# Language follows a distinct mode of extra-genomic evolution

Balthasar Bickel, Anne-Lise Giraud Mamessier, Klaus Zuberbühler, Carel van Schaik

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Balthasar Bickel, Anne-Lise Giraud Mamessier, Klaus Zuberbühler, Carel van Schaik. Language follows a distinct mode of extra-genomic evolution. 2024. pasteur-04627408

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# Language follows a distinct mode of extra-genomic evolution

Balthasar Bickel, Anne-Lise Giraud Mamessier, Klaus Zuberbühler, Carel van Schaik

► To cite this version:

Balthasar Bickel, Anne-Lise Giraud Mamessier, Klaus Zuberbühler, Carel van Schaik. Language follows a distinct mode of extra-genomic evolution. Physics of Life Reviews, 2024, 50, pp.211-225. 10.1016/j.plrev.2024.08.003. pasteur-04627408v2

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Submitted on 8 Apr 2025

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entific research documents, whether they are pub-
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Distributed under a Creative Commons Attribution 4.0 International License
# Language follows a distinct mode of extra-genomic evolution

Balthasar Bickel¹,², Anne-Lise Giraud³,⁴, Klaus Zuberbühler²,⁵,⁶, and Carel P. van Schaik²,⁷,⁸

¹Department of Comparative Language Science, University of Zurich; ²Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich; ³Department of Basic Neurosciences, University of Geneva; ⁴Institut de l'Audition, Institut Pasteur, INSERM, Université Paris Cité; ⁵Institute of Biology, University of Neuchâtel; ⁶School of Psychology and Neuroscience, University of St Andrews; ⁷Department of Evolutionary Biology and Environmental Science, University of Zurich; ⁸Max Planck Institute for Animal Behavior, Konstanz

## Abstract

As one of the most specific, yet most diverse of human behaviors, language is shaped by both genomic and extra-genomic evolution. Sharing methods and models between these modes of evolution has significantly advanced our understanding of language and inspired generalized theories of its evolution. Progress is hampered, however, by the fact that the extra-genomic evolution of languages, i.e. linguistic evolution, maps only partially to other forms of evolution. Contrasting it with the biological evolution of eukaryotes and the cultural evolution of technology as the best understood models, we show that linguistic evolution is special by yielding a stationary dynamic rather than stable solutions, and that this dynamic allows the use of language change for social differentiation while maintaining its global adaptiveness. Linguistic evolution furthermore differs from technological evolution by requiring vertical transmission, allowing the reconstruction of phylogenies; and it differs from eukaryotic biological evolution by foregoing a genotype vs phenotype distinction, allowing deliberate and biased change. Recognising these differences will improve our empirical tools and open new avenues for analyzing how linguistic, cultural, and biological evolution interacted with each other when language emerged in the hominin lineage. Importantly, our framework will help to cope with unprecedented scientific and ethical challenges that presently arise from how rapid cultural evolution impacts language, most urgently from interventional clinical tools for language disorders, potential epigenetic effects of technology on language, artificial intelligence and linguistic communicators, and global losses of linguistic diversity and identity. Beyond language, the distinctions made here allow identifying variation in other forms of biological and cultural evolution, developing new perspectives for empirical research.

## Introduction

Key concepts of historical linguistics and evolutionary biology share their roots in the tree models that became established in the 19th century [1]: extant similarities are best explained by descent with modification from a shared root. Just like the combination of non-accidental similarity and systematic differentiation between beak shapes among Galapagos finches point to a shared ancestral species, so does the combination of non-accidental similarity and systematic differentiation among words in Indo-European languages (e.g. *English hand*, *Nepali hāt*; *English eight*, *Nepali āṭh*) point to a shared ancestral language. Despite this joint insight, research on biological and linguistic evolution developed independently from each other for more than a century. It is only recently that contact between the two fields was re-established, driven by spectacular successes in estimating language phylogenies and trait histories [2,3]. Independently of this, biologists have increasingly recognized the impact of extra-genomic modes of evolution, launching a highly successful program on cultural transmission [4, 5].

These success stories suggest a far-reaching formal unity of all evolutionary processes. Biological evolution and the various manifestations of cultural evolution all seem to rest on es-
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# Language follows a distinct mode of extra-genomic evolution

Balthasar Bickel<sup>a, b, *</sup>, Anne-Lise Giraud<sup>c, d</sup>, Klaus Zuberbühler<sup>b, e, f</sup>,
Carel P. van Schaik<sup>b, g, h</sup>

## ARTICLE INFO

Edited by: E. Di Mauro

## ABSTRACT

As one of the most specific, yet most diverse of human behaviors, language is shaped by both genomic and extra-genomic evolution. Sharing methods and models between these modes of evolution has significantly advanced our understanding of language and inspired generalized theories of its evolution. Progress is hampered, however, by the fact that the extra-genomic evolution of languages, i.e. linguistic evolution, maps only partially to other forms of evolution. Contrasting it with the biological evolution of eukaryotes and the cultural evolution of technology as the best understood models, we show that linguistic evolution is special by yielding a stationary dynamic rather than stable solutions, and that this dynamic allows the use of language change for social differentiation while maintaining its global adaptiveness. Linguistic evolution furthermore differs from technological evolution by requiring vertical transmission, allowing the reconstruction of phylogenies; and it differs from eukaryotic biological evolution by foregoing a genotype vs phenotype distinction, allowing deliberate and biased change. Recognising these differences will improve our empirical tools and open new avenues for analyzing how linguistic, cultural, and biological evolution interacted with each other when language emerged in the hominin lineage. Importantly, our framework will help to cope with unprecedented scientific and ethical challenges that presently arise from how rapid cultural evolution impacts language, most urgently from interventional clinical tools for language disorders, potential epigenetic effects of technology on language, artificial intelligence and linguistic communicators, and global losses of linguistic diversity and identity. Beyond language, the distinctions made here allow identifying variation in other forms of biological and cultural evolution, developing new perspectives for empirical research.

## 1. Introduction

Key concepts of historical linguistics and evolutionary biology share their roots in the tree models that became established in the

<footnote><sup>a</sup> Department of Comparative Language Science, University of Zurich, Switzerland</footnote>

<footnote><sup>b</sup> Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Switzerland</footnote>

<footnote><sup>c</sup> Department of Basic Neurosciences, University of Geneva, Switzerland</footnote>

<footnote><sup>d</sup> Institut de l'Audition, Institut Pasteur, INSERM, Université Paris Cité, France</footnote>

<footnote><sup>e</sup> Institute of Biology, University of Neuchâtel, Switzerland</footnote>

<footnote><sup>f</sup> School of Psychology and Neuroscience, University of St Andrews, United Kingdom</footnote>

<footnote><sup>g</sup> Department of Evolutionary Biology and Environmental Science, University of Zurich, Switzerland</footnote>

<footnote><sup>h</sup> Max Planck Institute for Animal Behavior, Konstanz, Germany</footnote>

<footnote>* Corresponding author at: Department of Comparative Language Science, University of Zurich, Affolternstrasse 56, 8050 Zurich, Switzerland. E-mail address: balthasar.bickel@uzh.ch (B. Bickel).</footnote>
sentially the same principles and therefore afford a generalized theory and a shared toolkit of methods [5, 6]. Yet, this unification continues to be challenged by unresolved debates about how biological evolution interacts with cultural evolution in language [7–11] and by methodological developments that are increasingly responsive to problems that seem specific to biological [12, 13] or linguistic [14, 15] evolution.

These ongoing challenges call for a re-assessment of how the different modes of evolution compare and interact with each other. To be sure, all modes of evolution rely on the same core process: a mechanism producing variation (“mutation”, “change”) and a mechanism transmitting this variation (“inheritance”, “learning”), potentially with reduction (“selection”, “drift”, “bias”) (see Box 1 for a glossary of terms). Beyond this core, profound differences arise. This has been repeatedly noted, mostly by contrasting biological and cultural evolution [5, 16], or biological and linguistic evolution [1, 6]. However, linguistic evolution, i.e. the way in which languages change and diversify, differs in critical ways from other types of cultural

**Box 1. Glossary**

**Absorption:** transition into a state (“absorbing state”) that cannot be left.

**Adaptiveness (fitness):** extent to which a structure is experiencing stabilizing selection in the relevant niche, i.e. the state at which the structure responds to the affordances of the niche sufficiently well for preservation and propagation.

**Bias:** differences in the probability of a mutation or change in evolution (or the causes or the outcomes of such differences).

**Complexity:** amount of components linked together in a structure in unpredictable ways.

**Drift:** reduction of diversity through random sampling.

**Epigenetics:** environmentally induced alterations of genome activity with phenotypic effects, inherited through the germ line over a few generations and potentially modulating selection pressure and gene mutability (evolvability).

**Ergodicity:** property of a process where all states or parts of a system can always be reached within finite but randomly varying time.

**Evolution:** process that generates variation and transmits this variation, possibly in reduced form.

**Language:** either (i) a system of conventions governing the structure, composition, expression, and interpretation of meaningful signals, used by specific people at a specific time in a specific place, or (ii) the faculty for any such system, as a phenotypic trait of the human species. We refer to (ii) as the **language faculty** (understood in the broad sense [17]).

**Macroevolution vs microevolution:** processes between (macro) vs within (micro) distinct evolving lineages, i.e., when they no longer evolve as a single unit (for example because organisms cannot interbreed, speakers cannot understand each other or tool parts cannot inter-operate).

**Substrate of evolution:** the physical environment(s) within which evolutionary processes take place, e.g. nucleotide chemistry, neural systems, ecology.

**Mode of evolution:** the combination of specific mechanisms of variation, transmission, and selection that is characteristic of an evolving system.

**Neutral evolution:** transmission with variation but without bias or selection.

**Niche:** the ecology in which a structure is expressed and exposed to selection.

**Plasticity:** varying adaptiveness of phenotypes generated by differences in genome activity (for example through epigenetics), thereby modulating selection pressure.

**Selection:** differential choice of variants for their adaptiveness, limited by the amount of available genotypic variation and constrained by developmental pathways or scaling laws (e.g. Kleiber's Law or Zipf's Law).

**Stationarity:** distribution of state and state transition probabilities towards which ergodic Markov chains converge in infinity.

**Transmission:** replication of a structure, possibly with errors.

**Variation:** alteration (mutation) vs preservation of a structure, subject to physical and computational constraints.
19th century [1]: extant similarities are best explained by descent with modification from a shared root. Just like the combination of non-accidental similarity and systematic differentiation between beak shapes among Galapagos finches point to a shared ancestral species, so does the combination of non-accidental similarity and systematic differentiation among words in Indo-European languages (e.g. English *hand*, Nepali *hāt*; English *eight*, Nepali *āth*) point to a shared ancestral language. Despite this joint insight, research on biological and linguistic evolution developed independently from each other for more than a century. It is only recently that contact between the two fields was re-established, driven by spectacular successes in estimating language phylogenies and trait histories [2,3]. Independently of this, biologists have increasingly recognized the impact of extra-genomic modes of evolution, launching a highly successful program on cultural transmission [4,5].

These success stories suggest a far-reaching formal unity of all evolutionary processes. Biological evolution and the various manifestations of cultural evolution all seem to rest on essentially the same principles and therefore afford a generalized theory and a shared toolkit of methods [5,6]. Yet, this unification continues to be challenged by unresolved debates about how biological evolution interacts with cultural evolution in language [7–11] and by methodological developments that are increasingly responsive to problems that seem specific to biological [12,13] or linguistic [14,15] evolution.

These ongoing challenges call for a re-assessment of how the different modes of evolution compare and interact with each other. To be sure, all modes of evolution rely on the same core process: a mechanism producing variation ("mutation", "change") and a mechanism transmitting this variation ("inheritance", "learning"), potentially with reduction ("selection", "drift", "bias") (see Box 1 for a glossary of terms). Beyond this core, profound differences arise. This has been repeatedly noted, mostly by contrasting biological and cultural evolution [5,16], or biological and linguistic evolution [1,6]. However, linguistic evolution, i.e. the way in which

**Box 1**

**Glossary.**

**Absorption:** transition into a state ("absorbing state") that cannot be left.

**Adaptiveness (fitness):** extent to which a structure is experiencing stabilizing selection in the relevant niche, i.e. the state at which the structure responds to the affordances of the niche sufficiently well for preservation and propagation.

**Bias:** differences in the probability of a mutation or change in evolution (or the causes or the outcomes of such differences).

**Complexity:** amount of components linked together in a structure in unpredictable ways.

**Drift:** reduction of diversity through random sampling.

**Epigenetics:** environmentally induced alterations of genome activity with phenotypic effects, inherited through the germ line over a few generations and potentially modulating selection pressure and gene mutability (evolvability).

**Ergodicity:** property of a process where all states or parts of a system can always be reached within finite but randomly varying time.

**Evolution:** process that generates variation and transmits this variation, possibly in reduced form.

**Language:** either (i) a system of conventions governing the structure, composition, expression, and interpretation of meaningful signals, used by specific people at a specific time in a specific place, or (ii) the faculty for any such system, as a phenotypic trait of the human species. We refer to (ii) as the **language faculty** (understood in the broad sense [17]).

**Macroevolution vs microevolution:** processes between (macro) vs within (micro) distinct evolving lineages, i.e., when they no longer evolve as a single unit (for example because organisms cannot interbreed, speakers cannot understand each other or tool parts cannot inter-operate).

**Substrate of evolution:** the physical environment(s) within which evolutionary processes take place, e.g. nucleotide chemistry, neural systems, ecology.

**Mode of evolution:** the combination of specific mechanisms of variation, transmission, and selection that is characteristic of an evolving system.

**Neutral evolution:** transmission with variation but without bias or selection.

**Niche:** the ecology in which a structure is expressed and exposed to selection.

**Plasticity:** varying adaptiveness of phenotypes generated by differences in genome activity (for example through epigenetics), thereby modulating selection pressure.

**Selection:** differential choice of variants for their adaptiveness, limited by the amount of available variation and constrained by developmental pathways and scaling laws (e.g. Kleiber's Law or Zipf's Law).

**Stationarity:** distribution of state and state transition probabilities towards which ergodic Markov chains converge in infinity.

**Transmission:** replication of a structure, possibly with errors.

**Variation:** alteration (mutation) vs preservation of a structure, subject to physical and computational constraints.
evolution. This restrains the utility of two-way comparisons. Here we will show that linguistic evolution has specific characteristics of its own, siding with biological evolution in some and with cultural evolution in other respects. These specific differences are important because they have major methodological consequences and open new avenues for understanding the relationship between biological, cultural, and linguistic processes.

We proceed by developing these differences mainly on the basis of those well-understood instances of evolution that have dominated the discussion. In biology, we focus on eukaryotic evolution of multicellular organisms. In culture, we focus on technological evolution in humans, i.e. the archeologically documented history of artifacts and tools. In language, we focus on the reconstructible history of languages over the past ten thousand years or so. We then take these differences to define a new framework for exploring interactions and transitions between modes of evolution, expanding our purview onto less well-understood instances of evolution, such as the possible co-evolution of language with technology or the evolution of communicative plasticity in early hominins.

## Novelty and stability vs ergodicity and stationarity

While all modes of evolution subsist on continuous variation, a hallmark of biological evolution is the notion of **novelty** and **stability** at the level of the phenotype [18]. Biological evolution tends to yield novel forms that fit into specific niches, supported by varying degrees of plasticity and niche (co-)construction, and limited by architectural, phylogenetic, developmental and demographic constraints. Once they sufficiently fit under these conditions, phenotypes are roughly stable. When fitness is reduced, mostly due to environmental change, natural selection favors new phenotypes, often without precedent [12].

The faculty of language is a defining trait of the human phenotype. It evolved once as a novel trait fitting into the specific niche of human communication, sociality, and biology. It is a stable trait: no matter which language we look at, the faculty of language enables open-ended communication, creative cognition, and richly structured cooperation, it can be learned and processed by any human (pathologies aside), and it operates under similar physical and anatomical conditions. There is no evidence for any fundamental change of the faculty, not even when the primary modality changes from spoken to signed [19], or after extreme social disruption [20] and profound cultural transformations, such as the introduction of farming [21]. Innovations in response to niche changes are rare and limited to specific sound patterns. Most notably, changes in tooth use enabled new labiodental sounds (e.g. “f” or “v”) [22, 23], and genetically-influenced changes in pitch processing decreased the use of pitch distinctions for meaning differentiation (known as linguistic tone) in some populations [24, 25]. No other innovations have been reported for the faculty of language in reconstructible history.

At the same time, the language faculty is fundamentally dynamic. Within the state space defined by its niche (Box 2), language is subject to relentless diversification into varieties (distributions, sociolects, etc) and new languages [10, 26]. We refer to this intrinsic dynamics of the language faculty as **linguistic evolution**, as opposed to its biological evolution. Linguistic evolution follows from the social function of language: languages diversify in response to social clustering [27, 28], providing ubiquitous, continuously updated identity markers and creating a sense of belonging [29–34].

These dynamics and their function are a key design feature of language. But how do they square with the biological stability of the language faculty? The answer for this apparent contradiction comes from an intriguing observation that linguists have made since the 19th century (Box 3): the dynamic is full of cyclic developments [47]. A famous case is known as “Jespersen's cycle”, where negation oscillates between short and long forms: English, for
languages change and diversify, differs in critical ways from other types of cultural evolution. This restrains the utility of two-way comparisons. Here we will show that linguistic evolution has specific characteristics of its own, siding with biological evolution in some and with cultural evolution in other respects. These specific differences are important because they have major methodological consequences and open new avenues for understanding the relationship between biological, cultural, and linguistic processes.

We proceed by developing these differences mainly on the basis of those well-understood instances of evolution that have dominated the discussion. In biology, we focus on eukaryotic evolution of multicellular organisms. In culture, we focus on technological evolution in humans, i.e. the archeologically documented history of artifacts and tools. In language, we focus on the reconstructible history of languages over the past ten thousand years or so. We then take these differences to define a new framework for exploring interactions and transitions between modes of evolution, expanding our purview onto less well-understood instances of evolution, such as the possible co-evolution of language with technology or the evolution of communicative plasticity in early hominins.

## 2. Novelty and stability vs ergodicity and stationarity

While all modes of evolution subsist on continuous variation, a hallmark of biological evolution is the notion of **novelty** and **stability** at the level of the phenotype [18]. Biological evolution tends to yield novel forms that fit into specific niches, supported by varying degrees of plasticity and niche (co-)construction, and limited by architectural, phylogenetic, developmental and demographic constraints. Once they sufficiently fit under these conditions, phenotypes are roughly stable. When fitness is reduced, mostly due to environmental change, natural selection favors new phenotypes, often without precedent [12].

The faculty of language is a defining trait of the human phenotype. It evolved once as a novel trait fitting into the specific niche of human communication, sociality, and biology. It is a stable trait: no matter which language we look at, the faculty of language enables open-ended communication, creative cognition, and richly structured cooperation, it can be learned and processed by any human (pathologies aside), and it operates under similar physical and anatomical conditions. There is no evidence for any fundamental change of the faculty, not even when the primary modality changes from spoken to signed [19], or after extreme social disruption [20] and profound cultural transformations, such as the introduction of farming [21]. Innovations in response to niche changes are rare and limited to specific sound patterns. Most notably, changes in tooth use enabled new labiodental sounds (e.g. “f” or “v”) [22,23], and genetically-influenced changes in pitch processing decreased the use of pitch distinctions for meaning differentiation (known as linguistic tone) in some populations [24,25]. No other innovations have been reported for the faculty of language in reconstructible history.

