Echolocating accumulate information from acoustic snapshots to predict auditory object motion

Angeles Sallesa,1,2, Clarice Anna Diebolda,2, and Cynthia F. Mossa

aDepartment of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218

Edited by Ranulfo Romo, National Autonomous University of Mexico, Mexico City, D.F., Mexico, and approved October 1, 2020 (received for review June 8, 2020) Unlike other predators that use vision as their primary sensory echo reception (6). Bats actively modify the duration, direction, system, bats compute the three-dimensional (3D) position of flying timing, intensity, and spectral content of their calls in response to insects from discrete echo snapshots, which raises questions about information carried by echoes, which allows them to flexibly react to the strategies they employ to track and intercept erratically mov- changes in the environment, such as the trajectory of a target (7). ing prey from interrupted sensory information. Here, we devised The big brown , fuscus, produces frequency-modulated an ethologically inspired behavioral paradigm to directly test the echolocation signals to hunt for flying insects (8). When the big hypothesis that echolocating bats build internal prediction models brown bat approaches prey and initiates target capture, it increases from dynamic acoustic stimuli to anticipate the future location of the rate of its sonar calls and locks its sonar beam aim, aligned with moving auditory targets. We quantified the direction of the bat’s the head, onto the target ∼300 ms before contact (6, 9). This head/sonar beam aim and echolocation call rate as it tracked a therefore presents a powerful model to investigate internal models target that moved across its sonar field and applied mathematical of target motion, which govern auditory tracking strategies. models to differentiate between nonpredictive and predictive When a bat is tracking free-flying insect prey, it must rely on tracking behaviors. We discovered that big brown bats accumulate brief and interrupted echo snapshots while also accommodating information across echo sequences to anticipate an auditory tar- the acoustic and neural delays inherent to sonar imaging, as well get’s future position. Further, when a moving target is hidden as abrupt changes in a target’s trajectory and temporary occlu- from view by an occluder during a portion of its trajectory, the sions. The delays include the time it takes for a sonar call to bat continues to track its position using an internal model of the travel through the air from the bat to an object, the returning ’ PSYCHOLOGICAL AND COGNITIVE SCIENCES target s motion path. Our findings also reveal that the bat in- echo to arrive at the bat’s ears, and the brain to process echo creases sonar call rate when its prediction of target trajectory is features and to generate a motor response. The acoustic and violated by a sudden change in target velocity. This shows that the neural delays can add up to over 100 ms for each sonar snapshot bat rapidly adapts its sonar behavior to update internal models of (7), during which time the spatial location of the target relative auditory target trajectories, which would enable tracking of eva- to the bat has likely changed. Moreover, insects may show erratic sive prey. Collectively, these results demonstrate that the echolo- flight behaviors and move behind obstacles. How does the bat cating big brown bat integrates acoustic snapshots over time to ’ overcome these challenges? The fact that bats commonly execute build prediction models of a moving auditory target s trajectory successful prey captures, despite acoustic and neural delays, and enable prey capture under conditions of uncertainty. leads us to hypothesize that they accumulate information about target motion from a sequence of echoes to build internal prediction Eptesicus fuscus | predictive models | biosonar | prey tracking | auditory localization Significance ensing and action operate in concert to enable a broad suite Research on visual tracking of moving stimuli has contributed of animal behaviors, such as navigation, reaching, and object S to our understanding of sensory-guided behaviors; however, tracking. Decades of research on visual tracking of two-dimensional the processes that support auditory object tracking in natural moving stimuli have contributed to our understanding of sensory- three-dimensional environments remain largely unknown. This guided behaviors; however, the mechanisms that support auditory is important, not only to diverse groups of , but also to object tracking in natural three-dimensional (3D) environments humans that rely on hearing to track objects in their environ- remain largely unknown. This is important, not only to animals, but ment. For visually impaired individuals, hearing is paramount also to humans who rely on hearing to track objects in their envi- for auditory object tracking and navigation, and in recent ronment. For visually impaired individuals, hearing is paramount years, mobility training programs for the blind include in- for auditory object tracking and navigation, and many blind hu- struction on echolocation using tongue clicks. In this work, we mans use echolocation, the production of sound (often tongue provide conclusive demonstration that echolocating bats use clicking) to generate echoes that provide information about the predictive strategies to track moving auditory objects, which environment (1, 2). Here, we report discoveries on auditory object can inform future comparative work on auditory motion tracking from studies of the echolocating bat, a powerful and processing. tractable model system for quantifying 3D object localization under conditions of uncertainty. The bat relies on active acoustic sensing Author contributions: A.S. and C.F.M. designed research; A.S. and C.A.D. performed re- to compute the 3D trajectory of insects flying in and around veg- search; A.S., C.A.D., and C.F.M. contributed new reagents/analytic tools; A.S. and C.A.D. etation from sequences of acoustic “snapshots,” which it uses to analyzed data; and A.S., C.A.D., and C.F.M. wrote the paper. guide interception. Notably, the bat’ssonartrackingbehaviors The authors declare no competing interest. provide a direct metric of its spatial gaze at discrete instants in time. This article is a PNAS Direct Submission. Echolocating bats employ an active audiomotor feedback system Published under the PNAS license. to localize and track targets in their environment. By emitting dis- 1To whom correspondence may be addressed. Email: [email protected]. crete ultrasonic signals, bats probe their surroundings and listen to 2A.S. and C.A.D. contributed equally to this work. the features of “acoustic snapshots” returning from objects (3–5). This article contains supporting information online at https://www.pnas.org/lookup/suppl/ Successful tracking requires the coordination of dynamic echolo- doi:10.1073/pnas.2011719117/-/DCSupplemental. cation behavior and head aim to control sonar beam direction and

