Unlike other predators that use vision as their primary sensory system, bats compute the three-dimensional (3D) position of flying insects from discrete echo snapshots, which raises questions about the strategies they employ to track and intercept erratically moving prey from interrupted sensory information. Here, we devised an ethologically inspired behavioral paradigm to directly test the hypothesis that echolocating bats build internal prediction models from dynamic acoustic stimuli to anticipate the future location of moving auditory targets. We quantified the direction of the bat’s head/sonar beam aim and echolocation call rate as it tracked a target that moved across its sonar field and applied mathematical models to differentiate between nonpredictive and predictive tracking behaviors. We discovered that big brown bats accumulate information across echo sequences to anticipate an auditory target’s future position. Further, when a moving target is hidden from view by an occluder during a portion of its trajectory, the bat continues to track its position using an internal model of the target’s motion path. Our findings also reveal that the bat increases sonar call rate when its prediction of target trajectory is violated by a sudden change in target velocity. This shows that the bat rapidly adapts its sonar behavior to update internal models of auditory target trajectories, which would enable tracking of evasive prey. Collectively, these results demonstrate that the echolocating big brown bat integrates acoustic snapshots over time to build prediction models of a moving auditory target’s trajectory and enable prey capture under conditions of uncertainty.

Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.

Target tracking and interception in a dynamic world proves to be a fundamental challenge faced by both animals and artificial systems. To track moving objects under natural conditions, agents must employ strategies to mitigate interference and conditions of uncertainty. Animal studies of prey tracking and capture reveal biological solutions, which can inspire new technologies, particularly for operations in complex and noisy environments. By reviewing research on target tracking and interception by echolocating bats, we aim to highlight biological solutions that could inform new approaches to artificial sonar tracking and navigation systems. Most bat species use wideband echolocation signals to navigate dense forests and hunt for evasive insects in the dark. Importantly, bats exhibit rapid adaptations in flight trajectory, sonar beam aim, and echolocation signal design, which appear to be key to the success of these animals in a variety of tasks. The rich suite of adaptive behaviors of echolocating bats could be leveraged in new sonar tracking technologies by implementing dynamic sensorimotor feedback control of wideband sonar signal design, head, and ear movements.

Insectivorous bats capture their prey in flight with impressive success. They rely on the echoes of their own ultrasonic vocalization that yield acoustic snapshots, which enable target tracking on a rapid time scale. This task requires the use of intermittent information to navigate a dynamically changing environment. Bats may solve this challenging task by building internal models that estimate target velocity to anticipate the future location of a prey item. This has been recently tested empirically in perched bats tracking a target moving across their acoustic field. In this report, we build on past work to propose a new model that describes bat flight trajectories employing predictive strategies. Furthermore, we compare this model with a previous model of bat target interception that has also been employed by some visually guided animals: parallel navigation.

Echolocating bats rely on precise temporal processing of sound, as echolocation calls may be emitted at rates as high as 150–200 sounds per second in the terminal “buzz” phase that precedes prey capture by insectivorous species. High call repetition rates could introduce forward masking effects that interfere with echo detection; however, echolocating bats may have evolved auditory specializations to prevent repetition suppression of auditory responses and facilitate detection of sounds separated by very brief time intervals. We assessed the time course of post-stimulus recovery of the auditory brainstem response (ABR) in two bat species that differ in the temporal characteristics of their sonar behaviors: the insectivorous Eptesicus fuscus, which uses high sonar call rates to capture prey and the frugivorous Carollia perspicillata, which uses lower call rates to forage. We recorded forward-masking ABRs to paired clicks at varied inter-stimulus intervals and measured recovery from prior stimulation as the ratio of response amplitudes and latencies evoked by the second stimulus relative to the first. We observed significant species-specific effects of forward masking on ABR responses in which E. fuscus maintained comparable responses to ISIs less than 4 ms compared to longer post-stimulus response recovery times (>6 ms) in C. perspicillata.