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Title: Artificial sign language learning : a method for evolutionary linguistics
Author: Motamedi-Mousavi, Yasamin
ISNI:       0000 0004 6421 5123
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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Previous research in evolutionary linguistics has made wide use of artificial language learning (ALL) paradigms, where learners are taught artificial languages in laboratory 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 individual 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 language 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 modality with linguistic potential that is not normally used linguistically by hearing language 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. The implementation and development of ASLL in the present work provides an experimental window onto the cultural evolution of language in the manual modality. I detail a set of experiments that manipulate both linguistic features (investigating category structure and verb constructions) and cultural context, to understand precisely how the processes of interaction and transmission shape language structure. The findings from these experiments offer a more precise understanding of the roles that different cultural mechanisms play in the evolution of language, and further builds a bridge between data collected from natural languages in the early stages of their evolution and the more constrained environments of experimental linguistic research.
Supervisor: Kirby, Simon ; Smith, Kenneth ; Schouwstra, Marieke Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: artificial sign language learning ; evolution of linguistic structure ; language learning ; evolutionary linguistics ; artificial language learning