Language evolution as a Darwinian process: computational studies

This paper presents computational experiments that illustrate how one can precisely conceptualize language evolution as a Darwinian process. We show that there is potentially a wide diversity of replicating units and replication mechanisms involved in language evolution. Computational experiments allow us to study systemic properties coming out of populations of linguistic replicators: linguistic replicators can adapt to specific external environments; they evolve under the pressure of the cognitive constraints of their hosts, as well as under the functional pressure of communication for which they are used; one can observe neutral drift; coalitions of replicators may appear, forming higher level groups which can themselves become subject to competition and selection.

Iconicity and the Emergence of Combinatorial Structure in Language

In language, recombination of a discrete set of meaningless building blocks forms an unlimited set of possible utterances. How such combinatorial structure emerged in the evolution of human language is increasingly being studied. It has been shown that it can emerge when languages culturally evolve and adapt to human cognitive biases. How the emergence of combinatorial structure interacts with the existence of holistic iconic form‐meaning mappings in a language is still unknown. The experiment presented in this paper studies the role of iconicity and human cognitive learning biases in the emergence of combinatorial structure in artificial whistled languages. Participants learned and reproduced whistled words for novel objects with the use of a slide whistle. Their reproductions were used as input for the next participant, to create transmission chains and simulate cultural transmission. Two conditions were studied: one in which the persistence of iconic form‐meaning mappings was possible and one in which this was experimentally made impossible. In both conditions, cultural transmission caused the whistled languages to become more learnable and more structured, but this process was slightly delayed in the first condition. Our findings help to gain insight into when and how words may lose their iconic origins when they become part of an organized linguistic system.     

The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person’s linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.     

Acquiring and processing verb argument structure: distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.     

The Role of Social Network Structure in the Emergence of Linguistic Structure

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro-societies that varied in their network structure. We contrasted three types of social networks: fully connected, small-world, and scale-free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small-world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift).     

CUI BONO? A REVIEW OF BREAKING THE SPELL: RELIGION AS A NATURAL PHENOMENON BY DANIEL C. DENNETT

The three requirements for a Darwinian evolutionary process are replication, variation and selection. Dennett (2006) discusses various theories of how these three processes, especially selection, may have operated in the evolution of religion. He believes that the origins of religion, like the origins of language and music, may be approached scientifically. He hopes that such investigations will open a dialog between science and religion leading to moderation of current religious extremism. One problem with Dennett's program, illustrating the difficulty of breaking away from creationist thinking, is Dennett's own failure to consider how Darwinian methods may be used to study evolution of behavioral patterns over the lifetime of individual organisms.     

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n. We relax some of the simplifying assumptions of this model to explore how different assumptions about language evolution affect predictions about the time to convergence; in general, convergence time increases as the model becomes more realistic. This allows us to characterize the assumptions about language learning (given the models that we consider) that are sufficient for convergence to have taken place on a timescale that is consistent with the origin of human languages. These results clearly identify the consequences of a set of simple models of language evolution and show how analysis of convergence rates provides a tool that can be used to explore questions about the relationship between accounts of language learning and the origins of similarities across languages.     

Co‐occurrence statistics as a language‐dependent cue for speech segmentation

To what extent can language acquisition be explained in terms of different associative learning mechanisms? It has been hypothesized that distributional regularities in spoken languages are strong enough to elicit statistical learning about dependencies among speech units. Distributional regularities could be a useful cue for word learning even without rich language‐specific knowledge. However, it is not clear how strong and reliable the distributional cues are that humans might use to segment speech. We investigate cross‐linguistic viability of different statistical learning strategies by analyzing child‐directed speech corpora from nine languages and by modeling possible statistics‐based speech segmentations. We show that languages vary as to which statistical segmentation strategies are most successful. The variability of the results can be partially explained by systematic differences between languages, such as rhythmical differences. The results confirm previous findings that different statistical learning strategies are successful in different languages and suggest that infants may have to primarily rely on non‐statistical cues when they begin their process of speech segmentation.     