www.pnas.org/cgi/doi/10.1073/pnas.2011719117 PNAS Latest Articles | 1of10 Downloaded by guest on September 27, 2021 models of insect trajectories. Indirect support for predictive strate- gies used by echolocating bats comes mainly from reconstructed prey-capture events in the laboratory (10, 11) and mathematical models based on biological data from foraging bats (12, 13). Yet, previous experimental tests of sonar target tracking behaviors reported that echolocating bats use a nonpredictive strategy, based on information processed from the last returning echo, and this finding has never been replicated (14, 15). Here, using an etholog- ically inspired behavioral paradigm, we directly test the hypothesis that the echolocating bat accumulates information about target position from acoustic snapshots over time to predict an auditory object’strajectory. By using internal models to extrapolate the future position of an auditory object, bats can compensate for the delays due to the travel time of sound in air, neural processing of echo features, and motor response times. We hypothesize that bats integrate acoustic information to build predictive models that allow for the anticipatory tracking of targets moving across their acoustic field. We propose that such internal models permit anticipatory head aim, even when the target trajectory is temporarily occluded. In this work, we settle a longstanding controversy over bat sonar tracking strategies with empirical data, demonstrating that bats acquire information over streams of discrete echoes to anticipate the future location of a moving target, even when the object is temporarily occluded. Additionally, we show that when changes in target velocity occur, bats update their internal models by sampling echo information about target position at an increased rate. Results Evidence for Predictive Tracking of Targets with Simple Motion Paths. Bats were trained to track a moving target traveling along a simple trajectory across their acoustic field. During testing, six different target motions (standard simple motion, slow simple motion, catch [no target motion], velocity change fast, velocity change slow, back-and-forth motion) and occlusion conditions were presented to the bat randomly (Materials and Methods). The randomization of trials during testing prevented the bats from learning the motion path of an object and instead, required that they actively track the target according to the specific con- ditions of each trial (SI Appendix, Fig. S1). Bats began each trial by aiming their head in the direction of the start of the track. The bats tracked the target as it moved from left to right, exhibiting Fig. 1. Tracking of simple target motion. Time 0 indicates time at which the both head aim and sonar call rate adaptations. The target target is crossing in front of the bat. (A) Head aim tracking of the target, crossed in front of the bat and moved away to the right side of shown as angle over time for the standard simple motion and catch trials. the track; soon after, bats stopped tracking and turned to receive Target angle relative to the bat’s position is shown on the black dashed line. a food reward. (B) Tracking angle, defined by the difference between the target angle and In standard simple motion trials, bats exhibited anticipatory the head angle for standard simple motion trials, shows that the bat’s head aim of the head relative to target as they tracked a target moving aim is always ahead of the target. (C) Sonar call rate for standard simple across the acoustic field. We quantified the bat’s tracking strat- motion and catch trials during the target tracking period of the trial. (D) Head angle offset of data, defined by the difference between the head egy in a 730-ms window that started 660 ms before the target angle and the modeled head angle for standard simple motion trials (non- crossed in front of the bat and ended 70 ms after the target predictive model and predictive model). moved in front of the bat. Bats accurately tracked the target with their head aim, showing anticipatory aim toward future target positions compared with the actual trajectory of the target the bats reliably tracked the target but showed no adjustment of [t(97) = 4.06, P < 0.001, r = 0.3811] (Fig. 1 A and B). The bat’s head aim in anticipation of target motion, suggesting that the anticipation of future target locations, indicated by its head aim bat’s tracking strategy and head aim compensation are adapted during target tracking, provided evidence of a predictive strategy. to fast-moving targets (SI Appendix, Fig. S2 A–C). Sonar call rate increased as the target approached the bat, sta- bilized from ∼−400 to ∼−200 ms, and then started to decrease Models for Acoustic Tracking of Fast-Moving Targets. We developed (Fig. 1C), which reveals active sonar target tracking. mathematical models based on four separate hypotheses and Catch trials, where the motor moved the pulley without the evaluated their goodness of fit with our behavioral data. First, we target, showed no evidence of tracking behavior by the bat, con- incorporated the assumptions delineated in Masters et al. (15) firming that the motor sound did not elicit learned left-to-right (i.e., the bats would use the last returning echo to estimate the head motion. In catch trials, the bat aimed its head forward and position of the target) to develop a nonpredictive model. In this produced a low and steady sonar call rate. model, the bat’s head lags behind the target, as it uses the last Additionally, two bats were tested with the target moving at returning echo to determine the position of the target and guide half the velocity of the standard simple motion trials. In this case, its head aim. The nonpredictive model showed a poor fit to the

2of10 | www.pnas.org/cgi/doi/10.1073/pnas.2011719117 Salles et al. Downloaded by guest on September 27, 2021 behavioral data (residual sum of squares [RSS] = 3.1) (Figs. 1D adjusts the head aim to anticipate target motion. We tested the and 2A). A second hypothesis asserts that the bats show antici- model’s performance using between 2 and 10 echo returns from patory head motion simply by adjusting their head aim by a fixed the target at different positions to estimate target velocity and angle ahead of target position estimates computed from the last concurrently, explored a range of fixed angle head adjustments returning echo. This hypothesis was tested with a fixed head (between 0° and 20°) to determine which angle produced the best angle model. We sampled a range of fixed angles (between fit to the behavioral data (Fig. 2D). Our results show that the 0° and 20°) and found that for the fixed head angle model, a 10.2° predictive model using the information from the preceding five angle produced the best fit to our behavioral data (RSS = 0.98) echoes to estimate velocity and a fixed angle head adjustment of (Fig. 2 A and B). We considered a third hypothesis that the bat 6.24° generates the best fit to our behavioral data among all of uses the information from a sequence of returning echoes to the evaluated models (RSS = 0.146) (Figs. 1D and 2A). It is estimate target velocity and tested this hypothesis with a velocity noteworthy that increasing the number of echoes used to esti- estimation model. We investigated the performance of this mate target velocity from two to five did not add considerably to model using between 2 and 10 echo returns from the target at the goodness of fit of this model (using two echoes, RSS = 0.148) different positions to estimate its velocity and to determine the (SI Appendix, Table S1). In other words, it seems that the parameters that generated the best fit to the behavioral data number of echoes used for the velocity estimation does not affect (Fig. 2C). The velocity estimation model, tested using between 2 the performance of the model. These modeling results support and 10 echoes, fits the behavioral data only slightly better (RSS = the hypothesis that the bat develops internal predictive models, 0.886) (Fig. 2A) than the fixed head angle model, so we explored such as the one proposed here, which integrate the information a fourth model that is based on the hypothesis that the bat of returning echoes to estimate target velocity and invoke a head combines strategies to maximize head aim target tracking accu- adjustment to successfully track fast-moving prey. We fixed the racy. To test this hypothesis, we built a predictive model in which parameters of the predictive model using five echoes to estimate the bat estimates velocity from a sequence of echo returns and target velocity and a head angle shift of 6.24° to compare its PSYCHOLOGICAL AND COGNITIVE SCIENCES

