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Exploring the relationship between learnability and linguistic universals
"... Greater learnability has been offered as an explanation as to why certain properties appear in human languages more frequently than others. Languages with greater learnability are more likely to be accurately transmitted from one generation of learners to the next. We explore whether such a learnabi ..."
Abstract
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Greater learnability has been offered as an explanation as to why certain properties appear in human languages more frequently than others. Languages with greater learnability are more likely to be accurately transmitted from one generation of learners to the next. We explore whether such a learnability bias is sufficient to result in a property becoming prevalent across languages by formalizing language transmission using a linear model. We then examine the outcome of repeated transmission of languages using a mathematical analysis, a computer simulation, and an experiment with human participants, and show several ways in which greater learnability may not result in a property becoming prevalent. Both the ways in which transmission failures occur and the relative number of languages with and without a property can affect whether the relationship between learnability and prevalence holds. Our results show that simply finding a learnability bias is not sufficient to explain why a particular property is a linguistic universal, or even frequent among human languages. 1
Language evolution is shaped by the structure of the world: An iterated learning analysis
"... Human languages vary in many ways, but also show striking cross-linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends ..."
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Human languages vary in many ways, but also show striking cross-linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends only on their prior biases about language and the quantity of data transmitted at each point; the structure of the world being communicated about plays no role (Griffiths & Kalish, 2005, 2007). We revisit these findings and show that when certain assumptions about the independence of languages and the world are abandoned, learners will converge to languages that depend on the structure of the world as well as their prior biases. These theoretical results are supported with a series of experiments showing that when human learners acquire language through iterated learning, the ultimate structure of those languages is shaped by the structure of the meanings to be communicated.

