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First published online March 12, 2009
Journal of Experimental Biology 212, 1011-1020 (2009)
Published by The Company of Biologists 2009
doi: 10.1242/jeb.024620
Acoustic scanning of natural scenes by echolocation in the big brown bat, Eptesicus fuscus
1 Institute of Biology, University of Southern Denmark, Odense, DK-5230,
Denmark
2 Department of Psychology, Institute for Systems Research, University of
Maryland, College Park, MD 20742, USA
Author for correspondence (e-mail: cmoss{at}psyc.umd.edu)
Accepted 4 December 2008
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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5 deg. The bats also shifted the duration of
their sonar calls, revealing sequential sampling along the range axis. Changes
in duration and directional aim were correlated, showing that the bat first
inspected the hole, and then shifted its gaze to the more distant insect,
before flying through the net opening. Contrary to expectation based on the
sonar beam width, big brown bats encountering a complex environment accurately
pointed and shifted their sonar gaze to sequentially inspect closely spaced
objects in a manner similar to visual animals using saccades and fixations to
scan a scene. The findings presented here from a specialized orientation
system, echolocation, offer insights into general principles of active sensing
across sensory modalities for the perception of natural scenes.
Key words: echolocation, bat, active sensing, directionality, visual scanning, scene analysis
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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) or
by bottom up and top down processes in the central nervous system
(Bartels and Zeki, 1998;
Mitchinson et al., 2007).
Visually dominant animals tend to sequentially sample information by moving
their eyes or head to selectively fixate stimuli at the fovea or visual streak
(Liversedge and Findlay, 2000;
Eckmeier et al., 2008).
The echolocating bat presents an excellent animal model for exploring
whether sequential stimulus sampling extends to modalities beyond vision. Bats
produce ultrasonic vocalizations, and use information carried by sonar echoes
to forage and avoid obstacles (Popper and
Fay, 1995). Moreover, bats adaptively control the features of the
sonar sounds they use to probe the environment, and these signals can be
quantitatively analyzed to measure and model changes tied to perception.
Previous studies of echolocation in an open environment show that the big
brown bat locks its sonar beam onto a prey item hundreds of milliseconds
before capture (Ghose and Moss,
2003). The present study extended this work to measure the big
brown bat's sonar beam-directing behavior in a more complex environment that
included both obstacles and prey.
When an echolocating bat seeks insect prey in the proximity of vegetation,
because of the broadness of the sonar beam, each vocalization results in a
cascade of echoes, arriving from different directions and at different delays.
Field observations document that echolocating bats cope with acoustic clutter
by adapting the spectro-temporal characteristics of sonar signals to decrease
the duration and broaden the bandwidth of individual calls and increase the
repetition rate in groups of calls
(Schnitzler et al., 2003;
Surlykke and Moss, 2000).
These vocal-motor strategies are assumed to sharpen information about the
features and location of prey and to segregate prey echoes from clutter
echoes. In humans, sound frequency and pitch are particularly important for
parsing an auditory scene (Bregman,
1990), but such cues may not be as useful for the echolocating
bat, as its sonar calls constrain the echo frequency information, and
ambiguous spectral information can arise from the overlap of echoes from
multiple closely spaced objects. Thus, we hypothesize that spatial cues about
object direction and distance play a prominent role in the big brown bat's
analysis of auditory scenes. Furthermore, we hypothesize that the big brown
bat's perception of a complex environment depends on its active control over
sonar vocalizations to enhance spatial information about the direction and
distance of multiple objects. This is the focus of the present study.
In the experiments reported here, we required echolocating bats to perform
a dual task, obstacle avoidance and prey capture. Using high-speed stereo
videography and synchronized sound recordings with a microphone array, we
measured the directional aim of the big brown bat's sonar beam pattern in the
free-flying animal. The big brown bat's sonar beam is wide enough to generate
audible echoes simultaneously from multiple objects, and we conducted this
study to determine whether the animal's beam-directing behavior is consistent
with sequential or simultaneous sampling of sonar targets. Studies of eye
movements reveal that humans sequentially foveate parts of a visual scene,
even when features, such as those of a face, may be closely spaced
(Land and Hayhoe, 2001;
Kumar et al., 2005). The width
of the sonar beam suggests that the big brown bat does not need to scan
closely spaced objects sequentially, because all objects within as much as 30
deg. off the beam axis return echoes of comparable strength.