Fig. 2. Models for target tracking. (A) Head angle offset of data as defined by the difference between the head angle for standard simple motion trials and the modeled head angle for the nonpredictive model, fixed head angle model, velocity estimation model, and predictive model; these models integrate sonar call data to estimate head aim. (B) Goodness of fit for the fixed head angle model for different angular shifts; red dot indicates best fit. (C) Goodness of fit for the velocity estimation model for different numbers of echoes for the velocity estimation; red dot indicates best fit. (D) Goodness of fit for the predictive model for different numbers of echoes for the velocity estimation and different angle shifts; the area outlined with the red dashed line is shown in E.(E) Goodness of fit for the predictive model; red dashed line indicates best fit.

Salles et al. PNAS Latest Articles | 3of10 Downloaded by guest on September 27, 2021 performance with the nonpredictive model, which has no free velocity change fast trials the predictive model fit the data better parameters, across different experimental conditions. for the unoccluded trials (velocity change fast unoccluded after head aim tracking adjustment: predictive model RSS = 1.03, Internal Models Enable Tracking When the Target Trajectory Is nonpredictive model RSS = 1.28) (Fig. 4B and SI Appendix, Temporarily Occluded. In the wild, bats may encounter prey that Table S2) but not for the occluded trials (velocity change fast move in and out of acoustic clutter. We mimicked this scenario occluded after head aim tracking adjustment: predictive model by testing the bat’s ability to track moving objects that are tem- RSS = 3.38, nonpredictive model RSS = 0.05) (Fig. 4C and SI porarily occluded, akin to an insect darting behind a tree branch. Appendix, Table S2). This finding suggests that the bats may need We investigated whether bats rely on predictive strategies to more time to update their internal tracking and catch up with the anticipate the future location of a target that travels behind an moving target as it emerges from the occluder than what the occluder during a portion of its trajectory. We established that length of the trial allows. the occluder blocked echo returns from the moving target and For the velocity change slow trials, both occluded and unoc- prevented the bat from using sonar to track the target when it cluded trials, the predictive model fit the data better than the was behind the occluder. nonpredictive model before the head adjustment to the change As the bats tracked the target in the occlusion conditions, we in velocity (velocity change slow unoccluded before head aim found that they continued to aim their heads in the anticipated tracking adjustment: predictive model RSS = 5.93, nonpredictive position of the target as it moved behind the occluder, showing model RSS = 12.52 [Fig. 4F]; velocity change slow occluded no significant difference compared with standard simple motion before head aim tracking adjustment: predictive model RSS = trials [unoccluded trials; t(159) = 0.0991, P = 0.9212, r = 0.0079] 8.53, nonpredictive model RSS = 12.56 [Fig. 4G and SI Appen- (Fig. 3A). Furthermore, our predictive model shows a better fit dix, Table S2]). The predictive model also fit the data best after to the head aim data for the occluded trials than the non- the bat makes a head aim tracking adjustment in the unoccluded predictive model (occluded trials: predictive model RSS = 0.41, trials (velocity change slow unoccluded after velocity change: nonpredictive model RSS = 4.42) (Fig. 3B and SI Appendix, predictive model RSS = 0.75, nonpredictive model RSS = 4.71) Table S2). The difference between the goodness of fit of the (Fig. 4F and SI Appendix, Table S2) but not for the occluded predictive model for the occluded trials and the goodness of fit of trials (velocity change slow occluded after head aim tracking the predictive model for the unoccluded standard simple motion adjustment: predictive model RSS = 0.52, nonpredictive model trials is not statistically significant (Mann–Whitney U test, U = RSS = 0.29) (Fig. 4G and SI Appendix, Table S2). Similar to the 3006, P = 0.9982, r = 0.0002). Interestingly, the interruption of velocity change fast occluded trials, it appears that when the bat target echo information in the occluded trial segment was also has no access to acoustic information during the occlusion, it may evidenced by the bat’s reduced echolocation call rate, compared take longer to adjust its tracking strategy and anticipate target with unoccluded trials. Specifically, the bat’s sonar call rate in- motion. crement started to drop at the time the target moved behind the There was an increase in the echolocation call rate soon after occluder and remained significantly below baseline until after the the change in target velocity occurred for unoccluded trials (70 ± target reappeared from behind the occluder [t(62) = 5.7292, P < 93 ms for velocity change fast [Fig. 4D, green line] and 190 ± 0.001, r = 0.6012] (Fig. 3C). Our data indicate that bats can use 5 ms for velocity change slow [Fig. 4H, green line]). The delay trajectory information at the beginning of the trial to anticipate between the start of the increase in sonar call rate and the delay the future position of the target during the segment of the tra- of the update in head angle tracking is 290 ± 110 ms for velocity jectory when it moved behind the occluder. change fast and 270 ± 17 ms for velocity change slow. This suggests that the bat’s prediction of target motion is challenged Active Sensing by the Bat Is Used to Update Tracking of Erratically by a change in target speed, and it needs to gather more infor- Moving Targets. In the evolutionary arms race between bats and mation about the location of the target by increasing its sonar their prey, some insects have developed evasive flight strategies production rate to obtain more detailed position information to that involve sudden changes in direction and velocity (16). To update its tracking. explore the extent to which bats rely on predictive strategies to In the occluded trials, the bats did not have access to the target track erratic prey, we introduced a change in the velocity of the location when the velocity change occurred, so they continued to target motion. After the target had covered 50 cm of its trajec- move their heads appropriate for the preoccluded target motion tory, the velocity was either increased (condition: velocity change segment. We found that the bats started increasing their sonar fast) or reduced (condition: velocity change slow). We discov- call rate only when the target reappeared from behind the occluder ered that when there was a change in target velocity, there was a (Fig. 4 D and H, yellow lines). The increase in sonar sample rate in lag in the head angle adjustment to track the target: 360 ± 17 ms the occluded trials thus occurred at 160 ± 10 ms after the change in for velocity change fast (Fig. 4A, green line) and 460 ± 12 ms for velocity of the target for velocity change fast and at 280 ± 16 ms for velocity change slow (Fig. 4E, green line). To further investigate velocity change slow. The delay between the increase in sonar call the effects of a violation in the target trajectory prediction, we rate and the head aim tracking adaptation was 370 ± 45 ms for evaluated the bat’s tracking when the target velocity change velocity change fast and 280 ± 39 ms for velocity change slow. occurred behind the occluder. In this case, we found that there Thus, it appears that bats required additional echo information to was even a longer lag in the update of the bat’s head aim tracking update their internal tracking when the target’s velocity changed, with respect to target motion: 530 ± 35 ms for the velocity supporting the previous finding that after the change in velocity, change fast occluded trials and 560 ± 23 ms for the velocity the nonpredictive model presents a better fit for the occluded change slow occluded trials (Fig. 4 A and E, yellow lines). trials, both in velocity change fast and velocity change slow We found that for the velocity change fast trials (both oc- conditions. cluded and unoccluded), before the bat’s head aim adjusted to Additionally, two bats were tested on a target moving for- the change in velocity, the predictive model fit the data better ward, backward, and then forward again to simulate a prey than the nonpredictive model (velocity change fast unoccluded target changing directions in evasive flight. In this condition, before head aim tracking adjustment: predictive model RSS = referred to as back-and-forth motion, bats showed anticipa- 5.45, nonpredictive model RSS = 8.99 [Fig. 4B]; velocity change tory head aim tracking of the target, but the head aim track- fast occluded before head aim tracking adjustment: predictive ing error and goodness of fit of the models were worse than for model RSS = 6.87, nonpredictive model RSS = 11.77 [Fig. 4C the other target trajectories across the entire tracking window and SI Appendix, Table S2]). After the change in velocity, for the (predictive model RSS = 27.46, nonpredictive model RSS = 59.1)

