Cognitive Science, Volume 45, Issue 11, November 2021.

In recent years, multiple researchers working on the evolution of language have put forward the idea that the theoretical framework of usage-based approaches and Construction Grammar is highly suitable for modelling the emergence of human language from pre-linguistic or proto-linguistic communication systems. This also raises the question of whether usage-based and constructionist approaches can be integrated with the analysis of animal communication systems. In this paper, we review possible avenues where usage-based, constructionist approaches can make contact with animal communication research, (...) which in turn also has implications for theories of language evolution. To this end, we first give an overview of key assumptions of usage-based and constructionist approaches before reviewing some key issues in animal communication research through the lens of usage-based, constructionist approaches. Specifically, we will discuss how research on alarm calls, gestural communication and symbol-trained animals can be brought into contact with usage-based, constructionist theorizing. We argue that a constructionist view of animal communication can yield new perspectives on its relation to human language, which in turn has important implications regarding the evolution of language. Importantly, this theoretical approach also generates hypotheses that have the potential of complementing and extending results from the more formalist approaches that often underlie current animal communication research. (shrink)

Smith gives an excellent overview on research in language evolution, in which he discusses several recent models of how linguistic systems and the cognitive capacities involved in language learning may have co‐evolved. He illustrates how combined pressures on language learning and communication/use produce compositionally structured languages. Once in place, a (culturally transmitted) communication system creates new selection pressures on the capacity for acquiring these systems.

This response takes advantage of the diverse and wide-ranging series of commentaries to clarify some aspects of the target article, and flesh out other aspects. My central point is a plea to take graphic codes seriously as codes, rather than as a kind of visual art or as a byproduct of spoken language; only in this way can the puzzle of ideography be identified and solved. In this perspective, I argue that graphic codes do not derive their expressive power from (...) iconicity alone (unlike visual arts), and I clarify the peculiar relationship that ties writing to spoken language. I then discuss three possible solutions to the puzzle of ideography. I argue that a learning account still cannot explain why ideographies fail to evolve, even if we emancipate the learning account from the version that Liberman put forward; I develop my preferred solution, the “standardization account,” and contrast it with a third solution suggested by some commentaries, which says that ideographies do not evolve because they would make communication too costly. I consider, by way of conclusion, the consequences of these views for the future evolution of ideography. (shrink)

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously – not just as a mnemonic prop – to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language. Even if they also encode nonlinguistic information, they are (...) useless to someone who lacks linguistic competence in the encoded language or a related one. The versatility of writing is thus vicarious: Writing borrows it from spoken language. Why is it so difficult to build a fully generalist graphic code? The most widespread answer points to a learnability problem. We possess specialized cognitive resources for learning spoken language, but lack them for graphic codes. I argue in favor of a different account: What is difficult about graphic codes is not so much learning or teaching them as getting every user to learn and teach the same code. This standardization problem does not affect spoken or signed languages as much. Those are based on cheap and transient signals, allowing for easy online repairing of miscommunication, and require face-to-face interactions where the advantages of common ground are maximized. Graphic codes lack these advantages, which makes them smaller in size and more specialized. (shrink)

We present a theoretical framework bearing on the evolution of written communication. We analyze writing as a special kind of graphic code. Like languages, graphic codes consist of stable, conventional mappings between symbols and meanings, but (unlike spoken or signed languages) their symbols consist of enduring images. This gives them the unique capacity to transmit information in one go across time and space. Yet this capacity usually remains quite unexploited, because most graphic codes are insufficiently informative. They may only be (...) used for mnemonic purposes or as props for oral communication in real-time encounters. Writing systems, unlike other graphic codes, work by encoding a natural language. This allows them to support asynchronous communication in a more powerful and versatile way than any other graphic code. Yet, writing systems will not automatically unlock the capacity to communicate asynchronously. We argue that this capacity is a rarity in non-literate societies, and not so frequent even in literate ones. Asynchronous communication is intrinsically inefficient because asynchrony constrains the amount of information that the interlocutors share and limits possibilities for repair. This would explain why synchronous, face-to-face communication always fosters the development of sophisticated codes (natural languages), but similar codes for asynchronous communication evolve with more difficulties. It also implies that writing cannot have evolved, at first, for supporting asynchronous communication. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

The amount of information conveyed by linguistic conventions depends on their precision, yet the codes that humans and other animals use to communicate are quite ambiguous: they may map several vague meanings to the same symbol. How does semantic precision evolve, and what are the constraints that limit it? We address this question using a multiplayer gaming app, where individuals communicate with one another in a scaled-up referential game. Here, the goal is for a sender to use black and white (...) symbols to communicate colors. We expected that the players’ mappings between symbols and colors would grow more specific over time, through a selection process whereby precise mappings are preferentially copied. We found that players become increasingly more precise in their use of symbols over the course of their interactions. This trend did not, however, result from selective copying of precise mappings. We explore the implications of this result for the study of lexical ambiguity, Zipf's Law of Meaning, and disagreements over semantic conventions. (shrink)

Languages spoken in larger populations are relatively simple. A possible explanation for this is that languages with a greater number of speakers tend to also be those with higher proportions of non‐native speakers, who may simplify language during learning. We assess this explanation for the negative correlation between population size and linguistic complexity in three experiments, using artificial language learning techniques to investigate both the simplifications made by individual adult learners and the potential for such simplifications to influence group‐level language (...) characteristics. In Experiment 1, we show that individual adult learners trained on a morphologically complex miniature language simplify its morphology. In Experiment 2, we explore how these simplifications may then propagate through subsequent learning. We use the languages produced by the participants of Experiment 1 as the input for a second set of learners, manipulating (a) the proportion of their input which is simplified and (b) the number of speakers they receive their input from. We find, contrary to expectations, that mixing the input from multiple speakers nullifies the simplifications introduced by individuals in Experiment 1; simplifications at the individual level do not result in simplification of the population's language. In Experiment 3, we focus on language use as a mechanism for simplification, exploring the consequences of the interaction between individuals differing in their linguistic competence (as native and non‐native speakers might). We find that speakers who acquire a more complex language than their partner simplify their language during interaction. We ultimately conclude that adult learning can result in languages spoken by more people having simpler morphology, but that idiosyncratic simplifications by non‐natives do not offer a complete explanation in themselves; accommodation—by comparatively competent non‐natives to less competent speakers, or by native speakers to non‐natives—may be a key linking mechanism. (shrink)