Abstract
Birdsong is a rare example of learned vocal communication. Like speech, song consists of acoustic units (syllables) that are learned from adults and organized into temporal sequences (syntax) during development. Tutors’ and pupils’ syllables and syntax are often the same, suggesting that both song features are learned. Syntax is highly species-specific and consistent across individuals and populations of the same species, unlike syllables. Here, we hypothesized that song syntax can be accurately predicted by computational models based on the relationship between syllable acoustics and order that is consistently observed in the songs of conspecific individuals. We tested this hypothesis using techniques inspired by natural language processing to generate and test a species rules model for each of two species of estrildid finches whose songs are composed of stereotyped syllable sequences that differ in syntax. We modeled species rules as the significant correlations between a syllable’s acoustic features and sequence position in a song, measured by analyzing song acoustics in many individuals of the same species. We used only a bird’s syllable types and its species rules to predict syllable sequence in the birds’ adult songs, without using tutors’ songs to train or test the model. We then quantified the accuracy of the species rules model in predicting a bird’s syllable sequence compared to the accuracy of the tutor’s song in predicting a bird’s syllable sequence. Results showed that species rules models predicted birds’ actual sequences as well as did tutors’ sequences, in both species and across different colonies. Results support the hypothesis that species-specific rules based on syllable acoustics and order can explain the species-specific syntax of birdsong. The modeling approach developed here has general utility for detecting, predicting and comparing sequential structure in complex audio signals.
Similar content being viewed by others
Data availability
The analysis code and datasets used here are available from the corresponding author on request.
References
Cambria, E. & White, B. Jumping NLP curves: A review of natural language processing research. IEEE Comput. Intell. Mag. 9, 48–57 (2014).
Min, B. et al. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Comput. Surv. 56, 1–40 (2023).
Berwick, R. C., Okanoya, K., Beckers, G. J. L. & Bolhuis, J. J. Songs to syntax: The linguistics of birdsong. Trends Cogn. Sci. 15, 113–121 (2011).
James, L. S., Sun, H., Wada, K. & Sakata, J. T. Statistical learning for vocal sequence acquisition in a songbird. Sci. Rep. 10, 2248 (2020).
Lewis, R. N., Soma, M., de Kort, S. R. & Gilman, R. T. Like father like son: cultural and genetic contributions to song inheritance in an estrildid finch. Front. Psychol. 12, 654198 (2021).
Menyhart, O., Kolodny, O., Goldstein, M. H., DeVoogd, T. J. & Edelman, S. Juvenile zebra finches learn the underlying structural regularities of their fathers’ song. Front. Psychol. 6, 571 (2015).
Edwards, J. A., Rivera, M. & Woolley, S. M. N. The temporal organization of learned vocal behavior is predicted by species rather than experience. J. Neurosci. 45, e0576242025 (2025).
Marler, P. A comparative approach to vocal learning: Song development in white-crowned sparrows. J. Comp. Physiol. Psychol. 71, 1–25 (1970).
Liu, W. C., Gardner, T. J. & Nottebohm, F. Juvenile zebra finches can use multiple strategies to learn the same song. Proc. Natl. Acad. Sci. U. S. A. 101, 18177–18182 (2004).
Tchernichovski, O., Mitra, P. P., Lints, T. & Nottebohm, F. Dynamics of the vocal imitation process: How a zebra finch learns its song. Science 291, 2564–2569 (2001).
Lachlan, R. F., van Heijningen, C. A. A., ter Haar, S. M. & ten Cate, C. Zebra finch song phonology and syntactical structure across populations and continents—A computational comparison. Front. Psychol. 7, 980 (2016).
Marler, P. & Tamura, M. Culturally transmitted patterns of vocal behavior in sparrows. Science 146, 1483–1486 (1964).
Sturdy, C. B., Phillmore, L. S. & Weisman, R. G. Note types, harmonic structure, and note order in the songs of zebra finches (Taeniopygia guttata). J. Comp. Psychol. 113, 194–203 (1999).