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Fig. 3. Internal model of target trajectory enables head aim tracking during occlusion. Time 0 indicates time at which the target is crossing in front of the bat. (A) Tracking angles for standard simple motion in unoccluded trials (green line) and for occluded trials (yellow line) show that the head aim is always ahead of target (dashed line). Time at which target trajectory is behind occluder in occluded trials is marked as a gray box. (B) Head angle offset of data from occluded trials to nonpredictive model and to predictive model. (C) Sonar call rate for standard simple motion for unoccluded trials (green line) and for occluded trials (yellow line) during the target tracking period of the trial. Time at which target trajectory is behind occluder in occluded trials is marked as a gray box. Solid blue line denotes the window where significance between occluded and unoccluded trials was tested.

(SI Appendix, Fig. S2 D–F and Table S2). When evaluating the target (20). In the same way, when baseball players catch balls, goodness of fit in separate segments of the trajectory (segment 1: they use linear optical trajectories to adjust their running path to forward 50 cm and backward 50 cm; segment 2: forward until the ball’s position and ensure a successful catch (21). It is note- end), the RSS values for both segments still show a better fit for worthy that all these examples rely on the constant stream of visual the predictive model (segment 1: predictive model RSS = 19.1, information to adjust tracking. This raises the question: how do nonpredictive model RSS = 40.62; segment 2: predictive model humans and other animals track auditory targets using interrupted RSS = 17.58, nonpredictive model RSS = 34.96) (SI Appendix, snapshots of acoustic information? Table S2). This result supports the notion that erratically moving Echolocating bats present a distinct opportunity to investigate prey may increase survival success by challenging the bat’s internal target tracking strategies in animals that use discrete acoustic models of target trajectory. snapshots of moving objects. Bats are auditory specialists that are engaged in an evolutionary arms race with their prey: many in- Discussion sect species have evolved ultrasound hearing to detect foraging Humans and other animals track targets in a dynamically changing bats and employ evasive maneuvers when they hear the echolo- world, yet the strategies employed across stimulus modalities and cation calls of their predators. Bats must not only adopt strategies taxa are diverse. It is still unknown what strategies may be shared to outsmart evasive prey but also contend with the time it takes for across modalities and taxa and which might be distinct. Predators each emitted call to travel to a target and for the returning echo to like dragonflies and fish use visual information to update internal reach its ears, along with the time it takes for the brain to process models and predict the future location of a target (17–19). Dogs, the target location and to generate an appropriate motor re- also using vision, employ strategies to catch Frisbees by adjusting sponse. All of these factors have placed evolutionary pressure on their own speed and thus, maintain horizontal alignment with a bats to develop predictive tracking strategies for successful prey