At the same time, the language faculty is fundamentally dynamic. Within the state space defined by its niche (Box 2), language is subject to relentless diversification into varieties (dialects, sociolects, etc.) and new languages [10,26]. We refer to this intrinsic dynamic of the language faculty as **linguistic evolution**, as opposed to its biological evolution. Linguistic evolution follows from the social function of language: languages diversify in response to social clustering [27,28], providing ubiquitous, continuously updated identity markers and creating a sense of belonging [29–34].

This dynamic and its functions is a key design feature of language. But how does it square with the biological stability of the language faculty? The answer for this apparent contradiction comes from an intriguing observation that linguists have made since the 19th century (Box 3): the dynamic is full of cyclic developments [48]. A famous case is known as “Jespersen’s cycle”, where negation oscillates between short and long forms: English, for example, started with “ne”, extended it to “na wiht” and similar expressions meaning “not a thing”, then shortened it again to “not” and “n’t”, and now colloquially expands it again by “nothing” in some dialects (as in “she didn’t do nothing”). In modern work, this cyclic dynamic is successfully modeled by (first-order) Markov processes [49–53]

**Box 2**
The state space of language.

Variation in language is extensive but limited by the communicative (e.g. information-theoretical), social (e.g. trust-building, identificational), neuro-cognitive (e.g. developmental, computational, processual) and physical (e.g. acoustic, anatomical, geographical) niche where it operates. While linguists capture this variation by a wide range of descriptive tools (e.g. variables describing individual constructions, annotations of texts, features in syntax or in phonology, properties and meanings of words, etc.), current databases [35] converge on ever more narrowly defined variables (e.g. differentiating word orders not for entire languages, but conditioned on specific contexts) [36,37]. The data are mostly discrete, but there is a growing interest in continuous variation, measured for example acoustically [38,39] or neurobiologically [40–42]. An unresolved question is the extent to which variables are causally connected with each other, forming evolving systems [36,43], and it remains equally debated how exactly the various dimensions of the niche delimit the state space of the language faculty — for example whether the state space is mostly defined by communicative and conceptual or by computational and formal principles, or which aspects of the state space, if any, are fixed by learnability constraints [10,44].
**Box 2. The state space of language.** Variation in language is extensive but limited by the communicative (e.g. information-theoretical), social (e.g. trust-building, identificational), neuro-cognitive (e.g. developmental, computational, processual) and physical (e.g. acoustic, anatomical, geographical) niche where it operates. While linguists capture this variation by a wide range of descriptive tools (e.g. variables describing individual constructions, annotations of texts, features in syntax or in phonology, properties and meanings of words, etc.), current databases [35] converge on ever more narrowly defined variables (e.g. differentiating word orders not for entire languages, but conditioned on specific contexts) [36, 37]. The data are mostly discrete, but there is a growing interest in continuous variation, measured for example acoustically [38, 39] or neurobiologically [40, 41]. An unresolved question is the extent to which variables are causally connected with each other, forming evolving systems [36, 42], and it remains equally debated how exactly the various dimensions of the niche delimit the state space of the language faculty — for example whether the state space is mostly defined by communicative and conceptual or by computational and formal principles, or which aspects of the state space, if any, are fixed by learnability constraints [10, 43].

**Box 3. Myths about the dynamics of language.** Like other everyday phenomena, the dynamics of language are the target of myths and folk theories. Two popular myths in particular hinder evolutionary research:

**Myth 1:** "Languages become simpler over time." This myth seems inspired by the observation that Modern English word forms are simpler than those of related ancient languages like Latin or Sanskrit. But counter-examples abound. Maithili, for one, has much more complex verb forms than Old Indo-Aryan, from which it descends [44]. In general, languages usually become more complex because of what is technically known as grammaticalization [45] (where words become grammatical markers, e.g. the literal "she is going" became a future tense marker in "she is going to go"), and simpler because of erosion or regularization, in a cyclic manner (e.g. the future marker is often reduced to "gonna" in colloquial English).

**Myth 2:** "Languages evolve faster than organisms." This myth comes from comparing the millions of years it took humans to diverge from other primates with the hundreds of years it took English to diverge from other Germanic languages. But specific aspects of languages and specific aspects of human phenotypes have evolved within similar time depths. For example, words for 'leg' in Austronesian [46] (linguistic evolution) and aspects of pitch processing in Europe [25] (arguably genetically based, and thus biological evolution) apparently evolved within the same time span of roughly 10,000 years. The rate or speed of evolution is a property of the individual unit that evolves under a specific condition (with a lower bound set by the unit's generation time). It is not a property of the overall mode of evolution.

example, started with "ne", extended it to "na wiht" and similar expressions meaning "not a thing", then shortened it again to "not" and "n't", and now colloquially expands it again by "nothing" in some dialects (as in "she didn't do nothing"). In modern work, this cyclic dynamic is successfully modeled by (first-order) Markov processes [48–52] that are **ergodic** [48], i.e., all states in a transition chain can be reached in finite but randomly varying time (Figure 1A). This means that there can always be languages in all states (Figure 1B). For example, certain word order patterns have higher probabilities than others because they are easier and preferred, but, given ergodicity, there can always be some languages that use an otherwise dispreferred pattern [53]. A mathematical implication of ergodic processes is that they approach **stationarity** after some time, i.e., the process converges on unique transition probabilities between the states and their associated probability distribution. While the expected time varies greatly, current research suggests that stationarity is often well approached within reconstructible history, at least for grammar [53–57] (cf. *Implications for Methods*).

This property resolves the apparent contradiction between the stability of language in biological evolution and its dynamics in linguistic evolution. What has adapted and stabilized biologically is the total stationary probability distribution at the species level, not the individual languages. For biological evolution it suffices that the language faculty, as a phenotypic trait of the species, has stationary probabilities that are adapted to the niche where it operates.
Box 3

**Myths about the dynamics of language.** Like other everyday phenomena, the dynamics of language are the target of myths and folk theories. Two popular myths in particular hinder evolutionary research:

**Myth 1:** "Languages become simpler over time." This myth seems inspired by the observation that Modern English word forms are simpler than those of related ancient languages like Latin or Sanskrit. But counter-examples abound. Maithili, for one, has much more complex verb forms than Old Indo-Aryan, from which it descends [45]. In general, languages usually become more complex because of what is technically known as grammaticalization [46] (where words become grammatical markers, e.g. the literal "she is going" became a future tense marker in "she is going to go"), and simpler because of erosion or regularization, in a cyclic manner (e.g. the future marker is often reduced to "gonna" in colloquial English).

**Myth 2:** "Languages evolve faster than organisms." This myth comes from comparing the millions of years it took humans to diverge from other primates with the hundreds of years it took English to diverge from other Germanic languages. But specific aspects of languages and specific aspects of human phenotypes have evolved within similar time depths. For example, words for 'leg' in Austronesian [47] (linguistic evolution) and aspects of pitch processing in Europe [25] (arguably genetically based, and thus biological evolution) apparently evolved within the same time span of roughly 10,000 years. The rate or speed of evolution is a property of the individual unit that evolves under a specific condition (with a lower bound set by the unit's generation time). It is not a property of the overall mode of evolution.

that are ergodic [49], i.e., all states in a transition chain can be reached in finite but randomly varying time (Fig. 1A). This means that there can always be languages in all states (Fig. 1B). For example, certain word order patterns have higher probabilities than others because they are easier and preferred, but, given ergodicity, there can always be some languages that use an otherwise dispreferred pattern [54]. A mathematical implication of ergodic processes is that they approach stationarity after some time, i.e., the process converges on unique state and state transition probabilities. While the expected time varies greatly, current research suggests that

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Characteristic examples of linguistic evolution (A-B) as compared with technological evolution (C). A: The evolution of recursive compounds (such as "lemon tree garden", where each word modifies hierarchically the next one) in Indo-European is best captured by a continuous-time Markov chain that is ergodic (i.e., where all states are always accessible in finite but randomly varying time) and that approximates stationarity after ca. 8000 years, with transition probabilities as indicated in the graph [55] (median posterior estimates from a less constrained model than in the original publication). B: Stochastic character mapping [59,60] shows how state probabilities (on a gradient from yellow, absent, to green, present) fluctuate over time, consistent with historically attested changes. C: The evolution of cutting technologies is characterized by growth in complexity and application domains. Instead of reversals or cyclic developments, there are radical replacements of tools, with no clear descent along inheritance trees [61,62]. These replacements often come from technologies that had evolved for earlier functions (for example, stone flakes may have originated as byproducts from percussive use).</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Characteristic examples of linguistic evolution (A-B) as compared with technological evolution (C). **A:** The evolution of recursive compounds (such as "lemon tree garden", where each word modifies hierarchically the next one) in Indo-European is best captured by a continuous-time Markov chain that is ergodic (i.e., where all states are always accessible in finite but randomly varying time) and that approximates stationarity after ca. 8,000 years, with transition probabilities as indicated in the graph [54] (median posterior estimates from a less constrained model than in the original publication). **B:** Stochastic character mapping [58, 59] shows how state probabilities (on a gradient from yellow, absent, to green, present) fluctuate over time, consistent with historically attested changes. **C:** The evolution of cutting technologies is characterized by growth in complexity and application domains. Instead of reversals or cyclic developments, there are radical replacements of tools, with no clear descent along inheritance trees [60, 61]. These replacements often come from technologies that had evolved for earlier functions (for example, stone flakes may have originated as byproducts from percussive use).</figcaption></figure>

Individual languages can and will inevitably deviate from some of these preferences, but this does not affect the adaptiveness of the language faculty. If this were the case, the biological foundations of language would rapidly diverge among populations, leading to increasing inability for people to learn and use other languages. Instead, languages can freely change in response to social diversification and the bounds set by state space (Box 2). What emerges from this is a key difference between biological and linguistic evolution: while biological evolution generally leads to phenotypic novelty and stability, linguistic evolution generally leads to ergodicity and stationarity. Three consequences follow.

First, the language faculty, as a phenotype of our species, needs description in terms of ergodic systems and their stationary probabilities. It cannot be adequately captured by qualitative generalizations across or abstractions over individual languages [26, 62]. Such attempts leave no room for exceptions [36], but the search for exceptionless ("absolute") universals of language has run into profound statistical challenges [63]. Under ergodicity, exceptions are expected. Even the most disfavored, least probable structure will emerge at some point in time, such as languages that ban recursive embedding in syntax [26, 54, 64] or that collapse the distinction between questions and statements [65]. Exceptions are not a bug, but a feature of the language faculty.

Second, unlike biological evolution [12], linguistic evolution does not yield radically new forms [66]. The few cases of true innovation we noted happened all in response to changes
stationarity is often well approached within reconstructible history, at least for grammar [54–58] (cf. *Implications for Methods*).

This property resolves the apparent contradiction between the stability of language in biological evolution and its dynamic in linguistic evolution. What has adapted and stabilized biologically is the total stationary probability distribution at the species level, not the individual languages. For biological evolution it suffices that the language faculty, as a phenotypic trait of the species, has stationary probabilities that are adapted to the niche where it operates. Individual languages can and will inevitably deviate from some of these preferences, but this does not affect the adaptiveness of the language faculty. If this were the case, the biological foundations of language would rapidly diverge among populations, leading to increasing inability for people to learn and use other languages. Instead, languages can freely change in response to social diversification and the bounds set by state space (Box 2). What emerges from this is a key difference between biological and linguistic evolution: while biological evolution generally leads to phenotypic novelty and stability, linguistic evolution generally leads to ergodicity and stationarity. Three consequences follow.

First, the language faculty, as a phenotype of our species, needs description in terms of ergodic systems and their stationary probabilities. It cannot be adequately captured by qualitative generalizations across or abstractions over individual languages [26,63]. Such attempts leave no room for exceptions [36], but the search for exceptionless ("absolute") universals of language has run into profound statistical challenges [64]. Under ergodicity, exceptions are expected. Even the most disfavored, least probable structure will emerge at some point in time, such as languages that ban recursive embedding in syntax [26,55,65] or that collapse the distinction between questions and statements [66]. Exceptions are not a bug, but a feature of the language faculty.

Second, unlike biological evolution [12], linguistic evolution does not yield radically new forms [67]. The few cases of true innovation we noted happened all in response to changes in the niche of the language faculty, for example how the food we eat affects our teeth and thereby allows a new type of sound, labiodentals. This innovation is not a regular outcome of linguistic evolution. Quite the opposite, the innovation re-defined the niche conditions of linguistic evolution, changing to a state space that now allows Markovian transitions to and from labiodentals, with characteristic stationary probabilities [22]. The lack of radical novelty within linguistic evolution itself invalidates attempts to take patterns in linguistic evolution as potential mirrors of how the language faculty might have emerged in the biological evolution of our species [9,10,68–70]. Language change just does not display the directional development that marks the evolution of the language faculty as an entirely novel trait, unique to our species.

Third, linguistic evolution is far more prone to reversals and duplications than biological evolution, which tends to follow Dollo's Principle of Irreversibility. Because of ergodicity a linguistic structure can easily disappear from a language and reappear centuries later, like in the example in Fig. 1B [55]. For the same reason languages can develop structures repeatedly [48], sometimes accumulating forms that serve exactly the same function, as has happened for example in redundantly blown-up verb forms in Sino-Tibetan [71]. Given this, evolutionary models based on maximum-parsimony and distance criteria are fundamentally at odds with linguistic evolution. Furthermore, the ergodicity of linguistic evolution implies that processes of the Wright-Fisher type, which include absorbing states, cannot be applied to estimate coalescence times of languages. Such processes are successful in modeling how innovations spread in a speaker population [9,53,58,72–76], i.e. for linguistic micro-evolution. By contrast, linguistic macro-evolution requires models that exclude absorption and fixation. Otherwise, languages would stop changing once they reach an absorbing state. This conflicts with the function that language change has in social differentiation.

In all these respects, linguistic evolution is different not only from biological but also from many forms of cultural evolution. Technological evolution in particular is characterized by the same alternation between stable solutions and radical, discovery-driven innovations in specific utility niches that characterizes biological evolution. Just as technologies remain stable (e.g. metal knives) until they are replaced by new ones (e.g. laser cutters) serving new purposes (high-precision edging), so too organisms generally remain stable until they undergo directional changes as selective response to changes in the environment. Technological and biological evolution are not constrained by ergodicity. Instead, they are characterized by competition in adaptiveness, and tend toward ever-growing and cumulative ("ratcheting") complexity [77] (Fig. 1C).

Does the difference between stationarity and stability mean that the outcomes of linguistic evolution are different from biological and cultural evolution in all respects? Not quite. Stationary probabilities are independent of absolute transition rates [78]: Like in biological and cultural evolution, languages evolve at an uneven pace, sometimes faster, sometimes slower (Box 3). In language, the processes generating variation, and thus the rate of change and evolvability, are social ones, reflecting ideologies about language change, social network sizes and densities, the amount of second language learners or the need for, and effects of, group distinction and identity construction [29,30,32–34,79–81]. For example, deliberate exaggeration of differences after language splits ("schismogenesis") often speeds up evolution, similar in effect to bursts through adaptive radiation in biological evolution [47,82].

### 3. Obligate vs optional verticality

Like genes, languages must be passed on from generation to generation to evolve. When vertical transmission stops, languages and species go extinct, currently at an alarming rate in both cases [83].

This basic observation notwithstanding, it has often been cautioned (at least since Kroeber's 1923 anthropology textbook) that linguistic evolution might be affected by critically more horizontal transmission than (eukaryotic) biological evolution [84]. Words and structures spread from language to language by various copying and transfer processes [14,63,85]. For example, many languages of Europe share similarities not because of common descent but because of repeated copying over the centuries [86].

The reason for this apparent contradiction is that linguistic evolution relies on two distinct transmission processes of social learning [73,74,87–90]: First, alterations based on choice, copy, or error spread (horizontally) among adolescent and adult speakers, shifting and diversifying the probability distributions of words and structures. Second, if this statistical signal is strong enough, the alterations are acquired (vertically) by the next generation of children. Both processes are required for language change. Acquisition alone is
in the niche of the language faculty, for example how the food we eat affects our teeth and thereby allows a new type of sound, labiodentals. This innovation is not a regular outcome of linguistic evolution. Quite the opposite, the innovation re-defined the niche conditions of linguistic evolution, changing to a state space that now allows Markovian transitions to and from labiodentals, with characteristic stationary probabilities [22]. The lack of radical novelty within linguistic evolution itself invalidates attempts to take patterns in linguistic evolution as potential mirrors of how the language faculty might have emerged in the biological evolution of our species [9, 10, 67–69]. Language change just does not display the directional development that marks the evolution of the language faculty as an entirely novel trait, unique to our species.

Third, linguistic evolution is far more prone to reversals and duplications than biological evolution, which tends to follow Dollo's Principle of Irreversibility. Because of ergodicity a linguistic structure can easily disappear from a language and reappear centuries later, like in the example in Figure 1B [54]. For the same reason languages can develop structures repeatedly [47], sometimes accumulating forms that serve exactly the same function, as has happened for example in redundantly blown-up verb forms in Sino-Tibetan [70]. Given this, evolutionary models based on maximum-parsimony and distance criteria are fundamentally at odds with linguistic evolution. Furthermore, the ergodicity of linguistic evolution implies that processes of the Wright-Fisher type, which include absorbing states, cannot be applied to estimate coalescence times of languages. Such processes are successful in modeling how innovations spread in a speaker population [9, 52, 57, 71–74], i.e. for linguistic micro-evolution. By contrast, linguistic macro-evolution requires models that exclude absorption and fixation. Otherwise, languages would stop changing once they reach an absorbing state. This conflicts with the function that language change has in social differentiation.

In all these respects, linguistic evolution is different not only from biological but also from many forms of cultural evolution. Technological evolution in particular is characterized by the same alternation between stable solutions and radical, discovery-driven innovations in specific utility niches that characterizes biological evolution. Just as technologies remain stable (e.g. metal knives) until they are replaced by new ones (e.g. laser cutters) serving new purposes (high-precision edging), so too organisms generally remain stable until they undergo directional changes as selective response to changes in the environment. Technological and biological evolution are not constrained by ergodicity. Instead, they are characterized by competition in adaptiveness, and tend toward ever-growing and cumulative (“ratcheting”) complexity [75] (Figure 1C).

Does the difference between stationarity and stability mean that the outcomes of linguistic evolution are different from biological and cultural evolution in all respects? Not quite. Stationary probabilities are independent of absolute transition rates [76]: Like in biological and cultural evolution, languages evolve at an uneven pace, sometimes faster, sometimes slower (Box 3). In language, the processes generating variation, and thus the rate of change and evolvability, are social ones, reflecting ideologies about language change, social network sizes and densities, the amount of second language learners or the need for, and effects of, group distinction [29, 30, 32–34, 77–79]. For example, deliberate exaggeration of differences after language splits (“schismogenesis”) often speeds up evolution, similar in effect to bursts through adaptive radiation in biological evolution [46, 80].

