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@article{Kirby2026a,

title = {Language-like statistical structure arises in learned signaling: evidence from birdsong},

author = {Simon Kirby and Kazuo Okanoya and Ellen C. Garland and Miki Takahasi and Inbal Arnon},

doi = {10.1126/sciadv.aea3015},

year  = {2026},

date = {2026-01-01},

journal = {Science Advances},

volume = {12},

number = {32},

pages = {eaea3015},

abstract = {All languages have statistically coherent subsequences (e.g., words) whose frequency distribution follows a power law. These properties facilitate language learning in humans, making them good candidates for arising through cultural transmission as a way to help faithful transmission across generations. Recently, both properties were found in whale song, which is also culturally transmitted, leading to the strong prediction that they should be found wherever complex sequential signaling is culturally transmitted. Here, we use the same tools used to analyze human data and whale song to reveal that culturally transmitted Bengalese finch song also has statistically coherent subsequences whose distribution follows a power law. We additionally show that statistical coherence increases over development, but the power law is present throughout, suggesting that it reflects a fundamental principle of learned representations. Finding these parallels between evolutionarily distant species illustrates the importance of cultural transmission in shaping communication and suggests that core properties of language arise through convergent evolution. Tools inspired by how human infants learn speech reveal language-like statistical structure in birdsong.},

keywords = {Zipf\'s rank-frequency law},

pubstate = {published},

tppubtype = {article}

}

@article{Youngblood2026a,

title = {Zebra finches transform manipulated songs with shuffled syllables to exhibit linguistic laws},

author = {Mason Youngblood},

url = {http://dx.doi.org/10.1007/s10071-026-02058-0},

doi = {10.1007/s10071-026-02058-0},

year  = {2026},

date = {2026-01-01},

journal = {Animal Cognition},

abstract = {Linguistic laws are increasingly used as markers of efficiency in non-human communication, but it remains unclear how rapidly these patterns can emerge. In this re-analysis of experimental data from James and Sakata (James and Sakata 2017), I assessed whether zebra finches tutored with songs with shuffled syllables, where each syllable type is equally common and songs have equal length, transform them to exhibit three linguistic laws associated with efficiency in human language. Menzerath's law and Zipf's rank-frequency law are present in the learned songs to a similar extent as in human language, while there is only weak support for Zipf's law of abbreviation. These results suggest that some measures of language-like efficiency can emerge extremely rapidly, while others may require iterated social learning.},

keywords = {Menzerath-Altmann law, Zipf\'s law of abbreviation, Zipf\'s rank-frequency law},

pubstate = {published},

tppubtype = {article}

}

@article{Kershenbaum2021a,

title = {Shannon entropy as a robust estimator of Zipf's Law in animal vocal communication repertoires},

author = {Arik Kershenbaum and Vlad Demartsev and David E. Gammon and Eli Geffen and Morgan L. Gustison and Amiyaal Ilany and Adriano R. Lameira},

doi = {10.1111/2041-210X.13536},

year  = {2021},

date = {2021-01-01},

journal = {Methods in Ecology and Evolution},

volume = {12},

number = {3},

pages = {553-564},

abstract = {Abstract Information complexity in animals is an indicator of advanced communication and an intricate socio-ecology. Zipf's Law of least effort has been used to assess the potential information content of animal repertoires, including whether or not a particular animal communication could be ‘language-like’. As all human languages follow Zipf's law, with a power law coefficient (PLC) close to −1, animal signals with similar probability distributions are postulated to possess similar information characteristics to language. However, estimation of the PLC from limited empirical datasets (e.g. most animal communication studies) is problematic because of biases from small sample sizes. The traditional approach to estimating Zipf's law PLC is to find the slope of a log\textendashlog rank-frequency plot. Our alternative option uses the underlying equivalence between Shannon entropy (i.e. whether successive elements of a sequence are unpredictable, or repetitive) and PLC. Here, we test whether an entropy approach yields more robust estimates of Zipf's law PLC than the traditional approach. We examined the efficacy of the entropy approach in two ways. First, we estimated the PLC from synthetic datasets generated with a priori known power law probability distributions. This revealed that the estimated PLC using the traditional method is particularly inaccurate for highly stereotyped sequences, even at modest repertoire sizes. Estimation via Shannon entropy is accurate with modest sample sizes even for repertoires with thousands of distinct elements. Second, we applied these approaches to empirical data taken from 11 animal species. Shannon entropy produced a more robust estimate of PLC with lower variance than the traditional method, even when the true PLC is unknown. Our approach for the first time reveals Zipf's law operating in the vocal systems of multiple lineages: songbirds, hyraxes and cetaceans. As different methods of estimating the PLC can lead to misleading results in real data, estimating the balance of a communication system between simplicity and complexity is best performed using the entropy approach. This provides a more robust way to investigate the evolutionary constraints and processes that have acted on animal communication systems, and the parallels between these processes and the evolution of language.},

keywords = {Zipf\'s rank-frequency law},

pubstate = {published},

tppubtype = {article}

}