This article introduces the Special Issue and its focus on research in language evolution with emphasis on theory as well as computational and robotic modeling. A key theme is based on the growth of evolutionary developmental biology or evo-devo. The Special Issue consists of 13 articles organized in two sections: A) Theoretical foundations and B) Modeling and simulation studies. All the papers are interdisciplinary in nature, encompassing work in biological and linguistic foundations for the study of language evolution as well (...) as a variety of computational and robotic modeling efforts shedding light on how language may be developed and may have evolved. (shrink)

Evolutionary developmental systems theory stresses that selection pressures operate on entire developmental systems rather than just genes. This study extends this approach to language evolution, arguing that selection pressure may operate on two quasi-independent timescales. First, children clearly must acquire language successfully and evolution must equip them with the tools to do so. Second, while this is developing, they must also communicate with others in the moment using partially developed knowledge. These pressures may require different solutions, and their combination may (...) underlie the evolution of complex mechanisms for language development and processing. I present two case studies to illustrate how the demands of both real-time communication and language acquisition may be subtly different. The first case study examines infant-directed speech. A recent view is that IDS underwent cultural to statistical learning mechanisms that infants use to acquire the speech categories of their language. However, recent data suggest is it may not have evolved to enhance development, but rather to serve a more real-time communicative function. The second case study examines the argument for seemingly specialized mechanisms for learning word meanings. Both behavioral and computational work suggest that learning may be much slower and served by general-purpose mechanisms like associative learning. Fast-mapping, then, may be a real-time process meant to serve immediate communication, not learning, by augmenting incomplete vocabulary knowledge with constraints from the current context. Together, these studies suggest that evolutionary accounts consider selection pressure arising from both real-time communicative demands and from the need for accurate language development. (shrink)