## Obligate vs optional verticality

Like genes, languages must be passed on from generation to generation to evolve. When vertical transmission stops, languages and species go extinct, currently at an alarming rate in both cases [81].
insufficient. Children are highly resilient and conservative learners, approximating, not changing, the input they receive. But spread alone is also insufficient. Not every slang expression, loanword, or other alteration makes it into the next generation. Evolution only happens when an alteration is acquired by the next generation. This is comparable to the social spread of gene expression in epigenetics: to have an effect on evolution, it must make it into the germline.

This **obligate verticality** explains the original 19th century observation that both languages and organisms diversify by stepwise modification from a common root, with true homology or “cognacy”, as it is known in linguistics. Obligate verticality guarantees the regular, step-by-step modification that is necessary to establish this. For example, the cognacy of German *Zahn* ‘tooth’ with Latin *dens* and Nepali *dāt* *t* is established by stepwise and regular modification of *d* into *t*, as still attested in English *tooth*, and then of *t* into *z* (pronounced *ts*). Deviations from regularity give away loanwords. English *dentist*, for example, was borrowed, and is not a direct cognate, because *d* regularly changed to *t* in English (and it was borrowed after this sound change happened).

It is this regularity in word forms that allows detection of common descent and therefore inference of trees of the kind shown in Fig. 1B [3,91]. The trees define vertical lineages through generations, channelling the transmission of any kind of structure, beyond cognates. Once adopted, loanwords and copies (e.g. perfect tenses with “have” in Europe) also follow the same vertical channel as cognates, blending in with native words and structures [86,92]. Again, the vertical channel is essential; copying alone would have no evolutionary consequences. This obligate verticality makes it possible to statistically separate spread events from the vertical tree structures [14,47,92–95], much like in biology [96–98].

Nonlinguistic cultural evolution is fundamentally different in these regards. Cultural traits evolve by copying horizontally and obliquely, such as in the spread of farming or cutting technologies (Fig. 1C), or even through systematically skipping one generation, such as the transmission of specific ritual practices in parts of indigenous Australia [99]. Technology can die out and be re-discovered after long gaps, such as cement-based concrete [100]. None of these processes critically necessitates a vertical process akin to the germline in biology or the acquisition process in language. Unlike in language, the transmission of technology does not hinge on feeding (noisy) statistical input to children who have matching neuro-cognitive learning skills [101]. Instead, it requires a variety of teaching, copying, and trial-and-error procedures [102] that are usually free of critical period constraints in infants.

As a result, cultural evolution is typically less gradual and less incremental than linguistic or biological evolution [4,5]. The evolution of cutting technology, for example, is full of abrupt breaks and parallel developments when new possibilities are discovered and new tools developed (Fig. 1C). The lack of homology/cognacy in cultural data furthermore means that its evolution is typically better captured by directed networks than trees, and research focuses more on micro-evolutionary diffusion than on macro-evolutionary diversification [103]. In some cases, however, a linguistically inferred tree can be taken as a proxy of social differentiation and then the macro-evolution of technology or other cultural traits can be successfully modeled [103,104]; and sometimes there are enough historical data to trace tree-like evolution longitudinally [105,106].

## 4. Two substrates vs. one substrate

A key property of biological evolution is that selection operates in a different physical substrate than the generation and inheritance of variation: selection operates through the interaction of phenotypes — as gene “vehicles” [107] or “ interactants” [108] — with their ecology; variation and inheritance operate through processes in nucleotide chemistry, the genotype. Both processes are usually involved in creating stability, while the genotype alone typically rests on stationary processes of nucleotide substitution at each site (often adaptively neutral), comparable to the stationary processes of linguistic evolution. But in linguistic evolution there is only one substrate for the operations; there is no genotype vs phenotype distinction [89], and selection operates in the very same physical substrate as variation generation and inheritance, viz. in the social learning processes enabled when utterances are communicated and processed by interlocutors’ brains [89]. This is similar in cultural evolution, which also foregoes the genotype vs. phenotype distinction and relies on a single substrate for variation generation, inheritance, and selection in the form of interaction between individuals. Two consequences follow.

First, because variation is generated in the same substrate in which selection operates, the forces that shape selection, such as processing and learning preferences, or cultural utility, can also directly bias probabilities of change. Indeed, some changes in language [6,58,109,110] and culture [111] are intrinsically more likely than others, yielding results similar to those of selection among random replication errors. Disentangling these two options — sometimes called ‘source-oriented’ (or ‘mutational’) vs. ‘result-oriented’ (or ‘selectional’) evolution in language [112] — is challenging but increasingly possible by contrasting source-conditioned entry rates with the overall exit rates of a given state [113]. The issues do not arise as much in biology because biased mutations, although important, are limited to the genotype level [114]. It is only through epigenetics that genomic expression can be environmentally biased and thereby operate in a similar fashion as natural selection [115].

Second, since linguistic and cultural evolution operate in a single substrate that relies on the interaction between individuals, they are necessarily open to deliberate intervention in the form of conformity needs (for example strategically adopting new words, or new tools and rituals) or strategic tinkering (for example through language policies or in engineering), unlike biological evolution (which can be tampered with only by cultural innovations like selective breeding and genetic engineering). In practice, however, the amount of active intervention is limited in linguistic evolution. Indeed, many properties of language (for example verb inflection as in *eat* vs *eats* vs *ate*) tend to resist conscious choice [32,47]. They are instead determined by the total success in processing, learning, and communication over thousands of daily utterances [22,36,58,116] (avoiding for example too many irregularities of the *eat/ate* type), appearing in the end as an “invisible hand” process [117].
This basic observation notwithstanding, it has often been cautioned (at least since Kroeber's 1923 anthropology textbook) that linguistic evolution might be affected by critically more horizontal transmission than (eukaryotic) biological evolution [82]. Words and structures spread from language to language by various copying and transfer processes [14, 62, 83]. For example, many languages of Europe share similarities not because of common descent but because of repeated copying over the centuries [84].

The reason for this apparent contradiction is that linguistic evolution relies on two distinct transmission processes of social learning [72, 73, 85-88]: First, alterations based on choice, copy, or error spread (horizontally) among adolescent and adult speakers, shifting and diversifying the probability distributions of words and structures. Second, if this statistical signal is strong enough, the alterations are acquired (vertically) by the next generation of children. Both processes are required for language change. Acquisition alone is insufficient. Children are highly resilient and conservative learners, approximating, not changing, the input they receive. But spread alone is also insufficient. Not every slang expression, loanword, or other alteration makes it into the next generation. Evolution only happens when an alteration is acquired by the next generation. This is comparable to the social spread of gene expression in epigenetics: to have an effect on evolution, it must make it into the germline.

This **obligate verticality** explains the original 19th century observation that both languages and organisms diversify by stepwise modification from a common root, with true homology or "cognacy", as it is known in linguistics. Obligate verticality guarantees the regular, step-by-step modification that is necessary to establish this. For example, the cognacy of German *Zahn* 'tooth' with Latin *dens* and Nepali *dāt* is established by stepwise and regular modification of *d* into *t*, as still attested in English *tooth*, and then of *t* into *z* (pronounced *ts*). Deviations from regularity give away loanwords. English *dentist*, for example, was borrowed, and is not a direct cognate, because *d* regularly changed to *t* in English (and it was borrowed after this sound change happened).

It is this regularity in word forms that allows detection of common descent and therefore inference of trees of the kind shown in Figure 1B [3, 89]. The trees define vertical lineages through generations, channelling the transmission of any kind of structure, beyond cognates. Once adopted, loanwords and copies (e.g. perfect tenses with "have" in Europe) also follow the same vertical channel as cognates, blending in with native words and structures [84, 90]. Again, the vertical channel is essential; copying alone would have no evolutionary consequences. This obligate verticality makes it possible to statistically separate spread events from the vertical tree structures [14, 46, 90-93], much like in biology [94-96].

Nonlinguistic cultural evolution is fundamentally different in these regards. Cultural traits evolve by copying horizontally and obliquely, such as in the spread of farming or cutting technologies (Figure 1C), or even through systematically skipping one generation, such as the transmission of specific ritual practices in parts of indigenous Australia [97]. Technology can die out and be re-discovered after long gaps, such as cement-based concrete [98]. None of these processes critically necessitates a vertical process akin to the germline in biology or the acquisition process in language. Unlike in language, the transmission of technology does not hinge on feeding (noisy) statistical input to children who have matching neuro-cognitive learning skills [99]. Instead, it requires a variety of teaching, copying, and trial-and-error procedures [100] that are usually free of critical period constraints in infants.

As a result, cultural evolution is typically less gradual and less incremental than linguistic or biological evolution [4, 5]. The evolution of cutting technology, for example, is full of abrupt breaks and parallel developments when new possibilities are discovered and new tools developed (Figure 1C). The lack of homology/cognacy in cultural data furthermore means that its evolution is typically better captured by directed networks than trees, and research focus-
## 5. Implications for methods

Our comparisons suggest that, despite a common core in mechanisms of variation, transmission and selection, linguistic evolution has a dynamic distinct from other modes of evolution. It differs from the others in the ergodicity and stationary nature of its states. It sides with biological evolution in the necessity of vertical transmission, but with cultural evolution by foregoing a genotype vs. phenotype distinction (Fig. 2).

This profile has several methodological consequences. We have noted in our discussion (i) that at the species level language is best described by stationary probabilities, and not, as traditionally done, by “universals” that are qualitatively derived by abstraction over, or generalization across, individual languages, (ii) that regular patterns in linguistic evolution cannot recapitulate the biological evolution of the language faculty, (iii) that linguistic evolution supports true homology and therefore tree construction, unlike cultural, but like biological evolution, and (iv) that in linguistic and cultural evolution we can (and should) tease apart source-oriented (mutation-biasing) and result-oriented (selection-driven) effects.

But the profile also highlights the most important methodological challenge that remains for linguistic evolution: how to estimate stationary probabilities at the species level. Much progress has been made, especially because, unlike in biological evolution [12,118], available likelihood methods for Markov chain models are not challenged by absolute novelty, and because these methods have almost completely superseded parsimony and distance methods that are (as noted earlier) at odds with ergodic processes. But there are also many open issues that remain.

For one, despite their wide-ranging success, we do not know whether current Markov chain models are sufficient in all areas of linguistic evolution. One open question is whether and how ergodicity and stationarity in linguistic evolution can also be captured for continuous traits (cf. Box 2), for example by adding bounds [119] or constraining forces [120,121] to basic Brownian motion processes. This has been explored only very rarely [122,123], and it is unknown how well these models correspond to language change at the microevolutionary level.

Another challenge comes from language's state space. The state space of word forms is large, as there are, for example, many ways of naming things. This considerably increases the time to visit, let alone re-visit, each state. The upside of this is that word forms preserve signals of cognacy over longer time than any other property of language, allowing reconstruction of language trees (cf. *Obligate* vs *Optional Verticality*) and occasional approximations of individual word form evolution with non-ergodic models [52]. After about 10,000 years, however, states start to re-appear in the form of look-alikes [3,91], word forms that resemble each other by mere chance or because of recurrent sound-meaning associations [124,125] and word formation patterns [126]. This explains why older language families are exceedingly difficult to demonstrate and why attempts to estimate a world tree of languages meet skepticism among linguists.

The downside of the large state space in word forms is that it is difficult to assess whether estimates of stationary probabilities only characterize the current sample or whether they allow inferences to human language as a whole. Structural traits in grammar and phonology usually have smaller state spaces, but when they are causally connected with each other (cf. Box 2), the time to reach stationarity increases there as well, possibly beyond known linguistic history [49].

The issue is compounded by the fact that in the absence of linguistic data older than ca. 5000 years (from which we have the earliest writings), models need to co-estimate the impact of population history on language distributions (as well as the uncertainty about this impact). Extant linguistic distributions are heavily skewed by geographical conditions [127–129] and the vagaries of population history [49,79,80,92,95,130], such as the agriculturally induced spreads of large families like Indo-European and Sino-Tibetan [131].

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Linguistic, biological, and cultural (in particular, technological) evolution share a common core but each mode of evolution also has a property that distinguishes it from the other two: the ergodicity and stationarity of linguistic evolution in contrast to the alternation between stability and novelty in biological and cultural evolution; the genotype vs phenotype distinction of biological evolution in contrast to a single substrate for variation, transmission, and selection in linguistic and cultural evolution; and the optionality of vertical transmission in cultural evolution in contrast to the obligate transmission from one generation to the next in linguistic and biological evolution.</figcaption></figure>
es more on micro-evolutionary diffusion than on macro-evolutionary diversification [101]. In some cases, however, a linguistically inferred tree can be taken as a proxy of social differentiation and then the macro-evolution of technology or other cultural traits can be successfully modeled [101, 102]; and sometimes there are enough historical data to trace tree-like evolution longitudinally [103, 104].

## Two substrates vs. one substrate

A key property of biological evolution is that selection operates in a different physical substrate than the generation and inheritance of variation: selection operates through the interaction of phenotypes — as gene “vehicles”[105] or “interactants”[106] — with their ecology; variation and inheritance operate through processes in nucleotide chemistry, the genotype. Both processes are usually involved in creating stability, while the genotype alone typically rests on stationary processes of nucleotide substitution at each site (often adaptively neutral), comparable to the stationary processes of linguistic evolution. But in linguistic evolution there is only one substrate for the operations; there is no genotype vs phenotype distinction [87], and selection operates in the very same physical substrate as variation generation and inheritance, viz. in the social learning processes enabled when utterances are communicated and processed by interlocutors’ brains [87]. This is similar in cultural evolution, which also foregoes the genotype vs. phenotype distinction and relies on a single substrate for variation generation, inheritance, and selection in the form of interaction between individuals. Two consequences follow.

First, because variation is generated in the same substrate in which selection operates, the forces that shape selection, such as processing and learning preferences, or cultural utility, can also directly bias probabilities of change. Indeed, some changes in language [6, 57, 107, 108] and culture [109] are intrinsically more likely than others, yielding results similar to those of selection among random replication errors. Disentangling these two options — sometimes called ‘source-oriented’ (or ‘mutational’) vs. ‘result-oriented’ (or ‘selectional’) evolution in language [110] — is challenging but increasingly possible by contrasting source-conditioned entry rates with the overall exit rates of a given state [111]. The issues do not arise as much in biology because biased mutations, although important, are limited to the genotype level [112]. It is only through epigenetics that genomic expression can be environmentally biased and thereby operate in a similar fashion as natural selection [113].

Second, since linguistic and cultural evolution operate in a single substrate that relies on the interaction between individuals, they are necessarily open to deliberate intervention in the form of conformity needs (for example strategically adopting new words, or new tools and rituals) or strategic tinkering (for example through language policies or in engineering), unlike biological evolution (which can be tampered with only by cultural innovations like selective breeding and genetic engineering). In practice, however, the amount of active intervention is limited in linguistic evolution. Indeed, many properties of language (for example verb inflection as in *eat* vs *eats* vs *ate*) tend to resist conscious choice [32, 46]. They are instead determined by the total success in processing, learning, and communication over thousands of daily utterances [22, 36, 57, 114] (avoiding for example too many irregularities of the *eat/ate* type).

## Implications for methods

Our comparisons suggest that, despite a common core in mechanisms of variation, transmission and selection, linguistic evolution has a dynamic distinct from other modes of evolution. It differs from the others in the ergodicity and stationary nature of its states. It sides with
Partly as a result of these spreads, nearly half of the known language families are attested only by a single, isolate member [92]. There are only few proposals for how to estimate the evolution of such degenerate families together with that of language families with more informative phylogenies [54,92].

On the other hand, more and more studies identify causal mechanisms under which stationarity is expected, with specific asymmetries. Various experimental techniques and analyses of naturally produced language data have revealed, for example, that during linguistic evolution, compositional expression is preferred over holistic expression [132,133], recursive over one-off hierarchy [55], shorter over longer dependencies [123,134], simpler agent than patient coding [135,136], anterior over posterior voicing contrasts [137], and semantically more over less coherent categories [138–140]. These lead us to expect asymmetries in stationary probabilities — sometimes called “diachronic biases” [36] — that shape the intrinsic dynamic in the language faculty. Some of the asymmetries are grounded in biology, as manifest in independently traceable processing and learning preferences (for example favoring agents and coherent categories), while others derive from the general laws of information theory (for example expression/compression trade-offs) or (bio)physics (for example the acoustics of consonants) [26,36].

We suspect such asymmetries to be similar for all human populations because they hold at the species level, and, allowing for some amount of random variation, this has been confirmed in recent studies that test the idea explicitly [54,135]. When they differ among populations, this points to potential variation in the niche of the language faculty, and we will review possible cases below. Importantly, however, since asymmetries bear only on the stationary probabilities of a state, and do not introduce novel states into the language faculty, variation in asymmetries remains firmly within the ergodicity of linguistic evolution. This explains why such variation does not hinder any human infant from learning any language. When a language runs counter to global preference it incurs slight disadvantages in one or the other aspect of learning or processing, but it can still be learned. Brains are plastic enough to cope with any choice in the ergodic space of the language faculty.

# 6. Implications for interaction and co-evolution

If linguistic evolution differs from both biological and cultural evolution, how do these modes of evolution interact with each other? Could they co-evolve? Specifically, how does linguistic evolution interact with the biological evolution of the language faculty, and how does it interact with cultural evolution? Can language evolution interact with cultural evolution, for example in technology? These questions are difficult to ask, let alone answer, without the distinctions we propose. As we suggest below, many of these potential interactions open exciting avenues for research (Fig. 3).

## 6.1. Linguistic evolution and biological evolution

Linguistic evolution is the product of biological evolution. The biological evolution of the language faculty has not only defined the common state space within which all languages serve their communicative and cognitive functions, but it has also produced the ergodic dynamics that makes language a tool for social distinction. It is unknown why and when this combination of functions evolved. The combination is evolutionarily expensive because it requires intensive learning and regular coping with neurocognitively and communicatively dispreferred states. Progress here will depend on a deeper understanding of how social structures evolved in the hominin lineage. One intriguing hypothesis is that these aspects of linguistic evolution emerged when societies of mutual trust and mating grew beyond the point where one could know all members individually [33,141]. The idea is that linguistic evolution creates identity tags for these societies that are continuously available and hard to imitate for adult outsiders [34], perhaps extending earlier uses of vocalizations as group signatures [142]. As a result, language has become a privileged instrument in schismogenesis (although of course not the sole instrument that humans use).

Biological evolution is also likely to have defined specific asymmetries in stationary probabilities. To pick up some of the findings

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Interactions of the three modes of evolution proposed. Much is understood conceptually, though less so empirically, about how biology constrains linguistic evolution and how linguistic evolution interacts with cultural evolution (plain arrows). The other three links (arrows with '?') raise new questions, opening avenues for future research.</figcaption></figure>
biological evolution in the necessity of vertical transmission, but with cultural evolution by
foregoing a genotype vs. phenotype distinction (Figure 2).