Salles et al. PNAS Latest Articles | 5of10 Downloaded by guest on September 27, 2021 Fig. 4. Target tracking in velocity changes and occlusion conditions. Time 0 indicates time at which the target is crossing directly in front of the bat. Red arrows indicate change in target velocity, which separates the two evaluation windows (before and after change in velocity). Time at which target trajectory is behind occluder in occluded trials is marked as gray boxes. (A) Tracking angle in velocity change fast motion for unoccluded trials (unoccluded; green line) and for occluded trials (occluded; yellow line). (B) Head angle offset of data from velocity change fast unoccluded trials to nonpredictive model and to “predictive model.” (C) Head angle offset of data from velocity change fast occluded trials to nonpredictive model and to predictive model. (D) Sonar call rate for velocity change fast for unoccluded trials (green line) and for occluded trials (yellow line) during the target tracking period of the trial for velocity change fast trials. (E) Tracking angle in velocity change slow motion for unoccluded trials (unoccluded; green line) and for occluded trials (occluded; yellow line). (F) Head angle offset of data from velocity change slow unoccluded trials to “nonpredictive model” and to predictive model. (G) Head angle offset of data from velocity change slow occluded trials to nonpredictive model and to predictive model. (H) Sonar call rate for velocity change slow for unoccluded trials (green line) and for occluded trials (yellow line) during the target tracking period of the trial.

capture. Yet, there has been some controversy as to whether bats We developed four computational models that use sonar in- use predictive tracking strategies. Masters and collaborators formation to quantitatively assess the bat’s target tracking be- reported that E. fuscus used the last returning echo to orient head haviors across a variety of conditions, including targets moving at aim when tracking a moving target in a simple trajectory, indi- different speeds, with trajectory segments under occlusion, and cating a nonpredictive model of target motion where the bat’s exhibiting abrupt changes in velocity and direction. We built a head lags behind the target, but this result may be attributed to the nonpredictive model, motivated by the report by Masters et al. head-tracking gear bats wore in this study (15). While some studies (15), asserting that bats use the last returning echo to estimate in bats have offered indirect evidence that bats anticipate target the target position. This model failed to match the bat’s head aim motion (10, 11, 13), our results provide direct empirical evidence tracking performance in our experiments. We also generated a that E. fuscus can predict the spatial location of a moving auditory fixed head angle model that was based on the nonpredictive target and reliably aim their heads ahead of a fast-moving target. model but included a fixed head aim offset. The performance of

6of10 | www.pnas.org/cgi/doi/10.1073/pnas.2011719117 Salles et al. Downloaded by guest on September 27, 2021 this model showed only minor improvement over the non- when the occluder was present, the bat’s head aim was consistent predictive model. We also developed a velocity estimation model, with the velocity of the target prior to moving behind the occluder which uses a sequence of target echoes to estimate target velocity. and showed a lag in the head aim when the target emerged from This model fit the behavioral data only slightly better than the the occluder, which was quickly corrected after the bat obtained fixed head angle model, so we combined these two parameters to new target information. Echolocation call rate significantly in- develop a predictive model, which combines an estimation of creased in both velocity change conditions as soon as the target target velocity from a sequence of target echoes and a fixed head emerged from behind the occluder, consistent with the view that angle offset. The performance of this model was assessed using a the bat must update its representation of the target’smotionby variable set of echoes and a range of head angle offsets. A pre- probing the environment with sonar information sampled at a dictive model that estimated target velocity from five echoes at higher rate. Our results suggest that when there is a change in the changing locations and an angular head offset of 6.24° best fit the velocity of a target, the bat increases the rate of the sonar calls to behavioral data taken from bats tracking a fast-moving target in obtain higher resolution acoustic information about the position this study. We hypothesize that this fixed angle correction could of the target to update its internal tracking model. When the ensure that the target always remains within the sonar beam of the change in velocity occurs behind an occluder and the bat does not bat, sending signals slightly ahead so that they reach the target just have access to the information about target position, the bat in time and improve returning echo reception. When target speed continues to move its head, guided by its predictive internal model was slow, no anticipatory head aim was exhibited by the bat. of target trajectory, until the target emerges from behind the Bats increase their sonar call rate as they track and approach a occluder, allowing the bat to update information about target target (7). The increase in sonar call rate is also evidenced when location. a target approaches a bat perched on a platform (22). Our data In flying big brown bats, quantitative analyses of sonar target show an increase in call rate as the target approaches the bat, tracking suggest the animal’s use of a time-optimal strategy for signifying that the bat is actively tracking the target. This increase intercepting a target that moves erratically, but not when fol- in call rate demonstrates that the bat continuously probes the lowing conspecifics or tracking a target moving in a relatively environment and uses echoes to update its computed trajectory straight trajectory (10, 29). In the Japanese house bat, of the target, rather than moving its head in a stereotyped abramus, it has been shown that bats start to orient their flight learned behavior to receive a food reward. Furthermore, a va- behavior to a second prey item, even before intercepting the first, riety of stimulus conditions (e.g., changing velocity and occlu- suggesting that foraging bats plan ahead to capture prey (13). sion) was randomly interleaved during testing to prevent the bat This finding has implications for decision making and provides

