Modeling Syntactic Devices: An Exploration of Language Evolution from Connectionist and Memetic Perspectives

This thesis introduces the subject of language evolution and provides insights into the evolution of several syntactic devices by modeling the acquisition of artificial languages by humans and neural networks. Previous work has shown that some linguistic universals can be learned by sequential-learning devices with no languagespecific biases (e.g., Ellefson & Christiansen, 2000; Christiansen & Devlin, 1997; Van Everbroeck, 1999). Here, we conduct a series of connectionist simulations, as well as an artificial-grammar experiment to examine the ways in which case markings and word order may function as cues for the acquisition grammatical roles—”who did what to whom.” We hypothesize that languages that use rare devices to communicate “who did what to whom” are rare partially because they are more difficult to acquire. The results confirm out hypotheses and accommodate patterns of syntactic development across several different languages. Our results are consistent with the view of “language as an organism” (e.g., Christiansen, 1994). On this account, language universals may reflect non-linguistic, cognitive constraints on learning and processing of sequential structure, rather than constraints prescribed by an innate universal grammar. Languages that are easy to learn would proliferate, while those that are difficult to learn would die out, or never come into existence in a process of cultural evolution. I extend this evolutionary approach by framing language as a powerful replicator in its own right, and by investigating interactions between language and thought in a cognitive-linguistic process of self-stimulation.