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Linguistic, biological, and cultural (in particular, technological) evolution share a common core but each mode of evolution also has a property that distinguishes it from the other two: the ergodicity and stationarity of linguistic evolution in contrast to the alternation between stability and novelty in biological and cultural evolution; the genotype vs phenotype distinction of biological evolution in contrast to a single substrate for variation, transmission, and selection in linguistic and cultural evolution; and the optionality of vertical transmission in cultural evolution in contrast to the obligate transmission from one generation to the next in linguistic and biological evolution.</figcaption></figure>

This profile has several methodological consequences. We have noted in our discussion (i) that at the species level language is best described by stationary probabilities, and not, as traditionally done, by “universals” that are qualitatively derived by abstraction over, or generalization across, individual languages, (ii) that regular patterns in linguistic evolution cannot recapitulate the biological evolution of the language faculty, (iii) that linguistic evolution supports true homology and therefore tree construction, unlike cultural, but like biological evolution, and (iv) that in linguistic and cultural evolution we can (and should) tease apart source-oriented (mutation-biasing) and result-oriented (selection-driven) effects.

But the profile also highlights the most important methodological challenge that remains for linguistic evolution: how to estimate stationary probabilities at the species level. Much progress has been made, especially because, unlike in biological evolution [12, 115], available likelihood methods for Markov chain models are not challenged by absolute novelty, and because these methods have almost completely superseded parsimony and distance methods that are (as noted earlier) at odds with ergodic processes. But there are also many open issues that remain.

For one, despite their wide-ranging success, we do not know whether current Markov chain models are sufficient in all areas of linguistic evolution. One open question is whether and how ergodicity and stationarity in linguistic evolution can also be captured for continuous traits (cf. Box 2), for example by adding bounds [116] or constraining forces [117, 118] to basic Brownian motion processes. This has been explored only very rarely [119, 120], and it is unknown how well these models correspond to language change at the microevolutionary level.

Another challenge comes from language's state space. The state space of word forms is large, as there are, for example, many ways of naming things. This considerably increases
mentioned above, the preference for compositional and recursive expression in linguistic evolution rests on the neurobiological evolution of signal segmentation and recombination abilities [7,143,144], which have likely been selected for their communicative, acquisitional, and computational efficiency [9,10,145]. Similarly, the preference for simpler agent than patient expressions in linguistic evolution probably derives from a deeply rooted preference for agents in primate event cognition [136].

Can there be reverse effects? Can linguistic evolution impact the biological evolution of the language faculty? At first sight this is plausible in analogy to how cultural evolution impacts biological evolution in domains other than language [146], as when for example the invention of dairy farming has forced genetic adaptation in the form of developmental persistence of lactase, or the invention of sunscreen and vitamin D supplements masks genetic drivers of skin cancer and calcium absorption, respectively [147]. However, for such effects to impact genomes, cultural patterns must stabilize in a population for long enough periods. This is different in linguistic evolution, where traits never stabilize: their ergodicity means that they always remain “moving targets” [10] (Fig. 1B). While this does not necessarily exclude any language-gene coevolution (as it could be rapid in principle, see Box 3), no case has been demonstrated with biological evidence, despite two decades of intensive search and suggestive computer simulations [9,11,70]. The relationship between sign language and monogenic deafness is sometimes cited as an example [148]. However, the evidence for co-evolution remains controversial because spontaneously rather than institutionally established sign language communities show much less assortative mating than is needed for an impact of language on genes [149,150]. The drift towards sign language is therefore more likely a special case of linguistic evolution with a change of sensory-motor support that has emerged in populations with prevalent deafness.

An alternative but unexplored possibility is that epigenetics might be at play in linguistic evolution, like in other aspects of how humans interact with their social environment. Epigenetic effects operate within an individual's lifespan and are therefore exempt from the moving target problem. Epigenetics works by adding chemical tags to DNA strands, which then switch gene expression on or off, while the DNA sequence remains unchanged. To be inherited, however, the tagging has to be present in the germline, with sufficient presence in the gametes, and escape cleaning processes. A particularly promising avenue to address language-gene interactions at this level are genes expressed in brain regions that have most recently evolved, such as Broca's area [151], or genes expressed in computations that are key for language, such as neural oscillations for parsing, chunking and planning speech [152–158]. If such epigenetic tags make it to the next generation, they could potentially also alter the biological evolution of the language faculty.

A further possibility is that linguistic diversification leads to gene flow barriers, thereby structuring genetic populations. While current samples indeed reveal many cases of parallel genetic and linguistic diversification [159], the effects of this are expected to be adaptively neutral. To date, we are not aware of any mechanism that would make allele frequencies depend on the distribution of specific linguistic structures or words.

## 6.2. Linguistic evolution and cultural evolution

The cultural evolution of subsistence types [128], social complexity [160], economic resources [161], and language attitudes [30] is likely to have impacted diversification rates in linguistic evolution, although effects are often difficult to distinguish from co-varying factors of demography and ecology [79,80,127,129] that might already have been at play in the biological evolution of the language faculty as well (e.g. driving the use of language for social identity tagging).

Cultural evolution has also impacted the stationary probabilities of specific language states. The most pervasive effect is in the lexicon where cultural innovations put pressure on speakers' application of linguistic operations like metaphor [162,163] to label new artifacts and structures (e.g. using a word for "circle" for the newly invented wheel, or a word for "web" for the newly invented internet). Effects outside the lexicon include for example how changes in language attitude might have impacted the stationary probability of grammatical complexity [30], along with demographic admixture of second-language speakers who tend to lower specific aspects of this complexity [164–166].

A more controversial case is the potential impact of literacy on linguistic evolution. While literacy is known to impact spoken language processing [167–169], and often favors more complex expression styles (cf. written legal documents vs. everyday conversations), there is no evidence to date that it impacts linguistic evolution. An in-depth case study found only indirect and marginal effects [170], but large-scale models are lacking. At present, the evolution of autocompletion technologies and machine translation seems to impact preferences for certain formulations and word choices over others [171,172], but the long-term consequences of this are unknown.

Effects from linguistic on cultural evolution are less well studied, chiefly because they demand complex co-evolutionary models [173] where changes in language reinforce changes in cultural practices and vice-versa, with potential time lags. There are many intriguing observations and open questions — for example on the relationships between kinship terminology and social interdependence norms [174], or between spatial reference and architectural or ritual norms [175] — but co-evolutionary modeling at a global scale is lacking. Another promising area for research on language-culture co-evolution is how the application of specific metaphors might have impacted discoveries in science and developments in technology, for example how the “tree” metaphor has shaped evolutionary theories and phylogenetic estimation techniques.

## 6.3. Biological evolution and cultural evolution

The language faculty is often claimed to have co-evolved with advanced stone tool technology [176–178], although the underlying neurobiological mechanisms and evolutionary processes continue to be debated [179–181]. Ongoing technological evolution at present poses similar but more pressing questions. We see at least two ways in which current technology might reshape the niche
the time to visit, let alone re-visit, each state. The upside of this is that word forms preserve
signals of cognacy over longer time than any other property of language, allowing recon-
struction of language trees (cf. *Obligate vs Optional Verticality*) and occasional approxima-
tions of individual word form evolution with non-ergodic models [51]. After about 10,000
years, however, states start to re-appear in the form of look-alikes [3, 89], word forms that
resemble each other by mere chance or because of recurrent sound-meaning associations
[121, 122] and word formation patterns [123]. This explains why older language families are
exceedingly difficult to demonstrate and why attempts to estimate a world tree of languages
meet skepticism among linguists.

The downside of the large state space in word forms is that it is difficult to assess whether
estimates of stationary probabilities only characterize the current sample or whether they
allow inferences to human language as a whole. Structural traits in grammar and phonology
usually have smaller state spaces, but when they are causally connected with each other (cf.
Box 2), the time to reach stationarity increases there as well, possibly beyond known linguis-
tic history [48].

The issue is compounded by the fact that in the absence of linguistic data older than ca.
5,000 years (from which we have the earliest writings), models need to co-estimate the im-
pact of population history on language distributions (as well as the uncertainty about this im-
pact). Extant linguistic distributions are heavily skewed by geographical conditions [124–126]
and the vagaries of population history [48, 77, 78, 90, 93, 127], such as the agriculturally in-
duced spreads of large families like Indo-European and Sino-Tibetan [128]. Partly as a result
of these spreads, nearly half of the known language families are attested only by a single,
isolate member [90]. There are only few proposals for how to estimate the evolution of such
degenerate families together with that of language families with more informative phyloge-
nies [53, 90].

On the other hand, more and more studies identify causal mechanisms under which station-
arity is expected, with specific asymmetries. Various experimental techniques and analyses
of naturally produced language data have revealed, for example, that during linguistic evolu-
tion, compositional expression is preferred over holistic expression [129, 130], recursive over
one-off hierarchy [54], shorter over longer dependencies [120, 131], simpler agent than pa-
tient coding [132, 133], anterior over posterior voicing contrasts [134], and semantically more
over less coherent categories [135–137]. These lead us to expect asymmetries in stationary
probabilities --- sometimes called “diachronic biases” [36] --- that shape the intrinsic dynamic
in the language faculty. Some of the asymmetries are grounded in biology, as manifest in
independently traceable processing and learning preferences (for example favoring agents
and coherent categories), while others derive from the general laws of information theory (for
example expression/compression trade-offs) or (bio)physics (for example the acoustics of
consonants) [26, 36].

We suspect such asymmetries to be similar for all human populations because they hold at
the species level, and, allowing for some amount of random variation, this has been con-
firmed in recent studies that test the idea explicitly [53, 132]. When they differ among popu-
lations, this points to potential variation in the niche of the language faculty, and we will re-
view possible cases below. Importantly, however, since asymmetries bear only on the sta-
tionary probabilities of a state, and do not introduce novel states into the language faculty,
variation in asymmetries remains firmly within the ergodicity of linguistic evolution. This ex-
plains why such variation does not hinder any human infant from learning any language.
When a language runs counter to global preference it incurs slight disadvantages in one or
the other aspect of learning or processing, but it can still be learned. Brains are plastic
enough to cope with any choice in the ergodic space of the language faculty.
within which language operates and thereby potentially impact the biological evolution of the language faculty.

First, technological evolution has reached a point where we can engineer the brain, and directly act on the brain’s language system. The need to intervene on the brain’s language system primarily arises from the desire to correct disorders that are either acutely acquired (for example post-stroke aphasia) or neurodevelopmental (dyslexia, autism, stuttering, dysphasia, specific language impairment, etc.). Gene therapy of the inner ear is soon to become available as a therapy for monogenic congenital deafness [182,183]. Neurostimulation already enables functional alleviation of neurodevelopmental, highly polygenic language disorders [184]. The globalization of such direct interventions, gene therapy in particular, will in the long run remove selection against genes that limit engagement with the dominant modality of language, speech.

Second, technological evolution is increasingly delivering efficient brain-computer interfaces, even cortico-computer interfaces [185], allowing for direct decoding of unspoken language from neural activity. To date, most attempts have opted for decoding motor plans [186], a strategy that could in the best-case scenario work as fast as real speech production, though currently working at much slower rates. However, by moving beyond motor plans [40,187], it might become possible to decode more abstract forms of language, eventually allowing us to bypass the linearization bottleneck and leading to faster and different forms of communication and thinking. If talking mentally to one's computer via for example EEG became standard, the audio-phonatory loop, which has been a major condition of the language faculty, would be replaced by a loop where the feedback is freed from (bio-)mechanical constraints. In other domains (e.g. sense of agency) such a lack of feedback is known to provoke delusions and schizophrenic symptoms as the brain cannot suppress self-generated neural signals [188]. It is entirely unclear what risks such developments incur and to what extent they might change fundamental operations in language.

## 7. Implications for transitions, or the “evolution of evolution”

Another set of questions elicited by our distinctions concerns possible transitions between, and mixtures of, different modes of evolution. Fig. 4 charts two transitions that play a central role in language. The red arrow sketches the likely transition from primate to linguistic communication. It started from a communicative and cognitive system that was chiefly subject to biological evolution, changing mainly between species, in response to varied communicative niches. It ended with the within-species dynamic of language’s ergodicity that no longer relies on a genotype-phenotype interaction to evolve.

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Two transitions between modes of evolution. B = biological (eukaryotic) evolution; C = cultural (technological) evolution; L = linguistic evolution.</figcaption></figure>
Implications for interaction and co-evolution

If linguistic evolution differs from both biological and cultural evolution, how do these modes
of evolution interact with each other? Could they co-evolve? Specifically, how does linguistic
evolution interact with the biological evolution of the language faculty, and how does it inter-
act with cultural evolution? Can language evolution interact with cultural evolution, for exam-
ple in technology? These questions are difficult to ask, let alone answer, without the distinc-
tions we propose. As we suggest below, many of these potential interactions open exciting
avenues for research (Figure 3).

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Interactions of the three modes of evolution proposed. Much is understood conceptually, though less so empirically, about how biology constrains linguistic evolution and how linguistic evolution interacts with cultural evolution (plain arrows). The other three links (arrows with '?') raise new questions, opening avenues for future research.</figcaption></figure>

Linguistic evolution and biological evolution

Linguistic evolution is the product of biological evolution. The biological evolution of the lan-
guage faculty has not only defined the common state space within which all languages serve
their communicative and cognitive functions, but it has also produced the ergodic dynamics
that makes language a tool for social distinction. It is unknown why and when this combina-
tion of functions evolved. The combination is evolutionarily expensive because it requires
intensive learning and regular coping with neurocognitively and communicatively dispre-
ferred states. Progress here will depend on a deeper understanding of how social structures
evolved in the hominin lineage. One intriguing hypothesis is that these aspects of linguistic
evolution emerged when societies of mutual trust and mating grew beyond the point where
one could know all members individually [33, 138]. The idea is that linguistic evolution cre-
ates identity tags for these societies that are continuously available and hard to imitate for
adult outsiders [34], perhaps extending earlier uses of vocalizations as group signatures
[139]. As a result, language has become a privileged instrument in schismogenesis (al-
though of course not the sole instrument that humans use).

Biological evolution is also likely to have defined specific asymmetries in stationary probabili-
ties. To pick up some of the findings mentioned above, the preference for compositional and
recursive expression in linguistic evolution rests on the neurobiological evolution of signal
segmentation and recombination abilities [7, 140, 141], which have likely been selected for
their communicative, acquisitional, and computational efficiency [9, 10, 142]. Similarly, the
preference for simpler agent than patient expressions in linguistic evolution probably derives
from a deeply rooted preference for agents in primate event cognition [133].
The transition is ill-understood but, like so often in evolution, it is likely to have started with a gradual increase in phenotypic plasticity. This relaxed selection pressure, most likely at different rates for different aspects of prelinguistic communication and cognition, e.g. earlier for syntax than phonology [144]. Over time, diversification in vocal behavior was no longer tied to biological evolution, similar to the effects of domestication [189]. In addition, this more diverse behavior has fundamentally re-structured the niche of communication, triggering still increasing plasticity and neural de-differentiation in learning and processing. It is likely that these developments in turn spiraled up diversification until the process outpaced biologically driven evolution. At this point, the system hit the bounds of the ergodic state space that defines our language faculty (Box 2) [10], again quite possibly at different times for different aspects of language.

At the functional level, one candidate mechanism favoring the transition is the use of communicative signals for social tagging, possibly triggered by growing population size (as noted earlier) and enhanced by epigenetic effects from this tagging. An alternative mechanism is variation as a result of population splits in changing ecologies while inter-breeding and exchange continued among them, a dynamic that has characterized human history early on [190–192].

The transition was likely a lengthy process. Given what can be inferred about Neanderthal behavior [193] and hominin brain evolution [151,181], it probably started in the early Paleolithic, at least for some aspects of language. It ended only recently, with the emergence of labiodental sounds as arguably the latest addition to the state space.

The blue arrow sketches other key transitions of language, the transitions into culturally evolving technologies. We have already surveyed the impact of neurological engineering and brain-computer interfaces that might change language and its future evolution in fundamental ways. Another example is the use and transmission of language by artificial systems, for example by the various versions of generative pretrained transformer models (GPT) that produce and consume language at rapidly increasing scale.

The earliest transition of language into evolving technology came with writing. Driven by cultural rather than linguistic evolution, writing is not bound to vertical inheritance. It can spread in any direction and with temporal gaps, as when we can now re-learn ancient cuneiform by imitating inscriptions. As a result, writing allows communication across arbitrary distances, to millions of people worldwide and simultaneously, and without identifying ourselves as the source. Furthermore, we can read 2000 year-old texts, and this makes it possible to borrow long-extinct earlier words (for example so-called Latinisms in Romance, or Tatsama words in Indic languages); it even allowed the re-introduction of a language back into linguistic evolution, as in the rare case of Hebrew.

As a technology, writing also differs from linguistic evolution in that it is not ergodic. It is instead driven by waves of radical novelty, both in forms (alphabets, syllabaries, logographies, emojis, memes etc.) and techniques (etching, hand-writing, typing, speech-to-text conversion, etc.). One of the most impactful innovations was arguably the introduction of formulas and visualizations (e.g. the number line) when writing down mathematics in the 17th century [194]. Strong impact can also be expected in the domain of emotions, where the ongoing evolution of emojis and video-clip memes changes the way we communicate experiences.

Many more transitions could be drawn in Fig. 4 outside the domain of language. For example, the transition from prokaryotic to eukaryotic evolution was characterized by the addition of the germline and the reduction of horizontal gene transfer. These were radically novel structures that enabled rapid increases in complexity. Insights from this transition promises to open new avenues for quantifying the impact of horizontal transfer in language and for better understanding the transition away again from obligate verticality, as in the case of writing.

Some transition lines are arguably more complex. For example, we suspect that the evolution of music and fashion is best modeled as an alternation between innovation-driven technological evolution and ergodic cycles. New instruments (e.g. electric guitars) and fabric (e.g. polyester) allow new styles, but these then seem to be recycled in a stationary process until they are in turn superseded by new instruments and fabrics. More research is now needed to explore how modes of evolution play together in these and other areas of culture.

## 8. Conclusions

Our analysis reveals three dimensions in which modes of evolution differ from each other: whether evolution is ergodic or novelty-driven, whether or not it requires vertical transmission, and whether it rests on one or two physical substrates. Each of these dimensions greatly affects the kinds of evolutionary outcomes one can expect as well as the choice of methods to model them. We have developed the three dimensions on the basis of relatively well-understood types of biological, linguistic and cultural evolution. But as noted in the discussion of interactions (Fig. 3) and transitions (Fig. 4), the three dimensions lead to a richer space of possibilities. We propose that this framework allows more precise discussions about how various evolutionary processes come about and affect each other. In the long run, we expect broad dichotomies like “cultural” or “biological” to lose importance and to yield to sharper notions. What remains as a unifying principle is the core of evolution: the combination of specific mechanisms for variation generation, transmission and selection.

The distinct mode of evolution that characterizes language opens perspectives that have been off limits for traditional (qualitative) linguistics, i.e. the stationary probabilities of states, with their intriguing asymmetries. This finding in turn expands the question of the biological evolution of the language faculty from its possible states to their characteristic dynamics. These dynamics shed new light on biological evolution, separating for example the effects of inherited traits (like agency detection) from derived traits (like social differentiation) on specific properties of language (default syntax and change dynamics, respectively) when it emerged during hominin history.