from learning an open loop motor behavior and to ensure active indirect evidence for path planning (30), but it does not explicitly PSYCHOLOGICAL AND COGNITIVE SCIENCES sonar tracking in these experiments. demonstrate that bats predict the future position of a moving With compelling evidence that bats anticipate target motion target. Here, we present direct empirical evidence of predictive by accumulating information about its trajectory from a sequence auditory object tracking in echolocating bats by analyzing head of echo snapshots, we then examined how bats contend with un- aim and timing of echolocation calls. Future experiments in free- certainty in target motion. The first way we tested this was to flying bats may reveal the contribution of other sonar features, temporarily occlude the target from the bat’s sonar view. Our such as bandwidth, intensity, and duration, to the generation of results show no change in head aim behavior in conditions with internal models. occlusion compared with nonoccluded conditions, indicating the Diverse groups of animals show predictive tracking strategies bat maintains a representation of the spatial location of its target (17–20). In this context, most studies of target tracking have even during the ∼200 ms when target echoes are blocked by the considered visually dominant species that use a continuous stream occluder (Fig. 3). Interestingly, call production rate increases of optical information to generate and update internal models of normally before the target passes behind the occluder, but while stimulus motion. Echolocating bats, however, have only access to the target is behind the occluder, the bat shows no increase in call discontinuous acoustic snapshots to build a scene of their sur- rate, even while its head aim remains consistently ahead of where roundings in both open and cluttered environments. Yet, bat the target would be in space. The bat’s reduction in call rate may predators are extremely successful, even when pursuing evasive be in response to static echo returns from the occluder, which do insect prey on the wing. Here, we demonstrate that bats generate not provide the bat with relevant information about the target’s internal models of target trajectories to intercept moving prey and motion path. It is noteworthy that aerial hawking insectivorous modify these models in response to dynamic sensory information. bats sometimes hunt insect prey in forests with densely packed Our findings show conclusively that bats integrate acoustic snap- trees. Our results suggest that bats are able to track targets and shots over time to build internal models that allow them to track maintain a representation of the spatial location of a target even moving auditory targets. These findings are ethologically relevant, when it is temporarily occluded. and they serve to inform future research on auditory integration The second approach we took to probe the bat’s use of pre- and stimulus tracking, further knowledge of evolutionary adapta- dictive tracking strategies was to introduce an abrupt change in tions driving predator/prey dynamics, and inspire algorithms for target velocity, with and without the occluder. These changes in sonar–radar guidance in robotic design. velocity mirror evasive and erratic flight behaviors that insect prey employ to reduce capture success. Many species of insects Materials and Methods have developed hearing structures that are sensitive to fre- Animals. Four big brown bats (Eptesicus fuscus, one male and three females) quencies in the ultrasonic range and exhibit specialized flight recovered from exclusion sites served as subjects in this study. The bats were maneuvers to avoid predation by bats (23–25). For example, collected under the permit issued by the Maryland Department of Natural tend to show stereotyped evasive flight behaviors with Resources (no. 55440). All procedures were approved by the Institutional low variability, where they move steadily away from a predatory Animal Care and Use Committees at Johns Hopkins University (protocol no. bat (24, 26, 27), and show evasive flight behaviors that BA17A107), where this research was conducted. become increasingly erratic as a bat gets closer to the attack phase Experimental Setup and Training. Bats were trained to perch on an elevated of a prey-capture sequence (25, 28). In the two velocity change platform and track a moving target (mealworms; Tenebrio molitor larvae), conditions without the occluder, the bat increased sonar call rate which served as a food reward administered by the investigator with forceps within 130 ms of target velocity change, demonstrating an ability upon successful tracking of the moving prey item, as defined as adaptations to quickly update internal models of target motion in response to in sonar call rate and head aim. This paradigm was adapted from a previ- changes in prey behavior (Fig. 4). In velocity change conditions, ously established method (22, 31), where the bat rests on a platform and