Zann, R. Structure, sequence and evolution of song elements in wild Australian zebra finches. Auk 110, 702–715 (1993).
Hinde, R. A. Bird Vocalizations: Their Relation to Current Problems in Biology and Psychology (Cambridge University Press, 1969).
Love, J., Hoepfner, A. & Goller, F. Song feature specific analysis of isolate song reveals interspecific variation in learned components. Dev. Neurobiol. 79, 350–369 (2019).
Searcy, W. A., Soha, J., Peters, S. & Nowicki, S. Variation in vocal production learning across songbirds. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200257 (2021).
Thorpe, W. H. The learning of song patterns by birds, with special reference to the song of the chaffinch, Fringilla coelebs. Ibis 100, 535–570 (1958).
James, L. S., Davies, J. R., Mori, C., Wada, K. & Sakata, J. T. Manipulations of sensory experiences during development reveal mechanisms underlying vocal learning biases in zebra finches. Dev. Neurobiol. 80, 132–146 (2020).
Price, P. H. Developmental determinants of structure in zebra finch song. J. Comp. Physiol. Psychol. 93, 260–277 (1979).
Williams, H., Kilander, K. & Sotanski, M. L. Untutored song, reproductive success and song learning. Anim. Behav. 45, 695–705 (1993).
Marler, P. & Sherman, V. Innate differences in singing behaviour of sparrows reared in isolation from adult conspecific song. Anim. Behav. 33, 57–71 (1985).
Clayton, N. S. The effects of cross-fostering on selective song learning in estrildid finches. Behaviour 109, 163–175 (1989).
Moore, J. M. & Woolley, S. M. N. Emergent tuning for learned vocalizations in auditory cortex. Nat. Neurosci. 22, 1469–1476 (2019).
Gardner, T. J., Naef, F. & Nottebohm, F. Freedom and rules: The acquisition and reprogramming of a bird’s learned song. Science 308, 1046–1049 (2005).
James, L. S. & Sakata, J. T. Learning biases underlie “universals” in avian vocal sequencing. Curr. Biol. 27, 3676–3682 (2017).
Peters, S., Soha, J., Searcy, W. A. & Nowicki, S. Are song sequencing rules learned by song sparrows?. Anim. Behav. 192, 75–84 (2022).
Rose, G. J. et al. Species-typical songs in white-crowned sparrows tutored with only phrase pairs. Nature 432, 753–758 (2004).
Soha, J. A. & Marler, P. A species-specific acoustic cue for selective song learning in the white-crowned sparrow. Anim. Behav. 60, 297–306 (2000).
Wohlgemuth, M. J., Sober, S. J. & Brainard, M. S. Linked control of syllable sequence and phonology in birdsong. J. Neurosci. 30, 12936–12949 (2010).
Woolley, S. M. N. & Moore, J. M. Coevolution in communication senders and receivers: Vocal behavior and auditory processing in multiple songbird species. Ann. N. Y. Acad. Sci. 1225, 155–165 (2011).
Zann, R. A. Variation in the songs of three species of estrildine grassfinches. Emu 76, 97–108 (1976).
Sainburg, T. & Gentner, T. Q. Toward a computational neuroethology of vocal communication: From bioacoustics to neurophysiology, emerging tools and future directions. Front. Behav. Neurosci. 15, 811737 (2021).
Kershenbaum, A. et al. Acoustic sequences in non-human animals: a tutorial review and prospectus. Biol. Rev. Camb. Philos. Soc. 91, 13–52 (2016).
Yeh, Y., Rivera, M. & Woolley, S. M. N. Auditory sensitivity and vocal acoustics in five species of estrildid songbirds. Anim. Behav. 195, 107–116 (2023).
Immelmann, K. in Bird vocalisations (ed R. A. Hinde) 61–74 (Cambridge University Press, 1969).
Slater, P. J. B., Eales, L. A. & Clayton, N. S. Song learning in zebra finches (Taeniopygia guttata): Progress and prospects. Adv. Study Behav. 18, 1–34 (1988).
