Using Network Models of Absolute Pitch to Compare Frequency-Range Discriminations Across Avian Species

Behavioural Processes 84 (2010) 421–427

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Behavioural Processes

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Using network models of absolute pitch to compare frequency-range discriminations across avian species

Ronald G. Weisman a,∗, Marisa Hoeschele b, Laurie L. Bloomﬁeld c, Douglas Mewhort a, Christopher B. Sturdy b a Queen’s University, Canada b University of Alberta, Canada c Algoma University, Canada article info abstract

Article history: The spectral frequency ranges of song notes are important for recognition in avian species tested in the Received 9 September 2009 field. Frequency-range discriminations in both the field and laboratory require absolute pitch (AP). AP Received in revised form 15 January 2010 is the ability to perceive pitches without an external referent. The authors provided a network model Accepted 18 January 2010 designed to account for differences in AP among avian species and evaluated it against discriminative performance in eight-frequency-range laboratory tests of AP for five species of songbirds and two species Keywords: of nonsongbirds. The model’s sensory component describes the neural substrate of avian auditory percep- Absolute pitch tion, and its associative component handles learning of the discrimination. Using only two free parameters Animals Birds to describe the selectivity and the sensitivity of each species’ auditory sensory filters, the model provided Quantitative network model highly accurate predictions of frequency-range discrimination in songbirds and in a parrot species, but Frequency-range discrimination performance and its prediction were less accurate in pigeons: the only species tested that does not learn Song and call recognition its vocalizations. Here for the first time, the authors present a model that predicted individual species’ performance in frequency-range discriminations and predicted differences in discrimination among avian species with high accuracy. © 2010 Elsevier B.V. All rights reserved.

1. Introduction oscines. The flexibility and subtlety of oscines’ learned songs and calls are important determinants of oscines’ success as a suborder. Birds and mammals are rarely without a voice, and their In fact, oscines are highly successful; they constitute roughly half vocalizations are of demonstrable importance in communication of the approximately 9000 living species of birds. among members of the same species (termed conspecifics). Natu- ral selection has experimented with vocal communication learned by imitation in only a tiny number of distantly related orders 1.1. Auditory perception in song playback experiments and suborders of birds and mammals. Among birds, only true songbirds (oscines), humming birds (Apodiformes), and parrots Bird songs are most commonly studied in playback experi- (Psittaciformes) learn their vocalizations. Among mammals, only ments conducted in the field. In a playback study, territorial males whales and dolphins (Cetaceans), bats (Chiroptera), elephants (Ele- hear songs either recorded from conspecifics or synthesized to phantidae loxdonta), and of course humans (Homo sapiens) learn resemble conspecifics’ songs. The quantification of vigorous male their vocalizations. Comparisons in communication between vocal territorial responses (e.g., approaches to the loud speaker) mea- learning and nonlearning species, and comparisons among vocal sures the potency of the perceptual features or characteristics of learning species are the most powerful tools comparative scien- songs manipulated during playback experiments. Playback studies tists can apply to understand the evolution of communication (see measure the biological importance of a song’s perceptual features Jarvis, 2006, for a review). In this article, we have focused on the directly in nature, without any laboratory artifice. perceptual basis of vocal communication in birds, and especially in There are hundreds of published studies of song playback in dozens of oscine species (see McGregor, 1992; Slater, 2003). From these field experiments, it is now well known that songbirds col- ∗ lect information about several perceptual features of songs (e.g., Corresponding author at: Department of Psychology, Queen’s University, 99 Uni- number of notes, note duration, and the harmonic structure of versity Avenue, Kingston, Ontario, K7L3N6, Canada. Tel.: +1 613 540 4150; fax: +1 613 533 2499. notes) in order to identify conspecifics (e.g., Nelson, 1988). In study- E-mail address: [email protected] (R.G. Weisman). ing this vast literature, it became apparent to our research group

