Tuning movement for sensing in an uncertain world

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 movements predicted energetic cost. Trajectories generated in this way show good agreement with measured target tracking trajectories of electric fish. Similarly good agreement was found across three published datasets on visual and olfactory tracking tasks in insects and mammals. 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.