We report here, however, that the big brown bat does sample the environment sequentially with its sonar, adjusting the direction and distance of its acoustic gaze to closely spaced objects in a complex environment. This is an important finding, because it indicates that sequential scanning offers the animal an advantage that would not be predicted from the characteristics of the sonar beam and implies higher spatial resolution along the midline of the sonar receiver.
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MATERIALS AND METHODS |
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We conducted studies of the flight path and sonar signals used by E.
fuscus as it performed a task that required both obstacle avoidance and
target localization. Experiments were carried out in a large flight room
(Fig. 1), lined with acoustic
foam (Sonex, Illbruck, Minneapolis, MN, USA). Low level long wavelength
lighting excluded the big brown bat's use of vision
(Hope and Bhatnagar, 1979),
but still allowed for high-speed video recording and 3-D reconstruction of the
animal's flight path. In this experiment, the bat had to dectect and localize
a tethered insect behind one of two openings in a mist net that divided the
flight room. The positioning of the food reward was randomized from trial to
trial. The back end of the room was divided further by a partition net,
requiring the bat to select the correct opening to obtain its food. The mist
net was made of 0.1 mm threads, arranged in a grid and spaced 25 mm apart. The
net was moved every day to prevent the subjects from memorizing the positions
of the openings. The openings were circular, ca. 40 cm in diameter,
requiring the bats to maneuver quite accurately to fly through. E.
fuscus could detect the mist net and the two openings using echolocation,
but would occasionally crash into it, presumably because the weak echo returns
from this obstacle were difficult to detect and localize.
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Echo measurements
To characterize the echo levels experienced by the big brown bat in this
task, we measured echoes from the worm and the net. Using
Tucker-Davis-Technologies System 2 (Alachua, FL, USA; hardware and software),
amplified, filtered (Stanford Research Systems, Sunnyvale, CA, USA), frequency
modulated (FM) sounds were generated and broadcast through a speaker [Tweeter
LT800, frequency response flat (±3 dB) up to 100 kHz] powered with
Krone-Hite DC amplifier (Brockton, MA, USA). Echoes were picked up with a GRAS
1/4 in microphone (40BF, GRAS, Holte, Denmark) amplified 40 dB by a
Larsen-Davis amplifier/power supply (Provo, UT, USA), filtered by a WaveTech
filter (Karachi, Pakistan; band-pass filtered 10 kHz–100 kHz) and
digitized on-line with a Wavebook (IOtech, Cleveland, OH, USA), using a
sampling rate of 500 kHz. The broadcast signal was 1 ms in duration and
consisted of a downward linear FM sweep, from 90 kHz to 20 kHz, shaped by a
Hanning window function. The source level from the speaker was 77 dB peSPL
(peak equivalent sound pressure level) at 10 cm. The signal was repeated every
50 ms. The speaker and the microphone pointed towards the same position on the
target, but the microphone was placed 10 cm closer to the target than the
speaker to maximize the signal to noise ratio. The speaker was placed 50 cm
from the target. With a signal duration of 1 ms, there was no overlap between
the outgoing sound and returning echo. We measured the target strength of the
net at two angles, 90 and 45 deg., between the net and the sound beam. Target
strength of the mealworm was measured from the side. All target strength
calculations are referenced to 10 cm such that the target strength was
measured in dB as the echo level 10 cm from the target relative to the
incoming sound level at the target. The target strength of the net measured at
an angle of 90 deg. was –26 dB, whereas the target strength measured at
an angle of 45 deg. was –22 dB. The target strength of the tethered worm
was –16 dB. All sound levels are given in dB SPL re. 0.0002 Pa r.m.s.
The microphones were calibrated using a Brüel and Kjær sound level
calibrator (4231, Nærum, Denmark).
Video and sound recordings
Two frame-synchronized, high-speed video cameras (Kodak MotionCorder, 240
Hz, San Diego, CA, USA) were positioned in the upper corners of the flight
room. A calibration frame (Peak Performance, Boulder, CO, USA) was placed in
the room and filmed by both cameras prior to each recording session. The video
cameras were used to record the positions of the microphones, net and holes,
and the target, bat flight path and capture behavior.
