Neuromodulated attention and goal-driven perception in uncertain domains
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 two goal-driven perception tasks – one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scen...
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Published in | Neural networks Vol. 125; pp. 56 - 69 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.05.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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 two goal-driven perception tasks – one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as “eat”, “work-on-computer”, “read”, and “say-hi” that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. 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 for that goal. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0893-6080 1879-2782 |
DOI: | 10.1016/j.neunet.2020.01.031 |