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.

中文翻译：

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不确定领域的神经调节注意力和目标驱动的感知。

在不确定的领域中，目标通常是未知的，需要由有机体或系统预测。在本文中，对比激励反向支持（c-EB）用于两个目标驱动的感知任务-一个在基于动作的注意力场景中使用一对带有噪声的MNIST数字对，而另一个使用机器人进行。第一项任务包括注意偶数，奇数，低和高位数，而第二项任务包括导致目标的行动目标，例如“吃”，“计算机上工作”，“阅读”和“说嗨”注意与这些动作相关的对象。该系统需要增加对目标项目的关注，并减少对干扰项和背景噪声的关注。由于有效目标未知，基于胆碱能和去甲肾上腺素能神经调节系统的在线学习模型用于预测嘈杂的目标（预期的不确定性），并在目标发生变化时重新适应（预期的不确定性）。这种神经生物学上可行的模型演示了神经调节系统如何预测不确定区域中的目标以及注意力机制如何增强对该目标的感知。