Full bandwidth echolocation signal recordings were taken by two ultrasonic transducers placed on the floor within the calibrated space, and recorded digitally on an IOtech Wavebook (sample rate 250 kHz per channel).
Sonar beam measurements in free-flying bats
The echolocating bat controls not only the spectral–temporal features
of its echolocation cries but also the aim and directional characteristics of
its sonar beam. Eptesicus fuscus is an oral emitter, and the sonar
beam is aligned with the head. A linear microphone array was used to record
the big brown bat's sonar signals at 16 different locations in the flight
room. For each video frame in which a vocalization was detected, the
normalized beam intensity at each microphone was computed. E. fuscus
emits a broad band FM call with a first harmonic sweeping down from
ca. 60 to 25 kHz. The original signal was decomposed into three
sub-bands (20–30 kHz, 30–40 kHz, 40–50 kHz) and the
intensities computed in each sub-band for every vocalization. Using standard
tables (ISO 9613-1) a scaling factor correcting for the sound absorption due
to air was computed for each frequency band (see
Lawrence and Simmons, 1982).
Finally, the intensities in each band were corrected for spherical loss and
summed to yield a normalized intensity for each vocalization in the direction
of each microphone. Vectors were drawn from the bat to each microphone. The
length of each vector was made proportional to the normalized intensity at the
corresponding microphone. Based on these vectors we constructed a normalized
shading pattern such that the peak intensity has a value of 1.0 and is colored
black. Lighter colors denote progressively lower intensities. The resultant
shading pattern displays an estimate of the big brown bat's 2-D beam pattern.
The resultant vector provides an estimate of the bat's beam axis, and the
angular separation between the beam axis and an object of interest (obstacle
or prey) defines the tracking angle (for details, see
Ghose and Moss, 2003). A third
video camera was placed below the hole for close-up recordings in selected
trials at the start of the experiment to verify that the beam axis accurately
represented the aim of the bat's head.
The duration and pulse interval (time from start to start of pulses) defines the pulse–echo separation. These time measurements were recalculated to distance (m) using a velocity of sound of 340 m s–1 at room temperature and humidity, and depicted along the range axis (lower panel of Fig. 2). Plots of pulse duration, measured in meters, along with the distance from bat to net and bat to insect, respectively, revealed how the big brown bat, by decreasing the pulse duration, prevented overlap between its sonar vocalization and net or insect echoes.
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RESULTS |
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). In all net trials, pulse interval (PI) plots showed
that bats produced sonar sound groups for extended periods of time, indicating
how challenging this task was for the big brown bat. The plots show that sonar
strobing started well before (0.5–1 s) the bat crossed the net
(Fig. 3). With the worm in
front of the net, the task was comparable to prey capture in clutter and,
accordingly, the bats' performance rates were comparable to those observed in
an earlier experiment requiring the bats to take tethered insects between 40
and 10 cm from an artificial plant (Moss
et al., 2006). As in clutter experiments, the success was lower
the closer the prey was to the net. All bats were reluctant to fly through the hole at first, in particular bats W24, HP2 and BL52, and therefore it was only possible to collect a limited data set from these animals in the two behind net conditions (Table 1). Trials where the bats did not attempt to capture the prey were not included. HP4, on the other hand, learned quickly, and flew through the net to take insects as far as 110 cm behind the net. For these reasons, different numbers of trials were run for the four bats under the different conditions.
The bats' success dropped drastically as the reward was moved from positions in front of the net to positions behind the net. With the worm behind the net the task became more difficult because the bats faced two challenges: negotiating the net and capturing the insect prey. This task required the bats to maneuver through the (correct) hole before seizing the prey. We did not include trials with the target far behind the net if the bats did not attempt to fly through, as the primary goal of this study was to quantify the bat's sonar behavior in solving the task, rather than its success rate. The bats typically started by flying two or three rounds on the starting side of the net, where they approached the holes while emitting a series of high repetition rate pulses, inspection trains, presumably to decide which hole to fly through for capture.
When the worm was close behind the net many failures were misses, where the bat neither captured nor touched the worm. When the worm was far behind the net, many failures were due to the bat choosing to fly through the wrong hole (Table 1). In all trials, the echo from the worm, regardless of its position behind the net, was well above the bat's detection threshold when it flew within 1 m of the net before crossing it.
