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

an visual perception, cognition, and behavior. Deciphering the relative contribution of feed-forward and feedback processing may eventually help explain neuropsychological disorders such as autism and schizophrenia. The model also bridges the gap between the world of artificial intelligence (AI) and neuroscience because it may lead to better artificial vision systems and augmented sensory prostheses.

Rapid Categorization

During normal everyday vision, the eye moves around a scene, giving the brain time to focus attention on relevant cues, such as a snake curled in the path. Evolutionarily speaking, however, survival often depends on extracting vital information in one glance, so that we jump out of danger's way before we even realize what we've seen.

Cognitive neuroscientists have studied this phenomenon using a rapid categorization task during which subjects are asked to say whether a specific object (such as an animal) is present or not. In this task, subjects see an image flashed on a screen that is quickly replaced with an erasing mask (pink noise), which is presumed to shut down cognitive feedback. After just a 50 milliseconds glimpse of an image, less than the time it takes to flash two video frames, people can still accurately report an object's category, even though they are barely aware of what they have seen.

In parallel, computational neuroscientists have traced the flow of information from the retina through increasingly complex visual areas (V1, V2, V4) to the highest purely visual region, the inferotemporal cortex (IT), and on to higher areas such as prefrontal cortex (PFC) where object categorization is represented. The Poggio lab replicated the hypothetical computations the brain performs as information speeds forward through the visual pathway. They recently demonstrated that this biologically inspired model, which matches a number of different physiological data, can also learn to recognize objects from r
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Contact: Laurie Ledeen
ledeen@mit.edu
617-324-0134
McGovern Institute for Brain Research
2-Apr-2007


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