Artificial sign language learning: a method for evolutionary linguistics
Temas
Detalles
Previous research in evolutionary linguistics has made wide use of artificial language learning (ALL) paradigms, where learners are taught artificial languages in laborat- ory experiments and are subsequently tested in some way about the language they have learnt. The ALL framework has proved particularly useful in the study of the evolution of language, allowing the manipulation of specific linguistic phenomena that cannot be isolated for study in natural languages. Furthermore, this framework can test the output of individual participants, to uncover the cognitive biases of indi- vidual learners, but can also be implemented in a cultural evolutionary framework, investigating how participants acquire and change artificial languages in populations where they learn from and interact with each other. In this thesis, I present a novel methodology for studying the evolution of lan- guage in experimental populations. In the artificial sign language learning (ASLL) methodology I develop throughout this thesis, participants learn manual signalling systems that are used to interact with other participants. The ASLL methodology combines features of previous ALL methods as well as silent gesture, where hearing participants must communicate using only gesture and no speech. However, ASLL provides several advantages over previous methods. Firstly, reliance on the manual modality reduces the interference of participants’ native languages, exploiting a mod- ality with linguistic potential that is not normally used linguistically by hearing lan- guage users. Secondly, research in the manual modality offers comparability with the only current evidence of language emergence and evolution in natural languages: emerging sign languages that have evolved over the last century. Although the silent gesture paradigm also makes use of the manual modality, it has thus far seen little implementation into a cultural evolutionary framework that allows closer modelling of natural languages that are subject to the processes of transmission to new learners and interaction between language users.