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 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 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 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. (shrink)

For many years the evolution of language has been seen as a disreputable topic, mired in fanciful “just so stories” about language origins. However, in the last decade a new synthesis of modern linguistics, cognitive neuroscience and neo-Darwinian evolutionary theory has begun to make important contributions to our understanding of the biology and evolution of language. I review some of this recent progress, focusing on the value of the comparative method, which uses data from animal species to draw inferences about (...) language evolution. Discussing speech first, I show how data concerning a wide variety of species, from monkeys to birds, can increase our understanding of the anatomical and neural mechanisms underlying human spoken language, and how bird and whale song provide insights into the ultimate evolutionary function of language. I discuss the “descended larynx” of humans, a peculiar adaptation for speech that has received much attention in the past, which despite earlier claims is not uniquely human. Then I will turn to the neural mechanisms underlying spoken language, pointing out the difficulties animals apparently experience in perceiving hierarchical structure in sounds, and stressing the importance of vocal imitation in the evolution of a spoken language. Turning to ultimate function, I suggest that communication among kin (especially between parents and offspring) played a crucial but neglected role in driving language evolution. Finally, I briefly discuss phylogeny, discussing hypotheses that offer plausible routes to human language from a non-linguistic chimp-like ancestor. I conclude that comparative data from living animals will be key to developing a richer, more interdisciplinary understanding of our most distinctively human trait: language. (shrink)

Social ontology is the study of the nature and properties of the social world. It is concerned with analyzing the various entities in the world that arise from social interaction. -/- A prominent topic in social ontology is the analysis of social groups. Do social groups exist at all? If so, what sorts of entities are they, and how are they created? Is a social group distinct from the collection of people who are its members, and if so, how is (...) it different? What sorts of properties do social groups have? Can they have beliefs or intentions? Can they perform actions? And if so, what does it take for a group to believe, intend, or act? -/- Other entities investigated in social ontology include money, corporations, institutions, property, social classes, races, genders, artifacts, artworks, language, and law. It is difficult to delineate a precise scope for the field (see section 2.1). In general, though, the entities explored in social ontology largely overlap with those that social scientists work on. A good deal of the work in social ontology takes place within the social sciences (see sections 5.1–5.8). -/- Social ontology also addresses more basic questions about the nature of the social world. One set of questions pertains to the constituents, or building blocks, of social things in general. For instance, some theories argue that social entities are built out of the psychological states of individual people, while others argue that they are built out of actions, and yet others that they are built out of practices. Still other theories deny that a distinction can even be made between the social and the non-social. -/- A different set of questions pertains to how social categories are constructed or set up. Are social categories and kinds produced by our attitudes? By our language? Are they produced by causal patterns? And is there just one way social categories are set up, or are there many varieties of social construction? -/- The term ‘social ontology’ has only come into wide currency in recent years, but the nature of the social has been a topic of inquiry since ancient Greece. As a whole, the field can be understood as a branch of metaphysics, the general inquiry into the nature of entities. (shrink)

In the generative tradition, the language faculty has been shrinking—perhaps to include only the mechanism of recursion. This paper argues that even this view of the language faculty is too expansive. We first argue that a language faculty is difficult to reconcile with evolutionary considerations. We then focus on recursion as a detailed case study, arguing that our ability to process recursive structure does not rely on recursion as a property of the grammar, but instead emerges gradually by piggybacking on (...) domain-general sequence learning abilities. Evidence from genetics, comparative work on non-human primates, and cognitive neuroscience suggests that humans have evolved complex sequence learning skills, which were subsequently pressed into service to accommodate language. Constraints on sequence learning therefore have played an important role in shaping the cultural evolution of linguistic structure, including our limited abilities for processing recursive structure. Finally, we re-evaluate some of the key considerations that have often been taken to require the postulation of a language faculty. (shrink)

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. (shrink)

It is widely assumed that language in some form or other originated by piggybacking on pre-existing learning mechanism not dedicated to language. Using evolutionary connectionist simulations, we explore the implications of such assumptions by determining the effect of constraints derived from an earlier evolved mechanism for sequential learning on the interaction between biological and linguistic adaptation across generations of language learners. Artificial neural networks were initially allowed to evolve “biologically” to improve their sequential learning abilities, after which language was introduced (...) into the population. We compared the relative contribution of biological and linguistic adaptation by allowing both networks and language to change over time. The simulation results support two main conclusions: First, over generations, a consistent head-ordering emerged due to linguistic adaptation. This is consistent with previous studies suggesting that some apparently arbitrary aspects of linguistic structure may arise from cognitive constraints on sequential learning. Second, when networks were selected to maintain a good level of performance on the sequential learning task, language learnability is significantly improved by linguistic adaptation but not by biological adaptation. Indeed, the pressure toward maintaining a high level of sequential learning performance prevented biological assimilation of linguistic-specific knowledge from occurring. (shrink)

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. (shrink)

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. (shrink)

Historical language change (), like evolution itself, is a fact; and its implications for the biological evolution of the human capacity for language acquisition () have been ably explored by many contemporary theorists. However, Christiansen & Chater's (C&C's) revolutionary call for a replacement of phylogenetic models with glossogenetic cultural models is based on an inadequate understanding of either. The solution to their lies before their eyes, but they mistakenly reject it due to a supposed Gene/;culture co-evolution poses a series of (...) difficult theoretical and empirical problems that will be resolved by subtle thinking, adequate models, and careful cross-disciplinary research, not by oversimplified manifestos. (shrink)

