Our research involves applying mathematical and computational modelling techniques to traditional issues in the evolution of communication and language, historical linguistics, and language typology.

Our research is based on the hypothesis that language is a complex adaptive system that emerges through adaptive interactions between agents and continues to evolve in order to remain adapted to the needs and capabilities of the agents.

Since 1995 the laboratory has worked on the problem of the origins of language. The basic idea behind this work is that a community of language users (further called agents) can be viewed as a complex adaptive system which collectively solves the problem of developing a shared communication system. To do so, the community must reach an agreement on a repertoire of forms (a sound system in the case of spoken language), a repertoire of meanings (the conceptu-alisations of reality), and a repertoire of form-meaning pairs (the lexicon and grammar).

The aim of our research group is to investigate the evolution and organisation of natural and artificial cognitive systems using adaptive behaviour and cognitive modelling methods.

Spoken language is the behavior that most distinguishes Homo sapiens from other animals. The language faculty emerged recently in the Homo line, probably within the last 150,000 years. What were the neural, physiological and cultural bases that drove its emergence? Once the language faculty had emerged, how did it come to diffuse across the entire globe? What factors now drive the extant languages of the world to interact with each other and change? How are humans able to process speech so efficiently? Can knowledge of the human language faculty help us to enhance the ability of computers to recognize and process speech?

The Language Engineering Laboratory aims to better understand each of these issues. We approach these issues from a multi-disciplinary perspective, seeking to meld the latest theories from linguistics, cognitive science, anthropology and other empirical sciences with the latest modeling methods from engineering, computer science and mathematics.

We are fascinated by questions concerning the origin and evolution of human language. We have formulated a mathematical theory that explores how human language can evolve by natural selection. We analyze how associations between signals and objects form in a pre-linguistic society, explore the consequences of errors during communication or language acquisition, and describe a pre-linguistic error limit. We show how word-formation can overcome this error limit. Finally we analyze the emergence of simple grammatical rules. This work constitutes a systematic, mathematical approach for thinking about language evolution.

We are interested in every aspect of mathematical biology, but the common theme of our research is evolution. Evolution is the one theory that permeates all of biology.

Some of our recent studies include evolution of cooperation by indirect reciprocity, evolutionary graph theory, network reciprocity, group selection, somatic evolution of human cancer, and evolution of language.

Computational economics, simulating organizations, the evolution of language. (The study of the evolution of human language is a broad, multi-disciplinary research field. Many open questions remain pertaining both to the evolution of language ability and to the continued evolution of human languages themselves. Artificial Life based models have been used to investigate the coevolution of language ability and physiology, and also the necessary conditions for maintaining linguistic diversity in a population.)

Work in adaptive behavior and artificial life has been underway at Sussex since 1988. We are probably the most active European research group in this field... We are interested in understanding cognition in ethological, ecological, and evolutionary contexts. Some of our studies involve building autonomous mobile robots, while others take place in complex computer simulations that provide virtual realities for simulated agents. Some of our work also addresses wider issues in complex adaptive systems -- examples include: emergent computation, prebiotic artificial life, metatheoretical and philosophical issues and the global dynamics of discrete-time dynamical systems.

Dr Seth Bullock and Dr Jason Noble conduct interdisciplinary research that ingeniously fuses artificial intelligence techniques with challenging problems in biology. They have pioneered the use of computer simulations to model biological adaptive systems. This has had many important results such as the falsification of existing models of the evolution of symmetry, and the initiation of research into the simulation of the evolutionary dynamics of signaling systems.

The Max Planck Institute for Psycholinguistics conducts fundamental research in all major aspects of the interrelation between language and psychology. There are four permanent groups, focussed on the following topics: language production, language comprehension, language acquisition, and language and cognition, and in addition there is a group focussed on brain and language funded by the Dutch research council.

The foci of the center are on language processing, language learning, language disorders, and simulations of all these aspects of language in artificial systems. Research in the center is inter-disciplinary and draws upon the fields of linguistics, psychology, cognitive science, neuroscience, computer science, sociology, and anthropology.

The research carried out in this lab explores the nature of language and thought, primarily from a developmental perspective. Some of our main interests include: the role of ``theory of mind'' in word learning; notions of intention, design, and function, and the understanding of artifact categories; object change and object identity; the nature of fast mapping. The focus of this lab is diverse. All of this work is strongly interdisciplinary, bringing in theory and research from areas such as cognitive, social, and developmental psychology, evolutionary theory, linguistics, and philosophy.

Since 1993, we have been conducting research on evolutionary systems for brain communication, pursuing the principles of communication inspired by the mechanisms of genetics and evolution as occurring in nature. To enable human-to-computer communication that is similar to human-to-human communication, we are aiming to build computers that can generate information---information processing systems, rich in autonomy and creativity similar to the human brain. To achieve this, we have introduced the concept and methodologies of Artificial Life (ALife) and Evolutionary Computation into the modeling of brain functions (the central organ for communication) such as information generation and comprehension.

We study the relationship between biological and experiential constraints on cognitive behavior. The focus of our research is on the learning and processing of complex sequential structure, in particular as related to language. Being able to pay attention to sequentially presented information is important to many aspects of cognition. In our work, we study several aspects of sequential learning and processing, ranging from statistical learning of sequential information to the processing of complex recursive sentence constructions. Specifically, we're interested in what innate biases in sequential learning and processing may reveal about constraints on language acquisition and processing, and vice versa. We use a combination of cognitive and psycholinguistic experimentation, neurophysiological measures, and connectionist (neural network) modeling in our research.

Generally, communication between agents in sensor networks (as well as many other domains) is constrained by low bandwith, high noise, and heterogeneous sensing and processing capabilites of the nodes. We are focusing on this problem by developing an adaptive communication system that grounds sensor data into concepts which are communicated via a learned symbolic langauge.

Body and Mind - origins, evolution, functions, and relations - represent the major challenge for the life sciences in the 21st century. The KLI concentrates on the theoretical advances in the fields of biological and cognitive evolution. ...

... a collaborative effort involving linguistics, computer science, and statistics, aimed at various goals.

1. Producing and maintaining real linguistic datasets, in particular of Indo-European languages.
2. Formulating statistical models that capture the evolution of historical linguistic data.
3. Designing simulation tools and accuracy measures for generating synthetic data for studying the performance of reconstruction methods.
4. Developing and implementing statistically-based as well as combinatorial methods for reconstructing language phylogenies, including phylogenetic networks.