J Neurophysiol 104: 1426 –1437, 2010.
First published July 7, 2010; doi:10.1152/jn.00028.2010.
Differential Influence of Frequency, Timing, and Intensity Cues in a Complex
Acoustic Categorization Task
Katherine I. Nagel, Helen M. McLendon, and Allison J. Doupe
Keck Center for Integrative Neuroscience, Department of Physiology, University of California, San Francisco, California
Submitted 15 January 2010; accepted in final form 7 July 2010
INTRODUCTION
How our brains allow us to recognize words, faces, and other
patterns is an outstanding problem in neuroscience (Jusczyk
and Luce 2002; Kourtzi and DiCarlo 2006). Pattern recognition
is difficult because every stimulus is unique. To understand
speech, for example, our brains must be able to discount
variations in volume, prosody, and accent irrelevant to a
word’s meaning and to locate differences between words
essential to meaning. How does the brain achieve these two
tasks? One model (Cadieu et al. 2007; Serre et al. 2007; Sugrue
et al. 2005) is that it transforms the stimulus to emphasize the
most important differences, then classifies these representations according to some set of rules.
While neural representations of complex stimuli have been
studied extensively, the relationship between these representations and classification behavior remains much less well understood. Many physiological studies assume that animals
Address for reprint requests and other correspondence: A. J. Doupe, 513
Parnassus Ave., Box 0444, San Francisco, CA 94143-0444 (E-mail:
[email protected]).
1426
classify stimuli as we do (Hung et al. 2005; Kreiman et al.
2006) or explicitly train animals on categories chosen by
experimenters (Freedman et al. 2001). Finally, many investigations have focused on the visual system, using patterns that
are relatively stable over time. In contrast, acoustic patterns
vary in time, leading to distinct temporal activity patterns.
While several studies have demonstrated that these temporal
patterns of spiking carry significant auditory information
(Narayan et al. 2006), their role in acoustic pattern recognition
has not been tested behaviorally.
Songbirds provide a useful model for studying the relationship between neural representations and acoustic pattern recognition. Like humans, songbirds recognize both groups and
individuals (Appeltants et al. 2006; Becker 1982; Brooks and
Falls 1975a,b; Burt et al. 2000; Gentner et al. 2000; D. B.
Miller 1979; E. H. Miller 1982; Nelson 1989; Nelson and
Marler 1989; Prather et al. 2009; Scharff et al. 1998; Stripling
et al. 2003; Vignal et al. 2004, 2008) on the basis of their
learned vocalizations. A bird’s song is a high-dimensional
stimulus not unlike a human voice with structure that can be
parameterized in many ways. Although a bird’s song is recognizable, each rendition is different from the last: its volume
will depend on the distance between singer and listener; background noises— often other songs—will corrupt the signal
(Zann 1996); and the pitch and duration of individual notes will
vary slightly (Glaze and Troyer 2006; Kao and Brainard 2006).
To recognize a bird by his song, then, females must be able
both to discriminate between the songs of different individuals
and to ignore the variations in a single bird’s song.
The neural representation of songs—particularly in the primary auditory area field L (Fortune and Margoliash 1992,
1995; Vates et al. 1996)— has been studied in detail (Nagel and
Doupe 2006, 2008; Narayan et al. 2006; Sen et al. 2001;
Theunissen et al. 2000; Wang et al. 2007; Woolley et al. 2005,
2006, 2009). Most field L neurons are highly tuned in frequency, time, or both (Nagel and Doupe 2008; Woolley et al.
2009). In addition, field L responses change significantly with
mean volume (Nagel and Doupe 2006, 2008). The behavioral
limits of song recognition have received comparatively little
attention, however. How much variation in pitch or duration
can birds tolerate before a song becomes unrecognizable? Over
what range of volumes are birds able to discriminate songs of
different individuals? These questions are critical to understanding how the representation of song features or parameters
by neurons in the zebra finch brain are related to birds’
perception of these features and their behavioral responses to
song.
We used generalization of a learned discrimination (Beecher
et al. 1994; Gentner and Hulse 1998) as a model paradigm for
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Nagel KI, McLendon HM, Doupe AJ. Differential influence of
frequency, timing, and intensity cues in a complex acoustic categorization task. J Neurophysiol 104: 1426 –1437, 2010. First published
July 7, 2010; doi:10.1152/jn.00028.2010. Songbirds, which, like humans, learn complex vocalizations, provide an excellent model for the
study of acoustic pattern recognition. Here we examined the role of
three basic acoustic parameters in an ethologically relevant categorization task. Female zebra finches were first trained to classify songs as
belonging to one of two males and then asked whether they could
generalize this knowledge to songs systematically altered with respect
to frequency, timing, or intensity. Birds’ performance on song categorization fell off rapidly when songs were altered in frequency or
intensity, but they generalized well to songs that were changed in
duration by ⬎25%. Birds were not deaf to timing changes, however;
they detected these tempo alterations when asked to discriminate
between the same song played back at two different speeds. In
addition, when birds were retrained with songs at many intensities,
they could correctly categorize songs over a wide range of volumes.
Thus although they can detect all these cues, birds attend less to tempo
than to frequency or intensity cues during song categorization. These
results are unexpected for several reasons: zebra finches normally
encounter a wide range of song volumes but most failed to generalize
across volumes in this task; males produce only slight variations in
tempo, but females generalized widely over changes in song duration;
and all three acoustic parameters are critical for auditory neurons.
Thus behavioral data place surprising constraints on the relationship
between previous experience, behavioral task, neural responses, and
perception. We discuss implications for models of auditory pattern
recognition.
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
METHODS
Behavioral task and training
We trained adult female zebra finches (n ⫽ 21) to perform a
classification task in a two-alternative forced choice paradigm. Of
these, 10 were trained to classify the songs of two unfamiliar conspecific males played with a mean amplitude of 73 dB SPL, 5 were
trained to classify the same songs played at 30 dB SPL, and 6 were
trained to discriminate compressed from expanded songs from the
same male. Training took place in custom-built operant cages (Lazlo
Bocskai, Ken McGary, University of California at San Francisco) and
was controlled using a custom-written Matlab program that interfaced
with a TDT RP2 or RX8 board (Tucker Davis Technologies, Alachua,
FL). Playback of songs was made from small bookshelf speakers
(Bose, Framingham, MA). Intensity of playback was calibrated using
pure tones and a calibrated microphone (Brüel and Kjaer, Naerum,
Denmark).
