Language Evolution and Computation Bibliography

We consider a simple computational model of the evolution of a quantitative trait and its phenotypic plasticity based on directional and positive frequency-dependent selection in order to explore whether and how leaning might facilitate evolution under the dynamics that arise from communicative interactions among individuals. In the model, each individual expresses, at many different times in its lifetime, its real-valued trait depending on the probability distribution determined by its own genotypes. In communicative interactions between two individuals, the contribution of an interaction to the fitness is high when their trait values are close to each other as well as large, which represents the positive frequency-dependent and directional components of selection, respectively. The iterative interactions allow individuals to acquire a more adaptive trait pair through trial and error. Under the stochastic evolution process with the limited number of individuals, we show that learning allows the population to avoid getting stuck in the global but low optimum of the innate and individual-level fitness landscape via both aspects of the components of selection, and brings about the successful evolution by increasing the genetic variation of the population. We also analyze how such an effect of learning can be realized by measuring the degree of the two different contributions for increasing the adaptivity and similarity of communicative traits, respectively. We show that this effect of learning arises from these different types of contributions depending on the biological and environmental conditions such as the mutation rate and the duration of communicative interactions. We further show the condition for the complete genetic assimilation to occur.