A new computational model of how the primate brain recognizes objects creates a map of ?interesting? features (right) for a given image. The model’s predictions of which parts of the image will attract a viewer’s attention (green clouds, left) accord well with experimental data (yellow and red dots).
CAMBRIDGE, Mass. Researchers at MIT's McGovern Institute for Brain Research have developed a new mathematical model to describe how the human brain visually identifies objects. The model accurately predicts human performance on certain visual-perception tasks, which suggests that it's a good indication of what actually happens in the brain, and it could also help improve computer object-recognition systems. The model was designed to reflect neurological evidence that in the primate brain, object identification ? deciding what an object is ? and object location ? deciding where it is ? are handled separately. 'Although what and where are processed in two separate parts of the brain, they are integrated during perception to analyze the image,' says Sharat Chikkerur, lead author on a paper appearing this week in the journal Vision Research, which describes the work. 'The model that we have tries to explain how this information is integrated.' - The mechanism of integration, the researchers argue, is attention. According to their model, when the brain is confronted by a scene containing a number of different objects, it can't keep track of all of them at once.
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