Journal of Comparative Psychology
r998,V61. 112, No. 3,244-258
Copyright 1998 by the American Psychological Association, Inc.
073S-7036Y98/$3.00
Frequency-Range Discriminations: Special and General Abilities
in Zebra Finches (Taeniopygia guttatd) and Humans (Homo sapiens)
Ron Weisman, Milan Njegovan, Chris Sturdy, Leslie Phillmore, James Coyle, and Douglas Mewhort
Queen's University at Kingston
The acoustic frequency ranges in birdsongs and human speech can provide important pitch
cues for recognition. Zebra finches and humans were trained to sort contiguous frequencies
into 3 or 8 ranges, based on associations between the ranges and reward. The 3-range task was
conducted separately in 3 spectral regions. Zebra finches discriminated 3 ranges in the medium
and high spectral regions faster than in the low region and discriminated 8 ranges with
precision. Humans discriminated 3 ranges in all 3 spectral regions to the same modest standard
and acquired only a crude discrimination of the lowest and highest of 8 ranges. The results
indicate that songbirds have a special sensitivity to the pitches in conspecific songs and,
relative to humans, have a remarkable general ability to sort pitches into ranges.
Male songbirds rely on song as a long-distance social
signal to mark territory and to attract females for courtship.
An enduring question in the study of animal communication
is how birds recognize their own species' songs among a
plethora of other biological signals. A related comparative
question is how humans (Homo sapiens) recognize their
species-typical vocalizations at a distance in their acoustic
environment. In this article, we explore the perceptual
abilities that underlie these long-distance auditory recognition skills.
The most successful account of birdsong recognition is
known as the sound-environment hypothesis (Leroy, 1982;
Nelson, 1989); it states that songbirds are able to identify
conspecific songs because each species occupies an exclusive portion of multidimensional acoustic space. Locations
in acoustic space are defined by measurements of the
acoustic properties of song notes (e.g., frequency, frequency
change, duration). In support of the sound-environment
hypothesis, and of special interest for this article, individual
songbirds produce notes within highly circumscribed speciestypical ranges of acoustic frequencies, and birds use this
pitch information to help identify conspecifics in multidimensional acoustic space. The frequency range of song notes
appears repeatedly in the playback literature as important to
species recognition (e.g., Becker, 1982; Falls, 1962; Weary,
Lemon, & Date, 1986). Furthermore, frequency range is a
superordinate feature in song recognition, because when
pitted against other acoustic features in field playback
experiments, altering frequency outside species-typical ranges
(e.g., ±2 SD to ±3 SD) produces the greatest decrements in
territorial responses (e.g., calls, song, flying to the speaker;
see Nelson, 1989; Weary et al., 1986). In summary, the
species-typical range of the acoustic frequencies in song has
been demonstrated repeatedly to be particularly salient.
The use of the frequencies in conspecific song notes for
accurate sorting of conspecific from heterospecific songs
requires perception of note pitches and memory for the
range of pitches represented in a species' song notes. The
main purpose of this research was to explore the perception
of frequency ranges in laboratory experiments where songbirds were tested over a wide auditory continuum and with
operant discriminations that were even more difficult than
those required in the field for the recognition of conspecifics.
A second purpose of this research was to compare the
pitch-sorting abilities of songbirds and humans in frequencyrange discriminations. Comparison between songbirds and
humans provides unique knowledge about the perceptual
worlds of diverse species, convergent in their dependence on
learned acoustic communication.
Njegovan, Ito, Mewhort, and Weisman (1995) began the
series of frequency-range discrimination experiments that
we continue here. They trained zebra finches (Taeniopygia
guttata) and humans in an operant discrimination among
27 tones in the spectral region between 2000 Hz and 5120
Hz, spaced 120 Hz (an average of 3%) apart. Nine S +
(rewarded) tones formed a contiguous middle range, and
nine S — (unrewarded) tones each formed contiguous lower
and upper ranges. Zebra finches acquired a precise discrimination: They gave a low percentage of response to low-range
S— tones, then shifted accurately to a high percentage of
response to middle-range S+ tones, and then shifted accurately again to a low percentage of response to high-range
S- tones. Humans, too, discriminated among frequency
ranges, but their acquisition was slower and of a lower
standard than zebra finches. In other words, songbirds
classified pitches into ranges more accurately than humans.
These results suggest a perceptual basis for the superordi-
Ron Weisman, Milan Njegovan, Chris Sturdy, Leslie Phillmore,
James Coyle, and Douglas Mewhort, Department of Psychology,
Queen's University, Kingston, Ontario, Canada.
This research was supported by grants from the Natural Science
and Engineering Research Council (Canada).
We thank K. Laird and L. Tomilson for their assistance in
conducting this work and our participants for their efforts in this
research. The software used for modeling in mis article is written in
Pascal and is available from the authors.
Correspondence concerning this article should be addressed to
Ron Weisman, Department of Psychology, Queen's University,
Kingston, Ontario, Canada, K7L 3N6. Electronic mail may be sent
to ron@pavlov.psyc.queensu.ca.
244
245
RANGE DISCRIMINATION
nate salience of pitch ranges in species recognition by
songbirds. Here, we include potential effects of a superior
ability to memorize pitches in the general definition of
perceptual ability.
In two experiments, we sought to determine the source
and scope of (a) precise frequency-range discrimination by
zebra finches and (b) the striking species difference between
zebra finches and humans. Njegovan et al. (1995) suggested
several explanations, including hypotheses about differences
in perceptual processes and differences in motivation.
According to a perceptual explanation, songbirds sort
pitches with precision either because they have a special
sensitivity for the region of the frequency spectrum tested by
Njegovan et al. (1995) or because they have a general ability
to perceive and remember pitches accurately across a wide
spectrum of frequencies. In favor of the special sensitivity
hypothesis, Njegovan et al. (1995) may have tested in a
spectral region (i.e., about 2000 to 5000 Hz) more important
to songbirds than to humans. This region includes frequencies of the loudest harmonics in zebra finch songs (Williams,
Cynx, & Nottebohm, 1989), and it is where songbirds hear
best (Okanoya & Dooling, 1987). It is also possible that a
lower spectral region (i.e., 80 to 453 Hz) is more important
to humans because it includes the fundamental frequencies
of human voice (Bakan, 1987), which provide information
about gender and age (Handel, 1991). Against the specialsensitivity hypothesis, Njegovan et al. (1995) appear to have
tested in a spectral region that includes the formant frequencies associated with vowels and consonants and the frequencies where humans hear best. Therefore, whether humans
have a special sensitivity for sorting pitches in any spectral
region is undetermined.
To test the special-sensitivity and general-ability hypotheses, we trained songbirds and humans in frequency-range
discriminations in three spectral regions spanning the frequencies used in songbird and human vocalizations (between 100
Hz and about 5500 Hz). The special-sensitivity hypothesis
predicts that (a) humans might discriminate better in the
lower region than in other regions because it includes the
fundamental frequencies in human voices, and (b) songbirds
should excel in the middle and higher spectral regions
because these regions include most of the fundamentals and
loud harmonics heard in zebra finch songs.
In the second experiment, we trained songbirds and
humans in a multiple frequency-range discrimination that
required both species to sort pitches in several rewarded and
nonrewarded ranges across a wide spectral region (about
1000 Hz to 6000 Hz). The general-ability hypothesis predicts that songbirds should strengthen their lead over humans
in this more challenging frequency-range discrimination.
According to a motivational explanation, humans may
have suffered under poor motivational reward conditions in
Njegovan et al.'s (1995) experiments. It is not unknown for
human participants to become inattentive because of poor
motivation. In the present experiments, we used instructions
and competitive rewards to ensure that the human participants were motivated to perform in the frequency-range
discriminations. If the level of motivation was the principal
determinant of species differences in Njegovan et al.'s
(1995) experiments, then humans should perform much
better with increased motivation in the present experiments.
