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First published online May 30, 2008
Journal of Experimental Biology 211, 1850-1858 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.017715
Comparing passive and active hearing: spectral analysis of transient sounds in bats
Department Biologie II, Neurobiologie, Ludwig-Maximilians-Universität München, Großhadernerstrasse 2, 82152 Martinsried, Germany
* Author for correspondence at present address: School of Biological Sciences, University of Bristol, Woodland Road, Bristol, BS8 1UG, UK (e-mail: holger.goerlitz{at}bristol.ac.uk)
Accepted 27 March 2008
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Key words: background noise, call analysis, hearing, spectral shape, perceptual constancy
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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). Humans
perceive the colour of a white object still as white and the colour of a red
object still as red, independent of illumination by yellowish candle light or
greenish neon light.
The timbral constancy of the auditory system
(Risset and Wessel, 1982) is
an analogous perceptual phenomenon that allows the perception of the spectral
shape of a sound (i.e. its timbre or acoustic colour) despite spectral changes
caused by the environment (Watkins,
1991). The spectral shape of a sound signal is one major component
for the identification, grouping and segregation of different auditory signals
(Bregman, 1990;
Moss and Surlykke, 2001;
Griffiths and Warren, 2004).
However, the spectral shape of acoustic waves is distorted during propagation
in an unpredictable manner (Wiley and
Richards, 1978; Lawrence and
Simmons, 1982), thus blurring the information content of the
sound. The spectrum of a sound reaching the ear is hence the product of the
spectrum of the sound source and the spectrum of the surrounding.
Two options exist to cope with such acoustic distortions: either the sender
adapts its vocalization behaviour, or the receiver compensates for these
distortions. Species of almost all vertebrate classes show the first option,
i.e. changes in their vocalization behaviour, e.g. frogs
(Feng et al., 2006), birds
(Lengagne et al., 1999;
Slabbekoorn and Peet, 2003;
Brumm, 2004;
Slabbekoorn and den Boer-Visser,
2006), monkeys (Egnor et al.,
2007) and whales (Au et al.,
1985; Miller et al.,
2000; Foote et al.,
2004). Bats, in addition, are generally very adept in adjusting
their echolocation calls to changed acoustic conditions
(Kalko and Schnitzler, 1993;
Gillam et al., 2007). The
second option, the timbral constancy of perceived sounds, has only been
investigated in humans. Despite the ubiquitous distortions of the spectral
envelope of acoustic signals, humans can easily identify them. The auditory
system compensates for the spectral characteristics of the environment,
resulting in an undistorted perception of the original spectrum of the signal
(Risset and Wessel, 1982;
Watkins, 1991). When the
frequency response of the environment is experimentally manipulated, timbral
constancy leads to several perceptual phenomena, such as the `phoneme boundary
shift' between intergradient vowels
(Watkins, 1991), the `flat
spectrum vowel effect' (Summerfield et
al., 1987) and the `Zwicker tone'
(Zwicker, 1964).
Echolocating bats allow comparison between potentially different mechanisms
of auditory spectral processing, since they use both passive and active
hearing. The spectral information in both contexts is of great importance and
needs to be perceived correctly. Passive hearing is used for vocal
communication and rustling prey detection
(Esser and Schubert, 1998;
Boughman and Wilkinson, 1998;
Arlettaz et al., 2001) and we
would expect bats to compensate for spectral distortions in the same way as
humans. Active hearing, or echolocation, is used for spatial orientation and
airborne prey detection (Schnitzler et
al., 2003) by listening for the echoes of self-generated
ultrasonic calls and evaluating their temporal and spectral properties. For
example, bats identify and discriminate objects exclusively on the basis of
their different spectral reflection patterns
(von Helversen and von Helversen,
2003). Bats even separate and generalize the size-invariant
spectral reflection pattern of the same object with different sizes from the
size-variant spectral reflection pattern
(von Helversen, 2004;
Simon et al., 2006).
The neuronal processing of auditory information is different in passive
hearing and active echolocation and is closely coupled to and directly
influenced by the vocalizations during echolocation
(Suga and Schlegel, 1972;
Schuller, 1979). The
behavioural responses to auditory stimuli may thus differ in the same
experimental task between passive and active hearing. However, whether this is
the case was only investigated in one study on the temporal processing of
communication sounds and echoes
(Schuchmann, 2006).
