observers relied mostly on the region around the eyes and eyebrows,
regardless of whether the faces were upright or upside-down.

The devil is in the details, says Sekuler. Although
most of the relevant information for recognizing our faces was right
around the eyes, people seem much more efficient at picking up that
information in just the right way when the face is right side-up.
These results fly in the face of previous theories of face recognition.
Instead, the researchers suggest that the face inversion effect
may be an example of the old saying, practice makes perfect
 people simply have a lot more experience recognizing upright
faces, and that makes them better.

According to this view, the inversion effect is a fascinating example
of how the human brain processes information, and how our brains
can be trained to process difficult tasks more efficiently. In a
related study, to be published in April in the journal Cognitive
Science, Sekuler and her research team applied similar noise
obstructions to faces and unfamiliar textures to determine how people's
recognition skills improved with learning. With both types of patterns,
everyone who was tested improved. For faces, people became more
efficient at picking out the relevant information around the eyes
and eyebrows. For textures, different individuals adopted different
strategies for improvement. Although everyone became more efficient
at picking out the right details, the locations of those details
differed dramatically (some people relied more on information in
a top corner, whereas others relied on information in the middle
or bottom).

In working with textures, we found that people learned to
recognize them in different ways, even though they all ended up
performing the task equally well, says Sekuler. For
the first time, we were able to get a direct view of what strategies
the brain used to improve recognition. Understanding the unconscious
learning strategies people use, and how those strategies vary across
individuals, will help us to establish more effective training techniques.

Sekuler hopes that by identifying how the brain normally processes
this kind of information, she and her group will be able to develop
training programs for people who have impaired facial recognition
skills, such as autistic individuals and some stroke victims.

The first step toward improving performance in impaired
populations is to understand how the typical brain processes information,
she says. With this work, we've made a big leap toward
that end.

Sekuler's research team includes Patrick Bennett, professor
of psychology and Canada Research Chair in Vision Science, and Carl
Gaspar, graduate student, from McMaster University, and Jason Gold
assistant professor of psychology from Indiana University. The work
was funded by the Natural Sciences and Engineering Research Council
of Canada and the Canada Research Chairs.