Perceptual classification, whether in one continuum (as in
this article) or in multiple continua (as in the classifying
exemplars of visual concepts; e.g., Wasserman, Kiedinger,
& Bhatt, 1988), can be usefully conceptualized as simple
discrimination and generalization (see models by Astley &
Wasserman, 1992; Shepard & Kannappan, 1991). Njegovan
et al. (1995) implemented Shepard and Kannappan's (1991)
neural network model of stimulus generalization to account
for the classification of tones into frequency ranges by zebra
finches and humans. In the model, the pitch continuum
varied with increases in frequency and was represented in a
tonotopic spatial arrangement of neural units. Anatomical
and physiological evidence supports the existence of neural
tonotopic maps in mammalian and avian brains. Neural
circuits extend from the cochlea to Al (mammalian primary
auditory cortex; Brugge, 1985) and to Field L2 (the avian
homologue of Al; Vates, Broome, Mello, & Nottebohm,
1996) to provide tonotopic spatial representations of pitch in
mammals and birds, respectively.
In Shepard and Kannappan's (1991) model, individual
input units were connected directly to units in successive
layers of a sensory network (the tonotopic pitch map), and
the strength of connections between sensory units reflected
the spread of excitation across units in the network pitch
map. Weights for the strength of excitation in the model fell
off exponentially from sensory unit to unit in Shepard and
Kannappan's (1991) model. Generally, in neural network
models, the strength of connections between units in the
sensory network and the unit for some outcome (e.g.,
reward) reflects the activation of an association. In other
words, the strength of activation between units in the
sensory network and the outcome unit represents memories
for specific pitch-outcome associations. In Shepard and
Kannappan's (1991) model, weights for the activation of
links between sensory units and the outcome unit increased
and decreased in accord with a simple linear learning rule.
Njegovan et al. (1995) concluded that the model gave a
useful account of frequency-range discriminations in zebra
finches and humans. Here, we extend network modeling to
frequency-range discriminations across most of the perceptible auditory spectrum of songbirds and humans.
Experiment 1
According to the special-sensitivity hypothesis, zebra
finches were the superior classifiers in Njegovan et al.'s
(1995) experiments because they were tested in a region of
maximum pitch sensitivity where humans may be less
sensitive. In Experiment 1, we tested zebra finches and
humans in frequency-range discriminations in three spectral
regions: a low region (100-453 Hz) a medium region
(359-1633 Hz) and a high region (1200-5459 Hz). This
design allowed us to determine whether zebra finches or
humans have special pitch-sorting abilities in any of the
three spectral regions.
246
WEISMAN ET AL.
Method
Participants
Zebra finches. Eighteen experimentally naive adult male zebra
finches participated in the experiment. All birds had unlimited
access to water, grit, and cuttlebone in their cages. During training
and testing, access to food (Finch Mix, Armstrong Milling Co.,
Ltd., Cantield, Ontario, Canada) was provided during feeder visits.
Once a week, as a nutritional supplement, we gave the birds
hard-boiled egg and unlimited access to food. We added a
supplement (Vitamin Supplement Conditioner for Birds, Rolf C.
Hagen, Inc., Montreal, Quebec, Canada) to the birds' water every
2nd day. The birds lived on a 12-hr light-dark cycle, with the
temperature maintained at about 20 °C.
Humans. We recruited 12 humans, including 6 women and 6
men, who were 20-27 years old, through personal contact and paid
them to participate. The men and women were practicing musicians
with 4-20 years of musical training. We chose musicians because
they have extensive training with brief tonal stimuli. We excluded
people with extensive absolute-pitch abilities because they constitute a minuscule percentage of humans; generalizing findings
between them and most humans would be problematic.
Apparatus
Zebra finches. Each zebra finch lived and was observed in a
standard budgerigar cage (0.3 m wide X 0.4 m high X 0.4 m deep).
A wire floor, attached near the bottom of the cage, ensured that
spilled food was not eaten. Each cage was contained in a ventilated,
sound-attenuating enclosure and lighted by a 9-W twin-tube
fluorescent bulb. Each cage contained several perches, a water
bottle, a grit container, and a cuttlebone. An opening (11 cm
wide X 16 cm high) in the cage allowed birds access to the feeder
(Njegovan, Hilhorst, Ferguson, & Weisman, 1994). A microcomputer controlled the experiment and recorded responses in each
chamber. Tone stimuli were constructed and stored in Macintosh
Plus or SE computers (Apple Computer, Cupertino, CA) and
played using a NAD 310 (NAD Electronics, Ltd., London)
integrated amplifier and a Realistic Minimus-7 speaker (Radio
Shack, Barrie, Ontario, Canada) beside the feeder.
Humans. Participants worked individually in a room (2.1 m
wide X 4.0 m long X 2.5 m high) containing a table, a chair, a
small desk lamp (on at all times), and the test apparatus. The
apparatus was a panel mounted on a box (24 cm X 23 cm X 8 cm
high at the rear and 4 cm high at the front) outfitted with two
response buttons and two light emitting diodes (LEDs). The
response buttons were located in the center of the panel, 7 cm from
the top edge and 5 cm apart. The LEDs were situated in the
right-top corner of the panel, 6 cm and 10 cm from the top edge and
3 cm from the right edge. All other aspects of the apparatus,
including control of the experiment, recording of responses, and
presentation of tones were identical to the zebra finch apparatus.
Stimuli
Individual tones, 440 ms in duration, were synthesized with the
computer and sound system used to present them during discrimination training and with SoundEdit (Farallon Computing, Berkeley,
CA) and SoundCap (Gibson, 1987) software set at 22-k, 8-bit
samples per second. Tones were ramped in amplitude upward at
onset for 5 ms and downward at offset for 5 ms to avoid transients.
We created 27 tones in each of three spectral regions (low, medium,
and high) of the auditory continuum (see Table 1). A 6% change in
frequency, A// / = 0.06, separated successive tones in each region.
Table 1
Frequencies (Hz) ofS+ (Rewarded) andS—
(Nonrewarded) Tones in the Low, Medium, and High
Spectral Region Frequency-Range Discriminations
Tone type
Low region
SS-
100
106
112
119
126
133
141
150
159
168
178
189
200
212
225
239
253
268
284
301
319
338
359
380
403
427
453
ssS-
sssss+
s+
s+
s+
s+
s+
s+
s+
s+
sssssssss-
Medium region
359
381
403
428
453
480
509
540
572
607
643
681
722
766
812
860
912
967
1025
1086
1151
1220
1294
1371
1454
1541
1633
High region
1200
1272
1348
1429
1515
1606
1702
1804
1913
2027
2149
2278
2415
2560
2713
2876
3048
3231
3425
3631
3849
4079
4324
4584
4859
5150
5459
To reduce the effects of amplitude cues on the frequency-range
discriminations, we made versions of each tone at 65 dB sound
pressure level (SPL) and 75 dB (SPL) measured at the bird's
position on the perch with a Type-2235 Precision Sound Level
Meter (BrUel & Kjaer Canada, Ltd., Pointe Claire, Quebec,
Canada). In alternate sessions (i.e., on alternate days), birds heard
the first or the second version of each tone. Spectral analysis found
no significant energy at harmonic multiples of the tones in
recordings taken at the bird's position on the perch. That is, energy
levels at frequencies above the training tones varied from —35 to
—45 dB relative to energy at the fundamental and in no noticeable
relation to the fundamental frequencies of the training tones.
