Aug 2026· Science Advances· Vol 12· 1 citation· 52 references
Medicine
TL;DR
The same tools used to analyze human data and whale song are used to reveal that culturally transmitted Bengalese finch song also has statistically coherent subsequences whose distribution follows a power law, suggesting that core properties of language arise through convergent evolution.
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.
The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have been shaped, at least partly, by repeated transmission from one language user to another. The key finding is that language compositionality can arise spontaneously as a consequence of language being passed repeatedly through a language learning bottleneck. Here we explore how changing the frequency of different meanings, so that some meanings occur much more frequently than others, affects the character of its compositionality. We find that, as observed in natural languages, high-frequency meanings can escape the pressure to conform to the grammar that characterizes lower-frequency meanings. However, when the frequency structure is instead imposed on parts rather than on whole meaning vectors, the language fails to transmit across generations. This occurs despite the fact that the most frequent elements are reliably learned. These results suggest that frequency can shape emergent linguistic structure only when the frequency distribution is defined over form-meaning units that learners can acquire holistically. When frequency is instead distributed over smaller units, it fails to support the relational structure required for compositional generalisation, thereby preventing stable language transmission.
Fabio De Ponte, Eloise Gaines-White, Conor J. Houghton et al.· 0 citations
Human language exhibits lawful structure at the level of words (frequency, vocabulary growth) and word pairs (co-occurrence across distance). Here we show that the arrangement of words in sequence -- a central determinant of meaning -- obeys a comparable law. Using large language models as probabilistic probes, we measured the reduction in target perplexity conferred by prior context at distance d beyond that of the same words scrambled; this difference, the contextual persistence function P(d), isolates the influence of arrangement. Across ten corpora spanning six language families and written and spoken modalities, P(d) decayed approximately as 1/d ($P(d) \propto d^{-\alpha}$, mean $\alpha = 1.04$; median $r^2 = 0.96$). The effect vanished in scrambled and synthetic controls, replicated across independent probes, and did not appear in genomic or protein sequences under domain-native models. An exponent near 1 distributes contextual influence approximately uniformly across logarithmic timescales. The results establish a scaling law of contextual persistence in human language.
Statistical learning (SL) - the ability to detect patterns in sensory input without explicit instruction - is crucial for building internal models of the environment. In humans, it notably supports language acquisition, including word segmentation and grammar learning. Evidence across primates, songbirds, rodents, and insects indicate that SL is a widely shared evolutionarily conserved ability. The computational complexity of the mechanisms involved; however, varies between species, likely reflecting specific cognitive limitations. Alternatively, specific competences may have evolved to support the emergence of demanding, ecologically relevant, functions such as complex communication systems. These findings challenge the idea of a human-specific SL module while raising key questions about its evolutionary drivers and underlying mechanisms. Addressing these questions requires the expansion of cross-species, cross-modal studies with ecologically valid, unsupervised paradigms. Comparative approaches are indeed essential to uncover shared properties and species-specific SL adaptations. Framing SL as a foundational component of cognition, informed by animal research, offers new insights into brain function, and implicit learning in light of the evolution of sophisticated, emergent cognitive abilities such as complex communication systems.
Laure Tosatto, A. Avarguès-Weber· Current Opinion in Neurobiol...· 0 citations
It is concluded that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms.
Samuel A. Nastase, Zaid Zada, A. Goldberg et al.· Neuron· 0 citations
Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects and in speakers'selection from linguistic material made available through interaction. Large language models (LLMs) complicate this architecture without requiring the locus of selection to move away from human speakers. This article argues that LLMs are best treated as distributional mediators: they aggregate language produced across human populations, transform its distribution through training and post-training, and redistribute model-specific outputs at scale. I call the resulting ecological process algorithmic reweighting of the speaker-accessible distribution: model mediation can alter the relative frequencies with which competing variants reach human selectors. Emerging evidence on model-specific linguistic profiles and lexical uptake is consistent with parts of this pathway, but does not establish inevitable convergence. Human social evaluation remains decisive: model-associated forms may diffuse and become conventionalized, become socially recognizable as'AI-like'and subsequently avoided, or fail to diffuse in the first place. The proposal extends Mufwene's feature-pool ecology one step upstream of speaker selection and yields testable predictions about uptake, model-version effects, convergence, and social reversal.
Abstract Analogies are a fundamental part of our cognition and communication. Humans are also a social species with shared cultural repertoires learnt throughout our lifetimes. As such, we expect analogies to enable the learning of complex, novel information by communication that takes advantage of shared cultural information. Here, we demonstrate the plausibility of this proposal and clarify its scope through computational modelling. We first model the individual-level process of learning via analogy with access to a shared repertoire of cultural information, using NK landscapes to represent high-dimensional solution spaces for complex problems. We then analyse a model of population dynamics to consider the conditions under which analogical communication will evolve and be maintained when costly. Our analyses suggest that even when analogy use is costly in terms of both search time and memory constraints, it can nevertheless be advantageous by allowing learners to obtain high-quality solutions more efficiently and more often than those who do not learn via analogy. Content of image described in text.
Thomas Holding, P. Smaldino, C. Brand· Evolutionary Human Sciences· 1 citation