  Searching for a target object in a cluttered scene constitutes a fundamental
challenge in daily vision. Visual search must be selective enough to
discriminate the target from distractors, invariant to changes in the
appearance of the target, efficient to avoid exhaustive exploration of the
image, and must generalize to locate novel target objects with zero-shot
training. Previous work has focused on searching for perfect matches of a
target after extensive category-specific training. Here we show for the first
time that humans can efficiently and invariantly search for natural objects in
complex scenes. To gain insight into the mechanisms that guide visual search,
we propose a biologically inspired computational model that can locate targets
without exhaustive sampling and generalize to novel objects. The model provides
an approximation to the mechanisms integrating bottom-up and top-down signals
during search in natural scenes.