Procedure
Zebra finches. Nondifferential training began after a bird
learned to use the perch and feeder. We used nondifferential
training to create a high and uniform rate of responding across
training tones. The within-trial sequence began when a bird landed
on the perch, in turn breaking an infrared beam. If the bird
remained on the perch for 1 s, a single tone was selected randomly
and without replacement and played. If the bird entered the feeder,
in turn breaking an infrared beam, it was rewarded with 1-s access
to food. A 30-s intertrial interval (ITI) followed. If the bird left the
perch without entering the feeder, a trial ended after 1 s. If the bird
remained on the perch, a trial ended after 1 s, and a 60-s m
followed. We did this to increase the probability of the bird leaving
the perch during all trials.
247
RANGE DISCRIMINATION
After a bird responded at a high and consistent level to the tones,
discrimination training began. Here, visits to the feeder after S+
tones (S+s) were still rewarded with access to food, but visits to
the feeder after S- tones (S-s) resulted in a 30-s ITI with the
chamber lights off. As during nondifferential training, for each trial,
a single tone was selected randomly and without replacement from
the stimulus pool. The birds were tested in daily 12-hr sessions.
Birds had nondifferential training and then discrimination training in one of three spectral regions (n = 6 per region). In the
low-region group, nine tones between 168 Hz and 268 Hz (the
middle-frequency range) were S+s, whereas nine tones between
100 Hz and 159 Hz (the lower frequency range) and 9 tones
between 284 Hz and 453 Hz (the upper frequency range) were S —
s (see Table 1). In the medium-region group, nine tones between
607 Hz and 967 Hz (the middle-frequency range) were S+s,
whereas nine tones between 359 Hz and 572 Hz (the lower
frequency range) and nine tones between 1035 Hz and 1633 Hz
(the upper frequency range) were S—s (see Table 1). In the
high-region group, nine tones between 2027 Hz and 3231 Hz
(the middle-frequency range) were S+s, whereas nine tones
between 1200 Hz and 1913 Hz (the lower frequency range) and
nine tones between 3425 Hz and 5459 Hz (the upper frequency
range) were S-s (seeTable 1).
Discrimination ratios were calculated during training by dividing
the average percentage of response to the 9 S+ tones by the
average percentage of response to the 9 S + tones and to the 18 S—
tones. Discrimination was at chance when the discrimination ratio
was 0.50 (responding to all tones about equally) and perfect when
the ratio was 1.00 (responding to only the 9 S + tones). Discrimination training ended once the bird maintained a discrimination ratio
greater than or equal to 0.80 in 3 consecutive sessions or after 15
sessions of training, whichever occurred first.
Humans. The procedure for humans and zebra finches was the
same except that (a) humans used their hands to press the response
buttons, (b) humans had a 3-s ITI, and (c) humans did not receive
nondifferential training but instead were shown how to use the
apparatus with three exemplar tones before training began. For
humans, their responses on S+ trials lit up the LEDs for 1 s,
whereas responses on S — trials turned off the overhead room lights
for 3 s (one 40-W lamp remained on). Each 40-min training session
(about 500 trials) was divided in half by a 5-min rest break. Four
humans (two men and two women) heard the tones in each of the
three spectral regions.
Participants were instructed to initiate a trial by pressing the left
response button (the response played a single tone) and then to
press the right response button if the tone was an S+. Participants
were instructed that responses to S + tones turned on the LEDs and
that responses to S— tones briefly turned off the room lights. We
told participants about the importance of pitch to the discrimination
and about the distribution of S+ and S— frequencies hi their
training group.
Participants monitored their progress on a wall chart. Separate
charts, mounted in plain view, showed the competition for additional money as separate horse races among the participants in each
of the three spectral region groups. Participants were represented
by horses that were identified by numbers on the chart. The match
between a numbered horse and a participant was known only by
that person and by the experimenters. Distances on the charts
represented differences in discriminative performance among participants, but the scale could differ between groups. We paid a
competitive reward to the participant who attained the highest
discrimination ratio in each group.
Statistical Analyses
We conducted analyses of variance (ANOVAs) on the results
(i.e., discrimination ratios and percentages of response). The
percentages of response and the discrimination ratios may not be
normally distributed when sample ratio and percentage values are
near 0 and 1, or 0% and 100%, respectively, so we conducted
parallel analyses of the square root arcsine transform of both
measures. Analysis of the transformed data yielded virtually the
same levels of significance as analysis of the untransformed data;
we report the untransformed data below.
Results and Discussion
Acquisition
All zebra finches and humans attained criterion discrimination in 4,000 to 5,000 trials (i.e., in the 15 sessions
allocated to the experiment). In an among-groups
(Species X Spectral Region) ANOVA, we found no significant differences between species or among groups (Fs < 1).
Inspection of discrimination ratios across sessions suggested
that although all subjects finished training in about the same
number of trials, the pattern across trials and the final level
of discrimination differed among groups.
Figure 1 shows mean discrimination ratios for zebra
finches and humans in the low, medium, and high spectral
region groups across Trials 1,000 to 4,000 and on the final
day of discrimination training. Humans learned frequencyrange discriminations in the three spectral regions at about
the same pace and to about the same level. Zebra finches
learned the medium- and high-region discriminations more
rapidly than the low-region discrimination, but by the final
session of training they were discriminating in all three
regions at the same high level. Early in training, zebra
1.00
<
0.90
CC
0.80
5
IT
«
Q
0.70
FINCHES
0.60
4 /
HUMANS
—•—
HIGH —o—
•••••••••
MED --0"-
IL'
--*--
LOW --&--
0.50
1
2
3
4
FINAL
THOUSANDS OF TRIALS
Figure 1. Mean discrimination ratios across thousands of trials of
acquisition and on the final day of training for zebra finches and
humans in the frequency-range discriminations in the low, medium
(MED), and high spectral regions in Experiment 1. When the
discrimination ratio equals 0.50 performance is at chance, or about
equal responding to all 27 tones.
248
WEISMAN ET AL.
finches discriminated more poorly than humans, yet by the
final session zebra finches discriminated better than humans.
Every subject completed the first 4,000 training trials, but
different subjects finished training to criterion after varying
amounts of training; therefore, we conducted separate
ANOVAs for results to 4,000 trials (in four 1,000-trial
blocks) and for the final day of training. Across the first
4,000 trials, in a mixed (Species X Spectral Region X Trials)
ANOVA, we found significant effects for Species, F(l, 24) =
7.88, p < .01, Trials, F(3, 72) = 100.08, p < .0001; and a
significant Species X Trials interaction, F(3, 72) = 24.60,
p < .0001. Analysis of simple effects (p < .05) showed that
discrimination improved significantly over trials for both
species. Zebra finches began with significantly lower discrimination ratios (at 1,000 trials) than humans, but there
were no significant differences at 4,000 trials.
In mixed (Spectral Region X Trials) ANOVAs conducted
separately for zebra finches and humans, we found a
significant effect of spectral region for zebra finches,
F(2, 15) = 4.99, p - .022, but not for humans, F < 1.
Individual comparisons (Tukey, p < .05) showed that in
zebra finches the low-region group learned to discriminate
significantly more slowly than the medium- and high-region
groups, which did not differ significantly across the first
4,000 trials.
For the final day of training, in an among-subjects
(Species X Spectral Region) ANOVA, we found significantly better discrimination for zebra finches than for
humans, F(l, 24) = 51.17, p < .0001; no significant
differences in discrimination among the spectral regions
within each species (F < 1); and no significant
Species x Spectral Region interaction (F < 1). Although
discrimination appeared to decline slightly for humans on
the final day, in fact, mean discrimination ratios at 4,000
trials and on the final day differed by less than 0.01, and a
mixed (Session X Spectral Region) ANOVA found no significant decline (Fs < 1).