Rivera, M., Edwards, J. A., Hauber, M. E. & Woolley, S. M. N. Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny. Sci. Rep. 13, 7076 (2023).
Tchernichovski, O., Nottebohm, F., Ho, C. E., Pesaran, B. & Mitra, P. P. A procedure for an automated measurement of song similarity. Anim. Behav. 59, 1167–1176 (2000).
Kao, M. H. & Brainard, M. S. Lesions of an avian basal ganglia circuit prevent context-dependent changes to song variability. J. Neurophysiol. 96, 1441–1455 (2006).
Zevin, J. D., Seidenberg, M. S. & Bottjer, S. W. Limits on reacquisition of song in adult zebra finches exposed to white noise. J. Neurosci. 24, 5849–5862 (2004).
Araya-Salas, M. & Smith-Vidaurre, G. warbleR: An R package to streamline analysis of animal acoustic signals. Methods Ecol. Evol. 8, 184–191 (2016).
Sueur, J., Aubin, T. & Simonis, C. Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics 18, 213–226 (2008).
Keen, S. C. et al. A machine learning approach for classifying and quantifying acoustic diversity. Methods Ecol. Evol. 12, 1213–1225 (2021).
Fehér, O., Wang, H., Saar, S., Mitra, P. P. & Tchernichovski, O. De novo establishment of wild-type song culture in the zebra finch. Nature 459, 564–568 (2009).
Matheson, A. M. M. & Sakata, J. T. Relationship between the sequencing and timing of vocal motor elements in birdsong. PLoS ONE 10, e0143203 (2015).
Mets, D. G. & Brainard, M. S. An automated approach to the quantitation of vocalizations and vocal learning in the songbird. PLoS Comput. Biol. 14, e1006437 (2018).
Sakata, J. T. & Brainard, M. S. Real-time contributions of auditory feedback to avian vocal motor control. J. Neurosci. 26, 9619–9628 (2006).
Warren, T. L., Charlesworth, J. D., Tumer, E. C. & Brainard, M. S. Variable sequencing is actively maintained in a well learned motor skill. J. Neurosci. 32, 15414–15425 (2012).
Okanoya, K. Song syntax in Bengalese finches: Proximate and ultimate analyses. Adv. Study Behav. 34, 297–346 (2004).
Takahasi, M., Yamada, H. & Okanoya, K. Statistical and prosodic cues for song segmentation learning by Bengalese finches (Lonchura striata var. domestica). Ethology 116, 481–489 (2010).
Sossinka, R. & Böhner, J. Song types in the zebra finch Poephila guttata castanotis. Z. Tierpsychol. 53, 123–132 (1980).
Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: Tests using humpback whale song. Behaviour 149, 1413–1441 (2012).
Kruskal, J. B. An overview of sequence comparison: Time warps, string edits, and macromolecules. SIAM Rev. Soc. Ind. Appl. Math. 25, 201–237 (1983).
Gil, D. & Slater, P. J. B. Song organisation and singing patterns of the willow warbler, Phylloscopus trochilus. Behaviour 137, 759–782 (2000).
Margoliash, D., Staicer, C. A. & Inoue, S. A. Stereotyped and plastic song in adult indigo buntings, Passerina cyanea. Anim. Behav. 42, 367–388 (1991).
Haldar, R. & Mukhopadhyay, D. Levenshtein distance technique in dictionary lookup methods: an improved approach. https://arxiv.org/abs/1101.1232v1 (2011).
Yujian, L. & Bo, L. A normalized Levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1091–1095 (2007).
Slater, P. J. B. in Perspectives in Ethology (eds P. P. G. Bateson & P. H. Klopfer) Ch. 5, 131–153 (Plenum Press, 1973).
Goffinet, J., Brudner, S., Mooney, R. & Pearson, J. Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires. Elife 10, e67855 (2021).
Mandelblat-Cerf, Y. & Fee, M. S. An automated procedure for evaluating song imitation. PLoS ONE 9, e96484 (2014).
