This book is the first to provide a comprehensive survey of the computational models and methodologies used for studying the evolution and origin of language and communication. Comprising contributions from the most influential figures in the field, it presents and summarises the state-of-the-art in computational approaches to language evolution, and highlights new lines of development. Essential reading for researchers and students in the fields of evolutionary and adaptive systems, language evolution modelling and linguistics, it will also be of interest to (...) researchers working on applications of neural networks to language problems. Furthermore, due to the fact that language evolution models use multi-agent methodologies, it will also be of great interest to computer scientists working on multi-agent systems, robotics and internet agents. (shrink)

Studying trust in the context of human-robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children’s beliefs (...) about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, as opposed to 7-years-olds, who displayed the reverse pattern. These findings align with results showing that, for 3 and 7-year-olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children’s behavior: while attributing to the robot significantly lower mental states than the human, in the trusting game children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay – during development – between children’s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children’s age as well as the interactive partner’s nature (human vs. robot). (shrink)

Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. By understanding the mechanisms underlying the evolution of linguistic abilities, it is possible to understand the evolution of cognitive abilities. Cognitivism, one of the current approaches in psychology and cognitive science, proposes that symbol systems capture mental phenomena, and attributes (...) cognitive validity to them. Therefore, in the same way that language is considered the prototype of cognitive abilities, a symbol system has become the prototype for studying language and cognitive systems. Symbol systems are advantageous as they are easily studied through computer simulation (a computer program is a symbol system itself), and this is why language is often studied using computational models. (shrink)

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to “switch” between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise (...) development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. (shrink)

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these (...) capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots. (shrink)

The topic is characterized by a highly interdisciplinary approach to the issue of action and language integration. Such an approach, combining computational models and cognitive robotics experiments with neuroscience, psychology, philosophy, and linguistic approaches, can be a powerful means that can help researchers disentangle ambiguous issues, provide better and clearer definitions, and formulate clearer predictions on the links between action and language. In the introduction we briefly describe the papers and discuss the challenges they pose to future research. We identify (...) four important phenomena the papers address and discuss in light of empirical and computational evidence: (a) the role played not only by sensorimotor and emotional information but also of natural language in conceptual representation; (b) the contextual dependency and high flexibility of the interaction between action, concepts, and language; (c) the involvement of the mirror neuron system in action and language processing; (d) the way in which the integration between action and language can be addressed by developmental robotics and Human-Robot Interaction. (shrink)

The double function of language, as a social/communicative means, and as an individual/cognitive capability, derives from its fundamental property that allows us to internally re-represent the world we live in. This is possible through the mechanism of symbol grounding, i.e., the ability to associate entities and states in the external and internal world with internal categorical representations. The symbol grounding mechanism, as language, has both an individual and a social component. The individual component, called the “Physical Symbol Grounding“, refers to (...) the ability of each individual to create an intrinsic link between world entities and internal categorical representations. The social component, called “Social Symbol Grounding“, refers to the collective negotiation for the selection of shared symbols and their grounded meanings. The paper discusses these two aspects of symbol grounding in relation to distributed cognition, using examples from cognitive modeling research on grounded agents and robots. (shrink)

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Simulations with neural networks living in a virtual environment can be used to explore and test hypotheses concerning concepts and language. The advantages that result from this approach include (1) the notion that a concept can be precisely defined and examined, (2) that concepts can be studied in both nonverbal and verbal artificial organisms, and (3) concepts have properties that depend on the environment as well as on the organism's adaptive behavior in response to the environment.

Computational approaches based on autonomous agents share with new ape language research the same principles of dynamical system paradigms. A recent model for the evolution of symbolization and language in autonomous agents is briefly described in order to highlight the similarities between these two methodologies. The additional benefits of autonomous agent modeling in the field of language origin research are highlighted.

This symposium includes a selection of articles on the origins and evolution of language. These are extended version of selected papers presented at “EVOLANG6: The Sixth International Conference on the Evolution of Language” that was held in Rome in April 2006. This selection of papers provides a multi-methodological view of different approaches to, and theoretical explanations of, the evolution of language.

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. (shrink)

It is well-established that toddlers can correctly select a novel referent from an ambiguous array in response to a novel label. There is also a growing consensus that robust word learning requires repeated label-object encounters. However, the effect of the context in which a novel object is encountered is less well-understood. We present two embodied neural network replications of recent empirical tasks, which demonstrated that the context in which a target object is encountered is fundamental to referent selection and word (...) learning. Our model offers an explicit account of the bottom-up associative and embodied mechanisms which could support children’s early word learning and emphasises the importance of viewing behaviour as the interaction of learning at multiple timescales. (shrink)

Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). There has been little research on user preference towards HRI interfaces. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of (...) the participants, including their attentional activities and conversational activities, the temporal characteristics of these social behaviours, as well as questionnaires. It has been revealed that social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life. (shrink)

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. (shrink)