A trial began when the female hopped on a central “song perch”
that faced a speaker (Fig. 1A). Following this hop, the computer
J Neurophysiol • VOL
selected a stimulus from a database of songs from two individuals,
described in detail in the following text. The female had to decide
which male the song belonged to and to report her choice by hopping
on one of two response perches, located to the right (for male A) and
to the left (for male B) of the song perch. If she classified the song
correctly, a feeder located under the song perch was raised for 2–5 s,
allowing her access to food. If she classified the song incorrectly, no
new trials could be initiated for a time-out period of 20 –30 s. Females
had ⱕ6 s after song playback ended to make a response and were
allowed to respond at any point during song playback (see Fig. 1B for
song durations); playback was halted as soon as a response was made.
If no response was made within 6 s of the song ending, the trial was
scored as having no response, and a new trial could be initiated by
hopping again on the song perch.
Birds required 1–2 wk of training to acquire this task. Training
consisted of four stages. In “song mode,” the female was habituated to
the operant cage. Food was freely available. Hopping on the song
perch produced a song drawn at random from a separate habituation
database, which contained no songs from the two individuals used for
classification trials. The habituation database included 28 song renditions from each of four zebra finches, for a total of 112 stimuli. In
“food mode,” food was restricted, and the female had to hop on one
of the two response perches to raise the feeder. The song perch
continued to produce songs from the habituation database. Food and
song could be procured independently. In “sequence mode,” the
female had to hop first on the song perch, then on either of the two
side perches, within 6 s, to receive a reward. Finally, in “discrimination mode,” the female began classification trials. The habituation
song set was replaced with two sets of training songs, and trials were
rewarded (with food) or punished (with a 20 –30 s time-out) depending on which response perch the female chose.
Birds spent 1 or 2 days on each of the first three training stages
(song, food, and sequence). Birds were moved to the next stage of
training when they performed ⬎200 hops per day (for song mode), or
earned ⬎200 rewards per day (for food and sequence mode). Females
generally learned the classification task (⬎75% correct) within 2–5
days of beginning classification trials (Fig. 2, A and C). One female
did not perform significantly above chance after three weeks of
training and was removed from the study.
Females occasionally developed a bias in which they classified
most of one individual’s songs correctly (⬎90% correct) but performed more poorly (70 –90% correct) on songs from the other
individual. When this happened, we altered the percent of correct
trials that received a reward so that the preferred perch was rewarded
at a lower rate (70 –90%). This procedure was usually successful at
correcting large biases in performance, and reward rates were equalized over 1–3 subsequent days. Of 21 birds used in the study, 12
developed occasional biases lasting 1–3 days. Data from days on
which the two perches were rewarded at different rates were not used
for analysis.
In addition to this manual correction, the computer automatically
adjusted the rate of song presentation to play more songs associated
with the nonpreferred perch. At the beginning of a session, the
computer formed a list of all songs in the database. When a song was
played and got a response, it was removed from this list. Songs that
did not receive a response remained on the list. Songs were drawn
from this list until all songs in the database had elicited a response. At
the end of such a “block”, the computer calculated the percent correct
for “A” songs and “B” songs over the last 25 responses to each type
of song. If these percentages were unequal, it randomly removed A or
B songs from the next block so that the percent of A (or B) songs
presented was inversely proportional to the fraction of correct responses to A (or B) songs P(next A) ⫽ P(correct on B)/[P(correct on
A) ⫹ P(correct on B)].
According to this formula, if the bird made the same number of
correct responses to A and B songs, both songs would be presented
equally. However, if she got only one set of songs correct (for
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studying song recognition. In this operant task, a bird is taught
to categorize two sets of songs obtained from two different
conspecific individuals. The birds can use any of the parameters available in the training set to make the categorization. In
this sense, the task differs greatly from other psychophysical
tasks in which a single stimulus parameter is varied, and the
subject is therefore intentionally focused on this parameter.
After learning the categorization task, birds continue to perform but are intermittently tested using novel “probe” stimuli.
Probe stimulus sets contain song renditions modified along a
single parameter. How the bird categorizes these altered songs
can thus reveal which aspects of the original songs are used for
categorization. Probe stimuli are rewarded without respect to
the bird’s choice, ensuring that the bird cannot learn the
“correct” answer for these stimuli from the reward pattern.
Performance significantly above chance on a probe stimulus is
considered evidence for “generalization” to that probe, i.e., the
altered stimulus still falls within the perceptual category defined by the bird in the task.
Here we used this generalization paradigm to investigate the
role of three simple parameters in behavioral song recognition:
pitch (fundamental frequency), duration, and intensity. We
chose these parameters because frequency, temporal, and intensity selectivity are the parameters most frequently used to
characterize neural responses to sound; thus we hoped that this
behavioral study would provide a basis for comparing neural
and behavioral responses to complex ethologically relevant
stimuli. We found that birds trained to classify songs did not
generalize well to pitch-shifted songs but were surprisingly
robust in their categorization of songs with altered duration.
Moreover, most birds generalized only to volumes within
20 – 40 dB of the training stimuli. However, when explicitly
trained to do so, birds could discriminate songs with different
durations and correctly classify songs over a 50 dB volume
range. Together these data suggest that birds use different
aspects of the neural representation of song to solve different
tasks and that behavioral experiments are necessary to determine how animals categorize stimuli in different contexts. By
studying behavioral categorization in animal models like songbirds, where neural representations can be well-characterized,
we may gain insights into how human brains perform pattern
recognition.
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K. I. NAGEL, H. M. McLENDON, AND A. J. DOUPE
A
response perch A
song perch
feeder
speaker
B
20
Male B
Frequency (kHz)
Frequency (kHz)
8
number of songs
Male A
6
4
2
8
6
4
2
0
1 2 3 4 5 6
70 72 74 76
duration (sec)
number of songs
0
number of songs
number of songs
1sec
20
20
0
15
0
1 2 3 4 5 6
RMS amplitude (dB)
70 72 74 76
duration (sec)
RMS amplitude (dB)
0
0
power
10
power
10
−10
10
−10
2
10
10
4
10
2
10
frequency (Hz)
4
10
frequency (Hz)
example, if she chose the A perch on all trials and so got all As correct
and all Bs incorrect), the computer would adjust song presentation
rates so that only songs associated with the nonpreferred perch were
presented during the next block. Together with manual adjustment of
reward rates, this adaptive song presentation scheme helped ensure
that females correctly classified both sets of songs as rewarded and did
not display too strong a bias toward one perch or the other.
Females were allowed to work continually for the duration of their
14 h day. In general, females received all of their food by performing
this task. However, bird weight was monitored closely, and birds
received supplemental food when their weight decreased ⱖ10%.
