Reclaiming saliency: Rhythmic precision-modulated action and perception.

Reclaiming saliency: Rhythmic precision-modulated action and perception.
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DOI:
10.3389/fnbot.2022.896229
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发表时间:
2022
影响因子:
3.1
通讯作者:
Sajid, Noor
Sajid, Noor
中科院分区:
计算机科学3区
文献类型:
--
作者:
Anil Meera, Ajith;Novicky, Filip;Parr, Thomas;Friston, Karl;Lanillos, Pablo;Sajid, Noor

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人工智能和机器人技术中视觉注意力的计算模型受到显着图概念的启发。这些模型解释了(当前)视觉信息与其估计原因之间的互信息。然而,他们没有考虑感知和行动之间的循环因果关系。换句话说,根据当前的信念,他们不会考虑下一步在哪里采样。在这里,我们将显着性重新定义为一种主动推理过程,它依赖于两个基本原则:不确定性最小化和有节奏的调度。为此,我们区分了注意力和显着性。简而言之,我们将注意力与精确控制联系起来,即根据采样的感官数据更新信念的置信度,以及支持未来感官数据选择的不确定性最小化的显着性。利用这一点,我们提出了一种基于节奏精确调制的新的注意力机制,并讨论了它在机器人技术中的潜力,提供了数值实验,展示了它在状态和噪声估计、系统识别和信息路径规划的动作选择方面的优势。
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimization and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimization that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase its advantages for state and noise estimation, system identification and action selection for informative path planning.
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