Language evolution by iterated learning with bayesian agents

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.     

Language acquisition meets language evolution

Recent research suggests that language evolution is a process of cultural change, in which linguistic structures are shaped through repeated cycles of learning and use by domain-general mechanisms. This paper draws out the implications of this viewpoint for understanding the problem of language acquisition, which is cast in a new, and much more tractable, form. In essence, the child faces a problem of induction, where the objective is to coordinate with others (C-induction), rather than to model the structure of the natural world (N-induction). We argue that, of the two, C-induction is dramatically easier. More broadly, we argue that understanding the acquisition of any cultural form, whether linguistic or otherwise, during development, requires considering the corresponding question of how that cultural form arose through processes of cultural evolution. This perspective helps resolve the "logical" problem of language acquisition and has far-reaching implications for evolutionary psychology.     

Language as shaped by the brain

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change are much more rapid than processes of genetic change, language constitutes a "moving target" both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG--the mesh between learners and languages--arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent "organism," which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.     

Quantitative Standards for Absolute Linguistic Universals

Absolute linguistic universals are often justified by cross‐linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross‐linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in both it is possible to find strict linguistic universals, but that the numbers of independent languages necessary to do so is generally unachievable. This suggests that methods other than typological statistics are necessary to establish absolute properties of human language, and thus that many of the purported universals in linguistics have not received sufficient empirical justification.     

Human Self-Domestication and the Evolution of Pragmatics

As proposed for the emergence of modern languages, we argue that modern uses of languages (pragmatics) also evolved gradually in our species under the effects of human self-domestication, with three key aspects involved in a complex feedback loop: (a) a reduction in reactive aggression, (b) the sophistication of language structure (with emerging grammars initially facilitating the transition from physical aggression to verbal aggression); and (c) the potentiation of pragmatic principles governing conversation, including, but not limited to, turn-taking and inferential abilities. Our core hypothesis is that the reduction in reactive aggression, one of the key factors in self-domestication processes, enabled us to fully exploit our cognitive and interactional potential as applied to linguistic exchanges, and ultimately to evolve a specific form of communication governed by persuasive reciprocity—a trait of human conversation characterized by both competition and cooperation. In turn, both early crude forms of language, well suited for verbal aggression/insult, and later more sophisticated forms of language, well suited for persuasive reciprocity, significantly contributed to the resolution and reduction of (physical) aggression, thus having a return effect on the self-domestication processes. Supporting evidence for our proposal, as well as grounds for further testing, comes mainly from the consideration of cognitive disorders, which typically simultaneously present abnormal features of self-domestication (including aggressive behavior) and problems with pragmatics and social functioning. While various approaches to language evolution typically reduce it to a single factor, our approach considers language evolution as a multifactorial process, with each player acting upon the other, engaging in an intense mutually reinforcing feedback loop. Moreover, we see language evolution as a gradual process, continuous with the pre-linguistic cognitive abilities, which were engaged in a positive feedback loop with linguistic innovations, and where gene-culture co-evolution and cultural niche construction were the main driving forces.     

A Difference of Some Consequence Between Conventions and Rules

Lewis’s view of the way conventions are passed on may have some especially interesting consequences for the study of language. I’ll start by briefly discussing agreements and disagreements that I have with Lewis’s general views on conventions and then turn to how linguistic conventions spread. I’ll compare views of main stream generative linguistics, in particular, Chomsky’s views on how syntactic forms are passed on, with the sort of view of language acquisition and language change advocated by usage-based or construction grammars, which seem to fit better with Lewis’s ideas. Then I will illustrate the interest of Lewis’s perspective on the dissemination of conventions with a variety of linguistic examples.     

