Language Evolution and Computation Bibliography

This paper describes a study in the use of digital evolution to produce cooperative communication behavior in a population of digital organisms. The results demonstrate that digital evolution can produce organisms capable of distributed problem solving through interactions between members of the population and their environment. Specifically, the organisms cooperate to distribute among the population the largest value sensed from the environment. These digital organisms have no 'built-in' ability to perform this task; each population begins with a single organism that has only the ability to self-replicate. Over thousands of generations, random mutations and natural selection produce an instruction sequence that realizes this behavior, despite continuous turnover in the population.

In this paper we describe a model in which artificial evolution is employed to design neural mechanisms that control the motion of two autonomous robots required to communicate through sound to perform a common task. The results of this work are a proof-of-concept : they demonstrate that evolution can exploit a very simple sound communication system, to design the mechanisms that allow the robots cooperate by employing acoustic interactions. The analysis of the evolved strategies uncover the basic properties of the communication protocol.

This paper presents computer simulations which investigate the effect that different group sizes have on the emergence of compositional structures in languages. The simulations are based on a model that integrates the language game model with the iterated learning model. The simulations show that compositional structures tend to emerge more extensively for larger groups, which has a positive effect on the time in which the languages develop and on communicative success, which may even have an optimal group size. A mathematical analysis of the time of convergence is presented that provides an approximate explanation of the results. The paper concludes that increasing group sizes among humans could not only have triggered the origins of language, but also facilitated the evolution of more complex languages.

Language learning dynamics is modelled by an ensemble of individuals consisting of the grammar carriers and the learners. Increasing the system population size results into the transition from the individual to the collective mode of learning. At low communication level, different grammars coexist in their own survival niches. Enhancement of the communication level in purely collective mode, when all individuals are the part of general communication network, leads to the selection of the fittest grammar. Adding the individual mode of learning results into the formation of the quasigrammar, with the dominant grammar prevailing over the set of coexisting grammars.

Language can be viewed as a complex adaptive system which is continuously shaped and reshaped by the actions of its users as they try to solve communicative problems. To maintain coherence in the overall system, different language elements (sounds, words, grammatical constructions) compete with each other for global acceptance. This paper examines what happens when a language system uses systematic structure, in the sense that certain meaning-form conventions are themselves parts of larger units. We argue that in this case multi-level selection occurs: at the level of elements (e.g. tense affixes) and at the level of larger units in which these elements are used (e.g. phrases). Achieving and maintaining linguistic coherence in the population under these conditions is non-trivial. This paper shows that it is nevertheless possible when agents take multiple levels into account both for processing meaning-form associations and for consolidating the language inventory after each interaction.

There is an ongoing debate about whether the words in the first languages spoken by humans expressed single concepts or complex holophrases. A computer model was used to investigate the nature of the protolanguages that would arise if speakers could associate words and meanings, but lacked any productive ability beyond saying the word whose past uses most closely matched the meaning that they wished to express. It was found that both words expressing single concepts, and holophrastic words could arise, depending on the conceptual and articulatory abilities of the agents. However, most words were of an intermediate type, as they expressed more than a single concept but less than a holophrase. The model therefore demonstrates that protolanguages may have been of types that are not usually considered in the debate over the nature of the first human languages.