The condition makes it harder to learn to read. But it also seems to offer visual advantages.
People with dyslexia, who have a bias in favor of the visual periphery, can rapidly take in a scene as a whole — what researchers call absorbing the “visual gist.”
Intriguing evidence that those with dyslexia process information from the visual periphery more quickly also comes from the study of “impossible figures,” like those sketched by the artist M. C. Escher. A focus on just one element of his complicated drawings can lead the viewer to believe that the picture represents a plausible physical arrangement.
A more capacious view that takes in the entire scene at once, however, reveals that Escher’s staircases really lead nowhere, that the water in his fountains is flowing up rather than down — that they are, in a word, impossible. Dr. Catya von Károlyi, an associate professor of psychology at the University of Wisconsin, Eau Claire, found that people with dyslexia identified simplified Escher-like pictures as impossible or possible in an average of 2.26 seconds; typical viewers tend to take a third longer. “The compelling implication of this finding,” wrote Dr. Von Károlyi and her co-authors in the journal Brain and Language, “is that dyslexia should not be characterized only by deficit, but also by talent.”
The discovery of such talents inevitably raises questions about whether these faculties translate into real-life skills. Although people with dyslexia are found in every profession, including law, medicine and science, observers have long noted that they populate fields like art and design in unusually high numbers. Five years ago, the Yale Center for Dyslexia and Creativity was founded to investigate and illuminate the strengths of those with dyslexia, while the seven-year-old Laboratory for Visual Learning, located within the Harvard-Smithsonian Center for Astrophysics, is exploring the advantages conferred by dyslexia in visually intensive branches of science. The director of the laboratory, the astrophysicist Matthew Schneps, notes that scientists in his line of work must make sense of enormous quantities of visual data and accurately detect patterns that signal the presence of entities like black holes.