Modelling Social Structures and Hierarchies in Language Evolution

Language evolution might have preferred certain prior social configurations over others. Experiments conducted with models of different social structures (varying subgroup interactions and the role of a dominant interlocutor) suggest that having isolated agent groups rather than an interconnected agent is more advantageous for the emergence of a social communication system. Distinctive groups that are closely connected by communication yield systems less like natural language than fully isolated groups inhabiting the same world. Furthermore, the addition of a dominant male who is asymmetrically favoured as a hearer, and equally likely to be a speaker has no positive influence on the disjoint groups.Comment: 14 pages, 3 figures, 1 table. In proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, 14-16 December 201

Dynamic Models of Language Evolution: The Linguistic Perspective

Language is probably the key defining characteristic of humanity, an immensely powerful tool which provides its users with an infinitely expressive means of representing their complex thoughts and reflections, and of successfully communicating them to others. It is the foundation on which human societies have been built and the means through which humanity’s unparalleled intellectual and technological achievements have been realized. Although we have a natural intuitive understanding of what a language is, the specification of a particular language is nevertheless remarkably difficult, if not impossible, to pin down precisely. All languages contain many separate yet integral systems which work interdependently to allow the expression of our thoughts and the interpretation of others’ expressions: each has, for instance, a set of basic meaningless sounds (e.g. [e], [l], [s]) which can be combined to make different meaningful words and parts of words (e.g. else, less, sell, -less ); these meaningful units can be combined to make complex words (e.g. spinelessness, selling ), and the words themselves can then be combined in very many complex ways into phrases, clauses and an infinite number of meaningful sentences; finally each of these sentences can be interpreted in dramatically different ways, depending on the contexts in which it is uttered and on who is doing the interpretation. Languages can be analysed at any of these different levels, which make up many of the sub-fields of linguistics, and the primary job of linguistic theorists is to try to explain the rules which best explain these complex combinations

Language Evolution as a Darwinian Process: Computational Studies

This paper presents computational experiments that illustrate how one can precisely conceptualize language evolution as a Darwinian process. We show that there is potentially a wide diversity of replicating units and replication mechanisms involved in language evolution. Computational experiments allow us to study systemic properties coming out of populations of linguistic replicators: linguistic replicators can adapt to specific external environments; they evolve under the pressure of the cognitive constraints of their hosts, as well as under the functional pressure of communication for which they are used; one can observe neutral drift; coalitions of replicators may appear, forming higher level groups which can themselves become subject to competion and selection

Iterated learning of language distributions

This dissertation presents the results of a series of simulations intended to expand the findings of Burkett and Griffiths (2009, 2010), whose model is shown to make a number of assumptions that may be unrealistic with regard to human language learners. These assumptions are modified to create a number of more realistic scenarios. A series of simulations shows that the concentration parameter if continues to affect the outcome of iterated learning with Bayesian learners in these new scenarios. To overcome the need for the concentration parameter to be specified by the modeller, a model is presented where agents learn a complex hypothesis composed of both a distribution of languages within a population and the appropriate value for. The outcome of the simulations based on this model are inconclusive but do hint at the possibility of _ being affected by iterated learning, potentially enabling learners to acquire a complex hypothesis

Cross-situational inference and meaning space structure

Humans use language to direct each other’s attention to complex meanings. Researchers hold differing opinions on the evolutionary origin of these meanings: some hypothesise that human ancestors had innate, pre-linguistic concepts, similar to the referents of present-day nouns and verbs; others that simple meanings were broken down from initially complex meanings associated with specific situations. Experimental investigations of language evolution, meanwhile, have generally assumed a structured, pre-existing meaning space. This dissertation argues that a closer look at the ostensive-inferential nature of human communication supports a different account of the emergence of meanings and challenges the assumption of a pre-existing meaning space. Building on an experimental paradigm from Xu & Tenenbaum (2007), participants were presented with scenes of complex events to test how their meaning inferences were affected by the interaction of suspicious patterns in training input with their world knowledge. The results showed that the complexity of meaning lexicalised depended on this interaction, with substantial variation caused by both differing salience effects of events and differing world knowledge of participants. This result shows that meaning grounding in humans is not simply a matter of matching words to pre-existing cognitive concepts, or of associating a word with a specific recurring situation, but is an intelligent process crucially sensitive to the speaker’s intention to communicate. Further investigation is called for into the role of communication in forming a meaning space, and what determines this meaning space’s structure

