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. B
Contact: Dennis Meredith
Duke University Medical Center