While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist—in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering—predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement’s predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance.

中文翻译：

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调整运动以在​​不确定的世界中进行感知

当动物跟踪或搜索目标时，感觉器官会在与主要任务相关的运动之上进行一些无法解释的小运动。虽然存在多种关于这些运动的理论——因为它们支持信息趋向性、增益适应、光谱白化和高通滤波——但预测轨迹显示出与测量轨迹的拟合不佳。我们为这些运动提出了一种新理论，称为能量约束比例投注，其中移动到某个位置的概率与预期将如何与运动的预测能量成本相平衡的信息量成正比。以这种方式生成的轨迹与使用电感应跟踪物体的鱼、定位气味源的哺乳动物和昆虫以及使用视觉跟踪花朵的飞蛾的测量轨迹非常吻合。我们的理论将运动的代谢成本与信息理论相结合。它可以预测动物的感觉器官运动，并可以为机器人规定传感器运动以提高性能。