When the reward was far behind the net, it could either be on-axis with the hole, i.e. on a straight line perpendicular to the net, starting approximately in the middle of the hole, or off-axis, i.e. at an angle of 45 deg. or more with respect to that line. The success in on-axis trials was not higher than in off-axis trials (on-axis, 57%; off-axis, 56%; P>90%, Chi-square). Thus, the results for on- and off-axis trials were pooled into one category: far behind.
Echo measurements
Target strength measurements were used to estimate detection distances
assuming sonar calls with a source level of 110 dB SPL and 30–35 kHz as
the dominant frequency (Surlykke and Moss,
2000), and a detection threshold of 20 dB in the cluttered lab.
These estimates indicate that the net echoes would be detectable by the bat at
a maximum distance of 3.1 m when ensonified at 90 deg. and at 3.8 m when
ensonified at 45 deg. The target strength of the mealworm was stronger,
indicating that it would be detected by the bat at distances up to 4.8 m,
greater than those encountered by the bat in this study.
Directional adjustments in sonar calls
The acoustic behavior of all big brown bats revealed distinctly sequential
sampling of echo information from the net and the worm. The changes in beam
angle clearly illustrate the sequential processing. The bat directed its beam
back and forth between the left and right edges of the net hole until around
100–300 ms before flying through the net opening, when the beam axis was
shifted from the edges and directed through the hole towards the worm
(Fig. 4). The scanning between
the left and right edge shows that the bat did not sample all elements of the
auditory scene simultaneously, but shifted its focus from object to object
sequentially. Animations of the bat's dynamic adjustments of the beam pattern
as it performed in this task can be found at
http://www.bsos.umd.edu/psyc/batlab/nethole/beamaim/.
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The precision with which the big brown bat aligned its head with a target
is far greater than needed to maintain a strong, audible echo. Indeed, this
bat species' sonar beam is sufficiently broad to yield detectable echoes
30–60 deg. off-axis (Ghose and Moss,
2003). The big brown bat's accurate directional aim of the sonar
beam axis at obstacles and prey may be driven by requirements for higher
spatial resolution along the auditory midline. This observation leads us to
infer that the directional aim of the sonar beam axis is related to target
localization and selection.
The directional aim data in Fig. 4 show sequential scanning of obstacles (right and left edges of the net hole) and prey in all trials. When the bats were far from the hole, the angles to the edges and the prey were so close it was difficult to determine precisely where they were directing the sonar beam axis, but when they came closer to the net, the data revealed that they directed the sonar beam axis to the left or to the right edge of the hole, or to the prey, but not to all of these objects simultaneously. They scanned back and forth between these objects, a behavior that shows parallels with saccadic eye movements. The tracking angle revealed that well before crossing the net, the beam axis shifted from the edges of the hole to the worm on the other side of the net.
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In some trials the subjects interspersed sonar beam fixations at the edges of the hole with fixations at the worm, but in most trials the bats did not direct any cries towards the insect until initiating their final approaches to the hole. Presumably, the bats did not need updated information on the insect's position, because during inspections earlier in the trial they had already found the worm, which guided the choice of which hole to fly through. The bats typically flew several rounds on the starting side of the room before flying through the net hole to retrieve a food reward. Our data collection was end triggered after capture of the insect, and the buffer size (video and sound recording) was limited to 8 s, which is much shorter than the total duration of most trials.
While scanning the edges of the net hole, the bat directed the beam axis to
one edge of the net opening over a number of successive sonar calls. Earlier
experiments (Ghose and Moss,
2003) in an open room showed that the big brown bat fixated the
beam axis on the worm ca. 300 ms before capture with an angular
offset close to zero (±3 deg.). In the experiments reported here,
locking the beam axis at an object with high accuracy started much earlier
with respect to the time of capture. In trials with the tethered worm 
50
cm behind the net, the bat started directing the beam at the edges of the hole
around 500–800 ms before crossing the net, and the worm was captured
between 100 and 600 ms after passing through the hole
(Fig. 5). Directing the beam
axis on the net edges was at least as accurate (±5 deg.) as directing
it on the worm, even though the flying bat changed its position from call to
call. The directional aim of calls to objects in the environment was assigned
by two independent observers who used beam axis measurements and beam aim
animations to score data for each trial. Inter-observer reliability was
greater than 0.9, and any trial segments in question were scored by a third
observer. The directional aim data reported here are based on the agreement of
at least two observers. Tracking angle distributions for the left and right
edges of the net hole, as well as the worm, are presented for bat HP4 in
Fig. 6. The criterion for
assigning where the bat directed its sonar beam before it crossed the net was
a tracking angle of 
15 deg. relative to the net hole edges or the worm.