Theunissen, F. E., Sen, K. & Doupe, A. J. Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J. Neurosci. 20, 2315–2331 (2000).
Wang, H. et al. Transcriptional regulatory divergence underpinning species-specific learned vocalization in songbirds. PLoS Biol. 17, e3000476 (2019).
Aronov, D., Andalman, A. S. & Fee, M. S. A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320, 630–634 (2008).
Lahti, D. C., Moseley, D. L. & Podos, J. A tradeoff between performance and accuracy in bird song learning. Ethology 117, 802–811 (2011).
Nelson, D. A. & Marler, P. Selection-based learning in bird song development. Proc. Natl. Acad. Sci. U. S. A. 91, 10498–10501 (1994).
Peters, S., Derryberry, E. P. & Nowicki, S. Songbirds learn songs least degraded by environmental transmission. Biol. Lett. 8, 736–739 (2012).
Bin Raies, A., Mansour, H., Incitti, R. & Bajic, V. B. Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text. PLoS ONE 8, e77848 (2013).
Siddharthan, R. Dinucleotide weight matrices for predicting transcription factor binding sites: Generalizing the position weight matrix. PLoS ONE 5, e9722 (2010).
Wang, D. et al. Machine learning reveals cryptic dialects that explain mate choice in a songbird. Nat. Commun. 13, 1630 (2022).
Catchpole, C. K. & Slater, P. J. B. Bird Song: Biological Themes and Variations (Cambridge University Press, 2008).
Lipkind, D. et al. Songbirds work around computational complexity by learning song vocabulary independently of sequence. Nat. Commun. 8, 1247 (2017).
Katahira, K., Suzuki, K., Okanoya, K. & Okada, M. Complex sequencing rules of birdsong can be explained by simple hidden Markov processes. PLoS ONE 6, e24516 (2011).
Woolley, S. M. N. & Rubel, E. W. Bengalese finches *Lonchura striata domestica* depend upon auditory feedback for the maintenance of adult song. J. Neurosci. 17, 6380–6390 (1997).
Garland, E. C. et al. Quantifying humpback whale song sequences to understand the dynamics of song exchange at the ocean basin scale. J. Acoust. Soc. Am. 133, 560–569 (2013).
Lawrence, C. E. & Reilly, A. A. An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 7, 41–51 (1990).
Plamondon, S. L., Rose, G. J. & Goller, F. Roles of syntax information in directing song development in white-crowned sparrows (Zonotrichia leucophrys). J. Comp. Psychol. 124, 117–132 (2010).
Jin, D. Z. & Kozhevnikov, A. A. A compact statistical model of the song syntax in Bengalese finch. PLoS Comput. Biol. 7, e1001108 (2011).
Honda, E. & Okanoya, K. Acoustical and syntactical comparisons between songs of the white-backed munia (Lonchura striata) and its domesticated strain, the Bengalese finch (Lonchura striata var. domestica). Zoolog. Sci. 16, 319–326 (1999).
Lim, Y., Lagoy, R., Shinn-Cunningham, B. G. & Gardner, T. J. Transformation of temporal sequences in the zebra finch auditory system. Elife 5, e18205 (2016).
Schneider, D. M. & Woolley, S. M. N. Sparse and background-invariant coding of vocalizations in auditory scenes. Neuron 79, 141–152 (2013).
Yu, A. C. & Margoliash, D. Temporal hierarchical control of singing in birds. Science 273, 1871–1875 (1996).
McCasland, J. S. & Konishi, M. Interaction between auditory and motor activities in an avian song control nucleus. Proc. Natl. Acad. Sci. U. S. A. 78, 7815–7819 (1981).
Searcy, W. A., Soha, J., Peters, S. & Nowicki, S. Long-distance dependencies in birdsong syntax. Proc. R. Soc. B Biol. Sci. 289, 20212473 (2022).
Cohen, Y. et al. Hidden neural states underlie canary song syntax. Nature 582, 539–544 (2020).