Word learning in preschoolers: are bilingual 3-year-olds less guided by mutual exclusivity than their monolingual counterparts?

A fundamental question in developmental linguistics and developmental psychology is how young children learn new words. While some researchers suggest that words are primarily learned through experience, others argue that the acquisition process is guided by innate lexical biases. One of the most widely studied biases is the Mutual Exclusivity Bias (ME), which describes children’s preference for just one label per concept. The disambiguation effect in ME has been demonstrated extensively with ostensive paradigms requiring young monolingual children to choose between familiar and novel labels in identifying unfamiliar objects. However, evidence for ME within languages in bilingual children is mixed. In the present study, a productive naming paradigm was used to assess 3-year-olds’ tendency to adopt novel labels for familiar items (a variant on Merriman and Bowman’s (1989) rejection/correction effect). Five monolingual and 5 bilingual children aged 2;11-3;6 were tested in English. Following a training session when the experimenter applied novel labels to 3 of 12 pictures of familiar objects, the children played two successive naming games. The first game involved further reinforcement of the novel labels by the experimenter while the second game did not. In the first game, the bilingual children adopted novel labels more frequently (Mdn=.40) than the monolingual children (Mdn=.13) and Mann-Whitney’s (one-tailed exact) U=3.0, was significant, p<0.05 with a large effect size (r=-.63). In contrast, only one bilingual produced a novel label in the second game. Measures of receptiveness in the training session displayed asymmetries between production and comprehension. Overall the results suggest that experience of two languages plays an important role in learning novel labels. The findings are consistent with an account of ME as a heuristic learned from monolingual input, the application of which varies in bilingual preschoolers according to both ambient language and socio-pragmatic context. The results are discussed in the context of what insights can be gained from possible extensions to the experiment

Interactive Concept Acquisition for Embodied Artificial Agents

An important capacity that is still lacking in intelligent systems such as robots, is the ability to use concepts in a human-like manner. Indeed, the use of concepts has been recognised as being fundamental to a wide range of cognitive skills, including classification, reasoning and memory. Intricately intertwined with language, concepts are at the core of human cognition; but despite a large body or research, their functioning is as of yet not well understood. Nevertheless it remains clear that if intelligent systems are to achieve a level of cognition comparable to humans, they will have to posses the ability to deal with the fundamental role that concepts play in cognition. A promising manner in which conceptual knowledge can be acquired by an intelligent system is through ongoing, incremental development. In this view, a system is situated in the world and gradually acquires skills and knowledge through interaction with its social and physical environment. Important in this regard is the notion that cognition is embodied. As such, both the physical body and the environment shape the manner in which cognition, including the learning and use of concepts, operates. Through active partaking in the interaction, an intelligent system might influence its learning experience as to be more effective. This work presents experiments which illustrate how these notions of interaction and embodiment can influence the learning process of artificial systems. It shows how an artificial agent can benefit from interactive learning. Rather than passively absorbing knowledge, the system actively partakes in its learning experience, yielding improved learning. Next, the influence of embodiment on perception is further explored in a case study concerning colour perception, which results in an alternative explanation for the question of why human colour experience is very similar amongst individuals despite physiological differences. Finally experiments, in which an artificial agent is embodied in a novel robot that is tailored for human-robot interaction, illustrate how active strategies are also beneficial in an HRI setting in which the robot learns from a human teacher