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Title: Induction and interaction in the evolution of language and conceptual structure
Author: Carr, Jon William
ISNI:       0000 0004 7963 251X
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2019
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Languages evolve in response to various pressures, and this thesis adopts the view that two pressures are especially important. Firstly, the process of learning a language functions as a pressure for greater simplicity due to a domain-general cognitive preference for simple structure. Secondly, the process of using a language in communicative scenarios functions as a pressure for greater informativeness because ultimately languages are only useful to the extent that they allow their users to express - or indeed represent - nuanced meaning distinctions. These two fundamental properties of language - simplicity and informativeness - are often, but not always, in conflict with each other. In general, a simple language cannot be informative and an informative language cannot be simple, resulting in the simplicity-informativeness tradeoff. Typological studies in several domains, including colour, kinship, and spatial relations, have demonstrated that languages find optimal solutions to this tradeoff - optimal solutions to the problem of balancing, on the one hand, the need for simplicity, and on the other, the need for informativeness. More specifically, the thesis explores how inductive reasoning and communicative interaction contribute to simple and informative structure respectively, with a particular emphasis on how a continuous space of meanings, such as the colour spectrum, may be divided into discrete labelled categories. The thesis first describes information-theoretic perspectives on learning and communication and highlights the fact that one of the hallmark feature of conceptual structure - which I term compactness - is not subject to the simplicity-informativeness tradeoff, since it confers advantages on both learning and use. This means it is unclear whether compact structure derives from a learning pressure or from a communicative pressure. To complicate matters further, some researchers view learning as a pressure for simplicity, as outlined above, while others have argued that learning might function as a pressure for informativeness in the sense that learners might have an a-priori expectation that languages ought to be informative. The thesis attempts to resolve this by formalizing these different perspectives in a model of an idealized Bayesian learner, and this model is used to make specific predictions about how these perspectives will play out during individual concept induction and also during the evolution of conceptual structure over time. Experimental testing of these predictions reveals overwhelming support for the simplicity account: Learners have a preference for simplicity, and over generational time, this preference becomes amplified, ultimately resulting in maximally simple, but nevertheless compact, conceptual structure. This emergent compact structure remains limited, however, because it only permits the expression of a small number of meaning distinctions - the emergent systems become degenerate. This issue is addressed in the second part of the thesis, which compares the outcomes of three experiments. The first replicates the finding above - compact categorical structure emerges from learning; the second and third experiments compare artificial and genuine pressures for expressivity, and they show that it is only in the presence of a live communicative task that higher level structure - a kind of statistical compositionality - can emerge. Working together, the low-level compact categorical structure, derived from learning, and the high-level compositional structure, derived from communicative interaction, provide a solution to the simplicity-informativeness tradeoff, expanding on and lending support to various claims in the literature.
Supervisor: Kirby, Simon ; Culbertson, Jennifer ; Smith, Kenny Sponsor: Economic and Social Research Council (ESRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Bayes ; categorization ; category learning ; communication ; complexity ; compositionality ; compression ; concept learning ; convexity ; cultural evolution ; cultural transmission ; expressivity ; generalization ; induction ; informativeness ; interaction ; iterated learning ; Kolmogorov complexity ; language evolution ; minimum description length ; semantic categories ; simplicity