Language Evolution and Computation Bibliography

configure a pattern on a board to communicate with each other. Distinct from related studies, players in this game have no explicit game scores or tasks to optimize. Any dynamics occurring in this game are therefore ad-hoc and on-going processes. There were three major findings in this paper. (i) The subjects mainly interacted in two modes: a dynamic mode where players proceed through the game without assigning any meanings to the pattern, and a metaphoric mode, where players process with narrative reflection. (ii) Subjects spontaneously switch between the two modes, but this switching is suppressed when playing alone. (iii) A transition diagram of the board pattern can be used to label the two modes, e.g. linearity of the diagram is correlated with the metaphoric mode. One of the main features of grammar is to display subjects' intentionality in a systematic way. We argue that the switching between the two modes observed in our experiment can be taken as a grammatical aspect that emerged in the process. These modes express the speaker's perspective in the same manner as grammatical elements do in natural language. The switching behavior should be seen as a process that embodies a player's intention using the medium (in this case, the patterns in the wall game), and a player's exploration of the medium is a necessary step before generating a grammar structure.

The mating song of the male Bengalese finch can be described by a finite-state grammar and has the feature that more complex songs are preferred by females [1]-[3]. These facts suggest that complex song grammars may have evolved via sexual selection. How, then, do the female birds gauge a song's complexity? Assuming that they can measure the complexity of a song while communicating with a male, but without making a model of the song, we studied the evolution of song grammars. In our simulation, it was demonstrated that song grammars became more complex through communication between coevolving males and females. Furthermore, when singing and listening were subject to fluctuations, peculiar features were observed in communication and evolution.

Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. Agents communicate with each other by deriving and accepting words via rewriting rule set. They are ranked according to their communicative effectiveness: an agent which can derive less frequent and less acceptable words and accept words in less computational time will have higher scores. They can evolve by mutational processes, which change rewriting rules in their symbolic grammars. Complexity and diversity of words increase in the course of time. The emergence of modules and loop structure enhances the evolution. On the other hand, ensemble structure lead to a net-grammar, restricting individual grammars and their evolution.