McCullough, M. H. & Goodhill, G. J. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr. Opin. Neurobiol. 70, 89–100 (2021).
Bottjer, S. W., Miesner, E. A. & Arnold, A. P. Forebrain lesions disrupt development but not maintenance of song in passerine birds. Science 224, 901–903 (1984).
Doya, K. & Sejnowski, T. J. A novel reinforcement model of birdsong vocalization learning. Adv. Neural Inf. Process. Syst. 7, 101–108 (1995).
Doya, K. & Sejnowski, T. J. A computational model of birdsong learning by auditory experience and auditory feedback. In Central Auditory Processing and Neural Modeling (eds Poon, P. F. & Brugge, J. F.) 77–88 (Plenum Press, 1998).
Xiao, L. et al. Expression of FoxP2 in the basal ganglia regulates vocal motor sequences in the adult songbird. Nat. Commun. 12, 2617 (2021).
Kobayashi, K., Uno, H. & Okanoya, K. Partial lesions in the anterior forebrain pathway affect song production in adult Bengalese finches. NeuroReport 12, 353–358 (2001).
Kubikova, L. et al. Basal ganglia function, stuttering, sequencing and repair in adult songbirds. Sci. Rep. 4, 6590 (2014).
Eales, L. A. Do zebra finch males that have been raised by another species still tend to select a conspecific song tutor?. Anim. Behav. 35, 1347–1355 (1987).
Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication (Sinauer Associates, 2011).
Brumm, H. & Slabbekoorn, H. Acoustic communication in noise. Adv. Study Behav. 35, 151–209 (2005).
Gerhardt, H. C. The evolution of vocalization in frogs and toads. Annu. Rev. Ecol. Syst. 25, 293–324 (1994).
Gooler, D. M. & Feng, A. S. Temporal coding in the frog auditory midbrain: The influence of duration and rise-fall time on the processing of complex amplitude-modulated stimuli. J. Neurophysiol. 67, 1–22 (1992).
Suga, N., O’Neill, W. E. & Manabe, T. Cortical neurons sensitive to combinations of information-bearing elements of biosonar signals in the mustache bat. Science 200, 778–781 (1978).
Woolley, S. M. N., Fremouw, T. E., Hsu, A. & Theunissen, F. E. Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds. Nat. Neurosci. 8, 1371–1379 (2005).
Basista, M. J. et al. Independent premotor encoding of the sequence and structure of birdsong in avian cortex. J. Neurosci. 34, 16821–16834 (2014).
Foster, E. F. & Bottjer, S. W. Lesions of a telencephalic nucleus in male zebra finches: Influences on vocal behavior in juveniles and adults. J. Neurobiol. 46, 142–165 (2001).
Hosino, T. & Okanoya, K. Lesion of a higher-order song nucleus disrupts phrase level complexity in Bengalese finches. NeuroReport 11, 2091–2095 (2000).
Koparkar, A. et al. Lesions in a songbird vocal circuit increase variability in song syntax. Elife 13, rp93272 (2024).
Williams, H. & Vicario, D. S. Temporal patterning of song production: Participation of nucleus uvaeformis of the thalamus. J. Neurobiol. 24, 903–912 (1993).
Acknowledgements
We thank Mimi Kao for generously providing song recordings of zebra finches from the Tufts University and University of California, San Francisco colonies.
Funding
This study was supported by a National Research Service Award (F31DC020904) to J.A.E. and grants from the US National Science Foundation (IOS-1656825) and National Institutes of Health (DC009810) to S.M.N.W.
Author information
Authors and Affiliations
Contributions
J.A.E. and S.M.N.W. conceived and designed this study. J.A.E. and S.M.N.W. conducted the experiments and analyses. J.A.E. and S.M.N.W. wrote the manuscript text. J.A.E. and S.M.N.W. reviewed and edited the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Edwards, J.A., Woolley, S.M.N. A species rules syntax model accurately organizes birdsong syllables into songs. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44602-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-44602-5
