The past decades have seen a massive expansion in the application of statistical and machine learning methods to natural language processing (NLP). This work has yielded impressive results in numerous speech and language processing tasks, including e.g. speech recognition, morphological analysis, parsing, lexical acquisition, semantic interpretation, and dialogue management. The good results have generally been viewed as engineering achievements. Recently researchers have begun to investigate the relevance of computational learning methods for research on human language acquisition and change. The use of computational modeling is a relatively recent trend boosted by advances in machine learning techniques, and the availability of resources like corpora of child and child-directed sentences, and data from psycholinguistic tasks by normal and pathological groups. Many of the existing computational models attempt to study language tasks under cognitively plausible criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in the acquisition and evolution of the language abilities. In doing so, computational modeling provides insight into the plausible mechanisms involved in human language processes, and inspires the development of better language models and techniques. These investigations are very important since if computational techniques can be used to improve our understanding of human language acquisition and change, these will not only benefit cognitive sciences in general but will reflect back to NLP and place us in a better position to develop useful language models. Success in this type of research requires close collaboration between the NLP, linguistics, psychology and cognitive science communities.