Language evolution and the Baldwin effect
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Recently, a new constructive approach has emerged characterized by the use of computational models for simulating the evolution of language. This paper investigates the interaction between the two adaptation processes in different time-scales, evolution and learning of language, by using a computational model. Simulation results show that the fitness increases rapidly and remains at a high level, while the phenotypic plasticity increases together with the fitness, but then decreases and gradually converges to a medium value. This is regarded as the two-step transition of the so-called Baldwin effect. We investigate the evolutionary dynamics governing the effect.
Key wordsLanguage evolution Baldwin effect Genetic algorithm Recurrent neural network Artificial life
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