  In uncertain domains, the goals are often unknown and need to be predicted by
the organism or system. In this paper, contrastive excitation backprop (c-EB)
was used in a goal-driven perception task with pairs of noisy MNIST digits,
where the system had to increase attention to one of the two digits
corresponding to a goal (i.e., even, odd, low value, or high value) and
decrease attention to the distractor digit or noisy background pixels. Because
the valid goal was unknown, an online learning model based on the cholinergic
and noradrenergic neuromodulatory systems was used to predict a noisy goal
(expected uncertainty) and re-adapt when the goal changed (unexpected
uncertainty). This neurobiologically plausible model demonstrates how
neuromodulatory systems can predict goals in uncertain domains and how
attentional mechanisms can enhance the perception of that goal.