In this study, we examined, in two experiments, how bats classify the spectral shape of transient foreground stimuli in relation to a simultaneous noise background with a constant spectral shape. The two experiments differed only in the type of signal that needed to be classified: in the passive-acoustic experiment, the bats had to classify passively presented filtered impulses, which were temporally uncorrelated to their echolocation calls. In the active-acoustic experiment, filtered replica of their recorded echolocation calls were played back in real-time through a loudspeaker, thus generating a virtual echoacoustic object. During both experiments, a continuous noise background was presented, the spectral shape of which was constant during a given trial and could be altered between trials.
During passive hearing, we expected the bats to use the background as a
reference for the spectral analysis of the transient foreground stimuli, as
humans do (Watkins, 1991).
Thus, if the spectral shape of the background was changed, we hypothesized
that there would be a shift in the classification as a result of the
compensation for the changed environmental transfer characteristics. During
echolocation, we hypothesized that there would be no influence of a passively
presented constant noise on the spectral classification of echoes, if bats
processed echoes differently from passive acoustic signals.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Animal housing and training procedure
Nine adult, male pale spear-nosed bats, Phyllostomus discolor
Wagner 1843, were trained in the experiments. They were housed in groups of
two to five animals per cage (80 cmx60 cmx80 cm) on a 12 h:12 h
dark:light cycle, but were allowed to fly in a large room for several hours
each day. Animals had free access to water. On training days, they received
food only as a reward in the experiment. Additional food (fruit and mealworms)
was given at the weekends. Bats were usually trained daily for 20–30 min
on 5 days per week, followed by a 2 days break.
Bats were trained in a two-alternative, forced-choice paradigm to
discriminate a highpass filtered training stimulus (+6 dB/octave) from a
lowpass filtered training stimulus (–6 dB/octave) during continuous
playback of white noise. Bats indicated their decision by crawling into the
left or right arm of a Y-shaped maze. Correct decisions were rewarded with
banana-pulp; wrong decisions were neither rewarded nor punished. The next
trial started when the animal had crawled back to the starting position at the
top of the Y-shaped maze. The presented stimulus, and thus the rewarded side,
was selected pseudo-randomly (Gellermann,
1933).
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We collected data of three bats in Experiment 1 (passive hearing) and of two bats in Experiment 2 (echolocation). Five animals (two in Experiment 1, three in Experiment 2) did not learn to classify the control stimuli and could thus not be used for testing. Most of these individuals were too inactive and did not explore the setup. This was, for example, the case for Bat 2, which was successfully trained in Experiment 1, but did not cooperate anymore in Experiment 2. Other bats were too active and crawled quickly through the setup without paying enough attention. For example, one bat in Experiment 2 learned the task, but only showed a stable performance once or twice within several weeks.
Experiment 1: passive hearing
Stimuli
A noise background of 45–55 dB SPL re. 20 µPa and 20–100 kHz
bandwidth was continuously presented to the bats. The spectral envelope of the
noise was either white [i.e. having a flat amplitude spectrum (0 dB/octave),
used during training and testing] or pink (i.e. an amplitude spectrum
decreasing by –3 dB/octave, test trials only;
Fig. 1).
Impulses of 7 µs duration with a flat frequency spectrum were used as foreground stimuli. They were presented at a repetition period of 500 ms, uncorrelated to the bats' echolocation calls. Bats were trained to discriminate impulses that were either high- or lowpass filtered, such that the amplitude of their spectrum either increased or decreased with 6 dB/octave (Fig. 1). After the bats had learned this task, we tested their spontaneous classification of impulses that were filtered by filters having transfer functions of intermediate slopes (±3.6 and ±1.2 dB/octave; Fig. 1). The level of the filtered impulses was roved between ±6 dB to prevent the bats from using loudness cues for classification.
Altogether, we had two training (control) conditions, which were the ±6 dB/octave stimuli filters superimposed on white noise, and 10 test conditions, which were the four remaining stimuli filters of ±3.6 and ±1.2 dB/octave superimposed on white noise and all six stimuli filters superimposed on pink noise.