0376-6357/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2010.01.010 422 R.G. Weisman et al. / Behavioural Processes 84 (2010) 421–427 that one song feature, the spectral frequency ranges of song notes, (2006) have described our laboratory operant equipment and pro- has been cited repeatedly as important to both individual recogni- cedures for songbirds in detail. In brief, a bird hears an acoustic tion and species recognition. That is, in field playback experiments, stimulus (here always a sinewave tone) presented via a speaker frequency range predominates over other acoustic features (e.g., after the bird lands on a monitored perch in a sound isolation cham- the timing of notes) in determining the strength of the territorial ber. If the stimulus is an S+ (a go stimulus), then flying or hopping response (see Nelson, 1989a; Weary et al., 1986). The finding that from the perch to a feeder is reinforced with food; if the stimulus the frequency range of song notes (typically from the lowest to the is an S− (a no-go stimulus), then hopping to the feeder is not rein- highest peak frequency) is important to conspecific recognition has forced with food and is punished by a delay before the next trial. been replicated in at least 12 oscine species (e.g., Dabelsteen and Pigeons were tested in standard operant chambers fitted with peck- Pedersen, 1985; Emlen, 1972; Falls, 1963; Nelson, 1989b; Lohr et ing keys and grain feeders (see Friedrich et al., 2007). The pigeons al., 1994; Nowicki et al., 1989; Thompson, 1969; Weary et al., 1986; pecked the left key to hear a tone stimulus. If the stimulus was an Weisman and Ratcliffe, 1989; Wunderle, 1979). In a particularly S+, then pecking the right key was reinforced with food, if the stim- cogent example, Nelson (1989a) demonstrated that field spar- ulus was an S−, then pecking was punished with a delay. Notice that rows’ (Spizella pusilla) songs—pitch-shifted upward in frequency only pigeons (Columba livia) were trained to key peck. by two standard deviations—elicit no more territory defense than Frequency-range discriminations are laboratory analogues to songs recorded from birds of other species, whereas songs played identify the frequency ranges of successive notes in conspecific back within the normal frequency range elicited vigorous territory songs in playback experiments. We trained songbirds in the most defense. difficult of these discriminations, the eight-frequency-range dis- In describing the results of playback studies, field biologists crimination, using 40 tones, spaced 120-Hz apart, in the spectral like to talk about the acoustic properties of song, one of which region between 980 and 5660 Hz, and parsed into eight ranges is the ranges of spectral frequencies found in a species’ songs of five tones each (see Table 1). In the S+ (odd) version of the in nature. Be aware, however, that it is not what songbirds sing task, responses (hopping or flying to the feeder) to tones in the but what they hear that determines responsiveness to song. What odd ranges (1st, 3rd, 5th, and 7th) were reinforced (with food) songbirds hear are pitches in response to the frequencies pro- and responses to tones in the even ranges were not reinforced. duced in song. This is not a trivial point; the relationship between In the counterbalanced, S+ (even), version of the task, responses frequencies and pitches varies between avian and mammalian to tones in the even ranges (2nd, 4th, 6th, and 8th) were rein- species, and among avian species, as a function of differences in forced. Here we report on performance only in the S+ (odd) version auditory neurobiology. The critical perceptual ability that enables of the discrimination, because performance in the S+ (even) ver- songbirds to sort song notes into frequency ranges is called abso- sion is virtually the mirror image of performance in the S+ (odd) lute pitch (AP). AP is defined as the ability to reproduce or version. classify pitches with high accuracy without an external referent Over frequency-range experiments, testing a wide range of (Ward, 1998). Songbirds need no pitch pipe or other external species, important facts have emerged. The first finding is that standard to identify frequency ranges of the notes in conspecific birds (for example, zebra finches, Taeniopygia guttata) succeed in songs. accurately tracking every shift between reinforcement and nonreinforcement across eight frequency ranges, whereas humans and rats (and perhaps other mammals) seem only to track the shift 1.2. AP and frequency-range discriminations from the 1st to the 2nd range and from the 7th to the 8th range but otherwise fail in the eight-range discrimination. The second Although song playback research remains the best method for finding is that, although avian species always track reinforcement establishing the importance of a perceptual feature to song recog- across frequency ranges, the accuracy of the discrimination varies nition, the methodology has several limitations when the goal is to considerably across species (Weisman et al., 2006; Friedrich et al., study the perceptual abilities (here AP) that support use of pitch in 2007). songs and calls. For example, in playback experiments, birds habit- uate to extensive and repeated exposure to the same vocalizations and fail to respond at all to simplified acoustic stimuli that do not resemble conspecifics’ vocalizations. In studies of auditory percep- 2. A network model for classification by AP tion, one often wishes to study the responses of individuals across repeated presentations of an array of stimuli. In our AP studies, The purpose of the present article was to provide a general array we present auditory stimuli at different frequencies and compare model to account for differences among avian species in their ability species in their AP abilities, but we used operant reinforcement to sort frequencies into pitch ranges in operant discriminations and, techniques to minimize habituation. by extension, in territorial defense and mate attraction in nature. Our research group (see Weisman et al., 2006 for a summary) Our model has a sensory component that describes the basic neural has developed a suite of operant discrimination tasks that provide substrate of avian auditory perception and an associative or learn- direct and comparable quantitative measures of frequency-range ing component that describes how the sensory array is connected discrimination across several species of birds. Sturdy and Weisman to the outcome unit that controls behavior.

