  Recent machine learning models have shown that including attention as a
component results in improved model accuracy and interpretability, despite the
concept of attention in these approaches only loosely approximating the brain's
attention mechanism. Here we extend this work by building a more brain-inspired
deep network model of the primate ATTention Network (ATTNet) that learns to
shift its attention so as to maximize the reward. Using deep reinforcement
learning, ATTNet learned to shift its attention to the visual features of a
target category in the context of a search task. ATTNet's dorsal layers also
learned to prioritize these shifts of attention so as to maximize success of
the ventral pathway classification and receive greater reward. Model behavior
was tested against the fixations made by subjects searching images for the same
cued category. Both subjects and ATTNet showed evidence for attention being
preferentially directed to target goals, behaviorally measured as oculomotor
guidance to targets. More fundamentally, ATTNet learned to shift its attention
to target like objects and spatially route its visual inputs to accomplish the
task. This work makes a step toward a better understanding of the role of
attention in the brain and other computational systems.
