ArticlePloS one2011
Complex sequencing rules of birdsong can be explained by simple hidden Markov processes.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Language-like statistical structure arises in learned signaling: evidence from birdsong.Science advances · 2026Article
- Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments.eNeuro · 2026Article
- A species rules syntax model accurately organizes birdsong syllables into songs.Scientific reports · 2026Article
- Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity.eLife · 2025Article
- Partially Observable Markov Models Inferred Using Statistical Tests Reveal Context-Dependent Syllable Transitions in Bengalese Finch Songs.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
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- Language-like efficiency and structure in house finch song.Proceedings. Biological sciences · 2024Article
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- Tracking human skill learning with a hierarchical Bayesian sequence model.PLoS computational biology · 2022Article
- Measuring context dependency in birdsong using artificial neural networks.PLoS computational biology · 2021Article
- Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions.Frontiers in behavioral neuroscience · 2021Review
- Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires.PLoS computational biology · 2020Article
- Parallels in the sequential organization of birdsong and human speech.Nature communications · 2019Article
- Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song.Royal Society open science · 2019Article
- Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales.PLoS computational biology · 2019Article
- Article
- Multifractal analysis reveals music-like dynamic structure in songbird rhythms.Scientific reports · 2018Article
- Sexual communication and domestication may give rise to the signal complexity necessary for the emergence of language: An indication from songbird studies.Psychonomic bulletin & review · 2017Article
- Article
- Itinerancy between attractor states in neural systems.Current opinion in neurobiology · 2016Review
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4 authors.
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Abstract
Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.
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