Birds had free access to water throughout the experiment. Water was
located on one side of the cage, next to one of the two response
perches. After birds reached criterion in their discrimination behavior,
the overall reward rate for correct trials was lowered to 75–95%, to
increase the total number of trials birds performed each day. Whether
a bird was rewarded or not on a given trial was determined randomly;
no songs were specifically chosen to be rewarded or not. Correct trials
J Neurophysiol • VOL
that did not produce a reward did not produce punishment. All
incorrect trials continued to be punished with a time-out. UCSF’s
Institutional Animal Care and Use Committee approved all experimental procedures.
Classification of novel and altered stimuli
Probe trials were used to test the trained females’ ability to
generalize the learned classification to new or altered stimuli. Probe
stimuli could be novel bouts from the two males used for training or
songs from the training set altered along a single parameter. Probe
trials were introduced only after birds reached criterion and were
performing stably at the lowered reward rate (75–95%). Probe trials
were randomly interspersed between training trials at a low rate
(10 –15% of trials), were rewarded at a fixed rate equal to the overall
reward rate (75–95%) regardless of which response perch the female
chose, and were never punished. This ensured that no information
about the “correct” category of the probe was contained in its reward
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FIG. 1. An operant discrimination task to
study song categorization. A: diagram of the
operant cage seen from above. Three perches
are located on three sides of the cage. After
a hop on the central perch, the computer
plays a song from the speaker located directly in front of it. The bird can respond by
hopping on either of the 2 response perches.
After a correct answer, a feeder (2 in diam,
0.5 in deep, located 0.5– 0.75 in beneath the
cage) is raised to be flush with the cage floor
for 2–5 s, allowing the bird access to seed.
After an incorrect answer, all perches cease
to function for a 20 –30 s time-out. B: songs
used for training. The goal of our study was
to train birds to associate each response
perch with the songs of 1 individual. Example songs from the 2 individuals chosen are
shown here both as oscillograms (sound
pressure vs. time, top) and spectrograms (frequency vs. time, 2nd row). Each song is
composed of several repeated motifs; in each
song, 1 motif is indicated by the bar over the
spectrograms. The 2 individuals differ in the
temporal pattern (oscillogram) and frequency
content (spectrogram) of their syllables. Fifty
songs from each individual were used in training to avoid over-training on a single song
rendition. The distribution of overall song durations, mean (RMS) amplitudes, and power
spectra are similar for the 2 individuals as
shown (bottom).
response perch B
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
A
B
300
A <rt> = 1.92
B <rt> = 2.02
percent correct
number of responses
1
1
2
3
0
4
0
5
days of discrimination training
D
5
reaction time (seconds)
1
percent correct
C
10
reaction time (sec)
0. 5
1
3
5
7
0
days of discrimination training
E
percent correct
bird
percent response
100
response time
4
0
probes
probes
probes
100
0
0
100
training trials
2
0
0
100
training trials
2
4
training trials
rate and that females could not easily discriminate probe trials from
normal trials on the basis of reward. Probe trials that did not receive
a response were not rewarded and remained on the probe stimulus list
until they elicited a response. In this task, females were considered to
generalize only if their performance was above chance for both A and
B probe types. To perform significantly below chance, birds would
have to systematically classify A probe songs as B songs, and B probe
songs as A songs because percent correct was always calculated for A
and B songs taken together.
Song stimuli
The training database consisted of 100 song bouts, 50 from one
male (“male A”, Fig. 1B), and 50 from another (“male B”, Fig. 1B).
We used this large database to ensure that females learned to associate
each perch with an individual and not with a few specific renditions of
his song. Each bout consisted of one to six repeated “motifs” (the
learned syllable sequence; see bars above the spectrograms in Fig. 1B)
and was drawn randomly from a set of recording sessions containing
J Neurophysiol • VOL
0
both directed (sung to a female) and undirected (sung alone) song. The
original song files for male A were recorded over the course of 4 days
and contained 13 directed and 59 undirected bouts. For male B, 14
directed and 96 undirected bouts were recorded over the course of 2
days. Bouts were separated by ⱖ2 s of silence. Song recordings were
made from a stationary speaker located within a normal housing cage
53 ⫻ 53 ⫻ 53 cm; males were allowed to move freely during
recording. For playback, we normalized the songs such that the mean
intensity over the stimulus set was the same for songs of both
individuals (73.0 ⫾ 1.1 dB RMS SPL, calculated over the total
duration of the stimulus, 91.8 ⫾ 1.2 dB SPL max for male A; 72.7 ⫾
1.8 dB RMS SPL average, 92.5 ⫾ 1.8 dB SPL max for male B) but
preserved the range of variation in volume recorded at the microphone. All intensities reported in the paper are dB SPL over the total
duration of the stimulus. The mean song volume was chosen to
approximate what a female housed with the male in a soundbox might
hear. (For comparison, the average loudness of a male’s song at 0.4
m—larger than the cage width or the typical male-female distance
during directed song, is 60 –70 dB) (Cynx and Gell 2004). When
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FIG. 2. Learning and generalization of the
song categorization task. A: learning curve for
1 individual showing percent of correct responses on A () and B (□) songs as a
function of the number of days of discrimination training. By the 2nd day of training, this
bird performed well above chance on both
stimulus types. This bird had 9 days of pretraining (song, food, and sequence modes, as
described in METHODS) before beginning discrimination trials. B: distribution of reaction
times (after training) for the bird in A. The
mean reaction times were 1.92 s for A songs
and 2 s for B songs. This indicates that the bird
generally heard ⱖ1 full motif of the song but
usually responded before the stimulus had
played to completion (Fig. 1B, 2– 6 s overall
duration). C: learning curves for 9 trained
birds. Means ⫾ SD of percent correct are
shown for each day. The dotted line at 0.5
represents chance behavior. All show greater
than chance performance by the second day of
training. D: mean and SD of reaction time for
all A () and B (Œ) songs for each bird.
E: responses to novel renditions of the same 2
birds’ songs were used as a control to test
whether birds had learned to associate the
perch with an individual rather than simply
memorizing a particular set of acoustic signals.
Novel renditions were presented as probes,
meaning that they occurred with low probability, and were rewarded at a fixed rate regardless of the birds’ response (see METHODS). Left:
the mean and 95% confidence intervals on the
percent of correct responses for novel probe
songs (y axis) vs. training songs (x axis). Middle: the mean and 95% confidence intervals for
the percent of responses to these 2 types of
stimuli. Right: the mean and SD of reaction
time. In all cases, responses to novel renditions
of the birds’ songs were not significantly different from responses to the songs used in
training.