Darwinism Extended: A Survey of How the Idea of Cultural Evolution Evolved

In the past 150 years there have been many attempts to draw parallels between cultural and biological evolution. Most of these attempts were flawed due to lack of knowledge and false ideas about evolution. In recent decades these shortcomings have been cleared away, thus triggering a renewed interest in the subject. This paper offers a critical survey of the main issues and arguments in that discussion. The paper starts with an explication of the Darwinian algorithm of evolution. It is argued that this ‘formula’ is substrate-neutral, which means that biological evolution might not be the only Darwinian process. Other dynamic systems could evolve as well provided that certain conditions are met. In the case of human culture this seems to be the case. The paper then focuses on the notion of niche construction. It is argued that niche construction plays a crucial role in human evolution because it has altered the sources of natural selection and thus the path of evolution. Next two approaches to cultural evolution are discussed: sociobiology and memetics. I will argue that both approaches have flaws because they either underestimate the influence of culture or they stretch analogies too far. Finally two common objections against the idea of cultural evolution are addressed: Lamarckian inheritance and the issue of guided variation. I will argue that although cultural evolution differs from biological evolution in several respects, these discrepancies do not jeopardize the claim that cultural evolution is essentially Darwinian.     

Theories of early language acquisition

What features of brain processing and neural development support linguistic development in young children? To what extent is the profile and timing of linguistic development in young children determined by a pre-ordained genetic programme? Does the environment play a crucial role in determining the patterns of change observed in children growing up? Recent experimental, neuroimaging and computational studies of developmental change in children promise to contribute to a deeper understanding of how the brain becomes wired up for language. In this review, the muttidisciplinary perspectives of cognitive neuroscience, experimental psycholinguistics and neural network modelling are brought to bear on four distinct areas in the study of language acquisition: early speech perception, word recognition, word learning and the acquisition of grammatical inflections. It is suggested that each area demonstrates how linguistic development can be driven by the interaction of general learning mechanisms, highly sensitive to particular statistical regularities in the input, with a richly structured environment which provides the necessary ingredients for the emergence of linguistic representations that support mature language processing. Similar epigenetic principles, guiding the emergence of linguistic structure, apply to all these domains, offering insights into phenomena ranging from the precocity of young infant's sensitivity to speech contrasts to the complexities of the problem facing the young child learning the arabic plural.     

Language structure: psychological and social constraints

In this article we discuss the notion of a linguistic universal, and possible sources of such invariant properties of natural languages. In the first part, we explore the conceptual issues that arise. In the second part of the paper, we focus on the explanatory potential of horizontal evolution. We particularly focus on two case studies, concerning Zipf’s Law and universal properties of color terms, respectively. We show how computer simulations can be employed to study the large scale, emergent, consequences of psychologically and psychologically motivated assumptions about the working of horizontal language transmission.     

Interactive alignment as an allostatic process: A literature review

Research on interactive alignment encompasses various perspectives, including the recognition of linguistic coordination patterns, mental representation processes between interlocutors, and linguistic and behavioral convergence. In the absence of a literature review that presents the advances in the study of interactive alignment, this study identified 64 theoretical and empirical articles developed over 25 years (1996–2021) following the PRISMA methodology. Methodological and theoretical aspects derived from the analysis of the aforementioned reports are presented, showing how synchronization parameters interact, the associated neural bases, the impact of alignment on the development of joint actions, cognitive load, social interactions in general, and the prediction of language. The discussion and conclusions analyze the information presented and propose a comprehensive analysis from an allostatic perspective (predictive regulation through change) to advance the understanding of interactive alignment.     

Bilingualism in infancy: first steps in perception and comprehension

Many children grow up in bilingual families and acquire two first languages. Emerging research is advancing the view that the capacity to acquire language can be applied equally to two languages as to one but that bilingual and monolingual acquisition nonetheless differ in some nontrivial ways. To probe the first steps toward acquisition, researchers recently have begun to use experimental methods to study preverbal bilingual infants. We review the literature in this growing field, focusing on how infants growing up bilingual use surface acoustic information to separate, categorize and begin to learn their two languages. These new data invite the expansion of standard linguistic theories to account for how a single architecture can support the acquisition of two languages simultaneously.