A team of researchers at the Massachusetts Institute of Technology have created a computer visualization model that mimics the way humans see and interpret images. The 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.
This new study supports a long–held 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 human 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.
Importantly, the results showed no significant difference between humans and the model. Both had a similar pattern of performance, with well above 90% accuracy for the close views dropping to 74% for distant views. The 16% drop in performance for distant views represents a limitation of the one feed-forward sweep in dealing with clutter. Still, the researchers caustion that "We have not solved vision yet." With more time for cognitive feedback, people would outperform the model because they could focus attention on the target and ignore the clutter.
This model is a big step forward toward a real artificial intelligence (is that an oxymoron?). Computers that use a visual model to interpret what they are seeing could allow for computer systems that perform many of the tasks that are restricted to people today.