Table 1 Frequencies (Hz) of S+ (go) and S− (no-go) tones in the eight-frequency-range discrimination (Weisman et al., 1998).

Frequency range S+ (go) S− (no-go) Frequency (Hz)

1 S+ 980 1100 1220 1340 1460 2S− 1580 1700 1820 1940 2060 3 S+ 2180 2300 2420 2540 2660 4S− 2780 2900 3020 3140 3260 5 S+ 3380 3500 3620 3740 3860 6S− 3980 4100 4220 4340 4460 7 S+ 4580 4700 4820 4940 5060 8S− 5180 5300 5420 5540 5660 R.G. Weisman et al. / Behavioural Processes 84 (2010) 421–427 423

2.1. The sensory component

We have anchored our sensory array model on the well- known architecture of auditory areas of the avian brain (Miller and Leppelsack, 1985). Tonotopic representations of pitch are seen in the auditory units found in Field L, the surrounding caudal medial nidopallium (NCM), and the caudomedial mesopallium (CMM). In the tonotopic maps, the anatomical position of auditory units is cor- related to their pitch response properties, and these networks are the principal means whereby avian brains extract pitch information from auditory signals. In our model, the auditory units are inte- grated into a tonotopic gradient according to the mid-frequency of effective stimuli (e.g., tones, or noise bands). Our starting point in modeling the avian anatomical network was Shepard and Kannappan’s (1991) model for stimulus generalization among pitches, which our research group (Njegovan et al., 1995) adopted to provide predictions of three-range frequency discriminations in zebra finches. Moore and Glasberg (1987) concluded that the spread of activation across a network of auditory filters is Gaussian, not exponential as Shepard and Kannappan (1991) suggested. Following Moore and Glasberg (1987), we have used a Gaussian model for the pattern of activation across auditory receptor units to provide highly accurate predictions of three- and eight-frequency-range discriminations in zebra finches (see Weisman et al., 1998). Shepard and Kannappan (1991) used a multi-layer model, with no connections from filter to filter (not feed-forward architecture). We tested single- and multi-layer models. In our multi-layer Fig. 1. A schematic representation of the network model of absolute pitch in birds. models sensory units at each level were connected directly to sen- Gaussian approximations (upper panel) for the spread of sensory activation across sory input, with activation controlled by location (see Shepard and a network of filters, or tonotopic neural receptor units, spaced about 3% apart in frequency for zebra finches. Activation is centered at the receptor unit maxi- Kannappan’s (1991), Fig. 1). Multi-layer models provided no greater mally responsive to the test tone. The horizontal axis (in Hz) is shown at the top precision in predicting performance than a single layer of receptors, of the panel, and the vertical axis is shown at the left (in ordinate units). In the so here we report only on the latter. The sensory units in current schematic of activation across the network (bottom panel), Wj, is the associative model are laid out as a row (a single layer connected further by weight assigned to the connection between each receptor unit, aj, and the outcome solid lines in Fig. 1). The pitch preferred by a receptor, , corre- unit, O. Connections from the receptor units to the outcome unit are influenced by positive (reinforcement) and negative (nonreinforcement) outcomes. To simplify sponds to the receptor’s position in the row: low-frequency pitches the schematic, we omitted several of the connections between the units and the at the left and high-frequency pitches at the right. The receptor outcome. units illustrated in Fig. 1 serve as band-pass filters, each tuned to a preferred pitch. When a tone is sounded, the preferred units and the well-known reduction in auditory sensitivity with increasing those nearby become active. Activation in units horizontally adja- pitch in songbirds (Dooling et al., 2000). Two parameters control cent to the preferred receptor is proportional to the ordinate of a activation in the following equation. The first parameter, B,isa Gaussian distribution centered at the preferred receptor. value of activation at the base frequency in the array. The second, S, We assume that each receptor is a constant relative distance, D, is a sensitivity-reducing multiplier for successive frequencies. We from the adjacent receptors, and D maps onto the species’ primary defined values of the geometric function, G, for successive values auditory area, Field L. Given the receptors are equally spaced, we of x as: set D to a standard distance of 1. Hence, to calculate activation, one needs only a single parameter: the standard deviation, ,ofthe G(x) = B × Sx,x= 1 ...100. Gaussian distribution. We used as a parameter of the model; that is, ordinate values of the filters were a function of . Put simply, the The equation shows the calculation for an ordinal set of x terms effective width of the filters varies inversely with . (x =1...100). The ordinal set of x terms can be used to describe the Activation, A, in the receptor aligned at the preferred pitch, a, intensities of any range of frequencies by varying B and S to select is equal to the ordinate of the Gaussian at 0: the appropriate values. We held B constant and used S as a free parameter to fit the decrease in sensitivity with increasing pitch in A = N(0,). different species.