Stimulus generation
The impulses (impulse generator HP 8012B, Hewlett-Packard, Palo Alto, CA,
USA) and white-noise background (noise generator 1405, Brüel &
Kjær, Nærum, Denmark) were separately digitized and continuously
filtered with 21-point digital filters (DAP-boards 5200a, Microstar
Laboratories, Bellevue, WA, USA; 250 kHz sampling rate). All filters were
normalized to their root mean square (r.m.s.) to maintain a constant signal
level. The analogue outputs of the DAP-boards were lowpass filtered at 100 kHz
(FT 6, Tucker-Davis Technologies, Alachua, FL, USA), attenuated (Crystal 3310,
Cirrus Logic, Austin, TX, USA) and then summed together (SM 3, Tucker-Davis
Technologies). The combined stimulus was amplified (Yamaha M 35, Yamaha Corp.,
Hamamatsu Shizuoka, Japan for Experiment 1 and Rotel RB 960 BX, Rotel, Halle,
Germany for Experiment 2) and played back via one ultrasonic
loudspeaker (Technics EAS 10 TH 800D; Matsushita Electric Industrial, Osaka,
Japan), which was placed in the middle between the two arms of the Y-shaped
maze.
Subjects
Data from three bats were collected in Experiment 1, with 23–30
trials per subject and test condition. The number of control trials for white-
and pink-noise background, respectively, were 346/354 (Bat 1), 270/268 (Bat 2)
and 273/250 (Bat 3).
Experiment 2: echolocation
Stimuli
Instead of impulses, filtered replicas of the bats' echolocation calls were
used as foreground stimuli, thus presenting virtual objects reflecting an echo
every time the bat was emitting a call. The filters used for the foreground
and background were the same as in Experiment 1
(Fig. 1). We thus presented
again two training/control conditions and 10 test conditions.
Stimulus generation
Echolocation calls were picked up with a microphone (MTG MV301, protection
grid off, Microtech Gefell, Gefell, Germany; pre-amplifier 2671, Brüel
& Kjær), which was placed on top of the loudspeaker in the middle
between the two arms of the Y-shaped maze. Calls were amplified (measuring
amplifier 2610, Brüel & Kjær) and bandpass filtered (model 3550
4th order bandpass filter 30–100 kHz, Krohn-Hite, Brockton, MA, USA),
before they were processed by the DAP-board as described for Experiment 1 and
then played back via the loudspeaker. In addition to normalizing the
stimulus filters to their own r.m.s., they were normalized to the r.m.s. of a
model call of P. discolor filtered with the respective filter.
Virtual object target strength was about –10 dB, which is around the
upper bound of large three-dimensional objects relevant for orientation, such
as tree trunks (Stilz, 2004).
The electronic delay was 3 ms, thus positioning the virtual object about 50 cm
behind the loudspeaker. The noise background was generated and filtered as in
Experiment 1.
Subjects
Data of two bats were collected with 42 (Bat 4) or 64 (Bat 5) trials per
subject and test condition. The number of control trials for white- and
pink-noise background, respectively, were 878/916 (Bat 4) and 1416/1439 (Bat
5).
Echolocation call recording and analysis
We recorded echolocation call sequences of the preceding 4 s before
decision during almost all trials in Experiment 2 (phase 24, Terratec,
Herrenpfad, Germany; 192 kHz sampling rate, 24 bit), altogether 2134 call
sequences for Bat 4 and 3457 for Bat 5. We analysed the calls automatically by
a custom-written routine (Matlab 7.1, The Mathworks, Natick, MA, USA). Calls
were detected as regional maxima above a constant threshold in the lowpass
filtered (700 Hz) Hilbert envelope and then extracted form the time signal,
containing 5–95% of the total noise-corrected call energy. All calls
with a signal-to-noise ratio <30 dB and a peak amplitude <–20 dB
FS (decibel full scale) were removed and the remaining calls were checked
visually to exclude obvious artefacts, e.g. clicks or external noise. From the
five calls with the largest signal-to-noise ratio per sequence (=per trial),
we calculated the mean per sequence of six call parameters (best frequency,
–20 dB bandwidth and its corresponding lower and upper cut-off
frequencies, frequency centroid and fundamental frequency). For further
analysis, the sequence means were grouped, either per stimulus or per noise
background, to calculate the respective second-order means per stimulus or per
noise background.