A songs
B songs
0.5
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K. I. NAGEL, H. M. McLENDON, AND A. J. DOUPE
necessary, bouts were cropped at the end of a motif so that the total
duration was not more than 6 s. All stimuli were filtered between 250
and 8 kHz (Butterworth 2-pole filter). When necessary, low levels of
white noise were added to the recordings to ensure that the background noise level was the same for all stimuli (37 dB SPL). The two
stimulus sets thus had similar distributions of total duration, RMS
volume, and overall power spectrum (Fig. 1B, bottom) but had quite
distinct syllable structure and timing (Fig. 1B, top). Human listeners
(lab members) could easily identify the singer of each song.
Song manipulations
S(t, f) ⫽ (A, phi)
We then chose a new time scale for the song and interpolated the
magnitude and phase of the signal at the new time points based on the
magnitude and phase at nearby times.
S ' (t', f) ⫽ (A', phi')
Finally, the vocoder reassembled the stretched or compressed song
from the interpolated spectrogram. This process yielded a song that
was slower or faster than the original with largely unchanged frequency information.
To alter the pitch of a song without changing its duration, we first
used the preceding algorithm to stretch or compress the song in time.
We then resampled it back to its original length yielding a song with
a higher (for stretched songs) or lower (for compressed songs) pitch
but the same duration and temporal pattern of syllables as the original.
Songs altered in pitch or duration were not re-filtered after this
manipulation.
The pitch and duration of individual zebra finch notes are known to
vary across song renditions. Analyses in other papers indicate that
pitch can vary by 1–3% (0.1– 0.4 octaves) (Kao and Brainard 2006),
while duration can vary by 1– 4% (Cooper and Goller 2006; Glaze and
Troyer 2006). Variation within the normal range for individual birds
should not affect the bird’s classification of an individual’s song in an
operant classification task, whereas variation beyond that range might
or might not, depending on the importance of the parameter to
recognition and classification. We therefore created songs with durations and pitches increased and decreased by 0, 1, 2, 4, 8, 16, 32, and
64%. This resulted in durations ranging from 1/1.64 or 61% to 164%
of the original song length and pitches ranging from 0.71 octaves
lower to 0.71 octaves higher than the original pitch. Pitch-shifted and
time-dilated songs were generated from 2 or 10 different base stimuli:
1 or 5 from the A training set and 1 or 5 from the B training stimuli.
We computed the averaged responses to each manipulation of the 2 or
10 base stimuli to calculate performance as a function of pitch shift or
duration change. Songs with 0% change were processed in the same
way as other manipulated stimuli and were presented as probe stimuli
to ensure that nothing about our manipulation process or probe
stimulus presentation alone caused the birds to respond differently.
The loudness or volume of song stimuli was adjusted by scaling the
digital representation of the song before outputting it to the speaker.
This ensured that baseline noise levels in the recorded song presentations were consistently 46 dB below the mean amplitude of the song.
J Neurophysiol • VOL
A trial was initiated every time the bird hopped on the song perch.
Each trial could be scored as correct, incorrect, or no response. The
percent of correct responses (“percent correct”) was calculated by
dividing the total number of correct responses by the total number of
trials to which the bird responded. The percent of responses (“percent
response”) was calculated by dividing the total number of responses
by the total number of trials. Reaction time was calculated from the
song perch hop to the subsequent response hop. Ninety-five percent
confidence intervals on percent correct were calculated by fitting the
total number of correct responses, and the total number of responses,
to a binomial model (binofit.m, Matlab). The same algorithm was used
to estimate confidence intervals on percent response. Error bars on
reaction time measurements represent the mean SD across several
days. For estimates of behavior on control trials, we show the mean
and SD of percent correct and percent response across days. We use
this estimate rather than the binomial estimate because there are so
many more control training trials (9 control trials for every probe) that
the binomial estimate of error on control trials is exceedingly small.
As a convention, in this paper we state that a female generalized to
a probe stimulus when the lower 95% confidence interval on her
percent correct was ⬎50% (chance); in addition, the term “correct”
response here refers to correct classification of a song as belonging to
individual A or B.
RESULTS
Classification training and generalization
Nine of the 10 initial females trained on the classification
task at 73 dB reached criterion within 7 days of beginning
classification trials (Fig. 2, A and C) consistent with this being
a task that is ethologically relevant to the birds. One bird failed
to reach criterion (75%) after 17 days of training and was
removed from the study. Performance by the remaining birds
was good with ⬎80% of songs classified correctly. Mean
response times ranged from 1.4 to 2.7 s (Fig. 2, B and D), long
enough to hear one to two motifs of each song but generally
shorter than the entire song bout.
As a control, to test that females had learned to associate
different individuals with different perches rather than memorizing the rewarded classification of all 100 songs in the
database, we tested the responses of seven females to 10 novel
song bouts from each male that were not included in the
original training set. These novel song bouts were presented as
probe stimuli (see METHODS). Responses to novel bouts were
not significantly different from responses to training songs, in
percent of correct responses (Fig. 2E, left), percent of songs
responded to (E, middle), and reaction time (right). These data
indicate that classification was robust to bout-to-bout variations
in individual song structure and suggest that these females had
learned to associate each perch with an individual rather than
with the exact song variants used in the training.
Classification of pitch-shifted songs
To examine the role of pitch in classifying songs of different
individuals, we presented trained females with pitch-shifted
versions of training songs and observed how the females
classified these novel stimuli. Pitch-shifted songs were presented in a probe configuration as described in the preceding
text and in METHODS. We shifted the pitch of songs from each
male by ⫾ 0, 0.01, 0.03, 0.06, 0.11, 0.21, 0.40, and 0.71
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We used a phase vocoder algorithm (Flanagan and Golden 1966) to
independently manipulate the overall pitch and duration of training
songs. To alter the duration of a song without changing its pitch, the
vocoder broke it into short overlapping segments and took the Fourier
transform of each segment, yielding a magnitude and phase for each
frequency. We used segments of 256 samples, or 10.5 ms at 25 kHz,
overlapping by 128 samples. This produced a function of time and
frequency similar to the spectrogram often used to visualize an
auditory stimulus (such as those shown in Fig. 1B, middle) but with a
phase value as well as a magnitude for each point
Data analysis
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
octaves, equal to multiplying and dividing the frequency by
100, 101, 102, 104, 108, 116, 132, and 164%. This logarithmic
series of shifts was chosen to span and exceed the range of
natural variability in note pitch; the pitch of individual notes
has been shown to vary by 1–3% of their fundamental frequency, in undirected song (Kao and Brainard 2006). As
illustrated in Fig. 3A, the frequency content of pitch-shift
probes was altered, but their temporal pattern—including the
sequence of harmonic and noisy notes—was not. As a result,
human listeners could readily distinguish even strongly pitchshifted songs of one individual from those of the other.
