Interaction Studies - Volume 12, Issue 1, 2011

An important question in language evolution is whether segmentation as a linguistic process is able to yield compositionality. Segmentation is hypothesized to be a process to bridge the gap between holistic and compositional lexicons. However, to date no thorough analytical method has been provided to test the feasibility of segmentation. In this paper, an analytical model is presented that can predict the probability of encountering various kinds of overlaps by observing utterance pairs, and the probability of finding confirmation in the language for newly extracted segments. Language users start by using a previously evolved holistic lexicon to communicate about simple environments. They segment these holistic utterances to smaller pieces, which can be used as elements of a compositional lexicon. The model reveals that the feasibility of segmentation depends on the definition of counterexamples, i.e. those associations (pairs), which either cause ambiguous extraction of segments, or do not allow segmentation at all. On one hand if inexact overlaps are considered to be contradictory (i.e. causing confusion) to a perfect exact overlap, then the probability is so minuscule that it renders the role of segmentation marginal during language evolution. On the other hand, if such inexact counterexamples are able to be segmented unambiguously due to extra cognitive capacities, segmentation may have a much higher feasibility. Keywords: segmentation; fractionation; analysis; holistic; protolanguage; compositionality

The present study explores the idea that human linguistic communication co-opted a pre-existing population-wide behavioural system that was shared among social group members and whose structure reflected the structure of the environment. This system is hypothesized to have emerged from interactions among individuals who had evolved the capacity to imitate arbitrary, functionless behaviour. A series of agent-based computer simulations test the separate and joint effects of imitation, pattern completion behaviour, environment structure and level of social interaction on such a population-wide behavioural system. The results support the view that a system that could be co-opted for linguistic communication might arise in a population of agents equipped with arbitrary imitation for the purposes of pattern completion interacting in certain kinds of structured environments. Such pre-linguistic behavioural system could have bootstrapped communication and paved the way for biological capacities widely believed to be necessary for communication, such as shared intentionality and symbolicity, to evolve.

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

Young children generally learn words from other people. Recent research has shown that children can learn new actions and skills from nonhuman agents. This study examines whether young children could learn words from a robot. Preschool children were shown a video in which either a woman (human condition) or a mechanical robot (robot condition) labeled novel objects. Then the children were asked to select the objects according to the names used in the video. The results revealed that children in the human condition were more likely to select the correct objects than those in the robot condition. Nevertheless, the five-year-old children in the robot condition performed significantly better than chance level, while the four-year olds did not. Thus there is a developmental difference in children’s potential to learn words from a robot. The results contribute to our understanding of how children interact with non-human agents. Keywords: developmental cybernetics; word learning; social cognition; cognitive development

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. Key words: lexicon bootstrapping; cross-situational learning; supervised learning; random occupancy problems

It has been proposed that the design of robots might benefit from interactions that are similar to caregiver–child interactions, which is tailored to children’s respective capacities to a high degree. However, so far little is known about how people adapt their tutoring behaviour to robots and whether robots can evoke input that is similar to child-directed interaction. The paper presents detailed analyses of speakers’ linguistic behaviour and non-linguistic behaviour, such as action demonstration, in two comparable situations: In one experiment, parents described and explained to their nonverbal infants the use of certain everyday objects; in the other experiment, participants tutored a simulated robot on the same objects. The results, which show considerable differences between the two situations on almost all measures, are discussed in the light of the computer-as-social-actor paradigm and the register hypothesis. Keywords: child-directed speech (CDS); motherese; robotese; motionese; register theory; social communication; human–robot interaction (HRI); computers-as-social-actors; mindless transfer