Analysis and statistics
Perceptual classification boundary
A perceptual classification boundary k was calculated for each
animal according to the phoneme boundary used by Tyler et al.
(Tyler et al., 1982) and
Watkins (Watkins, 1991). The
perceptual classification boundary is defined as the filter slope that results
in 50% of highpass and lowpass classification by:
 | (1) |
Roving level simulation
We analysed the possible influence of the roving level on echo
classification in Experiment 2 by applying a roving level of –6 to +6 dB
to the five selected calls per sequence and calculating their frequency
centroid on a logarithmic frequency axis. The frequency centroid divides the
amplitude spectrum into two parts with equal energy and can therefore be used
as a measure for the frequency distribution.
Statistics
As we were limited to a small number of animals, we used each animal as its
own control for the behavioural data. Behavioural classifications of the same
foreground stimuli with different noise backgrounds were compared using
Fisher's exact test. Behavioural mean data were tested using Student's paired
one-sided t-tests, testing the null hypothesis against the one-sided
alternative hypothesis that the percentage of highpass classification
increases with pink-noise background. Student's two-sided t-tests
were used to compare means of the call parameters across backgrounds (Matlab
7.1).
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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2-test, P<0.0001). The open symbols in
Fig. 2 show that in these
trained conditions, all three bats performed highly reliably, in that highpass
filtered impulses were classified as highpass in about 90% of the trials and
lowpass filtered impulses were classified as highpass in only about 10% of the
trials. With a white-noise background, the bats' spontaneous classification of
the test stimuli with intermediate filter slopes depended in general
monotonically on the filter slope (solid black symbols in
Fig. 2). The perceptual
classification boundary (vertical black lines in the lower part of the panels
in Fig. 2) is on average at
–0.1 dB/octave, and thus very similar to the physical filter boundary of
0 dB/octave. With a pink-noise background, however, the bats classified the
same stimuli more often as highpass (red symbols in
Fig. 2). Consequently, the
perceptual classification boundary shifted towards negative slopes (vertical
red lines in the lower part of the panels in
Fig. 2). For the individual
bats, this downward shift of the perceptual classification boundary amounted
to 1.8, 1.0 and 0.8 dB/octave, with a significant mean downward shift of 1.2
dB/octave [Student's one-sided paired t-test, t(2)=5.52,
P=0.0156]. Accordingly, the mean difference in highpass
classification between pink and white-noise background (black bar in
Fig. 2D) is significantly
larger than zero [Student's one-sided t-test, t(5)=2.21,
P=0.0391]. In summary, all three bats compensated for the spectral
shape of the noise background.
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Experiment 2: spectral processing during echolocation
In this experiment, the bats were trained to classify the spectral transfer
function of a filter, and not the presented sounds themselves. As the filter
was excited by the bats' echolocation calls, the perceptual task was to
evaluate spectral changes of the perceived echoes, relative to the emitted
calls.
Two bats were successfully trained to discriminate highpass filtered echoes
(+6 dB/octave) from lowpass filtered echoes (–6 dB/octave), superimposed
on white-noise background (control, 2-test,
P<0.0001; open symbols in Fig.
3). As we showed for the passive-acoustic classification of
impulses, the spontaneous classification of filters with intermediate spectral
slopes and a white-noise background depended monotonically on the filter slope
(solid black symbols in Fig.
3). However, in contrast to the passive-acoustic experiment, the
classification of the same test filters was not affected by the change from a
white to a pink-noise background (red symbols in
Fig. 3). The shift of the
perceptual classification boundary was positive in Bat 4 and negative in Bat
5, with a non-significant mean shift of –0.3 dB/octave. Accordingly, the
mean difference in highpass classification between pink- and white-noise
background equals almost zero (black bar,
Fig. 3C).
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Effect of the roving level
In both experiments, the foreground level was roved to prevent the bats
from using loudness cues for classification. In addition to the filter slope,
which was the reinforced cue, this roving level also influenced the
classification during Experiment 2 (echolocation), but not during Experiment 1
(passive hearing). Fig. 4A,B
illustrates this effect for the control data of Experiment 2, which are
plotted as a function of echo level. This shows that highpass controls were
mostly classified as highpass (>90%), with a small decrease in performance
for louder echoes. By contrast, lowpass controls were classified correctly at
high levels, but incorrectly at low levels with errors of up to 60%. A
similar, more pronounced pattern is visible for the test echoes
(Fig. 4C,D).
