Tuning movement for sensing in an uncertain world

Abstract

Sensory organs-be they independently movable like eyes or requiring whole body 10 movement as in the case of electroreceptors-are actively manipulated throughout 11 stimulus-driven behaviors. While multiple theories for these movements exist, such as infotaxis, 12 in those cases where they are sufficiently detailed to predict sensory organ trajectories, they 13 show poor fit to measured trajectories. Here we present evidence that during tracking, these 14 trajectories are predicted by energy-constrained proportional betting, where the probability of 15 moving a sense organ to a location is proportional to an estimate of how informative that 16 location will be combined with its energetic cost. Energy-constrained proportional betting 17 trajectories show good agreement with measured trajectories of four species engaged in visual, 18 olfactory, and electrosensory tracking tasks. Our approach combines information-theoretic 19 approaches in sensory neuroscience with analyses of the energetics of movement. It can predict 20 sense organ movements in animals and prescribe them in robotic tracking devices. 21 22 26 2010; Khan et al., 2012; Stamper et al., 2012; Catania, 2013; Sponberg et al., 2015; Lockey and 27 Willis, 2015; Rucci and Victor, 2015; Stockl et al., 2017) (Fig. 1). There are several models in the 28 literature that have been proposed (Stamper et al., 2012; Yovel et al., 2010; Khan et al., 2012; Rucci 29 and Victor, 2015; Najemnik and Geisler, 2005; Yang et al., 2016; Stockl et al., 2017). For example, 30in the related case of signal-emitter organ control, fruit bats are known to oscillate their tongue-31 click-based sonar signals on approach to their targets (Yovel et al., 2010). This can be effective 32 because for many signal sources, the signal intensity peaks at the target's location and tapers away 33 in all directions. The expected amount of information-in the bat study quantified by the Fisher 34 Information of the emitted sonar signal-is highest at the maximum slope of the signal profile 35 because at those locations, small variations in the emitter position leads to large changes in emitted 36 signal power on target and thus also in the returning echos. In contrast, at the flat peak of the 37 profile where the object is located, small variations in emitter position lead to small or no change 38 in signal and returning echo; the expected information is therefore low. For active sensing animals 39 like bats, dolphins, and electric fish, placing emitter organs so that the target is at a location of high 40 1 of 33 Manuscript submitted to eLife signal slope then leads to better information harvesting and hence better estimation of the target 41 location (Clarke et al., 2015;Yovel et al., 2010). The same is true for animals guided by light or 42 sound, through placement of sense organs at high slope locations. Puzzlingly, this would suggest 43 that animals should monitor an information peak (one location of high signal slope), while the 44 documented animal behavior suggests that they move bet...

Active sensing in bees through antennal movements is independent of odor molecule

“…The horizontal movement would by contrast serve to sample the air in specific regions around the insect to search for the source of odor by selecting the plane of the vertical oscillation. This type of strategy can be observed in the casting behavior of other animals ( 5,13 ) and was described by Chen et al ( 72 ) as energy-constrained proportional betting. It serves to maximize information gain during sampling.…”