Salles et al. PNAS Latest Articles | 7of10 Downloaded by guest on September 27, 2021 tracks a target that approaches it head on. All experiments were conducted (Table 1). Each target motion was presented either with or without a portion under infrared illumination to preclude the animals’ use of vision (32). In of the trajectory being occluded. In a second experiment, two bats were preliminary training, bats learned to associate the food reward with a tested on catch, standard simple motion, back-and-forth, and slow simple sound stimulus (click). After this association was established, the distance motion interleaved conditions (Table 1). of the target reward was manipulated using a rotary servo motor (Aerotech Catch trials were included as controls. Catch trials decoupled the sound of Ensemble MP10 motor controller connected to a BMS60 servo motor) the motor and the motion of the target by disconnecting the tether from the with monofilament line suspended to a pulley. The velocity, accelera- motor and then running the motor. The catch trials ensured that the bat’s tion, and direction of target movement could be controlled through behavior was not cued by the sound of the motor itself but instead, by the custom software written in MATLAB 2016b, as described in ref. 22, and movement of the target. The target trajectory conditions with and without modified by the authors to generate the target trajectories presented in the occluder were randomized for each bat with each testing day. Bats these experiments. This paradigm employed sonar behaviors that par- performed 15 to 20 trials per day over the course of ∼2 wk until each animal alleled tracking behaviors in free-flying bats. The accuracy of target completed a minimum of 20 trials of each condition. position was corroborated and corrected by using video data and man- ually annotating target position for all conditions to confirm velocity at Data Analysis. The timing of the bat’s echolocation calls for each trial was each point. marked manually using a MatLab toolbox described in Wohlgemuth and Initially, each bat was trained to track a target that approached head on, as Moss (31); sonar call rate was calculated in 1-ms bins and plotted in the same described previously (22, 31). The maximum velocity of the target was time reference frame as the target motion. Head movement was tracked 0.75 m/s, and it traveled a total distance of 150 cm. The bat was offered a using the Hedrick MatLab toolbox (33). Three white markers were placed on food reward when it successfully tracked the target with both head aim the bat, one on each ear and one on the center of the head. This yielded 3D and sonar call adjustments, which were closely monitored by the experi- coordinates of head position and orientation. Then, the x and y coordinates menter on each trial. To make real-time judgements in the course of each of the ear position were converted into an angle relative to the reference trial as to whether the bat was tracking the target, experimenters evalu- frame of the pulley track. Additionally, the head angle of the bat and the ated bat head turning and echolocation call rate through a bat detector target position were calculated from the target controller programs de- (Pettersson D100), which shifts ultrasonic signals into the human audible scribed above (modified from ref. 25). The window in which the target was range. These observations provided the basis for experienced experi- occluded was calculated based on the occluder size and distance from the menters to make the qualitative judgement whether to reward the bat bat. An absolute head angle of 0° means that the nose of the bat (as at the end of a trial. After the bat reliably tracked the approaching target, established by markers) was pointing directly toward the front and the the platform was gradually rotated in subsequent training sessions until axis of the head would be perpendicular to the pulley track. Tracking the bat was perpendicular to the pulley line. At this point, the platform angle is defined by the difference between the target angle and the head was at a distance of 30 cm from the pulley track. The bats tracked the angle. mealworm moving from left to right. Data collection began after the bat Mathematical models were developed to test different hypotheses of consistently tracked the laterally moving target, as indexed by changes in tracking strategy. To measure goodness of fit of the data to the models, the head aim and sonar call rate. RSS was calculated, and the lower numbers correspond to better fittings to To temporarily occlude the moving target from the bat’s view, a 25 × the data. All models were developed here using the same function based on 25-cm sheet of felt, reinforced with plastic mesh, was supported by a metal the sonar calls emitted by the bat. This function computed the arrival time of stand and placed occluding the view of the target from the bat’s perch for a the echo returning from each position of the mealworm. All models use the portion of the trajectory in a subset of trials (descriptions are given below) call times Ci(j), where i is the index for the experiment and j is the index of (Fig. 5). the jth call during the ith experiment. Ci(j) already has an embedded cor- rection for the time delay D introduced in the recording by the distance to Testing. To quantify each bat’s tracking behaviors, we combined high-speed the microphone: D = d = , where d is the distance from the bat to mic v mic infrared video capture (300 frames per second) with two calibrated video sound the microphone and v is the speed of sound. Then, the function that cameras (Phantom Miro) and audio recordings with a custom-made elec- sound describes the index of the last call that arrived at the target before the time trostatic microphone connected to a National instruments board and digi- point T is defined by J(T, i)=max({j ∈ 1..|C | :C(j) < T − y(T)}). This function is tized at a 250-kHz sample rate. All recording systems were triggered in i i corrected by the travel time of the echolocation call to the target and is synchrony at the beginning of the trial by a transistor–transistor logic pulse √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ s(T)2 +d2 track generated with the start of the movement of the motor. calculated as:y(T)= , where dtrack is the distance from the bat to vsound In the first experiment, four different target motion conditions were the track and s(T) describes the position of the target at time T. randomly presented to each of the four subjects on each test day: standard For the nonpredictive model, we followed the assumptions of Masters simple motion, velocity change fast, velocity change slow, and catch trials et al. (15), which describe target position estimates based on the last returning echo from the target. In this way, the model (m) over time (t)is N ∑ s(C (J(t−y(t))+y(t))=N calculated using the following equation: m(t)=atan( i=1 i ), dtrack where N is the number of total trials. This model is deduced considering the index of the last call emitted, the time it takes that call to arrive at the target, the position of the target at the point the call reaches it, and the time it takes the echo to return to the bat. In this way, the model is based on the last position of the target when it returned an echo. For the fixed head angle model, we added a shift of 10.2° to the nonpredictive model. This shift was found to be the one to produce the best performance of this model as measured by the RSS (Fig. 2 A and B). For both the velocity estimation model and the predictive model, we assume that the bat estimates target velocity based on the time difference between previous sampled locations. These models fol- N (∑ s(C (J(t−y(t))+y(t))+s (t ))=N low the equation m(t)=atan( i=1 i pred i ) + Best fit Angle°. dtrack

spred is the position compensation based on velocity estimation and fol- low the equation s (t, i)=v (t, i) · (t − C (J(t − y(t))) − y(t)),wherev Fig. 5. Experimental setup. The bat sits on a platform and tracks a target pred pred i pred is the velocity estimation calculated based on the change in posi- (mealworm) that is moved on a tether by a motor from left to right. A mi- tion between the last two calls following the equation v (t, i)= crophone was placed opposite to the bat to record the bat’s sonar calls. In pred ( ( ( − ( )))+ ( ))− ( ( ( − ( ))− )+ ( ( ( − ( ))− ))) “ ” s Ci J t y t y t s Ci J t y t 1 y Ci J t y t 1 . We empirically tested the number some trials, the target trajectory is partially occluded by the occluder. Ci (J(t−y(t)))−Ci (J(t−y(t))−1) Tracking angle is defined by the difference between the target angle (or- of echoes for the velocity estimation that would produce the best perfor- ange) and the head angle (red). Head angle offset to the predictive and mance of the velocity estimation model and found that two echoes pro- nonpredictive models is defined by the difference between the head angle duced the lowest RSS (Fig. 2C). As we found that this model only preformed and the modeled head angle. slightly better than the fixed head angle model, we decided to combine

8of10 | www.pnas.org/cgi/doi/10.1073/pnas.2011719117 Salles et al. Downloaded by guest on September 27, 2021 Table 1. Motion parameters for trial conditions Type of motion Movement parameters

Standard simple motion Average velocity of 0.75 m/s (maximum 0.9 m/s, 250 ms to maximum) Velocity change fast Average velocity of 0.75 m/s for 0.5 m; 200-ms pause; 3.6 m/s after 0.5 m Velocity change slow Average velocity of 0.75 m/s for 0.5 m; 200-ms pause; 0.45 m/s after 0.5 m Catch Average velocity of 0.75 m/s for 1 m; no tether insect Slow simple motion Average velocity of 0.375 m/s (maximum 0.4 m/s, 250 ms to maximum) Back-and-forth 50 cm forward, backward 50 cm, forward 1.5 m; average velocity 0.75 m/s

Standard simple motion, velocity change fast, velocity change slow, and catch trials in both occluded and unoccluded conditions were presented to all bats randomly on test days. Additionally, in a second experiment, two bats were presented with slow simple motion, catch, slow, and back-and-forth trials randomly interleafed in the unoccluded condition.