Activation, A , in the receptor aligned adjacent to the preferred j 2.2. The learning component receptor, aj, is:

Aj = N(|j|,), Each receptor unit in the array is connected to the outcome (response–reinforcer) unit. Only connections to the outcome unit where |j| indicates the absolute value of the deviation from the are modifiable during training. The strength of the connection to the mean in ordinal units and Aj indicates activation at the receptor outcome unit from each receptor in the filter array is defined by a aj (see Fig. 1). In the model, as the frequency of the preferred pitch weight (W). We used Wj to index the connection to the outcome increases, activation produced across receptors, decreases accord- unit, O, from the receptor at the jth pitch in the filter array (see ing to a geometric function. The geometric function is discrete (the Fig. 1). Once training has been completed, the weights are fixed, exponential function is the continuous form). Notice that the reduc- and the model is ready for testing. In practice, one can alternate tion in the sensitivity of our filters at higher frequencies maps to between training and testing the model. 424 R.G. Weisman et al. / Behavioural Processes 84 (2010) 421–427

When a training tone is presented, activation of the filter array the filter array weighted by the connection values determined by is set by the values associated with the tone. The weights from learning: each unit in the filter array to the outcome unit are changed on O = W × A . each training trial. The change depends on a unit’s current acti- j ( j j) vation, A , the nature of the feedback associated with the training j The values of the outcome unit represent the associa- trial (positive or negative). First, consider the change, , associated tion between the tones as discriminative stimuli and the with feedback. If the feedback is positive (excitatory), the weight is response–reinforcer association. increased by a factor equal to : + We have assumed a linear relationship between the value of O and actual performance in the frequency-range discrimination. = Aj × + j That is, final performance in the discrimination, obtained at each If the feedback is negative (inhibitory), the weight is decreased tone, was estimated using simple linear regression, with predicted by a factor equal to − performance as the output of the network. Predicted performance, correlations, between obtained and predicted values, and root = Aj × − mean square (RMS) estimates of error were calculated from a regression analysis for each species. The weights themselves are updated as a function of . Specif- ically, on successive training trials, the weights are changed using a squash function, 3. Using the network model to describe species differences

1 The model includes seven parameters. Five parameters Wj = Wj + , 1 + e− remained constant across species: (1) the number of stimuli; in the present eight-range discriminations, the number of stimuli = 40, where e is the base of the natural logarithms. (2) we set B, the value of activation at the base frequency in the array = .25, and the two learning parameters (3) + = .300 and (4) 2.3. Predicting discriminative performance − = .050. In dozens of tests, across species, wide variation in these parameters was without notable effect. The number of layers, , Once training is complete, the activation of the outcome unit in and S were ﬁrst ﬁt by the Downhill Simplex Method for multi- response to the jth tone, Oj, is set by summing the activation from dimensional minimization, taking the usual precautions to avoid

Fig. 2. Percentage of response in an eight-frequency-range discrimination observed for five species of songbirds (both male and female zebra finches are shown) and predicted from the network model organized as shown in Fig. 1. Pearson correlations and root mean square (RMS) errors are shown for the fit of the model to each species. Empirical results obtained from Weisman et al. (2006). R.G. Weisman et al. / Behavioural Processes 84 (2010) 421–427 425