Furthermore, carving out the specific properties of linguistic evolution allows us to more sharply examine how language interacts with, and transitions from and into, other modes of evolution, not only with regard to biological evolution, but also with regard to cultural, in particular technological, evolution. This will be especially important in the near future, as we are witnessing rapid
Can there be reverse effects? Can linguistic evolution impact the biological evolution of the language faculty? At first sight this is plausible in analogy to how cultural evolution impacts biological evolution in domains other than language [143], as when for example the invention of dairy farming has forced genetic adaptation in the form of developmental persistence of lactase, or the invention of sunscreen and vitamin D supplements masks genetic drivers of skin cancer and calcium absorption, respectively [144]. However, for such effects to impact genomes, cultural patterns must stabilize in a population for long enough periods. This is different in linguistic evolution, where traits never stabilize: their ergodicity means that they always remain "moving targets" [10] (Figure 1B). While this does not necessarily exclude any language-gene coevolution (as it could be rapid in principle, see Box 3), no case has been demonstrated with biological evidence, despite two decades of intensive search and suggestive computer simulations [9, 11, 69]. The relationship between sign language and monogenic deafness is sometimes cited as an example [145]. However, the evidence for co-evolution remains controversial because spontaneously rather than institutionally established sign language communities show much less assortative mating than is needed for an impact of language on genes [146, 147]. The drift towards sign language is therefore more likely a special case of linguistic evolution with a change of sensory-motor support that has emerged in populations with prevalent deafness.

An alternative but unexplored possibility is that epigenetics might be at play in linguistic evolution, like in other aspects of how humans interact with their social environment. Epigenetic effects operate within an individual's lifespan and are therefore exempt from the moving target problem. Epigenetics works by adding chemical tags to DNA strands, which then switch gene expression on or off, while the DNA sequence remains unchanged. To be inherited, however, the tagging has to be present in the germline, with sufficient presence in the gametes, and escape cleaning processes. A particularly promising avenue to address language-gene interactions at this level are genes expressed in brain regions that have most recently evolved, such as Broca's area[148], or genes expressed in computations that are key for language, such as neural oscillations for parsing, chunking and planning speech [149-155]. If such epigenetic tags make it to the next generation, they could potentially also alter the biological evolution of the language faculty.

A further possibility is that linguistic diversification leads to gene flow barriers, thereby structuring genetic populations. While current samples indeed reveal many cases of parallel genetic and linguistic diversification [156], the effects of this are expected to be adaptively neutral. To date, we are not aware of any mechanism that would make allele frequencies depend on the distribution of specific linguistic structures or words.

## Linguistic evolution and cultural evolution

The cultural evolution of subsistence types [125], social complexity [157], and language attitudes [30] is likely to have impacted diversification rates in linguistic evolution, although effects are often difficult to distinguish from co-varying factors of demography and ecology [77, 78, 124, 126] that might have been at play in the biological evolution of the language faculty as well (e.g. driving the use of language for social identity).

Cultural evolution has also impacted the stationary probabilities of specific language states. The most pervasive effect is in the lexicon where cultural innovations put pressure on speakers' application of linguistic operations like metaphor [158, 159] to label new artifacts and structures (e.g. using a word for "circle" for the newly invented wheel, or a word for "web" for the newly invented internet). Effects outside the lexicon include for example how changes in language attitude might have impacted the stationary probability of grammatical
developments in technology, in particular genetic and neurological engineering, artificial intelligence, brain-computer interfaces, and social media, which may yield entirely new modes of evolution, involving novel social entities (brain-to-brain, brain-to-machine, crowd-to-crowd) that overrule those that were hitherto built via language. At the same time, ever-faster globalization reduces linguistic diversity at an unprecedented pace. This disrupts the traditional function that language distinctions have as markers of trust and cooperation networks, but the impact of these developments on human society is barely understood.

All these developments come with enormous ethical and societal challenges. It is urgent to recognize and examine them in depth. The analytical framework we proposed here may facilitate this endeavor.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.

Acknowledgments

This research was funded by the NCCR Evolving Language, Swiss National Science Foundation Agreement # 51NF40_180888. We thank (in alphabetical order) Chiara Barbieri, Daphné Bavelier, Dan Dediu, Russell Gray, Thibaud Gruber, Steve Levinson, Sabine Stoll, Stuart Watson, Marcel Weber, and Aris Xanthos for helpful comments, although we of course retain full responsibility for any remaining errors. We are also very grateful to helpful comments from anonymous reviewers. BB furthermore thanks Chundra Cathcart for sharing his implementation of Felsenstein’s pruning algorithm in Stan.

References

[1] Bromham L. Curiously the same: swapping tools between linguistics and evolutionary biology. Biol Philos 2017;32:855-86.

[2] Pagel M. Darwinian perspectives on the evolution of human languages. Psychon Bull Rev 2017;24:151-7.

[3] Greenhill SJ, Heggarty P, Gray RD. Bayesian phylolinguistics. The handbook of historical linguistics. Wiley-Blackwell; 2021. p. 226-53.

[4] Richerson PJ, Boyd R. Not by genes alone: how culture transformed human evolution. University of Chicago Press; 2005.

[5] Mesoudi A, Whiten A, Laland KN. Towards a unified science of cultural evolution. Behav Brain Sci 2006;29:329-47.

[6] Croft W. Evolutionary linguistics. Annu Rev Anthropol 2008;37:219-34.

[7] Berwick RC, Chomsky N. Why only us: recent questions and answers. J Neurolinguist 2017;43:166-77.

[8] Mendvíl-Giró JL. Did language evolve through language change? On language change, language evolution and grammaticalization theory. Glossa J Gen Linguist 2019;4.

[9] Kirby S. Culture and biology in the origins of linguistic structure. Psychon Bull Rev 2017;24:118-37.

[10] Christiansen MH, Chater N. Language as shaped by the brain. Behav Brain Sci 2008;31:489-509.

[11] Smith K. How culture and biology interact to shape language and the language faculty. Top Cogn Sci 2020;12:690-712.

[12] Uyeda JC, Zenil-Ferguson R, Pennell MW. Rethinking phylogenetic comparative methods. Syst Biol 2018;67:1091-109.

[13] Pagel M, O'Donovan C, Meade A. General statistical model shows that macroevolutionary patterns and processes are consistent with Darwinian gradualism. Nat Commun 2022;13:1113.

[14] Ranacher P, et al. Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact. J R Soc Interface 2021;18: 20201031.

[15] Evans CL, et al. The uses and abuses of tree thinking in cultural evolution. Philos Trans R Soc B Biol Sci 2021;376.

[16] Creanza N, Kolodny O, Feldman MW. Cultural evolutionary theory: how culture evolves and why it matters. Proc Natl Acad Sci 2017;114:7782-9.

[17] Hauser MD, Chomsky N, Fitch WT. The faculty of language: what it is, who has it, and how did it evolve? Science 2002;298:1569-79 (1979).

[18] Bakhtin Y, Katsnelson MI, Wolf YI, Koonin EV. Evolution in the weak-mutation limit: stasis periods punctuated by fast transitions between saddle points on the fitness landscape. Proc Natl Acad Sci 2021;118:e2015665118.

[19] Goldin-Meadow S. Discovering the biases children bring to language learning. Child Dev Perspect 2020;14:195-201.

[20] Blasi DE, Michaelis SM, Haspelmath M. Grammars are robustly transmitted even during the emergence of creole languages. Nat Hum Behav 2017;1:723-9.

[21] Bickel B, Nichols J. Linguistic typology and hunter-gatherer languages. The language of hunter-gatherers. Cambridge University Press; 2020. p. 67-75. T. Güldemann, P. McConvell, R. Rhodes.

[22] Blasi DE, et al. Human sound systems are shaped by post-Neolithic changes in bite configuration. Science 2019;363:eaaav3218 (1979).

[23] Everett C, Chen S. Speech adapts to differences in dentition within and across populations. Sci Rep 2021;11:1066.

[24] Wong PCM, et al. ASPM-lexical tone association in speakers of a tone language: direct evidence for the genetic-biasing hypothesis of language evolution. Sci Adv 2020;6.

[25] Dediu D. Tone and genes: new cross-linguistic data and methods support the weak negative effect of the “derived” allele of ASPM on tone, but not of Microcephalin. PLoS One 2021;16:e0253546.

[26] Evans N, Levinson SC. The Myth of Language Universals: language diversity and its importance for cognitive science. Behav Brain Sci 2009;32:429-48.

[27] Kauhanen H. Neutral change. J Linguist 2017;53:327-58.

[28] Fagyal Z, Swarup S, Escobar AM, Gasser I, Lakkaraju K. Centers and peripheries: network roles in language change. Lingua 2010;120:2061-79.

[29] Sharma D, Dodsworth R. Language variation and social networks. Annu Rev Linguist 2020;6:341-61.

[30] Trudgill P. Sociolinguistic typology: social determinants of linguistic complexity. Oxford University Press; 2011.

[31] Eckert P. Third wave variationism. Oxford University Press; 2016.

[32] Thomason S. Language contact and deliberate change. J Lang Contact 2007;1:41-62.

[33] Moffett MW. Human identity and the evolution of societies. Hum Nat 2013;24:219-67.

[34] Cohen E. The evolution of tag-based cooperation in humans: the case for accent. Curr Anthropol 2012;53:588-616.

[35] Ivani J, Bickel B. Databases for comparative syntactic research. In: Barbiers S, Corver N, Polinsky M, editors. The Cambridge handbook of comparative syntax. Cambridge University Press; 2024. preprint at, https://arxiv.org/abs/2310.11187. in press.

[36] Bickel B. Distributional typology: statistical inquiries into the dynamics of linguistic diversity. The Oxford Handbook of Linguistic Analysis. 2nd ed. Oxford University Press; 2015. p. 901-23. B. Heine, H. Narrog.

[37] Round ER, Corbett GG. Comparability and measurement in typological science: the bright future for linguistics. Linguist Typology 2020;24:489-525.

[38] Salesky E, et al. A corpus for large-scale phonetic typology. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2020. p. 4526-46.

[39] Seifart F, et al. The extent and degree of utterance-final word lengthening in spontaneous speech from 10 languages. Linguist. Vanguard 2020;7:1-14.
complexity [30], along with demographic admixture of second-language speakers who tend to lower specific aspects of this complexity [160–162].

A more controversial case is the potential impact of literacy on linguistic evolution. While literacy is known to impact spoken language processing [163–165], and often favors more complex expression styles (written legal document vs. everyday conversations), there is no evidence to date that it impacts linguistic evolution. An in-depth case study found only indirect and marginal effects [166], but large-scale models are lacking. At present, the evolution of autocompletion technologies and machine translation seems to impact preferences for certain formulations and word choices over others [167, 168], but the long-term consequences of this are unknown.

Effects from linguistic on cultural evolution are less well studied, chiefly because they demand complex co-evolutionary models where changes in language reinforce changes in cultural practices and vice-versa, with potential time lags. There are many intriguing observations and open questions — for example on the relationships between kinship terminology and social interdependence norms [169], or between spatial reference and architectural or ritual norms [170] — but co-evolutionary modeling at a global scale is lacking. Another promising area for research on language-culture co-evolution is how the application of specific metaphors might have impacted discoveries in science and developments in technology, for example how the “tree” metaphor has shaped evolutionary theories and phylogenetic estimation techniques.

## Biological evolution and cultural evolution

The language faculty is often claimed to have co-evolved with advanced stone tool technology [171–173], although the underlying neurobiological mechanisms and evolutionary processes continue to be debated [174–176]. Ongoing technological evolution at present poses similar but more pressing questions. We see at least two ways in which current technology might reshape the niche within which language operates and thereby potentially impact the biological evolution of the language faculty.

First, technological evolution has reached a point where we can engineer the brain, and directly act on the brain's language system. The need to intervene on the brain's language system primarily arises from the desire to correct disorders that are either acutely acquired (for example post-stroke aphasia) or neurodevelopmental (dyslexia, autism, stuttering, dysphasia, specific language impairment, etc.). Gene therapy of the inner ear is soon to become available as a therapy for monogenic congenital deafness [177, 178]. Neurostimulation already enables functional alleviation of neurodevelopmental, highly polygenic language disorders [179]. The globalization of such direct interventions, gene therapy in particular, will in the long run remove selection against genes that limit engagement with the dominant modality of language, speech.

Second, technological evolution is increasingly delivering efficient brain-computer interfaces, even cortico-computer interfaces [180], allowing for direct decoding of unspoken language from neural activity. To date, most attempts have opted for decoding motor plans [181], a strategy that could in the best-case scenario work as fast as real speech production, though currently working at much slower rates. However, by moving beyond motor plans [40, 182], it might become possible to decode more abstract forms of language, eventually allowing us to bypass the linearization bottleneck and leading to faster and different forms of communication and thinking. If talking mentally to one's computer via for example EEG became standard, the audio-phonatory loop, which has been a major condition of the language faculty, would be replaced by a loop where the feedback is freed from (bio-)mechanical constraints. In other domains (e.g. sense of agency) such a lack of feedback is known to provoke delu-
[40] Sauppe S, et al. Neural signatures of syntactic variation in speech planning. PLoS Biol 2021;19:e3001038.

[41] Malik-Moralesa S, et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat Neurosci 2022;25:1014–9.

[42] Stehwien S, Henke L, Hale J, Brennan J, Meyer L. The little prince in 26 languages: towards a multilingual neuro-cognitive corpus. In: Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources. European Language Resources Association; 2020. p. 43–9.

[43] Picallo MC. Linguistic variation in the minimalist framework. Oxford University Press; 2014.

[44] Chomsky N. Three factors in language design. Linguist Inq 2005;36:1–22.

[45] Bickel B, Bisang W, Yádava YP. Face vs. empathy: the social foundations of Maithili verb agreement. Linguistics 1999;37:481–518.

[46] Heine B, Narrog H. The Oxford handbook of grammaticalization. Oxford University Press; 2012.

[47] Greenhill SJ, et al. Evolutionary dynamics of language systems. Proc Natl Acad Sci 2017. https://doi.org/10.1073/pnas.1700388114.

[48] van Gelderen E. The linguistic cycle and the language faculty. Lang Linguist Compass 2013;7:233–50.

[49] Maslova E. A dynamic approach to the verification of distributional universals. Linguist Typology 2000;4:307–33.

[50] Pagel M. Maximum likelihood models for glott chronology and for reconstructing linguistic phylogenies. Time-depth in historical linguistics. The MacDonald Institute of Archeology; 2000. p. 189–207. C. Renfrew, A. MacMahon, L. Trask.

[51] Cathcart CA. Modeling linguistic evolution: a look under the hood. Linguist Vanguard 2018;4:2–11.

[52] Hoffmann K, Bouckaert R, Greenhill SJ, Kühnert D. Bayesian phylogenetic analysis of linguistic data using BEAST. J Lang Evol 2021;6:119–35.

[53] Griffiths TL, Kalish ML. Language evolution by iterated learning with Bayesian agents. Cogn Sci 2007;31:441–80.

[54] Jäger G, Wahle J. Phylogenetic typology. Front Psychol 2021;12:682132.

[55] Widmer M, Auderset S, Widmer P, Nichols J, Bickel B. NP recursion over time: evidence from Indo-European. Language 2017;93:1–36.

[56] Cysouw M. Understanding transition probabilities. Linguist Typology 2011;15:415–31.

[57] Jäger G. Evolutionary game theory and typology: a case study. Language 2007;83:74–109.

[58] Blythe RA, Croft W. How individuals change language. PLoS One 2021;16:e0252582.

[59] Huelsenbeck JP, Nielsen R, Bollack JP. Stochastic mapping of morphological characters. Syst Biol 2003;52:131–58.

[60] Revell LJ. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol Evol 2013;4:754–9.

[61] Basalla G. The evolution of technology. Cambridge University Press; 1988.

[62] Brain AB. The nature of technology: what it is and how it evolves. Free Press; 2009.

[63] Bickel B. Typology in the twenty-first century: major current developments. Linguist Typology 2007;11:239–51.

[64] Piantadosi ST, Gibson E. Quantitative standards for absolute linguistic universals. Cogn Sci 2014;38:736–56.

[65] Everett DL. Cultural constraints on grammar and cognition in Pirahá: another look at the design features of human language. Curr Anthropol 2005;46:621–46.

[66] Dryer MS. Polar questions. The world atlas of language structures. Oxford University Press; 2005. p. 470–3. M. Haspelmath, M. S. Dryer, D. Gil, B. Comrie.

[67] Gray RD, Greenhill SJ, Ross RM. The pleasures and perils of Darwinizing culture (with phylogenies). Biol Theory 2007;2:360–75.

[68] Heine B, Kuteva T. The genesis of grammar: a reconstruction. Oxford University Press; 2007.

[69] Tomasello M. Origins of human communication. MIT Press; 2008.

[70] Briscoe T. Grammatical acquisition: inductive bias and coevolution of language and the language acquisition device. Language 2000;76:245–96.

[71] Bickel B, et al. Free prefix ordering in Chintang. Language 2007;83:43–73.

[72] Reali F, Griffiths TL. Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift. Proc R Soc B Biol Sci 2010;277:429–36.

[73] Niyogi P, Berwick RC. The proper treatment of language acquisition and change in a population setting. Proc Natl Acad Sci 2009;106:10124–9.

[74] Yang CD. Internal and external forces in language change. Lang Var Change 2000;12:231–50.

[75] Meakins F, Hua X, Algy C, Bromham L. Birth of a contact language did not favor simplification. Language 2019;95:294–332.

[76] Hruschka DJ, et al. Building social cognitive models of language change. Trends Cogn Sci 2009;13:464–9.

[77] Tomasello M, Kruger AC, Ratner HH. Cultural learning. Behav Brain Sci 1993;16:495–511.

[78] Cathcart CA, Herce B, Bickel B. Decoupling speed of change and long-term preference in language evolution: insights from Romance verb stem alternations in. In: Proceedings of the Joint Conference on Language Evolution; 2022.

[79] Padilla-Iglesias C, Gjesfjeld E, Vinicius L. Geographical and social isolation drive the evolution of Austronesian languages. PLoS One 2020;15:e0243171.

[80] Greenhill SJ, Hua X, Welsh CF, Schneemann H, Bromham L. Population size and the rate of language evolution: a test across Indo-European, Austronesian, and Bantu Languages. Front Psychol 2018;9.

[81] Raviv L, Meyer A, Lev-Ari S. The role of social network structure in the emergence of linguistic structure. Cogn Sci 2020;44:e12876.

[82] Atkinson QD, Meade A, Venditti C, Greenhill SJ, Pagel M. Languages evolve in punctuational bursts. Science 2008;319 (1979)588–588.

[83] Bromham L, et al. Global predictors of language endangerment and the future of linguistic diversity. Nat Ecol Evol 2021. https://doi.org/10.1038/s41559-021-01604-y.

[84] List JM, Nelson-Sathi S, Geisler H, Martin W. Networks of lexical borrowing and lateral gene transfer in language and genome evolution. Bioessays 2014;36: 141–50.

[85] Thomason SG. Language contact: an introduction. Georgetown University Press; 2001.

[86] Heine B, Kuteva T. The changing languages of Europe. Oxford University Press; 2006.

[87] Bybee JL, Slobin DI. Why small children cannot change language on their own: suggestions from the English past tense. Current issues in linguistic theory. John Benjamins Publishing Company; 1982. p. 29–37. A. Ahlqvist.

[88] Ohla JJ. Sound change is drawn from a pool of synchronic variation. Language change: contributions to the study of its causes. Breivik: Mouton de Gruyter; 1989. p. 171–98. Leiv Egil, E. H. Jahr.

[89] Croft W. Explaining language change: an evolutionary approach. Longman; 2000.

