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In uncertain domains, the goals are often unknown and need to be predicted bythe 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 digitscorresponding to a goal (i.e., even, odd, low value, or high value) anddecrease attention to the distractor digit or noisy background pixels. Becausethe valid goal was unknown, an online learning model based on the cholinergicand noradrenergic neuromodulatory systems was used to predict a noisy goal(expected uncertainty) and re-adapt when the goal changed (unexpecteduncertainty). This neurobiologically plausible model demonstrates howneuromodulatory systems can predict goals in uncertain domains and howattentional mechanisms can enhance the perception of that goal.

Neuromodulated Goal-Driven Perception in Uncertain Domains