both parameters and tested the number of echoes and angle shift that model, different experimental conditions) and the Wilcoxon signed-rank produced the best fit (Fig. 2D and SI Appendix, Table S1). This test produced test when there was pairing (different model, same experimental condi- an angle shift of 6.24° and five echoes to be the best fit to the standard tion) (SI Appendix, Table S2). Effect size r was calculated as the rank simple motion trials. These parameters were then fixed when testing the biserial correlation. other conditions to be able to fairly compare the model with the non- To determine when the head aim of the bat departed from baseline predictive model, which has no free parameters. behavior in the velocity change conditions (baseline determined by the Head angle offset to the models is defined by the difference between the head aim before the change in tracking behavior), we analyzed the av- head angle and the modeled head angle. For all models, when evaluating erage head angle tracking data (across trials of the same experimental occluded trials, the sonar calls during the occluded portion of the trajectory condition) in the region starting at the velocity change point and were not included in the model to emulate the lack of acoustic information extending over the subsequent 250 ms. We then computed a linear fit of during this section of the trial. For all models, goodness of fit was tested in the that region of data (least square approximation) and compared this linear

“tracking window” defined from 47 to 106 cm of the track as it was the fit with the measured head angle data (separated by bats and trials). At PSYCHOLOGICAL AND COGNITIVE SCIENCES spatial window where the bats were engaged with the tracking task as each time point, P values were calculated to test the null hypothesis that defined in the first section of the results. head angle data and linear fit are the same (Mann–Whitney U test). The inflection point was determined as the first time point where this dif- Statistical Analyses. In all experiments, data were pooled across trials for each ference was statistically significant. To determine the error of this mea- experimental condition for all bats (n = 4). SI Appendix, Table S3 shows the surement, a random sampling with replacement bootstrapping was numbers of trials analyzed for each bat for each condition. Trials were employed, and the SEM was reported. Effect size r wascalculatedasthe aligned at time 0, when the target was directly in front of the bat. Mean rank biserial correlation. head angle and call rate were averaged across corresponding time points For the call rate plots, the data were smoothed on a per-trial basis with a over all trials for a given condition. The SE interval around the mean was 25-ms sliding averaging window. To determine whether the call rate was also calculated for each time point, and this method was also applied to all significantly reduced during the time the target was behind the occluder in models, which were based on call rate data using the method described the simple motion trials, the calls were analyzed within a window above. starting at the center point of the occluder and extending 250 ms after To determine if bats showed significant anticipatory head aim tracking in the point of reappearance (350-ms total length). Within this window, the simple motion unoccluded trials, the measured head angle for each trial 10-ms bins were created, and for each trial and each condition, the was subtracted from the head angle that would correspond to direct, on- number of calls in that bin was counted. Using the Anderson–Darling test target head aim at each time point (n = 73, width 10 ms). The population of with a 95% CI, it was determined that these data were normally dis- trials was assessed for normality (n = 97, Anderson–Darling test, 95% CI), tributed. Therefore, these data were analyzed using a parametric test. and a Student’s two-tailed t test was performed on the population at each For each 10-ms bin, the number of calls (in the population over trials) was time point (using Bonferroni correction for the 73 tests conducted at the compared between conditions with the Welch two-tailed t test, and a same time). The significance values for all time points were collected, and Bonferroni correction was applied (35 comparisons, each corresponding the most conservative value was reported (all other time points showed to each 10-ms bin, were tested simultaneously). The most conservative P significance). Effect size “r” was calculated as the point biserial correla- value across these test results was reported (all other values showed tion. The reported significance value was consistent with the equivalent significance within the 350-ms tested window). Effect size r was calcu- Mann–Whitney U test. lated as the point biserial correlation. We also confirmed that the To determine whether there was a significant difference in the bats’ head reported significance level did not exceed that of an equivalent Mann– angle tracking between unoccluded and occluded conditions, the head Whitney U test analysis. position for each trial was subtracted from the head position that would To determine when the call rate of the bat increased after a change in produce on-target head aim at that time point, and the RSS (summing over target velocity, the call rates from the individual trials were averaged and all time points) was calculated for each trial. On this set of trials (unoc- smoothed on a per-trial basis with a 25-ms sliding window and randomly cluded and occluded; n = 97 and n = 62, respectively), an Anderson– divided into 10 groups of equal size. For each time point (1-ms width), the Darling test was performed to check for normal distribution. The two sets mean call rate was calculated within each group (four conditions × 10 of trials (unoccluded and occluded) were then compared with a Welch groups), and then, the time point T at which the call rate increased (local two-tailed t test (because of unequal population sizes of trials) (SI Ap- minimum in the window starting at the time of velocity change and pendix,TableS3). Effect size r was calculated as the point biserial extending 350 ms) was determined. The mean and SEM of T over the 10 correlation. groups were reported as the final result for each condition. To compare the performance of the various model fits (comparing both the same model for different conditions and different conditions for the same Data Availability. MATLAB files have been deposited in GitHub at https:// model), we calculated the RSS for each trial (by calculating the difference github.com/angiesalles/batTargetPrediction. between the model predicted head angle and the measured head angle for each sampled time point) and analyzed the population of these RSS values ACKNOWLEDGMENTS. This work was funded by Human Frontiers Science separately for each condition. Not all of these data were normally distrib- Program Fellowship LT000220/2018 (to A.S.) and NSF Fellowship GRFP uted according to the Anderson–Darling test, and therefore, we used the 2018261398 (to C.A.D.). NSF Brain Initiative Grant NCS-FO 1734744 (2017- nonparametric Mann–Whitney U test for unpaired comparisons (same 2021), Air Force Office for Scientific Research Grant FA9550-14-1-0398NIFTI,

Salles et al. PNAS Latest Articles | 9of10 Downloaded by guest on September 27, 2021 and Office of Naval Research Grant N00014-17-1-2736 to C.F.M. also supported Dean Sheehan, Kevin Duffy, and Ami Asokumar provided valuable assistance the project. We thank the anonymous reviewers for constructive comments on with data preprocessing and bat care. We thank Dr. Kathryne Allen, Kevin the manuscript. Ike Enenemoh, Cameron Chenault, Alexa Earls, Bruce Nguyen, Himberger, and Te Jones for comments on an early version of the manuscript.

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