In summary, humans began the frequency-range discrimination with better performance than zebra finches, but by the
end of training zebra finches discriminated to a higher
standard than humans. It seems likely that we obtained better
initial performance from humans than from zebra finches
because humans were instructed about the distribution of
S + s and S—s in their training group. Humans learned the
range discrimination at the same rate in all three regions,
whereas zebra finches learned the discrimination faster in
the medium and high regions than in the low region. In the
following analyses, we examined in detail the performance
on the final day to seek out differences in frequency-range
discrimination across spectral regions.
Discrimination Across Tones on the Final Day
Among Spectral Regions
To compare performances directly across frequencies
among frequency-range discriminations in the three spectral
regions, we put the three discriminations on a common x
axis. We scaled frequency in relative frequency units of
constant log increase, A/// = 0.06, tone-by-tone beginning
ZEBRA FINCHES
—o—
--0--
HIGH
MED
•-A-- LOW
LU
W
2
en
LU
OC
U.
o
HI
o
0
2
4
6
8
10 12 14 16 18 20 22 24 26
8
10 12 14 16 18 20 22 24 26
LU
Q.
20-
0J
2
4
6
CONSTANT 6% INCREMENTS
FROM 100, 359, OR 1200 HZ
Figure 2. Mean percentage of response across constant 6%
increments in frequency from 100, 359, or 1200 Hz in the low-,
medium- (MED), and high-frequency ranges, respectively, in the
final session of frequency-range discrimination for zebra finches
(upper panel) and humans (lower panel) in Experiment 1. The solid
symbols show S+ (rewarded) tones, and the open symbols show
S— (nonrewarded) tones.
with the initial tone, 100, 359, or 1200 Hz in the low,
medium, and high regions, respectively (see Figure 2).
Zebra finches outperformed humans by responding more to
S+ tones and less to S— tones near range boundaries.
Within each species, final performance (percentage of
response) in the frequency-range discrimination appears to
be highly similar tone-to-tone across relative frequencies in
the low, medium, and high spectral regions.
In humans, a mixed (Spectral Region X Relative Frequency) ANOVA showed no significant effect of spectral
region and no significant interaction with relative frequency,
Fs < 1. In zebra finches, a mixed (Spectral Region X Relative
Frequency) ANOVA found a significant Spectral Region X
Relative Frequency interaction, F(52, 390) = 1.41, p = .04.
In an analysis of simple effects for the interaction, discrimination differed significantly among spectral regions at
exactly 4 of 27 relative frequencies. Two differences involved the low region; one difference each involved the
249
RANGE DISCRIMINATION
tional conditions for humans, and the apparatus were the same as in
Experiment 1.
other two regions. Examination of these small but significant
differences in percentage of response revealed no systematic
difference in favor of frequency-range discrimination in any
particular spectral region.
In summary, within-species differences in the final level
of performance varied from nonsignificant to inconsequential from one spectral region to another. With the same few
inconsequential exceptions, the discrimination of lower and
higher range S- relative frequencies was symmetrical and
equal among regions within species. In the cross-species
comparison with humans, zebra finches appeared to have
both a special sensitivity for the frequencies in the fundamentals and loud harmonics in their songs and a general ability
to sort frequencies across spectral regions.
Stimuli
We synthesized 40 tones at 980-5660 Hz (see Table 2). In
Experiment 2, as in Njegovan et al. (1995), tones were spaced 120
Hz apart. Details of ramping, amplitudes, and spectral analysis of
the tones were the same as in Experiment 1.
Procedure
Experiment 2
Note that frequencies are among birdsongs' most salient
features, and it is important to know that songbirds can use
multiple-frequency features to recognize song (e.g., maximum and minimum note frequencies in conspecific song;
Nelson, 1988). Thus, we expected that songbirds could
discriminate more than one S+ range. In Experiment 2, we
challenged the pitch-sorting abilities of zebra finches and
humans in a multiple frequency-range discrimination contrasting four S + frequency ranges with four S - ranges. We
maintained the same procedural and motivational aspects of
the task as in Experiment 1. Nonetheless, we expected that
the increased perceptual challenge of multiple frequencyrange discrimination would affect discrimination performance more in humans than songbirds.
Method
Subjects and Apparatus
Ten experimentally naive adult male zebra finches participated
in the experiment. Also, we recruited 10 experimentally naive
humans, including 5 women and 5 men, who were practicing
musicians with 3—20 years of musical training and who were 20—42
years old, as in Experiment 1. As in Experiment 1, we chose
musicians because they have extensive training with brief tonal
stimuli and we excluded people with extensive absolute pitch
abilities. Housing and feeding conditions for zebra finches, motiva-
After birds responded at a high consistent level to tones during
nondifferential training, we added discrimination contingencies
(S- and S+) between successive frequency ranges; each frequency range consisted of five tones as shown in Table 2. In the S—
first-reward order groups (ns = 4 zebra finches and 4 humans),
responses to tones in Ranges 2, 4, 6, and 8 were rewarded (see
Table 2). In the S + first-reward order groups (ns = 6 zebra finches
and 6 humans), responses to tones in Ranges 1, 3, 5, and 7 were
rewarded (see Table 2). In each group, responses to tones in ranges
alternating with S+ ranges were not rewarded. In all other ways,
nondifferential training and discrimination training were the same
as in Experiment 1.
The procedure for humans was similar to Experiment 1, except
that the tone set was the same as the set for zebra finches in
Experiment 2. Also, in Experiment 2, humans were shown how to
use the apparatus with eight exemplar tones (one per range) before
training began. We told the participants about the importance of
pitch to the discrimination and about the distribution of S + and S—
frequencies over the eight frequency ranges in their training group
(i.e., the same instructions as in Experiment 1 but with eight
frequency ranges instead of three).
Discrimination ratios were calculated during training by dividing
the average percentage of response to the 20 S+ tones (4 S+
ranges of 5 tones each) by the average percentage of response to the
20 S+ tones and 20 S— tones (4 S— ranges of 5 tones each). As in
Experiment 1, discrimination was at chance when the ratio was
0.50 (response was about equal to all tones) and perfect when the
ratio was 1.00 (responding to only the 20 S+ tones). Discrimination training ended once a subject maintained a discrimination ratio
of greater than or equal to 0.80 for 3 consecutive sessions or after
12 sessions of training, whichever occurred first. Statistical analyses of discrimination ratios and percentages of response were
computed the same as in Experiment 1.
Table 2
Frequencies (Hz) ofS+ (Rewarded) and S— (Nonrewarded) Tones in the S— First and
S+ First Multiple Frequency—Range-Discrimination Groups
Frequency
range
1
2
3
4
5
6
7
8
Frequency (Hz)
S- first S+ first
group
group
SS+
S-
s+
ss+
ss+
S+
s~
s+
ss+
ss+
o
980
1580
2180
2780
3380
3980
4580
5180
1100
1700
2300
2900
3500
4100
4700
5300
1220
1820
2420
3020
3620
4220
4820
5420
1340
1940
2540
3140
3740
4340
4940
5540
1460
2060
2660
3260
3860
4460
5060
5660
250
WEISMAN ET AL.
Results and Discussion
Acquisition
All zebra finches attained criterion discrimination, on
average, in 5,000 to 6,000 trials, whereas no human participant met the criterion in the 12 sessions allocated for the
experiment. Given that there was no variability in the results
for humans, statistical testing was impractical, but it is
reasonable to conclude that (in the context of a very long
experimental test) zebra finches acquire the multiple frequency-range discrimination with high accuracy (discrimination
ratios > 0.8) and humans do not. As in Experiment 1, we
examined the pattern across trials and the final level of
discrimination between species to obtain more quantitative
information about discrimination performance.