Fig. 2, across species, correlations between obtained and predicted frequency-range discriminations were very high, ranging from >.92 to >.99. Also, RMS error (root mean square error) between predicted and obtained discrimination performance was small across species. On this basis, we concluded that our network model provides an excellent account of AP in the frequency-range discriminations of individual species of songbirds. We also examined predictions from the model for two nonsongbird species: budgerigars (Melopsittacus undulatus) and pigeons (see Table 2 and Fig. 3). In common with songbirds, both nonsongbird species tracked reinforcement across frequency ranges, although pigeons were less accurate than budgerigars. As shown in Fig. 3, the correlation between obtained and predicted frequency- range discrimination performance was higher and RMS error lower for budgerigars than for pigeons. Variability in discriminative performance across pigeons and mainly poorer accuracy (see results for individual pigeons plotted in Friedrich et al.’s, 2007, Fig. 9) are likely causes of the poorer fit between pigeon performance and the network model. Also unlike the other species, pigeons were tested using the key-pecking response. In pigeons at least, key pecking for food can be less sensitive to control by auditory than visual stimuli (see Foree and LoLordo, 1973). More recently, Panlilio and Weiss (2005) have suggested that the affective state (positive or negative) associated with the auditory stimulus determines the strength of the association between it and a reinforcer. Alternatively, power- Fig. 3. Percentage of response in an eight-frequency-range discrimination observed for two species of nonsongbirds and predicted from the network model organized as ful Pavlovian associations between pecking a visual stimulus and shown in Fig. 1. Pearson correlations and root mean square (RMS) errors are shown food may hinder operant associations with an auditory stimulus. for the fit of the model to each species. Empirical results obtained from Friedrich et To test these ideas, we suggest training pigeons, and a few other al. (2007) and Weisman et al. (2006). species that do not learn their vocalizations, in operant auditory tasks that reinforce perching rather than pecking responses. With- local minima (Nelder and Mead, 1965; also see Press et al., 1992 for out discounting these methodological issues, we suggest that the a sample subroutine). As noted earlier (5) one-layer models yielded most likely reason for the difference in the models for pigeons and as accurate fits as multi-layer models: so across species, all our budgerigars is that budgerigars (in common with other parrots predicted values for the percentages of response are for one-layer and songbirds) learn their calls from conspecifics in adolescence versions of the model. (Farabaugh et al., 1994), whereas pigeons (in common with other We fit the data to obtain values for the final two parame- columbines) show no evidence of learning their calls (Baptista and ters for seven avian species, five species of songbirds (see Fig. 2) Abs, 1983). Despite a poorer predictive fit to pigeons’ performance, and two nonsongbird species (see Fig. 3). Table 2 lists the sam- as we discuss shortly the model was still useful in predicting species ple sizes and Simplex fits for (6) , and (7) S; the only parameters differences in AP. found to vary across species. The discrimination data shown in In Table 2, we show the mean percentage correct across the Figs. 2 and 3 were reported by Weisman et al. (2006) and Friedrich 40 tones for each species. The percentage correct was calculated et al. (2007). as an average of the percentage response on go trials and 100% – Results for male and female songbirds were pooled in Fig. 2, the percentage response on no-go trials. We also show the sen- except the data for zebra finches were plotted separately. Unlike the sitivity, , and selectivity, S, parameters fit separately for each other species, female zebra finches were noticeably less accurate species. One might expect that birds that are selective (their audi- than males and are shown separately in Fig. 2. Sex differences in the tory receptors are well tuned to the target stimuli, i.e., have a frequency-range discriminations of male and female zebra finches smaller ) and whose receptors maintain sensitivity, remain selec- reflect well-known differences in zebra finch neural song systems tive even at higher frequencies (i.e., have a higher S) would be (Bailey and Wade, 2003; Nottebohm and Arnold, 1976). As shown in most accurate at discriminating frequency ranges, tone-by-tone. We computed step-wise regression to determine whether our fit- Table 2 ted values of , and S contributed to accurate prediction of overall Species tested: zebra finches (male and females shown separately), white-throated percentage correct across species. We found that , and S both sparrows (females only), black-capped chickadees (males and females pooled), contributed significantly (F(2,7) = 37.12, p = .001) to the prediction mountain chickadees (males and females pooled), boreal chickadees (males only), of overall accuracy as measured by the mean percentage correct budgerigars (males only), and pigeons (males only). Sample sizes, overall percent- among species (R = .968). In other words, the parameters fit by ages correct, and values for the two free sensory receptor parameters in the network, (2,7) (selectivity), and S (sensitivity), that predicted species differences are shown. our model accurately predicted differences in discriminative performance among species. Our use of step-wise regression is not an Species N % correct S example of data dredging; only two parameters were fit separately Finch males 4 83.2 0.4768 0.9965 to each species and both contributed significantly to the prediction Finch females 4 79.9 0.5296 0.9861 of species differences in the discrimination. This analysis demon- Sparrows 5 71.7 0.4953 0.9665 strates the usefulness of formal modeling to comparative cognition Black capped 8 76.0 0.4998 0.9815 Mountain 8 71.4 0.4911 0.9610 by providing specific parametric values for the comparison, and Boreal 4 60.7 2.4614 0.9790 in the present study, a network model generated parameters that Budgerigars 8 79.5 0.5394 0.9995 described differences between AP across avian species remarkably Pigeons 5 60.8 2.5217 1.0049 well. 426 R.G. Weisman et al. / Behavioural Processes 84 (2010) 421–427

4. Conclusions References

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