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0.4 ms; Fig.
5A). The mean fundamental frequencies were 18.4 and 19.5 kHz for
Bats 4 and 5, respectively (Fig.
5H). The –20 dB bandwidth was around 55 kHz with cut-off
frequencies at 36–38 kHz and at 91–93 kHz
(Fig. 5C–E), which was
about the same for both bats. Inter-individual differences were only found in
the energy distribution across frequencies: the frequency centroid of Bat 4 is
about 4–5 kHz lower than in Bat 5
(Fig. 5F), and its best
frequency is about 15 kHz lower than in Bat 5
(Fig. 5G). Thus, calls of Bat 4
had most energy in the third harmonic (58–60 kHz), whereas calls of Bat
5 had most energy in the fourth harmonic (75–76 kHz). Hence, both bats
performed the spectral classification task with partially different spectral
call structures.
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The noise background and the different filter slopes only partially influenced the call structure. Bat 4 showed no systematic background-correlated differences in its call parameters. Bat 5 slightly increased the level of the higher harmonics of its calls during the presentation of pink-noise background: the –20 dB bandwidth and the frequency centroid was about 1 kHz higher [Fig. 5D,F, t(3099)=–4.04, P<0.0001], which was caused by an increase in the upper cut-off frequency [Fig. 5E, t(3099)=–4.04, P<0.0001]. Alternatively, it is possible that the bat accomplished this frequency increase by reducing its fundamental frequency by 0.1 kHz [Fig. 5H, t(3099)=3.49, P=0.0005], thus also reducing its fifth harmonic by 0.5 kHz. In consequence, the fifth harmonic moves further down into the pass band of the bat's vocal tract, and thus shifts the frequency centroid upwards.
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Experiment 1: perceptual compensation during passive hearing
With pink-noise background, the bats classified the same passively heard
impulses more often as highpass than during the presentation of a white-noise
background. This shows that bats employ a compensatory mechanism during
passive hearing similar to the one described in humans (e.g.
Summerfield et al., 1987;
Watkins, 1991). The perceptual
compensation in humans is presumably based on the different rate of
spectrotemporal variation between two different filters
(Furui, 1986;
Repp, 1987;
van Dijkhuizen et al., 1987;
Haggard et al., 1987): while
the spectrum of transmission channels is fairly constant over time, the
spectrum of natural sound sources varies rapidly. By taking the constant
fraction of the overall perceived spectrum as a reference, the auditory system
compensates for it and evaluates only relative changes to it. This results in
a perceptual whitening of the environmental spectrum and in the perception of
the undistorted spectral shape of the transmitted sound.
We found the same compensatory mechanism during passive hearing in bats. When the noise background was filtered with a constant lowpass filter, the bats compensated for this filter and perceived the same transient foreground stimuli as containing more high frequencies. Such a compensation allows animals to perceive the spectral shape of acoustic signals as it had been at the sound source, i.e. to perceive the spectral characteristics of the sound source despite changes in the absolute spectral shape of the acoustic signal that is impinging on the ear.
This timbral constancy of the auditory system is a high-level perceptual
process (Watkins, 1991), as is
the colour constancy of the visual system
(Smithson, 2005); the colour
constancy being mediated by cells in area V1 and V4 of the visual cortex
(Zeki, 1983;
Wachtler et al., 2003). As
such, they are likely to be under cognitive control and may be employed only
if necessary.
Experiment 2: spectral processing during echolocation
In contrast to passive hearing, the classification of echoes during active
hearing was independent of the passively presented noise background. This
shows (i) that the spectral processing of echoes differs from the spectral
processing of transient passive acoustic stimuli, even in the identical
experimental situation, and (ii) that the spectral processing of echoes is
independent of simultaneous, passively presented acoustic signals.
In contrast to the auditory processing of passive sounds, the neuronal
processing of echoes is coupled to, and influenced by, the vocalisation
pattern (Suga and Schlegel,
1972; Schuller,
1979), for example by opening temporal processing windows
(Roverud and Grinnell, 1985).
Based on neurophysiological data, it was suggested for the gleaning bat
Antrozous pallidus that it processes passively heard prey sounds and
actively acquired echoes in two different, parallel auditory pathways
(Fuzessery, 1994;
Razak and Fuzessery, 2002),
which converge later in the auditory cortex
(Razak et al., 1999).