Figure 3 shows mean discrimination ratios for zebra
finches and humans in the S— and S+ first-reward order
groups across Trials 1,000 to 5,000 and on the final session
of discrimination training. We show 5,000 trials here, rather
than 4,000 trials as in Experiment 1, because the discrimination in Experiment 2 was more difficult; no subject learned it
to criterion in fewer than 5,000 trials. The S- and S+ first
discriminations were learned at about the same rate and to
about the same level of accuracy. Zebra finches outperformed humans by discriminating between S+ and Sranges more quickly and to a much higher level (0.85) than
humans (0.54). For humans, discrimination increased only
slightly from chance (0.50) over Trials 1,000 to 5,000 or on
the final day.
Every subject completed the first 5,000 training trials, but
different subjects finished after different amounts of train-
1.00
FINCHES
ing; therefore, we present separate analyses across Trials
1,000 to 5,000 and for the final session of training. Over
Trials 1,000 to 5,000, we conducted a three-factor mixed
(Species X Reward Order X Trials) ANOVA and found significant main effects for species, F(l, 16) = 5.68, p - .03,
and trials, F(4, 64) = 27.48, p £ .0001, and a significant
Species X Trials interaction, F(4,64) = 9.41, p •< .0001.
In an analysis of simple eifects (p < .05), we found
significantly higher discrimination ratios for zebra finches
than for humans at 3,000,4,000, and 5,000 training trials. In
zebra finches, performance increased progressively and
significantly over trials. In humans, the increase in discriminative performance was small but statistically significant, so
that performance in the multiple frequency-range discrimination was above chance at 4,000 and 5,000 trials.
In the final session of training, zebra finches greatly
outperformed humans by discriminating between S+ and
S- ranges to a much higher asymptote (see Figure 3). For
humans, the discrimination ratio varied only slightly from
chance (0.50). In a two-factor between-subjects
(Species X Reward Order) ANOVA of discrimination ratios
on the final day of training, we found only a significant main
effect for species, F(l, 16) = 266.57, p £ .0001, that is,
significantly higher discrimination ratios for zebra finches
than humans.
In summary, zebra finches and humans began the frequency-range discrimination at about chance performance;
by the end of training, zebra finches discriminated to a high
standard, whereas humans discriminated only slightly better
than chance. In the next analyses, we looked in detail at
performance on the final day to seek differences between the
S— and S+ first discriminations.
Discrimination Across Tones on the Final Day
HUMANS
Between the S— and S+ First-Reward Order Groups
0.90
—•—
S- FIRST —a—
--»--
S+FIRST - - A - -
0.80
5
£
0.70
0.60
0.50
1
2
3
4
5
FINAL
THOUSANDS OF TRIALS
Figure 3. Mean discrimination ratios across thousands of acquisition trials and on the final day of training for zebra finches and
humans in the S— (nonrewarded) and S+ (rewarded) first-reward
older multiple frequency-range discriminations in Experiment 2.
When the discrimination ratio equals 0.50, performance is at
chance, or about equal responding to all 40 tones.
To compare final day performance in detail among
multiple frequency-range discriminations, we show the
percentage of response as a function of frequency (Hz)
separately for S- and S+ first groups of zebra finches (left
panels) and humans (right panels) in Figure 4. For zebra
finches, the pattern of response increased steeply and
precisely from a low percentage of response to the tones in
each S- frequency range to a much higher percentage of
response to the tones in each adjacent S+ frequency range in
the S— and S+ first groups. For humans, no precise
alternation between low and high percentages of response
between S- and S+ ranges was observed. Instead, in the
S— first group, humans responded most at the highest
frequencies and least at the lowest frequencies, whereas in
the S+ first group, humans responded most at the lowest and
least at the highest frequencies.
We conducted separate two-factor mixed (Reward
Order X Frequency) ANOVAs for the percentage of response across frequencies for zebra finches and humans
and found significant interactions for both species, Fs(39,
312) s 8.43, ps s .0001. In analyses of simple effects
251
RANGE DISCRIMINATION
ZEBRA FINCHES
HUMANS
s
DC
Li.
O
0
1000
2000
3000
4000
5000
HI
H
6000
1000
2000
3000
4000
5000
6000
2000
3000
4000
5000
6000
S-f FIRST
100
100 -,
eo
80-
HI
o
HI
60-
40
40
1000
2000
3000
4000
5000
6000
1000
FREQUENCY (Hz)
Figure 4. Mean percentage of response across frequency (Hz) for S— (nonrewarded) first (upper
panels) and S+ (rewarded) first (lower panels) reward order groups in the final session of multiple
frequency-range discriminations in Experiment 2 for zebra finches (left panels) and humans (right
panels). The solid symbols represent S+ tones, and the open symbols represent S— tones.
(ps < .05), we found very different interactions for zebra
finches and humans (see Figure 4). For zebra finches, the
percentage of response differed significantly at every frequency, so that range-by-range when the percentage of
response was low in the S - first group it was high in the S +
first group and, conversely, when it was high in the S - first
group it was low in the S+ first group. For humans, the
percentage of response differed significantly at 16 of 40
frequencies, such that the percentage of response was low at
low frequencies and high at high frequencies in the S - first
group and the opposite in the S+ first group. In these two
very different ways, the percentage of response in the S—
first group was the inverse of the percentage of response in
S+ first group, as demonstrated by large, highly significant
negative correlations between percentages of response toneby-tone in the S— and S+ first-reward orders for zebra
finches, r(39) = -.98, p =£ .0001, and humans, r(39) =
-.77,/>=s.0001.
In summary, zebra finches discriminated every shift
between reward and nonreward range-for-range, whereas
humans discriminated only whether the first and last ranges
were associated with reward and nonreward, respectively, or
the opposite. The results support the hypotheses that songbirds can accurately discriminate several S+ ranges between
about 1000 Hz to 6000 Hz and that humans can acquire only
rudimentary discriminations of the highest and lowest
ranges in this multiple frequency-range task.
A Simple Hearing Test
Zebra finches responded accurately on over 85% of the
trials on the final training day. Clearly, the birds heard
the S+s and S-s. Humans responded accurately on only
about 55% of the trials on the final training day. To ensure
that poor human performance was not the result of poor
hearing, after multiple frequency-range-discrimination training, we conducted a simple hearing test with tones at 100,
212, 453, 980, 1200, 2060, 2560, 3080, 4100, 5129, 5459,
and 5660 Hz and no-tone "catch" trials. Tones were played
at 65, 70, 75, and 80 dB SPL. Individuals were instructed to
initiate a trial by pressing the left response button (the
response played a single tone or no tone) and then to press
the right response button if a tone of any frequency and
loudness was heard. As per our instructions, no feedback
from the LEDs or from the overhead light was provided
during the 20-min test. Humans accurately reported the
presentation of a tone on 85% to 100% of the trials in the
hearing test. It appears that humans heard the S+s and S—s
during multiple frequency-range training but were unable to
sort the tones with any precision.
252
WEISMAN ET AL.
General Discussion
Motivation and Perception as Determinants
of Frequency-Range Discrimination
Njegovan et al. (1995) reported an incisive difference in
performance between songbirds and humans; zebra finches
greatly outperformed humans at sorting frequencies into
ranges. Finding the source of a difference in performance
between species can be problematic. However, more extensive data sets available for comparison increase the likelihood that the source of a species difference can be determined. One purpose of the present research was to determine
whether motivational differences or differences in perception account for the superiority of songbirds over humans in
frequency—range discriminations.
We constructed a comparison of several data sets available from Njegovan et al. (1995) and Experiments 1 and 2
(see Figure 5). The presentation of results in Figure 5
requires some explanation. Discrimination results from
1.00
ZEBRA FINCHES
0.90
0.80
0.70
..*
a:
0.60
NJEGOVAN ET AL.
