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)

My goal in this paper 1 is, first, to collect together a number of themes and observations that have usually been kept apart, locked up in their respective disciplines. When these are brought together, some general and far reaching implications become really rather clear. In particular, I want to make a case for the implicit coherence of these themes in the idea that.

Dialog is a joint action at different levels. At the highest level, the goal of interlocutors is to align their mental representations. This emerges from joint activity at lower levels, both concerned with linguistic decisions (e.g., choice of words) and nonlinguistic processes (e.g., alignment of posture or speech rate). Because of the high‐level goal, the interlocutors are particularly concerned with close coupling at these lower levels. As we illustrate with examples, this means that imitation and entrainment are particularly pronounced during (...) interactive communication. We then argue that the mechanisms underlying such processes involve covert imitation of interlocutors’ communicative behavior, leading to emulation of their expected behavior. In other words, communication provides a very good example of predictive emulation, in a way that leads to successful joint activity. (shrink)

This article proposes a number of models to examine through which mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although we take no stance on which position is to be accepted as final truth with respect to human categorisation (...) and naming, we do point to theoretical constraints that make each position more or less likely and we make clear suggestions on what the best engineering solution would be. Specifically, we argue that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication. Key Words: autonomous agents; colour categorisation; colour naming; connectionism; cultural evolution; genetic evolution; memes; origins of language; self-organisation; semiotic dynamics; symbol grounding. (shrink)

The transitions between turns at talk in conversation tend to occur quickly, with only a slight gap of approximately 100 to 300 ms between them. This estimate of central tendency, however, hides a wealth of complex variation, as a number of factors, such as the type of turns involved, have been shown to influence the timing of turn transitions. This article considers one specific type of turn that does not conform to the statistical trend, namely turns that deal with troubles (...) of speaking, hearing, and understanding, known as other-initiations of repair. The results of a quantitative analysis of 169 other-initiations of repair in face-to-face conversation reveal that the most frequent cases occur after gaps of approximately 700 ms. Furthermore, other-initiations of repair that locate a source of trouble in a prior turn specifically tend to occur after shorter gaps than those that do not, and those that correct errors in a prior turn, while rare, tend to occur without delay. An analysis of the transitions before other-initiations of repair, using methods of conversation analysis, suggests that speakers use the extra time (i) to search for a late recognition of the problematic turn, (ii) to provide an opportunity for the speaker of the problematic turn to resolve the trouble independently, (iii) and to produce visual signals, such as facial gestures. In light of these results, it is argued that other-initiations of repair take priority over other turns at talk in conversation and therefore are not subject to the same rules and constraints that motivate fast turn transitions in general. (shrink)

We investigate the emergence of iconicity, specifically a bouba-kiki effect in miniature artificial languages under different functional constraints: when the languages are reproduced and when they are used communicatively. We ran transmission chains of participant dyads who played an interactive communicative game and individual participants who played a matched learning game. An analysis of the languages over six generations in an iterated learning experiment revealed that in the Communication condition, but not in the Reproduction condition, words for spiky shapes tend (...) to be rated by naive judges as more spiky than the words for round shapes. This suggests that iconicity may not only be the outcome of innovations introduced by individuals, but, crucially, the result of interlocutor negotiation of new communicative conventions. We interpret our results as an illustration of cultural evolution by random mutation and selection. (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)

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)

Language is shaped by its environment, which includes not only the brain, but also the public context in which speech acts are effected. To fully account for why language has the shape it has, we need to examine the constraints imposed by language use as a sequentially organized joint activity, and as the very conduit for linguistic diffusion and change.