Behaviourally, it was suggested that the auditory mechanisms for the temporal
analysis of spatial echo information are under cognitive control and that they
may be employed during passive hearing, but not during echolocation
(Schuchmann et al., 2006;
Schuchmann, 2006). The current
study provides behavioural evidence that spectral processing also differs
between passive and active hearing. The reference for the spectral evaluation
of echoes is thus not the constant spectral characteristics of external
passive acoustic signals, but an internal reference, which can either be an
efference copy of the motor signals that generated the last call, or an
auditory reference generated from hearing the outgoing call.
In the current experiments, the background was never loud enough to mask the transient foreground. The evaluation of the echo spectral shape independent of ambient background sounds, as it was demonstrated here, does not mean that the bats would not suffer from masking at considerably higher background levels.
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Second, the spectral shape of the echo above hearing threshold might vary
with echo level. To test this hypothesis, we applied a roving level of
–6 to +6 dB to every recorded call and calculated the frequency centroid
of the roved echo above hearing threshold on a logarithmic frequency axis as a
measure for the perceived spectral shape. The hearing threshold of P.
discolor was taken from Hoffmann et al.
(Hoffmann et al., 2008), and
for frequencies above 80 kHz from Esser and Daucher
[(Esser and Daucher, 1996);
magnitudes reduced by 28.5 dB to match the audiogram of Hoffmann et al.
(Hoffmann et al., 2008)]. The
frequency centroid increased by about 2 kHz for softer echoes
(Fig. 6C). In comparison, the
filters used for echo generation changed the frequency centroid by about 5 kHz
(Fig. 6A), which is about twice
as much as caused by the roving level. Frequency cues caused by the roving
level should therefore be overruled by the direct spectral changes and are
thus not sufficient to explain the influence of the roving level on their
own.
Third, timbre, pitch and loudness are auditory perceptual dimensions that
mutually interact in a complex non-linear fashion. For example the reaction
time for stimulus classification in one of these dimensions depends in
non-reciprocal ways on the other dimensions
(Melara and Marks, 1990), with
high pitch and high timbre both corresponding to loud signals, but high timbre
corresponding to low pitch. In echolocation, small targets give more high
frequent echo content than larger ones. Therefore, the bats may tend to
classify faint targets similarly to high-pass filtered ones.
In summary, the joined effects of the bats perceptual associations between different stimulus qualities and the spectral composition of the echolocation calls combined with the bats' absolute hearing threshold can at least qualitatively explain the level dependence of the behavioural performance in Experiment 2. Note that the shape of the psychometric functions did not change if only a part of the trials was analysed, e.g. all trials with a roving level above 0 dB. Thus, the observed effect of the roving level does not confound the main result, i.e. the lack of influence of background spectral shape on echo spectral classification.
Echolocation calls
In addition to perceptual compensatory mechanisms, which are used during
passive hearing, bats could compensate for spectral distortions during
echolocation by adapting the call spectrum to the spectral shape of passive
acoustic signals. Bats constantly adjust their signals to meet changing
perceptual requirements. For example, call bandwidth is changed during prey
interception (Schnitzler et al.,
2003), presumably to increase object resolution
(Siemers and Schnitzler, 2004;
Boonman and Ostwald, 2007), and
depending on the surroundings (Kalko and
Schnitzler, 1993) and the calls of conspecifics
(Gillam et al., 2007). If bats
compensated for the lowpass characteristic of the passively presented noise
background, they would need to shift the frequency centroid of their calls to
lower frequencies. However, every consistent effect we found was in the
opposite direction, i.e. with a pink-noise background, the frequency centroid
was shifted to higher frequencies. These findings do not support the
compensation hypothesis. Instead, this is further evidence that echo spectral
processing is not influenced by passive acoustic stimuli. Passive and active
hearing thus represent two different and separate modes of operation of the
auditory system, where the spectral shape of passive sounds does not influence
the evaluation of signals during active hearing.
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CONCLUSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Acknowledgments |
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11, Abs. 1
TierSchG has been given to the Dept. Biologie II der
Ludwig-Maximilians-Universität München by the Landratsamt
München, dated April 20, 2005. |
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