[90] Lightfoot D. How new languages emerge. Cambridge University Press; 2006.

[91] Nichols J. The comparative method as heuristic. The comparative method reviewed. Oxford University Press; 1996. p. 39–71. M. Durie, M. Ross.

[92] Bickel B. Large and ancient linguistic areas. Language dispersal, diversification, and contact: a global perspective. Oxford University Press; 2020. p. 78–101. M. Crevels, P. Muysken.

[93] Gray RD, Bryant D, Greenhill SJ. On the shape and fabric of human history. Philos Trans R Soc B 2010;365:3923–33.

[94] Neureiter N, et al. Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer. Humanit Soc Sci Commun 2022;9:205.

[95] Matsumae H, et al. Exploring correlations in genetic and cultural variation across language families in northeast Asia. Sci Adv 2021;7:eabd9223.

[96] Keeling PJ, Palmer JD. Horizontal gene transfer in eukaryotic evolution. Nat Rev Genet 2008;9:605–18.

[97] Crisp A, Boschetti C, Perry M, Tunnacliffe A, Micklem G. Expression of multiple horizontally acquired genes is a hallmark of both vertebrate and invertebrate genomes. Genome Biol 2015;16:50.

[98] Kambayashi C, et al. Geography-dependent horizontal gene transfer from vertebrate predators to their prey. Mol Biol Evol 2022;39. msac052.

[99] White L. Generation moieties in Australia: structural, social and ritual implications. Oceania 1981;52:6–27.

[100] Jackson MD, et al. Phillipsite and Al-tobermorite mineral cements produced through low-temperature water-rock reactions in Roman marine concrete. Am Mineral 2017;102:1435–50.

[101] Schick J, et al. The function and evolution of child-directed communication. PLoS Biol 2022;20:e3001630.

[102] Sterelyn K. The pleistocene social contract: culture and cooperation in human evolution. Oxford University Press; 2021.

[103] Gray RD, Watts J. Cultural macroevolution matters. Proc Natl Acad Sci 2017;114:7846–52.

[104] Sheehan O, Watts J, Gray RD, Atkinson QD. Coevolution of landesque capital intensive agriculture and sociopolitical hierarchy. Proc Natl Acad Sci 2018;115: 3628–33.

[105] Valverde S, Solé RV. Punctuated equilibrium in the large-scale evolution of programming languages. J R Soc Interface 2015;12:20150249.
sions and schizophrenic symptoms as the brain cannot suppress self-generated neural sig-
nals [183]. It is entirely unclear what risks such developments incur and to what extent they
might change fundamental operations in language.

Implications for transitions, or the “evolution of evolution”

Another set of questions elicited by our distinctions concerns possible transitions between, and mixtures of, different modes of evolution. Figure 4 charts two transitions that play a central role in language. The red arrow sketches the likely transition from primate to linguistic communication. It started from a communicative and cognitive system that was chiefly subject to biological evolution, changing mainly between species, in response to varied communicative niches. It ended with the within-species dynamic of language’s ergodicity that no longer relies on a genotype-phenotype interaction to evolve.

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Two transitions between modes of evolution. B = biological (eukaryotic) evolution; C: cultural (technological) evolution; L: linguistic evolution.</figcaption></figure>

The transition is ill-understood but, like so often in evolution, it is likely to have started with a gradual increase in phenotypic plasticity. This relaxed selection pressure, most likely at different rates for different aspects of prelinguistic communication and cognition, e.g. earlier for syntax than phonology [141]. Over time, diversification in vocal behavior was no longer tied to biological evolution, similar to the effects of domestication [184]. In addition, this more diverse behavior has fundamentally re-structured the niche of communication, triggering still increasing plasticity and neural de-differentiation in learning and processing. It is likely that these developments in turn spiraled up diversification until the process outpaced biologically driven evolution. At this point, the system hit the bounds of the ergodic state space that defines our language faculty (Box 2) [10], again quite possibly at different times for different aspects of language.

At the functional level, one candidate mechanism favoring the transition is the use of com-
municative signals for social tagging, possibly triggered by growing population size (as noted
earlier) and enhanced by epigenetic effects from this tagging. An alternative mechanism is
[106] Youngblood M, Baraghith K, Savage PE. Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975–1999). *Evol Hum Behav* 2021;42:573–82.

[107] Dawkins R. The extended phenotype: the long reach of the gene. Oxford University Press; 2016.

[108] Hull DL. Science as a process: an evolutionary account of the social and conceptual development of science. University of Chicago Press; 1998.

[109] Blevins J. Evolutionary phylogeny: the emergence of sound patterns. Cambridge University Press; 2004.

[110] Cristofaro S. Cognitive explanations, distributional evidence, and diachrony. *Stud Lang* 2012;36:645–70.

[111] Mesoudi A. Cultural selection and biased transformation: two dynamics of cultural evolution. *Philos Trans R Soc B Biol Sci* 2021;376:1–12.

[112] Schmidtko-Bode K, Levshina N, Michaelis SM, Serzant IA. Explanation in typology: diachronic sources, functional motivations and the nature of the evidence. *Language Science Press*; 2019.

[113] Cathcart C, Hölzl A, Jäger G, Widmer P, Bickel B. Numeral classifiers and number marking in Indo-Iranian: a phylogenetic approach. *Lang Dyn Change* 2020; 11:273–325.

[114] Palazzo AF, Kejiou NS. Non-darwinian molecular biology. *Front Genet* 2022;13.

[115] Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. *Nature* 2019;571:489–99.

[116] Reali F, Griffiths TL. The evolution of frequency distributions: relating regularization to inductive biases through iterated learning. *Cognition* 2009;111: 317–28.

[117] Keller R. Sprachwandel: von der unsichtbaren Hand in der Sprache. Francke; 1990.

[118] FitzJohn RG, Maddison WP, Otto SP. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. *Syst Biol* 2009;58: 595–611.

[119] Boucher FC, Démery V. Inferring bounded evolution in phenotypic characters from phylogenetic comparative data. *Syst Biol* 2016;65:651–61.

[120] Boucher FC, Démery V, Conti E, Harmon LJ, Uyeda J. A general model for estimating macroevolutionary landscapes. *Syst Biol* 2018;67:304–19.

[121] Blomberg SP, Rathnayake SI, Moreau CM. Beyond brownian motion and the Ornstein-Uhlenbeck process: stochastic diffusion models for the evolution of quantitative characters. *Am Nat* 2020;195:145–65.

[122] Witzlack-Makarevich A, Zakharko T, Bierkandt L, Zúñiga F, Bickel B. Decomposing hierarchical alignment: co-arguments as conditions on alignment and the limits of referential hierarchies as explanations in verb agreement. *Linguistics* 2016;54:531–61.

[123] Hahn M, Xu Y. Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality. *Proc Natl Acad Sci* 2022;119: e2122604119.

[124] Johansson NE, Anikin A, Carling G, Holmer A. The typology of sound symbolism: defining macro-concepts via their semantic and phonetic features. *Linguist Typology* 2020;24:253–310.

[125] Blasi DE, Wichmann S, Hammarström H, Stadler PF, Christiansen MH. Sound-meaning association biases evidenced across thousands of languages. *Proc Natl Acad Sci* 2016;113:10818–23.

[126] Chang W, Cathcart C, Hall D, Garrett A. Ancestry-constrained phylogenetic analysis supports Indo-European steppe hypothesis. *Language* 2015;91:194–244.

[127] Bentz C, Dediu D, Verkerk A, Jäger G. The evolution of language families is shaped by the environment beyond neutral drift. *Nat Hum Behav* 2018;2:816–21.

[128] Derungs C, Köhli M, Weibel R, Bickel B. Environmental factors drive language density more in food-producing than in hunter-gatherer populations. *Proc R Soc B Biol Sci* 2018;285:20172851.

[129] Pacheco Coelho MT, et al. Drivers of geographical patterns of North American language diversity. *Proc R Soc B Biol Sci* 2019;286:20190242.

[130] Efrat-Kowalsky N, Ranacher P, Neureiter N, Widmer P, Bickel B. Oldest attested languages in the Near East reveal deep transformations in linguistic landscapes. *BioRxiv* 2022. https://doi.org/10.1101/2024.06.25.600575.

[131] Diamond JM, Bellwood P. Farmers and their languages: the first expansions. *Science* 2003;300:597–603 (1979).

[132] Senghas A, Kita S, Ozyürek A. Children creating core properties of language: evidence from an emerging sign language in nicaragua. *Science* 2004;305: 1779–82 (1979).

[133] Kirby S, Cornish H, Smith K. Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. *Proc Natl Acad Sci U S A* 2008;105:10681–6.

[134] Futrell R, Levy RP, Gibson E. Dependency locality as an explanatory principle for word order. *Language* 2020;96:371–412.

[135] Bickel B, Witzlack-Makarevich A, Choudhary KK, Schlesewsky M, Bornkessel-Schlesewsky I. The neurophysiology of language processing shapes the evolution of grammar: evidence from case marking. *PLoS One* 2015;10:e0132819.

[136] Wilson V, Zuberbühler K, Bickel B. The evolutionary origins of syntax: event cognition in nonhuman primates. *Sci Adv* 2022.

[137] Everett C. The global dispreference for posterior voiced obstruents: a quantitative assessment of word-list data. *Language* 2018;94:e311–23.

[138] Mansfield J, et al. Category clustering and morphological learning. *Cogn Sci* 2022;46.

[139] Xu Y, Duong K, Malt BC, Jiang S, Srinivasan M. Conceptual relations predict colexification across languages. *Cognition* 2020;201:104280.

[140] Chemla E, Dautriche I, Buccola B, Fagot J. Constraints on the lexicons of human languages have cognitive roots present in baboons (*Papio papio*). *Proc Natl Acad Sci* 2019;116:14926–30.

[141] Layton R, O'Hara S, Bilsborough A. Antiquity and social functions of multilevel social organization among human hunter-gatherers. *Int J Primatol* 2012;33: 1215–45.

[142] Ruch H, Zürcher Y, Burkart JM. The function and mechanism of vocal accommodation in humans and other primates. *Biol Rev* 2018;93:996–1013.

[143] Townsend SW, Engesser S, Stoll S, Zuberbühler K, Bickel B. Compositionality in animals and humans. *PLOS Biol* 2018;16:e2006425.

[144] Collier K, Bickel B, van Schaik CP, Manser MB, Townsend SW. Language evolution: syntax before phonology? *Proc R Soc B Biol Sci* 2014;281.

[145] Nowak MA, Plotkin JB, Jansen VAA. The evolution of syntactic communication. *Nature* 2000;404:495–8.

[146] Deacon TW. The symbolic species: the co-evolution of language and the brain. 1st ed. W.W. Norton; 1997.

[147] Uchiyama R, Spicer R, Muthukrishna M. Cultural evolution of genetic heritability. *Behav Brain Sci* 2021;1–147.

[148] Aoki K, Feldman MW. Recessive hereditary deafness, assortative mating, and persistence of a sign language. *Theor Popul Biol* 1991;39:358–72.

[149] Gialluisi A, Dediu D, Francks C, Fisher SE. Persistence and transmission of recessive deafness and sign language: new insights from village sign languages. *Eur J Hum Genet* 2013;21:894–6.

[150] Mudd K, de Vos C, de Boer B. An agent-based model of sign language persistence informed by real-world data. *Lang Dyn Change* 2020;10:158–87.

[151] Ponce de León MS, et al. The primitive brain of early *Homo*. *Science* 2021;372:165–71 (1979).

[152] Berto S, et al. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. *Nat Neurosci* 2021;24:554–64.

[153] Colquitt BM, Merullo DP, Konopka G, Roberts TF, Brainard MS. Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits. *Science* 2021;371:eabd9704 (1979).

[154] Hyafil A, Giraud AL, Fontolan L, Gutkin B. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. *Trends Neurosci* 2015;38: 725–40.

[155] Hyafil A, Fontolan L, Kabdebon C, Gutkin B, Giraud AL. Speech encoding by coupled cortical theta and gamma oscillations. *eLife* 2015;4:e06213.

[156] Hovsepyan S, Olasagasti I, Giraud AL. Combining predictive coding and neural oscillations enables online syllable recognition in natural speech. *Nat Commun* 2020;11:3117.

[157] Ding N, Melloni L, Zhang H, Tian X, Poeppl D. Cortical tracking of hierarchical linguistic structures in connected speech. *Nat Neurosci* 2016;19:158–64.

[158] Getz H, Ding N, Newport EL, Poeppl D. Cortical tracking of constituent structure in language acquisition. *Cognition* 2018;181:135–40.

[159] Barbieri C, et al. A global analysis of matches and mismatches between human genetic and linguistic histories. *Proc Natl Acad Sci* 2022;119:e2122084119.

[160] Currie TE, Mace R. Political complexity predicts the spread of ethnolinguistic groups. *Proc Natl Acad Sci* 2009;106:7339–44.

[161] Mufwene SS. Language evolution from an ecological perspective. The Routledge handbook of ecolinguistics. Routledge; 2017. p. 73–88. A. F. Fill, H. Penz.

[162] von Humboldt W. Über die verschiedenheit des menschlichen sprachbaus und ihren einfluss auf die geistige entwickelung des menschengeschlechtes. *Dümmler*; 1836.
variation as a result of population splits in changing ecologies while inter-breeding and ex-
change continued among them, a dynamic that has characterized human history early on
[185–187].

The transition was likely a lengthy process. Given what can be inferred about Neanderthal behavior [188] and hominin brain evolution [148, 176], it probably started in the early Paleolithic, at least for some aspects of language. It ended only recently, with the emergence of labiodental sounds as arguably the latest addition to the state space.

The blue arrow sketches other key transitions of language, the transitions into culturally evolving technologies. We have already surveyed the impact of neurological engineering and brain-computer interfaces that might change language and its future evolution in fundamental ways. Another example is the use and transmission of language by artificial systems, for example by the various versions of generative pretrained transformer models (GPT) that produce and consume language at rapidly increasing scale.

The earliest transition of language into evolving technology came with writing. Driven by cultural rather than linguistic evolution, writing is not bound to vertical inheritance. It can spread in any direction and with temporal gaps, as when we can now re-learn ancient cuneiform by imitating inscriptions. As a result, writing allows communication across arbitrary distances, to millions of people worldwide and simultaneously, and without identifying ourselves as the source. Furthermore, we can read 2,000 year-old texts, and this makes it possible to borrow long-extinct earlier words (for example so-called Latinisms in Romance, or Tatsama words in Indic languages); it even allowed the re-introduction of a language back into linguistic evolution, as in the rare case of Hebrew.

As a technology, writing also differs from linguistic evolution in that it is not ergodic. It is in-
stead driven by waves of radical novelty, both in forms (alphabets, syllabaries, logographies,
emojis, memes etc) and techniques (etching, hand-writing, typing, speech-to-text conver-
sion, etc). One of the most impactful innovations was arguably the introduction of formulas
and visualizations (e.g. the number line) when writing down mathematics in the 17th century
[189]. Strong impact can also be expected in the domain of emotions, where the ongoing
evolution of emojis and video-clip memes changes the way we communicate experiences.

Many more transitions could be drawn in Figure 4 outside the domain of language. For ex-
ample, the transition from prokaryotic to eukaryotic evolution was characterized by the addi-
tion of the germline and the reduction of horizontal gene transfer. These were radically novel
structures that enabled rapid increases in complexity.

Some transition lines are arguably more complex. For example, we suspect that the evolu-
tion of music and fashion is best modeled as an alternation between innovation-driven tech-
nological evolution and ergodic cycles. New instruments (e.g. electric guitars) and fabric
(e.g. polyester) allow new styles, but these then seem to be recycled in a stationary process
until they are in turn superseded by new instruments and fabrics. More research is now
needed to explore how modes of evolution play together in these and other areas of culture.

Conclusions

Our analysis reveals three dimensions in which modes of evolution differ from each other: whether evolution is ergodic or novelty-driven, whether or not it requires vertical transmission, and whether it rests on one or two physical substrates. Each of these dimensions greatly affects the kinds of evolutionary outcomes one can expect as well as the choice of methods to model them. We have developed the three dimensions on the basis of relatively well-understood types of biological, linguistic and cultural evolution. But as noted in the discussion of interactions (Figure 3) and transitions (Figure 4), the three dimensions lead to a
[163] Meir I. Topic-open-endedness: why recursion is overrated. Sign Lang Linguist 2020;23:258–71.

[164] Lupyan G, Dale R. Language structure is partly determined by social structure. PLoS One 2010;5:e8559.

[165] Widmer M, Jenny M, Behr W, Bickel B. Morphological structure can escape reduction effects from mass admixture of second language speakers: evidence from Sino-Tibetan. Stud Lang 2021;45:707–52.

[166] Kantarovich J, Grenoble LA, Vinokurova A, Nesterova E. Complexity and simplification in language shift. Front Commun 2021;6.

[167] Dehaene S, et al. How learning to read changes the cortical networks for vision and language. Science 2010;330:1359–64 (1979).

[168] Conant LL, Liebenthal E, Desai A, Binder JR. fMRI of phonemic perception and its relationship to reading development in elementary- to middle-school-age children. Neuroimage 2014;89:192–202.

[169] Huettig F, Pickering MJ. Literacy advantages beyond reading: prediction of spoken language. Trends Cogn Sci 2019;23:464–75.

[170] Deutscher G. Syntactic change in Akkadian: the evolution of sentential complementation. Oxford University Press; 2000.

[171] Zaghir J, Goldman JP, Bjelogricl M, Gaudet-Blavignac C, Lovis C. Caregivers interactions with clinical autocomplete tool: a retrospective study. In: Proceedings of the 20th International Conference on Informatics, Management, and Technology in Healthcare; 2022.

[172] Vanmassenhove E, Shterionov D, Gwilliam M. Machine translationese: effects of algorithmic bias on linguistic complexity in machine translation. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics; 2021. p. 2203–13.

[173] Evans N. Coevolutionary approaches to the science of language. Evolution, origin of life, concepts and methods. Springer International Publishing; 2019. p. 195–213. P. Pontarotti.

[174] Evans N. Context, culture, and structuration in the languages of Australia. Annu Rev Anthropol 2003;32:13–40.

[175] Bickel B. Spatial operations in deixis, cognition, and culture: where to orient oneself in Belhare. Language and conceptualization. Cambridge University Press; 1997. p. 46–83. J. Nuyts, E. Pederson.

[176] Greenfield PM. Language, tools and brain: the ontogeny and phylogeny of hierarchically organized sequential behavior. Behav Brain Sci 1991;14:531–51.

[177] Stout D, Chaminade T. Stone tools, language and the brain in human evolution. Philos Trans R Soc B Biol Sci 2012;367:75–87.

[178] Thibault S, et al. Tool use and language share syntactic processes and neural patterns in the basal ganglia. Science 2021;374:eabe0874 (1979).

[179] Cataldo DM, Migliano AB, Vinicius L. Speech, stone tool-making and the evolution of language. PLoS One 2018;13:1–10.

[180] Putt SS, Wijeakumar S, Franciscus RG, Spencer JP. The functional brain networks that underlie Early Stone Age tool manufacture. Nat Hum Behav 2017;1: 0102.

[181] Stout D, Chaminade T, Apel J, Shafti A, Faisal AA. The measurement, evolution, and neural representation of action grammars of human behavior. Sci Rep 2021;11:13720.