For five birds, we manipulated a single song bout from each
male; for three of these birds, we repeated the experiment
manipulating five song bouts from each male. No significant
differences were found between these results, and the data
from both were pooled.
Figure 3B (left) shows one female’s response to pitchshifted probe songs. The gray bar represents the mean ⫾ SD
across days of her performance on control trials (rewarded
at 75–95% if correct, punished if incorrect). For small pitch
shifts (⬍0.05 octaves), her performance on probes overlapped with her control performance. However, her performance fell off rapidly for shifts larger than those naturally
observed (⫾3%, indicated by - - -), and she performed at
chance for pitch shifts of 0.2 octaves or greater. The middle
and right hand panels of Fig. 3B show this bird’s percent
response (middle) and reaction time (right) for probes (—)
and control trials ( ). Percent response and reaction times
+0.71 octaves
8kHz
6
unshifted
4
8kHz
2
6
4
-0.71 octaves
2
8kHz
6
4
2
500msec
responses to pitch−shifted songs
100
100
5
50
reaction time
(seconds)
percent response
percent correct
B
0
−0.8
−0.4
0
0.4
0.5
100
100
0
−0.4
0
0.4
pitch shift (octaves)
0.8
0
0.5
pitch shift (octaves)
reaction time
(seconds)
50
−0.8
−0.5
5
percent response
percent correct
0
pitch shift (octaves)
pitch shift (octaves)
C
0
−0.5
0.8
0
−0.5
0
0.5
pitch shift (octaves)
−0.5
0
0.5
pitch shift (octaves)
FIG. 3. Responses to pitch-shifted songs. A: examples of pitch-shifted songs. Left: unshifted song. Top right: same song shifted up by 0.71 octaves (64%).
Bottom right: same song shifted down by 0.71 octaves (64%). In this manipulation, the frequency content of the song was changed but the temporal pattern
remained the same. A shift in pitch leads to vertical expansion or compression of notes with harmonic structure. Pitch-shifted probe stimuli were presented at
low probability (10 –15%) between unaltered training trials and were rewarded without regard to the female’s response. B: performance of one bird on
pitch-shifted probe stimuli. Left: percent correct as a function of pitch shift (—). Error bars represent 95% confidence intervals on percent correct obtained by
fitting data to a binomial distribution. , the mean ⫾ SD of the bird’s performance on control training trials, averaged across the same days (n ⫽ 16). - - - the
range of natural pitch variation in individual’s songs (⫾3%) · · · , chance behavior. The bird’s performance overlaps with control performance within this range
and falls off rapidly outside it. Middle: percent response as a function of pitch shift (—) with 95% confidence intervals. , the mean ⫾ SD of percent response
for control trials. Response rates do not change with pitch and overlap with response rates for control trials. Right: reaction time as a function of pitch shift (—)
with SD. , the mean ⫾ SD of reaction time for control trials. Reaction times for probe trials fall within the range of reaction times for control trials.
C: responses of 5 birds (including the example in B) to pitch-shifted probe stimuli. Left: differently shaded symbols represent the percent correct for each bird
at each condition. The black line represents the mean across birds. Error bars at the top left represent the mean ⫾ SD of each birds’ performance on control trials.
Data from the same bird are represented by the same symbol as in the main plot. All birds show a decrease in performance for songs outside the range of natural
variability and chance behavior for the largest pitch shifts. Middle: percent response for all birds as a function of pitch shift with the mean across birds shown
in black. Right: mean reaction time for all birds with the mean across birds shown in black.
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A
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K. I. NAGEL, H. M. McLENDON, AND A. J. DOUPE
for probes were not statistically different from those for
control trials.
Similar results were seen in all birds tested on these probes
(n ⫽ 5). As shown in Fig. 3C (left), all birds performed close
to their control levels for probe stimuli within the range of
natural within-individual variation, but performed poorly for
shifts outside this range. All birds performed at chance for the
largest pitch shifts (⫾0.71 octaves). Percent response and
reaction time did not change significantly with pitch (Fig. 3C,
middle and right). Together these data indicate that pitch
strongly determines how songs are classified in this task.
Classification of songs with altered duration
A
164% duration
8kHz
100% duration
6
8kHz
4
6
2
4
61% duration
2
8kHz
6
B
4
2
responses to compressed and expanded songs
500msec
100
5
50
reaction time
(seconds)
percent response
percent correct
100
0
60
C
100
140
0
50
180
percent duration
150
100
0
100
140
percent duration
180
100
150
percent duration
reaction time
(seconds)
50
60
50
5
percent response
100
percent correct
100
percent duration
0
50
100
150
percent duration
50
100
150
percent duration
FIG. 4. Responses to songs with altered duration. A: examples of songs with altered duration. Left: unaltered song. Top right: same song expanded by 64%.
Bottom right: same song compressed by 64% (duration ⫽ 1/1.64 or 61%). Expansion or compression alters the duration of both syllables and intervals but does
not change their relative durations nor the frequency content of the song. Duration probes were presented at low probability between unaltered training trials and
were rewarded without regard to the female’s response. B: performance of 1 bird on duration probe stimuli. Left: percent correct as a function of song duration
(—). Error bars and as described in the legend for Fig. 3B. - - -, the range of natural variability in individuals’ song durations (⫾4%). From 76 to 132% duration
the bird’s performance on probes overlaps with her performance on control trials, and she performs significantly above chance on all probes. Middle: percent
response as a function of song duration. Response rates for probes were comparable to response rates for control trials except for the most compressed songs.
Right: reaction time as a function of duration. Reaction time generally did not increase with song duration. C: responses of the same 5 birds to probe stimuli
with altered durations. All symbols as in Fig. 3C. Left: percent correct as a function of probe song duration. All birds performed significantly above chance for
all duration probe stimuli, although their performance decreased below control levels for the largest changes (61 and 164% duration). Middle: percent response
as a function of song duration. Many birds showed a small decrease in response rate only to the most compressed (61% duration) stimuli. Right: reaction time
as a function of song duration. Most birds showed no change in reaction time with changes in song duration.
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To examine the importance of stimulus duration for classification of individuals’ songs, we presented probe versions of
the training stimuli that were stretched or compressed in time.
The frequency content of these songs was preserved, as were
the relative durations of different syllables and intervals (Fig. 4A,
METHODS). Probe stimuli with altered duration were constructed
from the same song bouts used to create pitch-shifted probes.