The term embodiment identifies a theory that meaning and semantics cannot be captured by abstract, logical systems, but are dependent on an agent’s experience derived from being situated in an environment. This theory has recently received a great deal of support in the cognitive science literature and is having significant impact in artificial intelligence. Memetics refers to the theory that knowledge and ideas can evolve more or less independently of their human-agent substrates. While humans provide the medium for this evolution, (...) memetics holds that ideas can be developed without human comprehension or deliberate interference. Both theories have profound implications for the study of language—its potential use by machines, its acquisition by children and of particular relevance to this special issue, its evolution. This article links the theory of memetics to the established literature on semantic space, then examines the extent to which these memetic mechanisms might account for language independently of embodiment. It then seeks to explain the evolution of language through uniquely human cognitive capacities which facilitate memetic evolution. (shrink)

This paper proposes a coevolutionary scenario on the origins of compositionality and word order regularity in human language, and illustrates it using a multi-agent, behavioral model. The model traces a `bottom-up' process of syntactic development; artificial agents, by iterating local orders among lexical items, gradually build up basic constituent word order(s) in sentences. These results show that structural features of language (e.g. syntactic categories and word orders) could have coevolved with lexical items, as a consequence of general learning mechanisms (e.g. (...) pattern extraction and sequential learning) initially not language-specific. Keywords: Computer simulation; language origin; coevolution; compositionality; word order regularity. (shrink)

At the heart of evolutionary theory lays the notion of replication. Unfortunately, this notion is far less exact than the weight of its importance. In this paper, it is argued that replication always involves coding. Furthermore, when a theory of evolution is built on replication based on coding, a unifying and coherent picture arises that sheds new light on some of the controversies and open questions in contemporary biology, such as what are the roles of phylogeny and ontogeny in evolution, (...) and how to characterize and explain the increase of biological complexity. (shrink)

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of (...) the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals. (shrink)

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. (shrink)

On the surface, bi- and multilingualism would seem to be an ideal context for exploring questions of typological proximity. The obvious intuition is that the more closely related two languages are, the easier it should be to implement the two languages in one mind. This is the starting point adopted here, but we immediately run into the difficulty that the overwhelming majority of cognitive, computational, and linguistic research on bi- and multilingualism exhibits a monolingual bias (i.e., where monolingual grammars are (...) used as the standard of comparison for outputs from bilingual grammars). The primary questions so far have focused on how bilinguals balance and switch between their two languages, but our perspective on typology leads us to consider the nature of bi- and multi-lingual systems as a whole. Following an initial proposal from Hsin ( 2014 ), we conjecture that bilingual grammars are neither isolated, nor (completely) conjoined with one another in the bilingual mind, but rather exist as integrated source grammars that are further mitigated by a common, combined grammar (Cook, 2016 ; Goldrick et al., 2016a, b ; Putnam and Klosinski, 2017 ). Here we conceive such a combined grammar in a parallel, distributed, and gradient architecture implemented in a shared vector-space model that employs compression through routinization and dimensionality reduction. We discuss the emergence of such representations and their function in the minds of bilinguals. This architecture aims to be consistent with empirical results on bilingual cognition and memory representations in computational cognitive architectures. (shrink)

We agree that language adapts to the brain, but we note that language also has to adapt to brain-external constraints, such as those arising from properties of the cultural transmission medium. The hypothesis that Christiansen & Chater (C&C) raise in the target article not only has profound consequences for our understanding of language, but also for our understanding of the biological evolution of the language faculty.

In this paper it is championed that a two stages hypothesis for the evolution of language must take into account a qualified approach to heterochrony and the available information from the archaeological record. As it seems, a protracted childhood and youth was already at work in Homo erectus, but early postnatal brain growth was only available to Homo sapiens. According to these facts, the term hysteron proteron is given here to the reversal that sets off linguistic capacity during the first (...) three years of life, upon the ancient abilities for social, natural and technological knowledge, which were already established during the last 2 Mya BP. The unfolding of linguistic capacity creates the typical effect that ontogeny reverses phylogeny: since late postnatal brain growth, quite recent in evolution, launches in ontogeny before the phylogenetically old processes of playing and learning take place during childhood and youth. Full symbolic abilities may be explained as emerging from older semiotic competences based on iconicity and indexicality, which would have presumably sustained human life for millennia. Furthermore, it is argued that the formation of linguistic categories follows from that overlaying of extended connectivity linked to early postnatal brain growth, inasmuch as it entangles and transforms previous iconic and referential information: i.e. the ancient semiotic capacities for proprioception, embodiment, extended mimesis and pragmatic knowledge, shaping a sort of self-organization process that provides the formal and semantic properties we find in articulated languages. (shrink)

A well‐established principle of language is that there is a preference for closely related words to be close together in the sentence. This can be expressed as a preference for dependency length minimization (DLM). In this study, we explore quantitatively the degree to which natural languages reflect DLM. We extract the dependencies from natural language text and reorder the words in such a way as to minimize dependency length. Comparing the original text with these optimal linearizations (and also with random (...) linearizations) reveals the degree to which natural language minimizes dependency length. Tests on English data show that English shows a strong effect of DLM, with dependency length much closer to optimal than to random; the optimal English grammar also has many specific features in common with English. In German, too, dependency length is significantly less than random, but the effect is much weaker than in English. We conclude by speculating about some possible reasons for this difference between English and German. (shrink)

Languages emerge in response to the negotiation of shared meaning in social groups, where transparency of grammar is necessitated by demands of communication with relative strangers needing to consult on a wide range of topics (Ragir 2002). This communal exchange is automated and stabilized through activity-dependent fine-tuning of information-specific neural connections during postnatal growth and social development.