[182] Akil O, et al. Dual AAV-mediated gene therapy restores hearing in a DFN89 mouse model. Proc Natl Acad Sci 2019;116:4496–501.

[183] Omichi R, Shibata SB, Morton CC, Smith RJH. Gene therapy for hearing loss. Hum Mol Genet 2019;28:R65–79.

[184] Marchesotti S, et al. Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia. PLoS Biol 2020; 18:e3000833.

[185] Metzger SL, et al. Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis. Nat Commun 2022;13:6510.

[186] Moses DA, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med 2021;385:217–27.

[187] Proix T, et al. Imagined speech can be decoded from low- and cross-frequency intracranial EEG features. Nat Commun 2022;13:48.

[188] Leptourgos P, Corlett PR. Embodied predictions, agency, and psychosis. Front Big Data 2020;3:27.

[189] Deacon TW. A role for relaxed selection in the evolution of the language capacity. Proc Natl Acad Sci 2010;11:9000–6.

[190] Scerri EML, Will M. The revolution that still isn't: the origins of behavioral complexity in Homo sapiens. J Hum Evol 2023;179:103358.

[191] Ragsdale AP, et al. A weakly structured stem for human origins in Africa | Nature. Nature 2023. https://doi.org/10.1038/s41586-023-06055-y.

[192] Padilla-Iglesias C, et al. Population interconnectivity over the past 120,000 years explains distribution and diversity of Central African hunter-gatherers. Proc Natl Acad Sci 2022;119:e2113936119.

[193] Dediu D, Levinson SC. Neanderthal language revisited: not only us. Curr Opin Behav Sci 2018;21:49–55.

[194] Núñez RE. No Innate Number Line in the Human Brain. J Cross Cult Psychol 2011;42:651–68.
richer space of possibilities. We propose that this framework allows more precise discus-
sions about how various evolutionary processes come about and affect each other. In the
long run, we expect broad dichotomies like “cultural” or “biological” to lose importance and to
yield to sharper notions. What remains as a unifying principle is the core of evolution: the
combination of specific mechanisms for variation generation, transmission and selection.

The distinct mode of evolution that characterizes language opens perspectives that have been off limits for traditional (qualitative) linguistics, i.e. the stationary probabilities of states, with their intriguing asymmetries. This finding in turn expands the question of the biological evolution of the language faculty from its possible states to their characteristic dynamics. These dynamics shed new light on biological evolution, separating for example the effects of inherited traits (like agency detection) from derived traits (like social differentiation) on specif- ic properties of language (default syntax and change dynamics, respectively) when it emerged during hominin history.

Furthermore, carving out the specific properties of linguistic evolution allows us to more
sharply examine how language interacts with, and transitions from and into, other modes of
evolution, not only with regard to biological evolution, but also with regard to cultural, in par-
ticular technological, evolution. This will be especially important in the near future, as we are
witnessing rapid developments in technology, in particular genetic and neurological engi-
neering, artificial intelligence, brain-computer interfaces, and social media, which may yield
entirely new modes of evolution, involving novel social entities (brain-to-brain, brain-to-ma-
chine, crowd-to-crowd) that overrule those that were hitherto built via language. At the same
time, ever-faster globalization reduces linguistic diversity at an unprecedented pace. This
disrupts the traditional function that language distinctions have as markers of trust and co-
operation networks, but the impact of these developments on human society is barely un-
derstood.

All these developments come with enormous ethical and societal challenges. It is urgent to
recognize and examine them in depth. The analytical framework we proposed here may fa-
cilitate this endeavor.

Acknowledgements

This research was funded by the NCCR Evolving Language, Swiss National Science Foundation Agreement # 51NF40_180888. We thank (in alphabetical order) Chiara Barbieri, Daphné Bavelier, Dan Dediu, Russell Gray, Thibaud Gruber, Steve Levinson, Sabine Stoll, Stuart Watson, Marcel Weber, and Aris Xanthos for helpful comments, although we of course retain full responsibility for any remaining errors. BB furthermore thanks Chundra Cathcart for sharing his implementation of Felsenstein's pruning algorithm in Stan.

References

[1] Bromham L. 2017. Curiously the same: swapping tools between linguistics and evolutionary biology. *Biol. Philos.* 32:855–86

[2] Pagel M. 2017. Darwinian perspectives on the evolution of human languages. *Psychon. Bull. Rev.* 24(1):151–57

[3] Greenhill SJ, Heggarty P, Gray RD. 2021. Bayesian phylolinguistics. In *The Handbook of Historical Linguistics*, pp. 226–53. London: Wiley-Blackwell

[4] Richerson PJ, Boyd R. 2005. *Not by genes alone: how culture transformed human evolution*. Chicago: University of Chicago Press. 332 pp.

[5] Mesoudi A, Whiten A, Laland KN. 2006. Towards a unified science of cultural evolution. *Behav. Brain Sci.* 29(4):329–47

[6] Croft W. 2008. Evolutionary linguistics. *Annu. Rev. Anthropol.* 37:219–34
[7] Berwick RC, Chomsky N. 2017. Why only us: Recent questions and answers. *J. Neurolinguistics*. 43:166–77

[8] Mendívil-Giró J-L. 2019. Did language evolve through language change? On language change, language evolution and grammaticalization theory. *Glossa J. Gen. Linguist*. 4(1):

[9] Kirby S. 2017. Culture and biology in the origins of linguistic structure. *Psychon. Bull. Rev*. 24:118–37

[10] Christiansen MH, Chater N. 2008. Language as shaped by the brain. *Behav. Brain Sci*. 31:489–509

[11] Smith K. 2020. How Culture and Biology Interact to Shape Language and the Language Faculty. *Top. Cogn. Sci*. 12(2):690–712

[12] Uyeda JC, Zenil-Ferguson R, Pennell MW. 2018. Rethinking phylogenetic comparative methods. *Syst. Biol*. 67(6):1091–1109

[13] Pagel M, O'Donovan C, Meade A. 2022. General statistical model shows that macroevolutionary patterns and processes are consistent with Darwinian gradualism. *Nat. Commun*. 13(1):1113

[14] Ranacher P, Neureiter N, van Gijn R, Sonnenhauser B, Escher A, Weibel R, Muysken P, Bickel B. 2021. Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact. *J. R. Soc. Interface*. 18(181):20201031

[15] Evans CL, Greenhill SJ, Watts J, List J-M, Botero CA, Gray RD, Kirby KR. 2021. The uses and abuses of tree thinking in cultural evolution. *Philos. Trans. R. Soc. B Biol. Sci*. 376(1828):

[16] Creanza N, Kolodny O, Feldman MW. 2017. Cultural evolutionary theory: How culture evolves and why it matters. *Proc. Natl. Acad. Sci*. 114(30):7782–89

[17] Hauser MD, Chomsky N, Fitch WT. 2002. The faculty of language: what it is, who has it, and how did it evolve? *Science*. 298:1569–79

[18] Bakhtin Y, Katsnelson MI, Wolf YI, Koonin EV. 2021. Evolution in the weak-mutation limit: Stasis periods punctuated by fast transitions between saddle points on the fitness landscape. *Proc. Natl. Acad. Sci*. 118(4):e2015665118

[19] Goldin-Meadow S. 2020. Discovering the Biases Children Bring to Language Learning. *Child Dev. Perspect*. 14(4):195–201

[20] Blasi DE, Michaelis SM, Haspelmath M. 2017. Grammars are robustly transmitted even during the emergence of creole languages. *Nat. Hum. Behav*. 1:723–29

[21] Bickel B, Nichols J. 2020. Linguistic typology and hunter-gatherer languages. In *The language of hunter-gatherers*, ed T Güldemann, P McConvell, R Rhodes, pp. 67–75. Cambridge: Cambridge University Press

[22] Blasi DE, Moran S, Moisik SR, Widmer P, Dediu D, Bickel B. 2019. Human sound systems are shaped by post-Neolithic changes in bite configuration. *Science*. 363(6432):eaav3218

[23] Everett C, Chen S. 2021. Speech adapts to differences in dentition within and across populations. *Sci. Rep*. 11:1066

[24] Wong PCM, Kang X, Wong KHY, So H-C, Choy KW, Geng X. 2020. ASPM-lexical tone association in speakers of a tone language: Direct evidence for the genetic-biasing hypothesis of language evolution. *Sci. Adv*. 6(22):

[25] Dediu D. 2021. Tone and genes: New cross-linguistic data and methods support the weak negative effect of the “derived” allele of ASPM on tone, but not of Microcephalin. *PLOS ONE*. 16(6):e0253546

[26] Evans N, Levinson SC. 2009. The Myth of Language Universals: language diversity and its importance for cognitive science. *Behav. Brain Sci*. 32:429–48

[27] Kauhanen H. 2017. Neutral change. *J. Linguist*. 53(2):327–58

[28] Fagyal Z, Swarup S, Escobar AM, Gasser L, Lakkaraju K. 2010. Centers and peripheries: Network roles in language change. *Lingua*. 120(8):2061–79

[29] Sharma D, Dodsworth R. 2020. Language Variation and Social Networks. *Annu. Rev. Linguist*. 6(1):341–61

[30] Trudgill P. 2011. *Sociolinguistic typology: social determinants of linguistic complexity*. Oxford: Oxford University Press

[31] Eckert P. 2016. *Third Wave Variationism*. Oxford: Oxford University Press

[32] Thomason S. 2007. Language Contact and Deliberate Change. *J. Lang. Contact*. 1(1):41–62

[33] Moffett MW. 2013. Human Identity and the Evolution of Societies. *Hum. Nat*. 24(3):219–67

[34] Cohen E. 2012. The Evolution of Tag-Based Cooperation in Humans: The Case for Accent. *Curr. Anthropol*. 53(5):588–616
[35] Ivani J, Bickel B. in press. Databases for comparative syntactic research. In *The Cambridge Handbook of Comparative Syntax*, ed S Barbiers, N Corver, M Polinsky. Cambridge: Cambridge University Press (preprint at https://arxiv.org/abs/2310.11187)

[36] Bickel B. 2015. Distributional typology: statistical inquiries into the dynamics of linguistic diversity. In *The Oxford Handbook of Linguistic Analysis, 2nd edition*, ed B Heine, H Narrog, pp. 901–23. Oxford: Oxford University Press

[37] Round ER, Corbett GG. 2020. Comparability and measurement in typological science: the bright future for linguistics. *Linguist. Typology*. 24:489–525

[38] Salesky E, Chodroff E, Pimentel T, Wiesner M, Cotterell R, Black AW, Eisner J. 2020. A Corpus for Large-Scale Phonetic Typology

[39] Seifart F, Strunk J, Danielsen S, Hartmann I, Pakendorf B, Wichmann S, Witzlack-Makarevich A, Himmelmann NP, Bickel B. 2020. The extent and degree of utterance-final word lengthening in spontaneous speech from 10 languages. *Linguist. Vanguard*. 7:1–14

[40] Sauppe S, Choudhary KK, Giroud N, Blasi DE, Norcliffe E, Bhattamishra S, Gulati M, Egurtzegi A, Bornkessel-Schleszewsky I, Meyer M, Bickel B. 2021. Neural signatures of syntactic variation in speech planning. *PLoS Biol*. 19:e3001038

[41] Malik-Moraleda S, Ayyash D, Gallée J, Affourtit J, Hoffmann M, Mineroff Z, Jouravlev O, Fedorenko E. 2022. An investigation across 45 languages and 12 language families reveals a universal language network. *Nat. Neurosci*. 25(8):1014–19

[42] Picallo MC, ed. 2014. *Linguistic variation in the minimalist framework*. Oxford: Oxford University Press. 262 pp.

[43] Chomsky N. 2005. Three factors in language design. *Linguist. Inq*. 36:1–22

[44] Bickel B, Bisang W, Yādava YP. 1999. Face vs. empathy: the social foundations of Maithili verb agreement. *Linguistics*. 37:481–518

[45] Heine B, Narrog H, eds. 2012. *The Oxford Handbook of Grammaticalization*. Oxford: Oxford University Press

[46] Greenhill SJ, Wu C-H, Hua X, Dunn M, Levinson SC, Gray RD. 2017. Evolutionary dynamics of language systems. *Proc. Natl. Acad. Sci.*

[47] van Gelderen E. 2013. The Linguistic Cycle and the Language Faculty. *Lang. Linguist. Compass*. 7(4):233–50

[48] Maslova E. 2000. A dynamic approach to the verification of distributional universals. *Linguist. Typology*. 4:307–33

[49] Pagel M. 2000. Maximum likelihood models for glottochronology and for reconstructing linguistic phylogenies. In *Time-Depth in Historical Linguistics*, ed C Renfrew, A MacMahon, L Trask, pp. 189–207. Cambridge: The MacDonald Institute of Archeology

[50] Cathcart CA. 2018. Modeling linguistic evolution: a look under the hood. *Linguist. Vanguard*. 4:2–11

[51] Hoffmann K, Bouckaert R, Greenhill SJ, Kühnert D. 2021. Bayesian phylogenetic analysis of linguistic data using BEAST. *J. Lang. Evol*. 6(2):119–35

[52] Griffiths TL, Kalish ML. 2007. Language Evolution by Iterated Learning With Bayesian Agents. *Cogn. Sci*. 31(3):441–80

[53] Jäger G, Wahle J. 2021. Phylogenetic Typology. *Front. Psychol*. 12:682132

[54] Widmer M, Auderset S, Widmer P, Nichols J, Bickel B. 2017. NP recursion over time: evidence from Indo-European. *Language*. 93:1–36

[55] Cysouw M. 2011. Understanding transition probabilities. *Linguist. Typology*. 15:415–31

[56] Jäger G. 2007. Evolutionary game theory and typology: a case study. *Language*. 83:74–109

[57] Blythe RA, Croft W. 2021. How individuals change language. *PLOS ONE*. 16(6):e0252582

[58] Huelsenbeck JP, Nielsen R, Bollback JP. 2003. Stochastic mapping of morphological characters. *Syst. Biol*. 52:131–58

[59] Revell LJ. 2013. Two new graphical methods for mapping trait evolution on phylogenies. *Methods Ecol. Evol*. 4:754–59

[60] Basalla G. 1988. *The Evolution of Technology*. Cambridge: Cambridge University Press

[61] Brain AB. 2009. *The Nature of Technology: What it is and how it evolves*. New York: Free Press

[62] Bickel B. 2007. Typology in the 21st century: major current developments. *Linguist. Typology*. 11:239–51

[63] Piantadosi ST, Gibson E. 2014. Quantitative standards for absolute linguistic universals. *Cogn. Sci*. 38:736–56
[64] Everett DL. 2005. Cultural constraints on grammar and cognition in Pirahã: another look at the design features of human language. *Curr. Anthropol.* 46:621–46

[65] Dryer MS. 2005. Polar questions. In *The world atlas of language structures*, ed M Haspelmath, MS Dryer, D Gil, B Comrie, pp. 470–73. Oxford: Oxford University Press

[66] Gray RD, Greenhill SJ, Ross RM. 2007. The pleasures and perils of Darwinizing culture (with phylogenies). *Biol. Theory*. 2:360–75

[67] Heine B, Kuteva T. 2007. *The genesis of grammar: a reconstruction*. Oxford: Oxford University Press

[68] Tomasello M. 2008. *Origins of human communication*. Cambridge, MA: MIT Press

[69] Briscoe T. 2000. Grammatical Acquisition: Inductive Bias and Coevolution of Language and the Language Acquisition Device. *Language*. 76:245–96

[70] Bickel B, Banjade G, Gaenszle M, Lieven E, Paudyal N, Rai IP, Rai M, Rai NK, Stoll S. 2007. Free prefix ordering in Chintang. *Language*. 83:43–73

[71] Reali F, Griffiths TL. 2010. Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift. *Proc. R. Soc. B Biol. Sci.* 277(1680):429–36

[72] Niyogi P, Berwick RC. 2009. The proper treatment of language acquisition and change in a population setting. *Proc. Natl. Acad. Sci.* 106(25):10124–29

[73] Yang CD. 2000. Internal and external forces in language change. *Lang. Var. Change.* 12(3):231–50

[74] Meakins F, Hua X, Algy C, Bromham L. 2019. Birth of a contact language did not favor simplification. *Language*. 95(2):294–332

[75] Tomasello M, Kruger AC, Ratner HH. 1993. Cultural learning. *Behav. Brain Sci.* 16(3):495–511

[76] Cathcart CA, Herce B, Bickel B. 2022. Decoupling speed of change and long-term preference in language evolution: insights from Romance verb stem alternations

[77] Padilla-Iglesias C, Gjesfjeld E, Vinicius L. 2020. Geographical and social isolation drive the evolution of Austronesian languages. *PLOS ONE*. 15(12):e0243171

[78] Greenhill SJ, Hua X, Welsh CF, Schneemann H, Bromham L. 2018. Population Size and the Rate of Language Evolution: A Test Across Indo-European, Austronesian, and Bantu Languages. *Front. Psychol.* 9:

[79] Raviv L, Meyer A, Lev-Ari S. 2020. The Role of Social Network Structure in the Emergence of Linguistic Structure. *Cogn. Sci.* 44(8):e12876

[80] Atkinson QD, Meade A, Venditti C, Greenhill SJ, Pagel M. 2008. Languages evolve in punctuational bursts. *Science*. 319(5863):588–588

[81] Bromham L, Dinnage R, Skirgård H, Ritchie A, Cardillo M, Meakins F, Greenhill S, Hua X. 2021. Global predictors of language endangerment and the future of linguistic diversity. *Nat. Ecol. Evol.*

[82] List J-M, Nelson-Sathi S, Geisler H, Martin W. 2014. Networks of lexical borrowing and lateral gene transfer in language and genome evolution. *BioEssays*. 36(2):141–50

[83] Thomason SG. 2001. *Language contact: an introduction*. Washington, D.C.: Georgetown University Press

[84] Heine B, Kuteva T. 2006. *The changing languages of Europe*. Oxford: Oxford University Press

[85] Bybee JL, Slobin DI. 1982. Why small children cannot change language on their own: suggestions from the English past tense. In *Current Issues in Linguistic Theory*, ed A Ahlqvist, pp. 29–37. Amsterdam: John Benjamins Publishing Company

[86] Ohala JJ. 1989. Sound Change is Drawn from a Pool of Synchronic Variation. In *Language change: Contributions to the study of its causes*, ed Breivik, Leiv Egil, EH Jahr, pp. 171–98. New York: Mouton de Gruyter

[87] Croft W. 2000. *Explaining language change: an evolutionary approach*. Harlow: Longman

[88] Lightfoot D. 2006. *How new languages emerge*. Cambridge: Cambridge University Press

[89] Nichols J. 1996. The comparative method as heuristic. In *The comparative method reviewed*, ed M Durie, M Ross, pp. 39–71. Oxford: Oxford University Press

[90] Bickel B. 2020. Large and ancient linguistic areas. In *Language dispersal, diversification, and contact: a global perspective*, ed M Crevels, P Muysken, pp. 78–101. Oxford: Oxford University Press

[91] Gray RD, Bryant D, Greenhill SJ. 2010. On the shape and fabric of human history. *Philos. Trans. R. Soc. B*. 365:3923–33
[92] Neureiter N, Ranacher P, Efrat-Kowalsky N, Kaiping GA, Weibel R, Widmer P, Bouckaert RR. 2022. Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer. *Humanit. Soc. Sci. Commun.* 9(1):205

[93] Matsumae H, Ranacher P, Savage PE, Blasi DE, Currie TE, Koganebuchi K, Nishida N, Sato T, Tanabe H, Tajima A, Brown S, Stoneking M, Shimizu KK, Oota H, Bickel B. 2021. Exploring correlations in genetic and cultural variation across language families in northeast Asia. *Sci. Adv.* 7(34):eabd9223

[94] Keeling PJ, Palmer JD. 2008. Horizontal gene transfer in eukaryotic evolution. *Nat. Rev. Genet.* 9(8):605–18

[95] Crisp A, Boschetti C, Perry M, Tunnacliffe A, Micklem G. 2015. Expression of multiple horizon- tally acquired genes is a hallmark of both vertebrate and invertebrate genomes. *Genome Biol.* 16(1):50

[96] Kambayashi C, Kakehashi R, Sato Y, Mizuno H, Tanabe H, Rakotoarison A, Künzel S, Furuno N, Ohshima K, Kumazawa Y, Nagy ZT, Mori A, Allison A, Donnellan SC, Ota H, Hoso M, Yanagida T, Sato H, Vences M, Kurabayashi A. 2022. Geography-Dependent Horizontal Gene Transfer from Vertebrate Predators to Their Prey. *Mol. Biol. Evol.* 39(4):msac052

[97] White I. 1981. Generation Moieties in Australia: Structural, Social and Ritual Implications. *Oceania*. 52(1):6–27

[98] Jackson MD, Mulcahy SR, Chen H, Li Y, Li Q, Cappelletti P, Wenk H-R. 2017. Phillipsite and Al-tobermorite mineral cements produced through low-temperature water-rock reactions in Roman marine concrete. *Am. Mineral*. 102(7):1435–50

[99] Schick J, Fryns C, Wegdell F, Laporte M, Zuberbühler K, Schaik CP van, Townsend SW, Stoll S. 2022. The function and evolution of child-directed communication. *PLOS Biol.* 20(5):e3001630

[100] Sterelny K. 2021. *The Pleistocene social contract: culture and cooperation in human evolution*. New York: Oxford University Press. 182 pp.