The few vocalizations that did not fall within this range were not assigned to
any object.
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Temporal adjustments in sonar calls
The results from measurements of temporal features of the sonar calls
confirmed the results of the beam aim data because pulse duration and
repetition rate also showed that the bats sampled the acoustic objects
sequentially. When echolocating bats home in on a prey item, they adjust the
duration and timing of their sonar signals to temporally isolate the prey
echoes in a pulse–echo overlap-free window. In these experiments bats
adapted their acoustic behavior in a similar manner. Pulse duration and PI
decreased when the bat approached the net opening, although not by as much as
in a typical terminal buzz before prey capture. Pulse duration decreased
monotonically such that there was no overlap between the outgoing sonar signal
and the echo return from the net, until around 100–300 ms before the bat
flew through the net opening, when pulse duration increased
(Fig. 7). As the bat approached
the net, the decrease in pulse duration served to prevent overlap between its
vocalizations and net echoes. When the call duration was increased before the
bat crossed the net, the beam extended well beyond the net towards the worm on
the other side. The results suggest that the increase in pulse duration,
allowing for overlap between sonar vocalizations and net echoes, marks the
point in time when the bat shifted its gaze along the range axis from the net
to the more distant worm.
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). Comparison of pulse duration with beam aim (dashed red line in Fig. 7A) further illustrates that the time when the bat shifted its gaze from the edges of the net opening to the worm (taken from tracking angle data) corresponds well with the time when the bat increased pulse duration, allowing overlap between net echo and pulse. The bottom panel of Fig. 7A shows how the pulse duration (measured as distance; blue curve) changed in relation to the distances between the bat and worm (black) and between the bat and net (magenta). Hence, both independent measurements, beam angle and pulse duration, showed that the bat shifted its sonar gaze from net hole to prey well before it flew through the hole. Furthermore, these shifts in beam direction and call duration co-occurred. On average, the shift in sonar beam axis to the prey occurred 220±140 ms before crossing the net, and the increase in pulse duration resulting in overlap with the net echo occurred 219±79 ms before crossing the net. The correlation between the time of the shift of beam axis and the time of increase in pulse duration (Fig. 7B) was statistically significant, R2=0.59 (P<0.01). The shift in gaze from net hole to worm may indicate the time when the bat had localized the hole with enough accuracy to fly through, and started planning its attack of the insect.
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DISCUSSION |
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Echolocation, like other active sensing systems, involves feedback between action and perception: the bat produces ultrasonic vocalizations and processes echo information to guide vocal-motor adjustments to further probe the environment. Similar to active visual scanning of the environment by eye movement, fixation and accommodation, the big brown bat scans the environment acoustically by directional control of the sonar beam (through movement and fixation of the head aim), coupled with range-dependent adaptive changes in sonar call duration and pulse interval. Measurement of the directional aim and features of the big brown bat's sonar cries allows us direct access to the signals that drive its behavior.
Sequential sampling of spatial information
The results of our study demonstrate that the big brown bat sequentially
samples the environment with its sonar vocalizations, adjusting the direction
and distance of its acoustic gaze, in a manner analogous to sequential visual
fixation of objects within a complex visual scene. Due to the broad
directionality of the emitted beam (Ghose
and Moss, 2003), almost equal sound energy impinges on objects
within a wide angle ahead of the bat. The sonar beam of the big brown bat is
sufficiently broad (>120 deg. at 6 dB down from the peak)
(Hartley and Suthers, 1989) to
return concurrent audible echoes from the closely spaced opening in the net
and the tethered worm. If the big brown bat was to simultaneously sample echo
information from the obstacle and prey in this task, we would expect the
animal to maintain its beam axis in the general direction of all these
objects. However, we found that the big brown bat points its sonar beam axis
sequentially at the edges of the net opening and prey, often scanning back and
forth between these objects. This behavior pattern resembles visual scanning
of objects in a natural scene by humans
(Land and Hayhoe, 2001).
