Investigating social interaction strategies for bootstrapping lexicon development

This paper investigates how different modes of social interactions influence the bootstrapping and evolution of lexicons. This is done by comparing three language game models that differ in the type of social interactions they use. The simulations show that the language games which use either joint attention or corrective feedback as a source of contextual input are better capable of bootstrapping a lexicon than the game without such directed interactions. The simulation of the latter game, however, does show that it is possible to develop a lexicon without using directed input when the lexicon is transmitted from generation to generation

Using Cultural and Social Beliefs in Language Games

Agreement on word-object pairing in communication depends on the intensity of the beliefs that gradually emerge in a society of agents, on the condition that no one was born with embedded knowledge. The agents search and exchange ideas about unknown word-object pairings, until they meet a consensus about what the object should be named. A language game is a social process of finding agreement on word-object pairings through communication in a multi-agent system. In this paper, a technique is proposed to discover the association between a word and the agents' beliefs on an object using self-organizing maps and a cultural algorithm in a multi-hearer environment. A conceptual space is implemented, which stores the agent's beliefs in three dimensions, represented by colors. The technique was evaluated for a variety of scenarios using four significant measures: coherence, specificity, success rate, and word size. The results showed that with the proposed method social agents can reach agreement fast and that their communication is effective

A Cognitively Founded Model of the Social Emergence of Lexicon

This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper, the agents are endowed with the cognitive skills necessary to categorize the input in a lexicographic way, a categorization process that is implemented by the means of data mining techniques. The second aspect is the social one, represented by the process of reiterate interactions among the agents who compose a population. The framework of this social process is that of evolutionary game theory, with a population of agents who are randomly matched in each period in order to play a game that, in this paper, is a kind of signaling game. The simulations show that the emergence of a socially shared meaning associated to a combination of symbols is, under the assumptions of this model, a statistically inevitable occurrence.Social Conventions, Fast and Frugal Heuristic Theory, Emergence of Lexicon, Data Mining, Signaling Games

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning

Emergence of Self-Organized Symbol-Based Communication \ud in Artificial Creatures

In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a \ud virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, \ud we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a \ud major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying

From EFL classroom language to classroom lexicon: Importing formulaic story language into teacher talk

This paper reports on a language activity carried out in an Italian University with student teachers attending a primary education course. The activity was designed to train them to use authentic children’s picturebooks as a source for EFL language learning. It consisted of identifying and ‘noticing’ (Mackey 2006) multiword expressions and ready-made utterances in a number of authentic picturebooks and simulating instances of weaving the picturebook language into the fabric of daily classroom talk. Following the activity, the students wrote individual pieces of reflective writing describing their experience. Comments by student teachers assessed the use of picturebook-derived formulaic language on both their learning and perceived ability to teach English, and revealed much about their pedagogical perspectives on teacher talk. Results suggest that promoting the use of authentic and meaningful language in context can help student teachers conceive of classroom communication as lexicon (a shared communicative practice the rules of which are fully known only by a restricted community of speakers) rather than mere language-based interactio

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs

Modeling Interactions Between Language Evolution and Demography

In this article I provide a review of studies that have modeled interactions between language evolution and demographic processes. The models are classifi ed in terms of three different approaches: analytical modeling, agent-based analytical modeling, and agent-based cognitive modeling. I show that these approaches differ in the complexity of interactions that they can handle and that the agent-based cognitive models allow for the most detailed and realistic simulations. Thus readers are provided with a guideline for selecting which approach to use for a given problem. The analytical models are useful for studying interactions between demography and language evolution in terms of high-level processes; the agent-based analytical models are good for studying such interactions in terms of social dynamics without bothering too much about the cognitive mechanisms of language processing; and the agent-based cognitive models are best suited for the study of the interactions between the complex sociocognitive mechanisms underlying language evolution