[101] Gray RD, Watts J. 2017. Cultural macroevolution matters. *Proc. Natl. Acad. Sci.* 114(30):7846–52

[102] Sheehan O, Watts J, Gray RD, Atkinson QD. 2018. Coevolution of landesque capital intensive agriculture and sociopolitical hierarchy. *Proc. Natl. Acad. Sci.* 115(14):3628–33

[103] Valverde S, Solé RV. 2015. Punctuated equilibrium in the large-scale evolution of programming languages. *J. R. Soc. Interface*. 12(107):20150249

[104] Youngblood M, Baraghith K, Savage PE. 2021. Phylogenetic reconstruction of the cultural evo- lution of electronic music via dynamic community detection (1975–1999). *Evol. Hum. Behav.* 42(6):573–82

[105] Dawkins R. 2016. *The extended phenotype: the long reach of the gene*. Oxford: Oxford Uni- versity Press

[106] Hull DL. 1998. *Science as a process: an evolutionary account of the social and conceptual development of science*. Chicago: University of Chicago Press. 586 pp.

[107] Blevins J. 2004. *Evolutionary phonology: the emergence of sound patterns*. New York: Cam- bridge University Press

[108] Cristofaro S. 2012. Cognitive explanations, distributional evidence, and diachrony. *Stud. Lang.* 36:645–70

[109] Mesoudi A. 2021. Cultural selection and biased transformation: two dynamics of cultural evolu- tion. *Philos. Trans. R. Soc. B Biol. Sci.* 376(1828):1–12

[110] Schmidtke-Bode K, Levshina N, Michaelis SM, Seržant IA. 2019. *Explanation in typology: Di- achronic sources, functional motivations and the nature of the evidence*. Leipzig: Language Science Press

[111] Cathcart C, Hölzl A, Jäger G, Widmer P, Bickel B. 2020. Numeral classifiers and number mark- ing in Indo-Iranian: A phylogenetic approach. *Lang. Dyn. Change.* 11(2):273–325

[112] Palazzo AF, Kejiou NS. 2022. Non-Darwinian Molecular Biology. *Front. Genet.* 13:

[113] Cavalli G, Heard E. 2019. Advances in epigenetics link genetics to the environment and dis- ease. *Nature*. 571(7766):489–99

[114] Reali F, Griffiths TL. 2009. The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning. *Cognition*. 111(3):317–28

[115] FitzJohn RG, Maddison WP, Otto SP. 2009. Estimating Trait-Dependent Speciation and Extinc- tion Rates from Incompletely Resolved Phylogenies. *Syst. Biol.* 58(6):595–611

[116] Boucher FC, Démery V. 2016. Inferring Bounded Evolution in Phenotypic Characters from Phylogenetic Comparative Data. *Syst. Biol.* 65(4):651–61
[117] Boucher FC, Démery V, Conti E, Harmon LJ, Uyeda J. 2018. A General Model for Estimating Macroevolutionary Landscapes. *Syst. Biol.* 67(2):304–19

[118] Blomberg SP, Rathnayake SI, Moreau CM. 2020. Beyond Brownian Motion and the Ornstein-Uhlenbeck Process: Stochastic Diffusion Models for the Evolution of Quantitative Characters. *Am. Nat.* 195(2):145–65

[119] Witzlack-Makarevich A, Zakharko T, Bierkandt L, Zúñiga F, Bickel B. 2016. Decomposing hierarchical alignment: co-arguments as conditions on alignment and the limits of referential hierarchies as explanations in verb agreement. *Linguistics*. 54:531–61

[120] Hahn M, Xu Y. 2022. Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality. *Proc. Natl. Acad. Sci.* 119(24):e2122604119

[121] Johansson NE, Anikin A, Carling G, Holmer A. 2020. The typology of sound symbolism: Defining macro-concepts via their semantic and phonetic features. *Linguist. Typology*. 24:253–310

[122] Blasi DE, Wichmann S, Hammarström H, Stadler PF, Christiansen MH. 2016. Sound-meaning association biases evidenced across thousands of languages. *Proc. Natl. Acad. Sci.* 113:10818–23

[123] Chang W, Cathcart C, Hall D, Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports Indo-European steppe hypothesis. *Language*. 91:194–244

[124] Bentz C, Dediu D, Verkerk A, Jäger G. 2018. The evolution of language families is shaped by the environment beyond neutral drift. *Nat. Hum. Behav.* 2(11):816–21

[125] Derungs C, Köhli M, Weibel R, Bickel B. 2018. Environmental factors drive language density more in food-producing than in hunter-gatherer populations. *Proc. R. Soc. B Biol. Sci.* 285:20172851

[126] Pacheco Coelho MT, Pereira EB, Haynie HJ, Rangel TF, Kavanagh P, Kirby KR, Greenhill SJ, Bowern C, Gray RD, Colwell RK, Evans N, Gavin MC. 2019. Drivers of geographical patterns of North American language diversity. *Proc. R. Soc. B Biol. Sci.* 286:20190242

[127] Efrat-Kowalsky N, Ranacher P, Neureiter N, Widmer P, Bickel B. 2022. Oldest attested languages in the Near East reveal deep transformations in linguistic landscapes. *Rev.*

[128] Diamond JM, Bellwood P. 2003. Farmers and their languages: The first expansions. *Science*. 300:597–603

[129] Senghas A, Kita S, Özyürek A. 2004. Children creating core properties of language: Evidence from an emerging sign language in nicaragua. *Science*. 305(5691):1779–82

[130] Kirby S, Cornish H, Smith K. 2008. Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language. *Proc. Natl. Acad. Sci. U. S. A.* 105:10681–86

[131] Futrell R, Levy RP, Gibson E. 2020. Dependency locality as an explanatory principle for word order. *Language*. 96(2):371–412

[132] Bickel B, Witzlack-Makarevich A, Choudhary KK, Schlesewsky M, Bornkessel-Schlesewsky I. 2015. The neurophysiology of language processing shapes the evolution of grammar: evidence from case marking. *PLoS ONE*. 10:e0132819

[133] Wilson V, Zuberbühler K, Bickel B. 2022. The evolutionary origins of syntax: Event cognition in nonhuman primates. *Sci. Adv.*

[134] Everett C. 2018. The global dispreference for posterior voiced obstruents: A quantitative assessment of word-list data. *Language*. 94:e311-323

[135] Mansfield J, Saldana C, Hurst P, Nordlinger R, Stoll S, Bickel B, Perfors A. 2022. Category Clustering and Morphological Learning. *Cogn. Sci.* 46(2):

[136] Xu Y, Duong K, Malt BC, Jiang S, Srinivasan M. 2020. Conceptual relations predict colexification across languages. *Cognition*. 201:104280

[137] Chemla E, Dautriche I, Buccola B, Fagot J. 2019. Constraints on the lexicons of human languages have cognitive roots present in baboons (*Papio papio*). *Proc. Natl. Acad. Sci.* 116(30):14926–30

[138] Layton R, O’Hara S, Bilsborough A. 2012. Antiquity and Social Functions of Multilevel Social Organization Among Human Hunter-Gatherers. *Int. J. Primatol.* 33(5):1215–45

[139] Ruch H, Zürcher Y, Burkart JM. 2018. The function and mechanism of vocal accommodation in humans and other primates. *Biol. Rev.* 93(2):996–1013

[140] Townsend SW, Engesser S, Stoll S, Zuberbühler K, Bickel B. 2018. Compositionality in animals and humans. *PLOS Biol.* 16:e2006425

[141] Collier K, Bickel B, van Schaik CP, Manser MB, Townsend SW. 2014. Language evolution: syntax before phonology? *Proc. R. Soc. B Biol. Sci.* 281(1788):
[142] Nowak MA, Plotkin JB, Jansen VAA. 2000. The evolution of syntactic communication. *Nature*. 404(6777):495-98

[143] Deacon TW. 1997. *The symbolic species: the co-evolution of language and the brain*. New York: W.W. Norton. 527 pp. 1st ed ed.

[144] Uchiyama R, Spicer R, Muthukrishna M. 2021. Cultural Evolution of Genetic Heritability. *Behav. Brain Sci.*, pp. 1-147

[145] Aoki K, Feldman MW. 1991. Recessive hereditary deafness, assortative mating, and persistence of a sign language. *Theor. Popul. Biol.* 39(3):358-72

[146] Gialluisi A, Dediu D, Francks C, Fisher SE. 2013. Persistence and transmission of recessive deafness and sign language: new insights from village sign languages. *Eur. J. Hum. Genet.* 21(9):894-96

[147] Mudd K, Vos C de, Boer B de. 2020. An agent-based model of sign language persistence informed by real-world data. *Lang. Dyn. Change*. 10(2):158-87

[148] Ponce de León MS, Bienvenu T, Marom A, Engel S, Tafforeau P, Alatorre Warren JL, Lord-kipanidze D, Kurniawan I, Murti DB, Suriyanto RA, Koesbardiati T, Zollikofer CPE. 2021. The primitive brain of early *Homo*. *Science*. 372(6538):165-71

[149] Berto S, Fontenot MR, Seger S, Ayhan F, Caglayan E, Kulkarni A, Douglas C, Tamminga CA, Lega BC, Konopka G. 2021. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. *Nat. Neurosci.* 24(4):554-64

[150] Colquitt BM, Merullo DP, Konopka G, Roberts TF, Brainard MS. 2021. Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits. *Science*. 371(6530):eabd9704

[151] Hyafil A, Giraud A-L, Fontolan L, Gutkin B. 2015. Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. *Trends Neurosci.* 38(11):725-40

[152] Hyafil A, Fontolan L, Kabdebon C, Gutkin B, Giraud A-L. 2015. Speech encoding by coupled cortical theta and gamma oscillations. *eLife*. 4:e06213

[153] Hovsepyan S, Olasagasti I, Giraud A-L. 2020. Combining predictive coding and neural oscillations enables online syllable recognition in natural speech. *Nat. Commun.* 11(1):3117

[154] Ding N, Melloni L, Zhang H, Tian X, Poeppel D. 2016. Cortical tracking of hierarchical linguistic structures in connected speech. *Nat. Neurosci.* 19:158-64

[155] Getz H, Ding N, Newport EL, Poeppel D. 2018. Cortical tracking of constituent structure in language acquisition. *Cognition*. 181:135-40

[156] Barbieri C, Blasi DE, Arango-Isaza E, Sotoripoulos AG, Hammarström H, Wichmann S, Greenhill SJ, Gray RD, Forkel R, Bickel B, Shimizu KK. 2022. A global analysis of matches and mismatches between human genetic and linguistic histories. *Proc. Natl. Acad. Sci.* 119(47):e2122084119

[157] Currie TE, Mace R. 2009. Political complexity predicts the spread of ethnolinguistic groups. *Proc. Natl. Acad. Sci.* 106:7339-44

[158] Humboldt W von. 1836. *Über die Verschiedenheit des menschlichen Sprachbaus und ihren Einfluss auf die geistige Entwicklung des Menschengeschlechtes*. Berlin: Dümmler

[159] Meir I. 2020. Topic-open-endedness: Why recursion is overrated. *Sign Lang. Linguist.* 23(1-2):258-71

[160] Lupyan G, Dale R. 2010. Language structure is partly determined by social structure. *PLoS ONE*. 5:e8559

[161] Widmer M, Jenny M, Behr W, Bickel B. 2021. Morphological structure can escape reduction effects from mass admixture of second language speakers: evidence from Sino-Tibetan. *Stud. Lang.* 45:707-52

[162] Kantarovich J, Grenoble LA, Vinokurova A, Nesterova E. 2021. Complexity and Simplification in Language Shift. *Front. Commun.* 6:

[163] Dehaene S, Pegado F, Braga LW, Ventura P, Filho GN, Jobert A, Dehaene-Lambertz G, Kolinsky R, Morais J, Cohen L. 2010. How Learning to Read Changes the Cortical Networks for Vision and Language. *Science*. 330(6009):1359-64

[164] Conant LL, Liebenthal E, Desai A, Binder JR. 2014. FMRI of phonemic perception and its relationship to reading development in elementary- to middle-school-age children. *NeuroImage*. 89:192-202

[165] Huettig F, Pickering MJ. 2019. Literacy Advantages Beyond Reading: Prediction of Spoken Language. *Trends Cogn. Sci.* 23(6):464-75

[166] Deutscher G. 2000. *Syntactic change in Akkadian: the evolution of sentential complementation*. Oxford: Oxford University Press
[167] Zaghir J, Goldman J-P, Bjelogrlic M, Gaudet-Blavignac C, Lovis C. 2022. Caregivers Interactions with Clinical Autocomplete Tool: a Retrospective Study

[168] Vanmassenhove E, Shterionov D, Gwilliam M. 2021. Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation

[169] Evans N. 2003. Context, culture, and structuration in the languages of Australia. *Annu. Rev. Anthropol.* 32:13–40

[170] Bickel B. 1997. Spatial operations in deixis, cognition, and culture: where to orient oneself in Belhare. In *Language and conceptualization*, ed J Nuyts, E Pederson, pp. 46–83. Cambridge: Cambridge University Press

[171] Greenfield PM. 1991. Language, tools and brain: The ontogeny and phylogeny of hierarchically organized sequential behavior. *Behav. Brain Sci.* 14(4):531–51

[172] Stout D, Chaminade T. 2012. Stone tools, language and the brain in human evolution. *Philos. Trans. R. Soc. B Biol. Sci.* 367:75–87

[173] Thibault S, Py R, Gervasi AM, Salemme R, Koun E, Lövden M, Boulenger V, Roy AC, Brozzoli C. 2021. Tool use and language share syntactic processes and neural patterns in the basal ganglia. *Science*. 374(6569):eabe0874

[174] Cataldo DM, Migliano AB, Vinicius L. 2018. Speech, stone tool-making and the evolution of language. *PLOS ONE*. 13:1–10

[175] Putt SS, Wijeakumar S, Franciscus RG, Spencer JP. 2017. The functional brain networks that underlie Early Stone Age tool manufacture. *Nat. Hum. Behav.* 1:0102

[176] Stout D, Chaminade T, Apel J, Shafti A, Faisal AA. 2021. The measurement, evolution, and neural representation of action grammars of human behavior. *Sci. Rep.* 11(1):13720

[177] Akil O, Dyka F, Calvet C, Emptoz A, Lahlou G, Nouaille S, Boutet de Monvel J, Hardelin J-P, Hauswirth WW, Avan P, Petit C, Safieddine S, Lustig LR. 2019. Dual AAV-mediated gene therapy restores hearing in a DFNB9 mouse model. *Proc. Natl. Acad. Sci.* 116(10):4496–4501

[178] Omichi R, Shibata SB, Morton CC, Smith RJH. 2019. Gene therapy for hearing loss. *Hum. Mol. Genet.* 28(R1):R65–79

[179] Marchesotti S, Nicolle J, Merlet I, Arnal LH, Donoghue JP, Giraud A-L. 2020. Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia. *PLoS Biol.* 18(9):e3000833

[180] Metzger SL, Liu JR, Moses DA, Dougherty ME, Seaton MP, Littlejohn KT, Chartier J, Anumanchipalli GK, Tu-Chan A, Ganguly K, Chang EF. 2022. Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis. *Nat. Commun.* 13(1):6510

[181] Moses DA, Metzger SL, Liu JR, Anumanchipalli GK, Makin JG, Sun PF, Chartier J, Dougherty ME, Liu PM, Abrams GM, Tu-Chan A, Ganguly K, Chang EF. 2021. Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria. *N. Engl. J. Med.* 385(3):217–27

[182] Proix T, Delgado Saa J, Christen A, Martin S, Pasley BN, Knight RT, Tian X, Poeppel D, Doyle WK, Devinsky O. Arnal LH, Mégevand P, Giraud A-L. 2022. Imagined speech can be decoded from low- and cross-frequency intracranial EEG features. *Nat. Commun.* 13(1):48

[183] Leptourgos P, Corlett PR. 2020. Embodied Predictions, Agency, and Psychosis. *Front. Big Data*. 3:27

[184] Deacon TW. 2010. A role for relaxed selection in the evolution of the language capacity. *Proc. Natl. Acad. Sci.* 11:9000–9006

[185] Scerri EML, Will M. 2023. The revolution that still isn't: The origins of behavioral complexity in Homo sapiens. *J. Hum. Evol.* 179:103358

[186] Ragsdale AP, Weaver TD, Atkinson EG, Hoal EG, Möller M, Henn BM, Gravel S. 2023. A weakly structured stem for human origins in Africa. *Nature*. 617(7962):755–63

[187] Padilla-Iglesias C, Atmore LM, Olivero J, Lupo K, Manica A, Arango Isaza E, Vinicius L, Migliano AB. 2022. Population interconnectivity over the past 120,000 years explains distribution and diversity of Central African hunter-gatherers. *Proc. Natl. Acad. Sci.* 119(21):e2113936119

[188] Dediu D, Levinson SC. 2018. Neanderthal language revisited: not only us. *Curr. Opin. Behav. Sci.* 21:49–55

[189] Núñez RE. 2011. No Innate Number Line in the Human Brain. *J. Cross-Cult. Psychol.* 42(4):651–68