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- About Us
Educated Guesses Gone Wrong
28 May 2002 (All day)
Tennis and golf players, at least the good ones, experience it with every shot: Their brains compute the velocity and direction of a flying ball with high precision in real-time. But under dim light, when the contrast is low, even the pros misjudge. Now theoretical neuroscientists show that such errors are not due to sloppy neuronal computation, but the result of educated--but erroneous--guesses as the brain struggles under difficult conditions.
Dim light can lead to some strange errors. In one classic example, a rhombus moving horizontally with the four corners covered appears to move vertically at low contrast (see link below with interactive animation). Similarly, car drivers underestimate their speed in foggy weather and tend to drive faster than on a sunny day. Cognitive neuroscientists have tried to exploit such observations to figure out how the brain perceives motion.
Theoretical neuroscientist Yair Weiss of the Hebrew University of Jerusalem now presents a simple theoretical model that explains most of the known illusions and misperceptions. The model, described in a paper published online 21 April by Nature Neuroscience, is based on a statistical theory that takes into account two basic assumptions about the real world: First, most objects in the world don't move at all, and the rest tend to move slowly. Second, it's hard to pin down the exact location of objects under low contrast. When Weiss fed the model numbers mimicking motion signals detected by the retina, he found that the model behaved surprisingly like a human: It came up with precise estimates of objects' motion in good "lighting" conditions and made the same mistakes when conditions were less optimal. Weiss says this suggests that the human brain uses the same assumptions to compute the motion of objects.
Cognitive neuroscientist Wilson Geisler of the University of Texas, Austin, is intrigued that illusions are not the result of "irrational" processes in the brain as some researchers had assumed. "Now it can simply be explained by a system that is making educated guesses," Geisler says. "That's what is so nice about it."