Duration, like pitch, was multiplied and divided by 100, 101,
102, 104, 108, 116, 132, and 164% (equivalent to durations
ranging from 61 to 164% of the original song length). The
duration of individual notes varies by 1– 4% in natural song
(Cooper and Goller 2006; Glaze and Troyer 2006; Kao and
Brainard 2006). Our manipulations of duration and pitch thus
spanned an equivalent range, both in terms of percent change
and in comparison to the range of natural within-individual
variability in each parameter.
Females showed far greater tolerance for percentage changes
in song duration than for percentage changes in song pitch.
Figure 4B (left) shows percent correct as a function of probe
song duration for the example bird shown in Fig. 3B. One
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
Classification of songs with altered mean volume
To examine the effects of song volume on song classification, we tested four birds, trained on songs with a mean
amplitude of 73 dB, with probe stimuli played at a range of
volumes (3–73 dB in 10 dB steps, Fig. 6A). Ten silent probes
(with the same durations as the original probe songs) were
included as well. To compare the birds’ generalization abilities
to their discrimination thresholds, we then retrained these birds
at the full range of volumes and repeated the generalization test
(Fig. 6B).
Surprisingly, birds did not generalize consistently across
volumes in this task, although there was variation across birds.
Two birds performed significantly worse than their control
behavior even on stimuli played 10 –20 dB softer than mean
training volume, a third performed significantly worse for
stimuli ⬎30 dB softer, while a fourth showed performance
significantly different from her behavior on training trials when
songs were ⬎40 dB softer than the training set. For all birds,
performance on low volume generalization trials after training
at 73 dB only was significantly worse than their performance
after retraining at all volumes (Fig. 6B), indicating that their
initial reduced performance was not due to an inability to hear
or to discriminate stimuli played at these low levels.
One explanation for these results is that birds trained at 73
dB do not attend to stimuli played at much softer volumes.
To address this possibility, we trained five naive birds to
discriminate songs played at 30 dB RMS, then tested them
on the same range of probe stimuli (3–73 dB; Fig. 6C). We
found that none of these birds correctly generalized the
learned classification to louder volumes. Birds responded
above chance only to stimuli within 20 dB of the training
volume. In response to the loudest stimuli (73 dB) all birds
performed at chance.
B
100
reaction time (seconds)
5
percent correct
A
is no harder than discriminating two individuals. These data
indicate that birds are capable of discriminating expanded from
contracted songs but that they treat them similarly in the
individual classification task.
50
4
3
2
1
0
1
2
3
4
5
6
7
days of discrimination training
bird
FIG.
5. Learned discrimination of songs with different durations. A: learning curves for 6 naive birds trained to discriminate songs that differed only in
duration. Mean ⫾ SD of percent correct are shown for each day. Birds were trained to discriminate 50 versions of the original B song, compressed to 76% of
the original duration, from the 50 versions of the same B songs expanded to 164% of their original duration. These two manipulations were chosen because when
birds were asked to categorize songs of two different individuals, the responses to these compressed and expanded songs were not significantly different from
responses to the original songs. When explicitly trained to discriminate based on duration, all birds learned to discriminate better than chance by the 2nd day of training.
B: mean ⫾ SD of reaction time for compressed () and expanded (Œ) songs for all birds. There was no significant difference in reaction time for the two different
song types.
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hundred percent duration indicates control interpolation without overall change; represents the bird’s performance on
normally rewarded control trials, and - - - represent the range
of natural variation (⫾4%). In contrast to her behavior on
pitch-shifted probes, this female responded well above chance
to all duration probes, including those compressed or expanded
well beyond the range of natural variability of duration within
an individual. For duration changes ⱕ32%, her performance
overlapped with her control performance. For a 64% change in
duration, her performance dropped but remained above chance.
Percent response (Fig. 4B, middle) was similar to control for all
probes except the most compressed (61% duration). Reaction
times (Fig. 4B, right) were not significantly different from
control for all probes.
Data from four additional birds (Fig. 4C) confirmed this
result. Birds behaved well above chance on all probes, covering a 2.6-fold range of song durations. Performance dropped
slightly for large duration changes (61 and 164% of original
duration) but remained over chance (95% confidence intervals)
for all birds. Most birds showed a small drop in response rates
for the most compressed songs (61% duration) but responded
equally well to all other probe stimuli. Reaction times for one
bird increased with song duration but remained similar for the
other birds. Together these data indicate that syllables and
intervals within a broad range of durations are sufficient for
classification of songs from different individuals.
Females might generalize to songs with altered duration
because they consider them to be similar to the training songs
or because they are unable to distinguish them from the
training songs. To test whether females generalized because
they were simply unable to discriminate compressed and expanded songs, we trained six naive females to discriminate
songs played at 132 from songs played at 1/132%, or 76%, of
the original duration. We chose these two values because
responses to both were indistinguishable from responses to
training stimuli in the first experiment.
Figure 5 shows percent correct as a function of training day
for birds trained on the duration classification task. These
curves are very similar to those for birds trained on the
individual classification task and suggest that the duration task
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K. I. NAGEL, H. M. McLENDON, AND A. J. DOUPE
Trained at 73 dB
A
100
percent correct
percent correct
100
50
0
20
40
RMS volume (dB)
60
0
20
40
RMS volume (dB)
80
60
80
percent correct
100
50
0
20
40
RMS volume (dB)
60
50
80
0
20
40
RMS volume (dB)
5
percent correct
reaction time (seconds)
100
50
4
3
2
1
0
1
2
3
4
5
6
FIG. 6. Testing generalization from one
volume to another. A: performance of 4 birds
trained at 73 dB RMS on probe songs played at
3–73 dB. Birds did not consistently generalize
across volumes: 1 bird performed significantly
worse than its control behavior for all tested
stimuli softer than 73 dB, whereas another performed significantly worse for stimuli below
63 dB. Two other birds showed a small amount
of generalization, but performed significantly
worse than control for stimuli softer than 43
and 33 dB, respectively. B: performance of the
same 4 birds after retraining on stimuli played
at 3–73 dB. Performance at low volumes
(13– 43 dB) improved for all birds, indicating
that their initial generalization performance at
these intensities was not as good as the birds’
ability to discriminate these stimuli. C: performance of 5 birds trained with 30 dB RMS
songs on probe songs at 3–73 dB. The birds
performed significantly above chance for songs
from 13– 43 dB but performed at chance level
for songs played at 63 and 73 dB. The vertical
line in (A) and (C) indicate the RMS volume of
the training songs. D: performance of the same
birds in (C) on identical probes after 1–2 days
of retraining with songs at many volumes. All
symbols as in (Fig. 3C). After retraining, the
birds performed above chance for all songs
between 13 and 73 dB. These data indicate that
failure to correctly classify loud songs in C was
not due to an inability to discriminate them. E,
left: Learning curves for 5 naive birds trained
to discriminate songs of two different individuals played at 30 dB RMS. Mean ⫾ SD of
percent correct are shown for each day. All
birds learned to discriminate better than chance
by the 3rd day of training. Right: Mean and SD
of reaction times for birds trained on 30 dB
songs.