X
Sj
|
GO
0.50
--•—
EXPERIMENT 1
A
EXPERIMENT 2
1.00
HUMANS
0.90
O
0.80
0.70
0.60
0.50
1
2
3
4
5
FINAL
THOUSANDS OF TRIALS
Figure 5. Mean discrimination ratios across thousands of acquisition trials and on the final day of training for zebra finches (upper
panel) and humans (lower panel) for the compact frequency-range
discrimination reported by Njegovan et al. (1995), the high spectral
region discrimination (Experiment 1), and the average for S—
(nonrewarded) and S+ (rewarded) first multiple-range discriminations in Experiment 2. Performance is at chance, or about equal
responding across tones, when the discrimination ratio equals 0.50.
Njegovan et al. (1995) are replotted as discrimination ratios
(with a range of 0.50 to 1.00) as a function of thousands of
trials to conform to the treatment of results in Experiments 1
and 2. Ratios from Experiment 1 are shown only for
discrimination in the high spectral region, which is about the
same region as for frequency-range discriminations in
Njegovan et al. (1995) and in Experiment 2. Also, highly
similar discrimination ratios for the S + and S - first-reward
orders in Experiment 2 have been averaged to simplify
presentation.
It is straightforward to construct an unambiguous ranking
of the perceptual challenge of the frequency-range discriminations tested here and by Njegovan et al. (1995); the
perceptual difficulty of a range increases with (a) the number
of alternating S+ and S- tones and (b) the proximity in
frequency between tones across ranges. The frequencyrange discrimination in Experiment 2 (eight ranges, tones
separated in absolute frequency by 120 Hz or an average
relative frequency change of about 3%) is more difficult
perceptually than the discrimination in Njegovan et al.
(1995; three ranges, with tones separated in frequency by
120 Hz or an average of about 3%), which, in turn, is more
difficult perceptually than the discrimination in Experiment
1 (three ranges, with tones separated in frequency by 6%).
For zebra finches and humans, discrimination ratios across
trials and on the final day (Figure 5) varied directly with this
simple and obvious scaling of perceptual difficulty. The
relationship holds for zebra finches, who remained under the
same motivational conditions in all three experiments, and
for humans, who had stronger motivation in Experiments 1
and 2 than in Njegovan et al.'s (1995) experiment.
We used instructions and competitive rewards to ensure
that human participants were well-motivated. There are
indications that these procedures were effective; humans in
both experiments attended to their position in the horse-race
simulation, continued to attend sessions regularly, and, in
Experiment 1, had high discrimination ratios (Final Day
M = 0.86). Therefore, the hypothesis that humans performed more poorly than songbirds mainly because of
inadequate motivation fails to provide a principled general
account of the available data sets. In Experiment 1, where
humans were well-motivated, they performed about as well
as the zebra finches in Njegovan et al.'s (1995) experiment
(Final Day Ms = 0.86 and 0.88, respectively). Yet, in
Experiment 2, where humans were identically wellmotivated, they fell to minimally effective (near-chance)
discrimination (Final Day M = 0.55). Put simply, given
evidence that the motivational conditions in Experiment 1
were sufficient to generate excellent range discrimination
(Final Day M = 0.88), it would be bizarre to conclude that
these same motivational conditions were the cause of dismal
(but significantly above-chance) performance in a more
difficult multiple frequency-range discrimination in Experiment 2 (Final Day M = 0.55); instead it seems credible that
the perceptual abilities of zebra finches and humans account
for most, if not all, of the differences observed among
frequency-range discriminations and species in Experiments 1 and 2 and in the experiments reported by Njegovan
etal.(1995).
253
RANGE DISCRIMINATION
Perceptual accounts of the ability of zebra finches and
humans to sort pitches in frequency-range discriminations
could include only immediate perception for pitches (pitch
resolution) or both immediate perception and perceptual
memory for S+ and S— pitches. Better pitch categorization
of more widely spaced tones (6% vs. 3%) suggests an
important role for immediate pitch perception, whereas
better performance with three rather than eight frequency
ranges could implicate either better pitch resolution or better
pitch memory.
Network Models for Pitch Classification
in Frequency-Range Discriminations
In this section, we use a simple neural network mode) to
provide a perceptual basis for how songbirds and humans
sort pitches. The model also provides estimates for the roles
of immediate perception and perceptual memory for pitch in
frequency—range discriminations.
Our starting point is Shepard and Kannappan's (1991)
network model of stimulus generalization; Njegovan et al.
(1995) reported that the model provided highly accurate
predictions of the percentages of response in frequencyrange discriminations. Njegovan et al. (1995) also described
a serious flaw in Shepard and Kannappan's (1991) model: It
requires a hefty 6 layers of sensory units to fit performance
for zebra finches and a mind-boggling 24 layers of sensory
units to fit performance for humans. There is no anatomical
basis for such voluminous tonotopic maps. Instead, the
anatomical and physiological evidence suggests that tonotopically arranged auditory filters consist of single cells or small
multicell units (Brugge, 1985; Vates et al., 1996). Here, we
present a revised, simplified, and highly predictive network
model of frequency-range discrimination in songbirds and
humans.
In our network model, we consider (a) the sensory effects
of the stimulus tones, that is, the spread of excitation across
an auditory network (in neural terms, a tonotopic pitch map),
and (b) the effects of reward and nonreward trial outcomes,
that is, the activation of connections between the auditory
units and the outcome unit.
In the revised model, we adopted a Gaussian approximation for excitation across a network of tonotopically arranged neural units (these filters are represented schematically at Line ai in Figure 6). The upper panel of Figure 6
shows the spread of excitation from a 1000-Hz tone to neural
units spaced about 3% apart in frequency from about 600 to
1800 Hz (see the horizontal axis at the top of the panel).
Excitation is expressed (on the vertical axis to the left in the
upper panel) as the height of an ordinate of the normal
(Gaussian) distribution centered at each neural unit. Sensory
excitation evoked by a 1000-Hz tone declines in Gaussian
function from the neural unit centered at 1000 Hz to other
units.
In our simulations, the height of the ordinate of the
function at one filter from the input tone defines the form of
the Gaussian excitation pattern. The spread of excitation
across auditory filters is a function of the frequency and
SPREAD OF EXCITATION ACROSS
3% INCREASES IN FREQUENCY (HZ)
1000
750
1250
1500
ZEBRA FINCHES
w
LLJ
-" r
/
: !
HUMANS
\
Z
a
DC
o
o.o
TONOTOPIC
UNITS
Wjj
The Spread of Sensory Excitation
OUTCOME UNIT
We traced the flaw in Shepard and Kannappan's (1991)
model to its requirement that sensory excitation decline
exponentially (over numerous layers of units). Experimental
evidence for humans about the shape of the excitation
pattern for pure tones is available (Moore & Glasberg, 1983,
1987). For example, Moore and Glasberg (1983) concluded
that for human observers and a 1000-Hz tone, a filter
centered at 950 Hz is excited about 94% as much as a filter
tuned directly to a 1000-Hz tone, whereas a filter tuned to
650 Hz is hardly excited at all. Most important, Moore and
Glasberg's (1983, 1987) data show that the spread of the
excitation across neural units (in their terms, "filters") is at
least approximately Gaussian and not exponential as Shepard
and Kannappan (1991) proposed.
ACTIVATION OF CONNECTIONS BETWEEN
TONOTOPIC UNITS AND THE OUTCOME UNIT
Figure 6. Gaussian approximations (upper panel) for the spread
of sensory excitation across a network of filters, or tonotopic neural
units, spaced about 3% apart in frequency in zebra finches and
humans. The horizontal axis (in Hz) is shown at the top of the
panel, and the vertical axis shown at the left in the upper panel is
the ordinate of the Gaussian (normal) function at each frequency. In
the schematic of activation across the network (bottom panel), Wjj is
an associative weight assigned to the connection between each
auditory filter, ai, and the outcome unit, BJ. To simplify the
schematic, we omitted several of the connections between filters
and the outcome.