The big brown bat's sequential acoustic scanning of the obstacle and prey
is consistent with findings reported by Barber and colleagues, who showed that
the gleaning pallid bat, Antrozous pallidus, cannot process two
streams of auditory information simultaneously
(Barber et al., 2003). However,
in Barber and colleagues' experiments, the two streams of information were
obtained through echolocation and passive listening. The central auditory
system of the pallid bat appears to have evolved to segregate the processing
of echolocation and prey-generated sounds into two separate populations of
neurons in the inferior colliculus
(Fuzessery, 1994). Here, all
streams of acoustic information, from both prey and obstacles, were obtained
through echolocation. As the bat sequentially adjusted the direction and
duration of the sonar beam to inspect the obstacle and prey, we infer that
directional and temporal cues are the most important acoustic parameters to
this animal for the segregation of auditory objects in the scene. This
indicates that the big brown bat's adaptive control of the beam aim and pulse
duration is key to its success in complex, cluttered environments.
Insectivorous bats of different species exploit a wide variety of habitats
ranging from very cluttered space to completely open air. It is hard to
imagine that it will be possible in the future to measure and track the beam
aim of a bat hunting in the wild. However, the fine correlation between beam
aim and temporal call features demonstrated by this study indicates that
changes in temporal parameters correlated to a bat's position with respect to
background may allow one to infer which objects in the natural environment the
bat was sampling along the range axis. Thus, future field studies may reveal
whether other bat species with different habitat preferences also sample
auditory objects sequentially to segregate streams in a complex natural
habitat.
In visual animals, eye movements during scanning of a scene consist of a
series of fixations interspersed by saccades to move the eye from one point to
the next. Birds move the whole head to fixate the eyes
(Eckmeier et al., 2008). In
primate vision, behavioral (Rizzolatti et
al., 1987) and neurophysiological studies
(Kustov and Robinson, 1996)
suggest a tight coupling between eye movement and attention systems. In the
echolocating bat, we suggest that beam axis direction may correspond to its
attention to different objects in the scene. Any listening animal will turn
its head toward a sound of interest, exploiting the maximum directional acuity
of hearing along the midline, which, for example, has been demonstrated
elegantly in a thorough series of experiments with barn owls
(Konishi, 1993). Echolocating
bats can take advantage of the added directionality of the outgoing sound to
reduce echo intensity from an off-axis object by controlling the aim of the
sonar beam.
In vision, fixation points are related to the behavioral task (e.g.
Yarbus, 1961). Pertinent to
our study are look-ahead fixations. Numerous studies of humans engaged in
natural tasks that require multiple, visually guided sequential behaviors have
shown that reaching for an object is preceded by fixations at the object up to
a few seconds in advance (e.g. Hayhoe et
al., 2003). These look-ahead fixations are thought to contribute
to task planning (Hodgson et al.,
2000). A study of humans stepping over an obstacle
(Patla and Vickers, 1997)
showed that subjects fixated on the obstacle before, but not during, the
period in which they navigated the obstacle. In our study we observed sonar
beam fixations by the big brown bat on the edges of the net hole before but
not when getting close to the time of net crossing, suggesting a link between
perception and action planning also in the echolocation system.
Accuracy of sonar beam fixations
Sonar fixations serve to lock the beam axis onto an object over successive
sounds, just as visual fixations lock the fovea accurately onto a target until
the next saccade. We measured the accuracy with which the big brown bat
directed its sonar beam aim at obstacles, and compared these measurements with
the accuracy of its beam aim at prey
(Ghose and Moss, 2003). In the
present study, the big brown bat directed its sonar beam at the obstacle with
high accuracy, locking the beam axis onto the edges of the net opening over
successive vocalizations with deviations of less than 5 deg., even though the
angle of the object with respect to the bat continuously changed as it flew.
Ghose and Moss (Ghose and Moss,
2006) proposed that this accurate locking of the sonar beam during
prey interception serves to simplify steering towards and, finally,
interception of the prey. As the results here show that the big brown bat's
accuracy of directing the sonar beam axis to the obstacles it must avoid is as
high as that to the prey, we infer that centering the beam axis also serves
perceptual functions related to scene analysis. This implies that receiver
width may be much narrower than the transmitted beam pattern.
