First impressions: Computer model behaves like humans on visual categorization task

Computers can usually out-compute the human brain, but there are some tasks, such as visual object recognition, that the brain performs easily yet are very challenging for computers. The brain has a much more sophisticated and swift visual processing system than even the most advanced artificial vision system, giving us an uncanny ability to extract salient information after just a glimpse that is presumably too fleeting for conscious thought. To explore this phenomenon, neuroscientists have long used rapid categorization tasks, in which subjects indicate whether an object from a specific class (such as an animal) is present or not in the image.

Now, in a new MIT study, a computer model designed to mimic the way the brain itself processes visual information performs as well as humans do on rapid categorization tasks. The model even tends to make similar errors as humans, possibly because it so closely follows the organization of the brain's visual system.

"We created a model that takes into account a host of quantitative anatomical and physiological data about visual cortex and tries to simulate what happens in the first 100 milliseconds or so after we see an object," explained senior author Tomaso Poggio of the McGovern Institute for Brain Research at MIT. "This is the first time a model has been able to reproduce human behavior on that kind of task." The study, issued on line in advance of the April 10, 2007 Proceedings of the National Academy of Sciences (PNAS), stems from a collaboration between computational neuroscientists in Poggio's lab and Aude Oliva, a cognitive neuroscientist in the MIT Department of Brain and Cognitive Sciences.

This new study supports a longheld hypothesis that rapid categorization happens without any feedback from cognitive or other areas of the brain. The results also indicate that the model can help neuroscientists make predictions and drive new experiments to explore brain mechanisms involved in hum

Contact: Laurie Ledeen
McGovern Institute for Brain Research

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