254
WEISMAN ET AL.
amplitude of the stimulus tones (see a review by Moore,
1989). The amplitudes of the stimulus tones were the same
in Experiments 1 and 2 and in Njegovan et al.'s (1995) work.
Hence, a single excitatory pattern specified by a speciestypical ordinate should suffice to model all the results from
these frequency-range discriminations. It is possible, of
course, that only one (species-general) ordinate might
describe the excitation patterns for both songbirds and
humans. However, the evidence presented here suggests that
differences in perception between songbirds and humans are
the main determinants of then- frequency-range discriminations. Therefore, we expected to find two different excitation
patterns: one for zebra finches and another for humans (see
the upper panel in Figure 6). The shape of the excitatory
pattern across neural units is the model's estimate of
immediate perception or pitch resolution for a species. In the
model, better pitch determination results in a steeper decline
in excitation at filters not centered on the tone.
Table 3
Parameters of the Gaussian Model for Experiments 1 and 2
Parameter
Zebra finches
Humans
Gaussian spread9
Learning rates (\s)
Rewarded outcomes \i
Unrewarded outcomes X2
Number of layers
0.150
0.391
0.10
0.05
0.10
0.05
1
1
"The spread parameter is the height of the ordinate of the normal
curve one unit from the stimulus tone.
Table 3 for parameters). Figure 7 includes Pearson rs for
prediction of observed discriminative performance from
linear regression on response activation values generated by
the best simulations from the model. These simulations
Activation of Connections in the Network
In the model, the strength of a connection (an association
between an auditory unit and the outcome unit) changes
from trial to trial as a function of reward and nonreward. The
lower panel of Figure 6 shows a schematic of this activation.
(In the model every auditory unit is connected to the output
unit, but to simplify the schematic many of the connections
are omitted in Figure 6.) Consider the weight (activation
strength) of the connection, w,j, between the auditory filter,
Oj, and the outcome unit, ar (Although we use only one
outcome unit here, we envision the possibility of more.) The
learning rule in the network is that the change in activation,
Aw,y, is equal to a:\i(l - H^-) on rewarded trials and
X2(0 - wy) on unrewarded trials (see Shepard & Kannappan, 1991). Parameters in the equation are:
a,, the spread of sensory excitation from the input tone to the
ith filter (in the Gaussian function already described);
\i, the learning rate parameter for a rewarded outcome; and
\2, the learning rate parameter for an unrewarded outcome.
In the equation, 1 is the maximum expected probability of
response on a rewarded trial and 0 is the maximum expected
probability of response on an unrewarded trial. As discrimination training progresses, the strength of the connections
between the outcome unit and sensory units near an S +
increases and the strength of the connections to units near an
S— decreases. The strength of connections between the
auditory units and the outcome unit is the model's estimate
of memory for the tone's relationship to reward. In the
model, strong connections between the tone and the outcome result in a better memory for the tone-outcome
relation.
To find the best fit between the results of our frequencyrange discriminations and the network model (see Table 3
for parameters), we conducted dozens of simulations of our
results (altering parameters of the model from simulation to
simulation) to obtain consistently high Pearson rs, which we
report here (see Figures 7, 8, and 9). In Figure 7, we show
percentages of responses simulated by the model and
observed for each spectral region group in Experiment 1 (see
100-,
ZEBRA FINCHES
80-
SIM
-o—
/
•••*•••
-D--
HIGH
MED
LOW
40-
CO
20-
LU
IT
U_
O
HI
£
0
2
4
6
8
10 12 14
16 18 20 22 24 26
8
10 12 14 16 18 20 22 24 26
10
°-| HUMANS
rs = .Q7
80 '
60-
20-
0
2
4
6
CONSTANT 6% INCREMENTS
FROM 100,359, OR 1200 HZ
Figure 7. Mean percentage of response across constant 6%
increments in frequency from 100, 359, or 1200 Hz in the low-,
medium- (MED), and high-ftequency ranges, respectively, in the
final session of frequency-range discrimination (Experiment 1)
obtained from zebra finches (upper panel) and humans (lower
panel) and simulated (SIM) by a Gaussian neural network model.
255
RANGE DISCRIMINATION
ZEBRA FINCHES
A = .97
u.
o
111
o
<
1000
2000
3000
4000
5000
6000
1000
2000
3000
4000
5000
6000
4000
5000
6000
S+ FIRST
r = .83
100-,
o
cc
UJ
0-
40
20-
0-
1000
2000
3000
4000
6000
6000
1000
2000
3000
FREQUENCY (Hz)
Figure 8. Mean percentage of response across frequency (Hz) for S— (nonrewarded) first (upper
panels) and S + (rewarded) first (lower panels) reward order multiple frequency—range discriminations (Experiment 2) obtained from zebra finches (left panels) and humans (right panels) and
simulated (SIM) by the Gaussian neural network model.
provide an excellent fit for (actual) performance in Experiment 1 for both zebra finches and humans.
Figure 8 shows observed and simulated (predicted) percentages of response and rs for the fit between the two; S—
and S+ first multiple frequency-range discriminations are
shown in the upper and lower panels, respectively. Using
identical parameters as for Experiment 1 (Table 3), the fit to
the discriminative performance of zebra finches is excellent.
The fit for humans is very good (again using the same
parameters as for Experiment 1), but nonsystematic variation in the generally increasing (S— first) and decreasing
(S+ first) observed percentages of responses reduced the
correlations between the results and the simulations.
Finally, we asked whether the parameters used to simulate
the results of Experiments 1 and 2 would serve in simulations of Njegovan et al.'s (1995) frequency-range data. In
Figure 9, we show percentages of response data reported by
Njegovan et al. (1995) and their simulation by the model
(see Table 3 for parameters) and rs for the fit between the
two. The Gaussian model provides excellent simulations of
the data without the extensive multilayered tonotopic map
required by Shepard and Kannappan's (1991) exponential
model.
After determining the parameters of best fit (shown in
Table 3), we conducted dozens more simulations to determine the effects of orderly variation in values for the
parameters on the model and its correlation with our results.
For example, we tested models with one to three layers,
using a separate Gaussian ordinate for spread among layers,
but none of those simulations gave better fits than the
one-layer model depicted in Figure 6. We conclude that
multiple layers are not critical to an accurate model for
frequency-range discriminations. This result agrees with the
neural-anatomical evidence about tonotopic maps in Al and
Field L2 in mammals and birds, respectively, which appear
to consist of single neurons or small neural units (see review
by Smith, Moody, & Stebbins, 1990).
We conducted simulations using a range of learning rate
(Xs) parameters from 0.2 to 0.02, with little change in the fits
to discriminative performance. Making the learning rate
parameter for unrewarded trials at least slightly smaller than
for rewarded trials increased some of the correlations by
about .02 and improved the visual appearance of the fits
between the data and simulations. In general, then, over a
wide range of values, learning rates were not critical to
obtaining good fits between discriminative performance and
256
WEISMAN ET AL.
100-, ZEBRA FINCHES
r = .99
SIM
DATA
60-
LU
CO
CO
LU
DC
u_
o-i
O
°^&y?«
2000
2500
3000
3500
4000
4500
5000
3000
3500
4000
4500
5000
HI
HUMANS
t =.97
O
80-
cc
LU
Q_
60-
20-
2000
2600
FREQUENCY
Figure 9.