The big brown bat's beam-directing behavior is much more precise than
required for target echo detection. In a variety of species, localization
accuracy is highest along the midline, falling off in the periphery [humans
(Mills, 1958); monkey
(Brown et al., 1982); cat
(Heffner and Heffner, 1988)].
For example, the minimum audible angle in humans is about 1 deg. along the
midline, increasing to 7.5 deg. at a test azimuth of 70 deg. As the
echolocating bat's auditory system follows a general mammalian plan
(Covey and Casseday, 1995), we
hypothesize that its localization accuracy is also highest along the midline
and falls off with azimuth, which is corroborated by the accurate directional
control of the sonar beam in flight. When sitting on a platform, the big brown
bat can track a target even more accurately, i.e. approximately 1 deg.
(Masters et al., 1985).
Enhanced processing of echoes along the midline shares some of the functional
advantages of a fovea or visual streak in the retina, but without
morphological specializations at the receptor level.
Rapid transitions between sonar fixations
The sequential scanning of the echolocating bat resembles results from
vision studies showing that humans and other primates sample information from
a complex display by a series of fixations, interrupted by saccadic eye
movements. The latency to make a visual saccade is typically longer than 200
ms (Liversedge and Findlay,
2000). When planning ahead, saccade latencies may be substantially
shorter (Fischer and Weber,
1993), but still far greater than the big brown bat's latency of
20–50 ms to re-direct its sonar beam between the net opening and prey in
this dual task.
Suppression of visual sensitivity during saccades gives rise to the
perception of a continuous and stable retinal image, in spite of the retinal
smear created by high velocity eye movements
(Dodge, 1905;
Volkmann et al., 1978). The
echolocating bat rarely produced sonar calls during the transition from beam
directing at one object to another. Instead of actively suppressing sensory
input during high velocity head movements, the big brown bat appears to
minimize auditory smear by reducing echo input when its head is turning. Thus,
this bat species reduces information processing demands on the receiving
auditory system through its vocal-motor control of the signal output.
Acoustic gaze along the range axis
When the echolocating bat shifts the direction of its sonar beam from
obstacle to obstacle it also makes range-dependent adjustments in the duration
of sonar calls to avoid pulse–echo overlap, suggesting accommodation to
objects at different distances. If the big brown bat encounters objects at
different distances, it typically shortens its calls to avoid pulse–echo
overlap with the closest object. Therefore, the duration of this bat species'
vocalizations provides an index of its acoustic gaze along the range axis.
When a bat tolerates overlap between its pulse and the echo from a closer
object, we infer that it has shifted its acoustic gaze to a more distant
object. This shift in acoustic gaze occurred regularly in this dual task:
after the bat found the net opening, it shifted its acoustic gaze to the worm
behind the obstacle, tolerating overlap between its sonar calls and net
echoes, sometimes for as long as 350 ms. In vision, shifts in fixation between
targets that lie in different directions and at varying depths also require a
combination of saccadic and vergence movements
(Kumar et al., 2005).
Conclusions
Our results show that the big brown bat sequentially samples spatial
information from closely spaced objects in a complex acoustic scene, even
though shifts up to 30 deg. in the directional aim of its sonar beam axis
should yield echoes of similar strength, due to the broad width of the beam
(Ghose and Moss, 2003;
Hartley and Suthers, 1989).
Ghose and Moss (Ghose and Moss,
2006) demonstrated a correlation between the big brown bat's head
direction and its flight control and suggested that this bat species uses its
head orientation to set up a common reference frame for sensorimotor
integration, as it converts its sonar information into flight motor commands.
The results of the present study extend this interpretation to suggest that
the big brown bat's accurate beam-directing behavior also plays a role in
spatial localization and attention. In particular, the big brown bat's active
control of sequential beam fixations to obstacles and prey may reflect its
higher spatial resolution and allocation of perceptual resources along the
auditory midline.
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Footnotes |
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We wish to thank Amaya Perez and Wei Xian for technical support. Marianne E. Jensen assisted with data collection and analysis. This work was funded by NSF grant IBN0111973 and NIH grant R01-MH056366 to C.F.M., and grants from the Danish Natural Science Research Council and the Danish National Research Foundation to A.S. We also thank the Wissenschaftkolleg zu Berlin for fellowship support of C.F.M. and A.S. Deposited in PMC for release after 12 months.
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References |
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