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Animals including humans adapt to the environment by 2 different mechanisms working on 2 levels, evolution and learning, a population level mechanism and an individual level mechanism, respectively. Learning can further be classified into individual learning and social learning (IL and SL, hereafter). Our purpose is to understand how complex systems, specifically language emerged through the evolutionary interaction between IL and SL by using an agent-based model. Language is a communication tool but also a cognitive tool while most evolutionary scenarios see it as the former (Reboul, 2015). Indeed, in the brain, utilizing language-related circuits, some form of linguistic knowledge is linked to the external world by producing/perceiving sounds and gestures, and at the same time, is connected to the inner mental world composed of concepts, intentions and reasoning (Berwick et. al., 2013), regardless whether there is a shared computational core (e.g. Strong Minimalist Thesis (Chomsky, 2000)). We assume the fitness function represents the 2 components of selection. One is the directional component (DC, hereafter) that drives the evolution of fundamental traits underlying the whole linguistic activities. For DC, we do not use a simple unimodal function but a minimal fitness function representing a multimodal fitness landscape with linguistic levels. A linguistic level represents a fitness value expected when communication succeeds, corresponding to the number of traits involved in the trait interaction. The basic idea is as follows. In general, language ability regardless of cognitive or communicative is based on multiple subordinate traits, and its mutual interactions are nonlinear. We can further assume that the more adaptive the trait set is, the more inter-trait 165

Communication of cognition? A minimal computational model of evolutionary interactions between individual and social learning