(HZ)
Mean percentage of response across frequency (Hz) in
the final session of frequency-range discrimination (Njegovan et
al., 1995) obtained from zebra finches (upper panel) and humans
(lower panel) and simulated (SIM) by the Gaussian neural network
model.
simulations from the Gaussian model, except that slightly
better fits were obtained with a separate and lower learning
rate for unrewarded trials. Also, the same learning rate
parameters generated good fits to songbird and human
performance.
Be aware, however, that we did not seek to model
acquisition here; human participants had instructions about
the discriminations that cannot be coded into the learning
rule (see Shepard & Kannappan, 1991), and a salience
parameter is needed to account for differences in acquisition
among zebra finches trained in different spectral regions.
Much more research is needed to develop a successful
network learning rule for these and similar acquisition
functions (Anderson, 1995).
In our systematic variation of the model's parameters, it is
important to note that only the Gaussian ordinate parameters
used to generate excitation patterns were critical to the
highly accurate simulations observed here. For humans,
decreasing or increasing the normal curve ordinate of the
nearest filter (to the stimulus) by as little as 0.005 (and thus
changing the spread of excitation across the tonotopic map)
reduced the Pearson r between simulation and data by at
least .10 and greatly affected the visual appearance of the fit.
For zebra finches, decreasing the normal ordinate of the
nearest filter by 0.20 had little effect, but further decreases
made the simulations for zebra finches appear more like
those for humans and, therefore, greatly reduced the fit.
The network model might be criticized as circular, (i.e.,
anchored only in the data it seeks to explain). In fact, the
model is anchored in a theoretical framework (i.e., Shepard
& Kannappan, 1991), in knowledge of the neural circuits for
tonotopic maps (e.g., Fortune & Margoliash, 1992), and in
estimates of the shape of auditory excitation patterns in
humans (Moore & Glasberg, 1983, 1987), all predating this
article and Njegovan's et al.'s (1995) experiments by several
years. In other words, the conceptual framework for the
model, its neural basis, and the excitation pattern for humans
were not invented by us to explain frequency-range discriminations. The narrower excitation pattern envisioned by the
model for songbirds is accurate for several separate frequency-range discriminations but is not yet anchored by independent evaluation. Here, the model has performed as a
heuristic, pointing to the usefulness of further perceptual and
neural research to determine the auditory excitation patterns
of songbirds.
This analysis suggests that the Gaussian ordinate parameter for the spread of excitation across the tonotopic map is
the single critical determinant of whether a simulation from
the model will predict the performance of a zebra finch or of
a human. Evidence that only variation in perception can
account for the very different discriminative performances
of zebra finches and humans in these experiments is
important. For example, humans were able to discriminate
only the first and last ranges in the multiple-range task in
Experiment 2, which might lead one to suppose that humans
were using very different strategies than songbirds. But the
network model provides a more elegant principled explanation: One simple quantitative difference in auditory excitation pattern accounts for all the observed differences between species. In this way, the model provides further
evidence that the precision of zebra finches' frequencyrange discriminations and the superiority of zebra finches
over humans in frequency-range discriminations have a
perceptual basis in the tonotopic maps of the two species.
Also, if narrow excitation patterns are the rule among
songbirds, these patterns may well provide the perceptual
basis for the importance of frequency to song recognition in
birds. Finally, failure to find an important role for the
strength of connections from the sensory units to the
outcome unit suggests that differences in memory for the
pitch-outcome relation may not play an important role in
determining the species' differences obtained in these experiments. In other words, the network model gives an excellent
account of frequency-range discriminations between species and among training conditions based on differences in
pitch perception between zebra finches and humans without
implicating differences in pitch memory between the species. Future research needs to determine whether evidence
favoring superior pitch memories in songbirds can be
achieved separate from evidence favoring superior pitch
perception.
257
RANGE DISCRIMINATION
Weber Fractions as Determinants
of Frequency-Range Discrimination
Weber fractions (relative difference limens) obtained
from simple successive two-tone discriminations show that
songbirds and humans detect smaller relative changes at
frequencies between 1000 Hz and 5000 Hz than below 1000
Hz; also, Weber fractions are about 1% and 0.25% in
songbirds and humans, respectively (see reviews by Dooling, 1980; Moore, 1989; Sinnott, Sachs, & Hienz, 1980).
The effects of spectral region on frequency-range discriminations (see Figure 1) are only partially predicted by Weber
fractions. Zebra finches, but not humans, learned to sort
pitches in the lower region more slowly than in higher
spectral regions, but in final performance zebra finches were
equally accurate in all three regions. Most important, the
psychophysical data predict that humans should acquire
frequency-range discriminations more quickly and to a
higher asymptote than songbirds, and yet in three extensive
experiments (see Figure 5) we found the opposite result.
Can our results and those from two-tone discriminations
be made consistent? To understand the extent of the
problem, one must know that difference limens derived from
simple two-tone frequency discriminations do poorly in
predicting excitatory patterns calculated from discriminations of complex tones (Moore & Glasberg, 1987). Recall
that these same patterns of excitation accurately predict the
results of our frequency-range discriminations. In other
words, we need to hedge conclusions about the prediction of
multiple frequency-range discriminations from auditory
difference limens until the relation between difference
limens and excitatory patterns is better understood (see a
discussion by Moore, 1989, pp. 160-165).
Are Humans and Songbirds Pitch Specialists
orGeneralists?
Humans acquired the three-range discrimination at the
same rate and reached the same moderate, final-day performance in all three spectral regions. This result indicates that
humans have no special sensitivity for discriminating frequency ranges in the spectral region of the fundamental
frequencies or the formant frequencies in their voices. In
frequency-range discriminations, humans appear to be generalists, capable of modest but useful categorization of
frequencies over a broad acoustic spectrum. This modest
ability may allow humans to sort the fundamental frequencies in, say, female voices from those in male voices and in
infant distress cries.
Humans are challenged to the margins of their perceptual
capacity by increasing the number of positive and negative
ranges to eight. Humans shifted from discriminating the S+
frequency range from the S— ranges in Experiment 1 (three
ranges) to discriminating only crude anchor points at the
lowest and highest frequency S+ and S— frequency ranges
in Experiment 2 (eight ranges, see Figure 4). Thus, the
pitch-sorting ability of humans, although general across the
frequency spectrum, is sharply limited in power.
Zebra finches acquired a frequency-range discrimination
more rapidly in the medium and high regions than in the low
region. This pattern suggests that songbirds have a special
sensitivity for classifying the frequencies predominant in
their vocalizations (e.g., fundamentals and loud harmonics).
By the final day of training, zebra finches discriminated to
the same high standard in all three spectral regions. Also, in
a multiple frequency-range discrimination over a broad
spectrum of frequencies (Experiment 2, Figure 4) songbirds
discriminated precisely in all eight ranges. So, although it is
possible to have either a special sensitivity or general ability,
in comparison with humans, zebra finches manage to have
both when sorting pitches.
Pitch Perception and Song Recognition
Frequency is not the only feature important to song
recognition, but it is the feature that weights highest in
discriminant analyses that distinguish one species from
many (Nelson, 1988), and when pitted against others it is the
feature that predominates in song recognition (e.g., Nelson,
1989). Our experiments revealed that zebra finches have a
remarkable ability to classify pitches, an ability that may be
central to the categorization of song notes and whole songs
as conspecific by songbirds. That is, superior frequencyrange discrimination in these experiments demonstrates that
songbirds have more than sufficient perceptual competence
to classify the several notes in song as conspecific. Using a
simple neural network model, we were able to find evidence
that songbirds' superior pitch-sorting abilities are likely due
to better pitch perception rather than to better pitch memory.
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Received June 3,1997
Revision received December 19,1997
Accepted December 21,1997 •
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