7
days of discrimination training
bird
As with the birds initially trained at 73 dB, retraining of
these birds on all volumes improved performance markedly.
Within 1 day of retraining, all birds responded above chance to
stimuli from 13 to 73 dB (Fig. 6D). Together these data suggest
that birds do not automatically generalize a song classification
learned at one volume to songs played at a very different
volume. Instead, they generalize only within a range of
⬃20 – 40 dB.
DISCUSSION
The goal of this study was to determine what parameters
govern whether a female zebra finch classifies a novel song as
belonging to a familiar individual and to provide a basis for
comparing neural response properties to an ethologically relevant classification behavior. In general, we expect that different
parameters of communication sounds may be important for
different tasks. Human listeners, for example, use different
cues to determine the identity of a word versus the identity of
its speaker (Avendaño et al. 2004; O’Shaughnessy 1986; Walden et al. 1978). Different aspects of song may carry information about a singer’s species, family, identity, distance, and
social intent (Becker 1982; Marler 1960; Miller 1982). In this
study, we chose to focus on the parameters underlying classification of songs from two conspecific individuals.
J Neurophysiol • VOL
We used an established method, generalization of a learned
classification task, as a paradigm for studying the cues birds
use to associate a song with an individual (Beecher et al. 1994;
Gentner and Hulse 1998). In this operant task, a bird is taught
to categorize many stimuli without any constraint on the cues
used for identification, then tested on novel probe stimuli in
which particular cues are modified. Probe stimuli are rewarded
without respect to the bird’s choice, ensuring that the bird
cannot learn the imposed ”correct“ answer for these stimuli
from the reward pattern. Performance significantly above
chance on a probe stimulus is considered evidence for generalization to that probe. By presenting sets of natural stimuli and
letting the bird choose to what to attend, this method allows
one to determine the strategies birds use to categorize complex
stimuli in a given context. This paradigm has several other
advantages: birds learn it quickly, it makes it possible to
present many test stimuli in a short period of time, it can be
used to test both male and female responses using the same
paradigm, and it is easy to distinguish incorrect responses
(choice of the wrong perch), which signify a failure to generalize the classification, from simple lack of response (no perch
chosen), which could signify tiredness, indifference, or lack of
attention. Other assays, such as measuring approach behavior
(Miller 1979), calls (Vicario 2004; Vicario et al. 2002), singing
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60
Retrained at 3−73 dB
D
100
percent correct
50
80
Retrained at 3−73 dB
B
E
Trained at 30 dB
C
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
J Neurophysiol • VOL
stimuli fell to chance rapidly once stimuli fell outside of the
range of natural variation. Studies in other songbird species
have shown that generalization of a tone sequence discrimination task to new frequencies is constrained to the frequency
range encompassed by the original training stimuli (Hulse and
Cynx 1985; Hulse et al. 1998). Our results are consistent with
these findings and illustrate that this frequency range constraint
holds for more complex natural stimuli.
In contrast, females performed significantly above chance
over a more than twofold range of syllable durations. Just
noticeable differences reported in the literature for timing vary
depending on the question being asked. Parakeets can detect a
change of 10 –20% in the duration of a signal (Dooling and
Haskell 1978), while zebra finches are able to detect gaps of
only 2–3 ms in broadband noise (Okanoya and Dooling 1990).
In our study, we found that the females were easily able to
discriminate songs compressed by 32% from those expanded
by 32%, although they generalized completely to both these
stimuli. Together with our results for pitch-shifted stimuli,
these data support the idea that discrimination abilities do not
necessarily predict generalization abilities and that generalization must be tested separately.
More naturalistic studies of songbirds, although they tend to
examine a smaller range of variation, also support the importance of frequency for vocal identification (Brooks and Falls
1975b; for review, see Falls 1982; Vignal et al. 2008). A
greater selectivity for frequency than timing cues for individual
identification has also been seen in mice. Auditory cortex
responses of mouse mothers carry more information about pup
calls than those of virgins, primarily because of changes in
frequency coding (Galindo-Leon et al. 2009; Liu and Schreiner
2007).
These results parallel aspects of human speaker identification. Like zebra finches, humans depend heavily on pitch cues
to identify speakers (O’Shaughnessy 1986) but not to identify
phonemes (Avendaño et al. 2004). Word duration can be
important as well (Walden 1978) but is less reliable than
spectral features. Thus the acoustic parameters used by these
songbirds to identify individuals may be closer to those used by
human beings to identify speakers rather than words.
These findings on pitch and duration have important consequences for understanding how neural responses to song may
be related to perceptual behavior in this task. Many neurons in
field L, the avian analog of primary auditory cortex, are tightly
tuned in time (Nagel and Doupe 2008), causing them to
respond to syllable onsets with precisely time-locked spikes
(Woolley et al. 2006). This temporal pattern of spiking provides significant information about what song was heard
(Narayan et al. 2006), and several papers have therefore suggested that these temporal patterns of spiking are critical for
birds’ discrimination and recognition of songs (Larson et al.
2009; Wang et al. 2007). Because these patterns are primarily
driven by syllable onsets, the absolute temporal pattern of
spiking in a single neuron should change as a song is expanded
or contracted in time. The ability of birds to generalize across
large changes in duration calls into question simple models that
use such temporal spiking patterns in field L to classify songs.
An alternate model of song classification could be based on
the particular population of cells activated by song rather than
individual neurons’ temporal pattern of firing. Because many
field L neurons are tightly tuned in frequency, the subset of
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(Stripling et al. 2003), or displays (Nowicki et al. 2002;
O’Lochlen and Beecher 1999) in response to song presentation, have also been successfully used to gauge mate recognition and song familiarity, but it is often difficult to determine
the difference between incorrect and absent responses. Techniques such as multi-dimensional scaling (e.g., Dooling et al.
1987) have also provided insight into what acoustic parameters
birds use to group or classify calls; however, this approach
requires the experimenter to parameterize the differences between the stimuli being classified. This can be difficult to do in
the case of complex songs, where syllable structure and timing
differ greatly between individuals.
