The term visual attention immediately evokes the idea of limited resources, serial processing, or a zoom metaphor. But evidence has slowly accumulated that computations that take into account probabilistic relationships among visual forms and the target contribute to optimizing decisions in biological and artificial organisms, even without considering these limited-capacity processes in covert attention or even foveation. The benefits from such computations can be formalized within the framework of an ideal Bayesian observer and can be related to the classic theory of sensory cue combination in vision science and context-driven approaches to object detection in computer vision. The framework can account for a large range of behavioral findings across distinct experimental paradigms, including visual search, cueing, and scene context. I argue that these forms of probabilistic computations might be fundamental to optimizing decisions in many species and review human experiments trying to identify scene properties that serve as cues to guide eye movements and facilitate search. I conclude by discussing contributions of attention beyond probabilistic computations but argue that the framework's merit is to unify many basic paradigms to study attention under a single theory.

中文翻译：

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注意，眼球运动和搜索的概率计算。

视觉注意力一词立即唤起了有限资源，串行处理或缩放隐喻的想法。但是证据逐渐积累，考虑到视觉形式与目标之间的概率关系的计算有助于优化生物和人造生物的决策，即使没有在秘密关注或什至偏爱中考虑这些能力有限的过程。这种计算的好处可以在理想的贝叶斯观察者的框架内形式化，并且可以与视觉科学中的感官提示组合的经典理论以及计算机视觉中对象检测的上下文驱动方法相关。该框架可以解释不同实验范式中的大量行为发现，包括视觉搜索，提示和场景上下文。我认为，这些概率计算形式可能是优化许多物种决策的基础，并复习了试图确定场景属性的人类实验，这些场景属性可作为指导眼球运动和促进搜索的线索。最后，我讨论了概率计算以外的注意力的贡献，但认为该框架的优点是统一许多基本范式以单一理论研究注意力。