There are some sights and noises that people just can't help but notice. Indeed, research in neuroscience now suggests that the recognition of salient objects is a key part of how we make sense of our environment. But building robots that can intelligently pick out items of interest using sight or sound remains a daunting challenge. So a handful of engineers are working on a new approach called selective-attention modeling, which attempts to program robots to evaluate scenes critically as some neuroscientists believe people do.

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