Because our goal was to identify parameters used to identify
conspecific individuals, we made an effort to choose a training
set that included a large range of the variations in an individual’s song that a female housed in our laboratory might
encounter. We therefore trained females on 50 song renditions
from each male, which varied in bout length, social intent
(directed vs. undirected), mean volume (over a range experienced in a standard laboratory cage), and the pitch and duration
of individual notes. We tested that females had learned to
associate each perch with an individual rather than a specific
set of song renditions, by testing that they correctly generalized
the task to novel song renditions drawn from the same set of
recordings. We note that some variations a female might
encounter even in the lab were not included in the training set
(such as background noise from other birds) nor were variations that a laboratory female would be unlikely to encounter
(recordings from the same male over a large range of distances). These choices may have affected females’ ability to
generalize to songs strongly altered in intensity, as we discuss
further below.
After verifying that females had correctly learned the task,
we probed their performance by asking whether they could
generalize the classification to songs altered in frequency,
duration, and intensity. In these experiments, our goal was to
test stimuli both inside and outside the range of natural variability to compare the birds’ generalization behavior to the
response properties of auditory neurons. From the perspective
of auditory neurons, which are highly sensitive to these parameters, one might expect that any change would strongly affect
classification. Alternatively, one might expect that a bird’s
experience is the best predictor of the critical parameters. A
given male zebra finch’s song varies little in pitch and duration
but can in principle vary over a large range of intensities. Thus
a strategy based on the cues most informative for song discrimination might dictate classification sensitive to changes in
pitch and duration but not intensity. Our results show, however, that birds do not classify stimuli according to either of
these simple predictions.
For one, we found that birds relied much more on pitch than
on duration (in terms of percentage difference) to categorize
modified songs as belonging to one of two individuals. Although these cues vary by similar amounts within naturally
occurring songs (1–3% for pitch, 1– 4% for duration), females
showed much narrower tolerance for pitch than for duration.
Females generalized over a wider range than the 0.5% just
noticeable difference for pitch measured in most avian species
(Dooling et al. 2000), suggesting that discrimination abilities
did not limit birds’ ability to perform the classification task on
pitch-shifted stimuli. However, performance on pitch-shifted
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K. I. NAGEL, H. M. McLENDON, AND A. J. DOUPE
J Neurophysiol • VOL
automatic function of the ascending auditory system; in the
visual system, some perceptual invariances can also be unexpectedly labile (Cox et al. 2005).
Understanding how the brain allows humans and animals to
recognize complex patterns is a fundamental goal of neuroscience. Often, however, physiological studies assume that we
know what tasks the brain solves—such as building invariance
to position and scale and luminance in the visual system,
concentration in the olfactory system, and intensity in the
auditory system. Our data suggest that animals can classify
stimuli in unexpected ways and do so differently depending on
the task at hand. Relating neural activity to perceptual behavior
will require detailed and quantitative assessments of behavior
as well as of neural activity.
ACKNOWLEDGMENTS
We thank T. Gentner, B. Dooling, and D. Schoppik for advice in setting up
the behavioral apparatus, L. Bocskai and D. Floyd for constructing the
behavioral boxes, K. McGary for help with electronics, S. C. Woolley for
recording the birdsongs, and S. Sober and M. Brainard for comments on the
manuscript. Phase vocoder code by D.P.W. Ellis.
Present address of K. Nagel: Dept. of Neurobiology, Harvard Medical
School, 220 Longwood Ave. Boston, MA 02115.
GRANTS
This work was supported by National Institute of Mental Health Grants
MH-55987 and MH-077970 to A. J. Doupe and by a Howard Hughes Medical
Institute fellowship to K. I. Nagel.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
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field L neurons that fire during the course of a song will depend
on the spectral content of that song and will thus vary between
songs of different individuals. For neurons that care primarily
about frequency, a pitch-shifted song should activate a different population of neurons than the original song, while a song
altered only in duration should activate the same ensemble as
the original. The observed tolerance for duration shifts over
pitch shifts supports this model. According to this scheme,
birds should tolerate only frequency shifts smaller than the
typical spectral tuning widths of field L neurons, which would
not shift the population of neurons activated. In fact, in our
behavioral task, females’ limited tolerance for pitch-shifted
songs showed remarkable similarity to spectral tuning widths:
the half-width of the pitch tuning curves was 0.11– 0.22 octaves, while the half-widths of field L neurons have a minimum
of 0.13 octaves, and a median of 0.33 octaves (Nagel and
Doupe 2008). This suggests that which cells are active at a
given time is more important for this classification task than the
precise pattern of activity in those cells.
Temporal patterns of spiking may be used for other types of
auditory tasks, however. For example, our data show that birds
can discriminate songs that differ only in duration, suggesting
that they can use timing information to make perceptual decisions. Moreover because behavioral categorization eventually
falls off at large duration shifts (64% change), temporal patterns likely play some role in song categorization. This may
reflect a dependence of perceptual classification on the slow
temporal evolution of an ensemble of neurons active during
song, however, rather than on the details of spike timing in
individual neurons.
In a second set of experiments, we asked birds whether they
could generalize a song classification task learned at one
stimulus intensity to another. The ability to generalize across
intensities is considered fundamental to pattern recognition
tasks in both the auditory and visual domains. Yet most
auditory neurons show strong nonlinearities in their response
as a function of sound level (Geissler 1998; Nagel and Doupe
2006). An outstanding question has therefore been where in the
auditory system invariance to volume emerges (Billimoria et
al. 2009; Drew and Abbott 2003; Wang et al. 2007).
Surprisingly, we found that in this task most females did not
generalize to songs ⬎20 dB louder or softer than the training
stimuli. This held true even when the probe songs were much
louder than the training stimuli (while still within naturalistic
sound levels) and belies the simple idea that birds automatically generalize singer identity across volumes. One possible
explanation for this finding lies in the role of intensity cues in
behavior: studies in the field have shown that male birds
respond differently to playback of a neighbor’s song from
different distances and positions (Brooks and Falls 1975a;
Naguib and Todt 1998; Stoddard et al. 1991). Our training set
contained only songs recorded over a relatively narrow range
of distances—those a male could reach within a standard
laboratory cage. That females did not generalize to songs
played back at very different volumes may indicate that the
category they learned was “male A at a given distance” rather
than simply “male A.” This could be tested by explicitly
training on recordings made at a larger range of distances.
These data also raise the possibility that the different representations of loud and soft sounds in field L have perceptual
consequences, and that intensity invariance is not a central or
EFFECTS OF ACOUSTIC PARAMETERS ON SONG CATEGORIZATION
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