New Book Explains Age-Old Mystery of Geometrical Illusions
DURHAM, N.C. -- The insights provided by neurobiologist Dale
Purves and his colleagues over the last few years about why the
brain doesn't see the world according to the measurements
provided by rulers, protractors or photometers suggest that
vision operates in way very different from what most
In a new book "Perceiving Geometry: Geometric Illusions
Explained by Natural Scene Statistics" (Springer), Purves and
colleague Catherine Howe explore why the brain generates
Visual perception is a daunting task for the brain, explains
Purves, because light streaming into the eye carries only
ambiguous information about the environment.
"The basic problem, recognized for several centuries, is
that the image on our retinas can't specify what's out there in
the world," said Purves. "The light received by our retinal
receptors tangles up illumination, reflectance, transmittance,
size, distance and orientation," said Purves. "This means that
there's no logical way to get back from the retinal image to
what's actually out there in the world."
Nevertheless, many neurobiologists have attempted to explain
vision by postulating that the brain's neural wiring can
definitively "calculate" the features of a visual scene,
despite the visual world's inevitable ambiguity. Such
"rule-based" theories, said Purves, have arisen because
neurobiologists have concentrated on understanding how neurons
in the brain's visual region extract and recognize specific
features such as edges in a visual scene.
"Because of the enormous power and success of modern
neurophysiology and neuroanatomy, there just didn't seem to be
any reason to think much about this issue," said Purves.
"However, we began worrying about it seven or eight years ago
because the physiology and anatomy people had described didn't
explain what we end up seeing. There was no instance, even in
the simplest aspects of vision, where the properties of visual
neurons in the brain explain the brightness, colors or forms
that we actually see."
Thus, Purves and his colleagues began exploring visual
illusions -- the name given to the more obvious discrepancies
between the physical world and the way people see it -- to
understand the strategy the brain uses in perceiving the world.
Basically, they statistically compared perceptions -- such as
the apparent length of a line -- with physical measurements of
what the line stimulus on the retina was most likely to
represent in the real world.
This sort of analysis, made by measuring a large set of
geometrical images with a device called a laser range scanner,
showed that the brain is not a calculating engine, cranking out
stimulus features, but a "statistical engine" wired by
evolution and a person's experience to make the best
statistical guess about objects in a visual scene, based on how
successful those guesses have been in the past.
"So, vision is not about extracting features from a scene;
it's about extracting statistics in the sense of relating the
image on your retina to the visually guided behavior that's
worked in the past," said Purves. "This framework for thinking
about vision explains quantitatively -- sometimes in amazing
detail -- what we end up seeing."
In 2003, Purves and colleague Beau Lotto published an
explanation of their "probabilistic" theory of vision in their
book "Why We See What We Do: An Empirical Theory of Vision"
(Sinauer Associates, Inc).
These two books and dozens of scientific papers have framed
the questions that Purves believes researchers must ask about
how vision works. But he emphasizes that those questions have
only begun to be addressed in neurobiological terms.
"The problem for colleagues in physiology and anatomy is
that our theory runs counter to what they've been doing for the
last fifty years," said Purves. "And their response has
understandably been 'Well, OK, that's interesting. But how do
you relate this concept of vision to physiology and this
anatomy?' It's perfectly valid to say, 'You've got a nice idea
and it does explain the phenomenology of what we see, but how
does that relate to the neurons that we know and love?'
"The answer is, we don't know," said Purves. "That's going
to be the next many years of vision research. It will mean
constructing a framework that explains how neurons and the
connections among them operate in service of this complex,
evolved statistical process called vision.
"Some bright people will certainly do this in the next ten,
twenty or thirty years," said Purves. "I don't expect to be
around to see it, but inevitably that will happen. But it's
going to take people who deeply understand statistics and
computer models of neural systems to develop a working theory
of how the properties of neurons and anatomical connections are
related to the end product of vision."
Purves said he hopes that the latest book that Catherine
Howe and he have written, along with the earlier work, will
continue the process of enlisting fellow neurobiologists in
tackling the immense question of how we perceive the
confusingly ambiguous visual world around us.