Tue 20th May, 11.00-12.30, 1.17 Dugald Stewart Building

Vanessa Ferdinand

What drives regularity in human language?

Languages evolve as they pass through generations of learners’ minds and adapt to selection pressures exerted by our cognitive architecture and learning biases. In this talk, I focus on one such pressure: our linguistic regularization bias, which drives learners to eliminate free variation in language. The nature of linguistic regularization is a subject of ongoing debate: some argue for domain-general causes such as memory limitations (Hudson Kam & Chang, 2009) and others argue for biases specific to the linguistic domain (Bickerton, 1984; Becker & Veenstra, 2003). I will present the results of a lexical frequency learning experiment which show that both domain-specific and domain-general factors independently contribute to regularization in adult learners. The domain-general component is then further explored in additional experimental conditions and analyses regarding concurrent frequency learning, cognitive load, and participant expectations / task framing. These results tell us how much regularity we expect to see after one generation of learning due to different components of our regularization bias. But how do these components contribute to the overall regularity of language? To address this question, participant behavior is extrapolated forward into evolutionary time (assuming the output of one learner becomes the input for the next learner). Using information-theoretic notions of similarity, I show that after one generation, participant behavior due to domain-specific regularization encodes more information about linguistic regularity, whereas after several generations of learners, behavior due to domain-general regularization encodes more. This raises important issues about the mapping of individual biases onto distributional features of human languages and demonstrates that evolutionary analyses provide more information about this mapping than the results of single-generation data alone.

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