Effect of symmetry bias on linguistic evolution

Abstract

As described in this paper, we investigated the effect of the symmetry bias on linguistic evolution. We specifically examined symmetry bias, which indicates the meaning in a state of environment. For this task, we constructed a meaning selection iterated learning model based on Simon Kirby's iterated learning Model, and used it for simulation with three strategies: perfect matching symmetry bias, imperfect matching symmetry bias, and random strategy. Results of applying imperfect matching symmetry bias show that the language of the agent evolved into more compositional language. The agent acquired a more expressive, and a more similar language to the parent's language than with the Random strategy agent. However, application of perfect matching symmetry bias showed that the language of the agent did not evolve. The agent acquired a less expressive and a more different language to the parent's language than with Random strategy agent. Our experimentally obtained results demonstrate that the effect of imperfect matching symmetry bias accelerates linguistic evolution into compositional language, whereas perfect matching symmetry bias disturbs linguistic evolution.