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Regularization in language learning and change

Date and Time: 
Thursday, January 15, 2015. 04:00 PM - 05:30 PM
Meeting Location: 
Building 460, Room 126
Meeting Description: 
About the speaker:
Janet Pierrehumbert is a Professor of Linguistics at Northwestern University and an Adjunct Professor at the New Zealand Institute of Language, Brain and Behaviour
 
Language systems are highly structured. Yet language learners still encounter inconsistent input. Variation is found both across speakers, and within the productions of individual speakers. If learners reproduced all the variation in the input they received, language systems would not be so highly structured. Instead, all variation across speakers in a community would eventually be picked up and reproduced by every individual in the community. Explaining the empirically observed level of regularity in languages requires a theory of regularization as a cognitive process.
 
This talk will present experimental and computational results on regularization. The experiments are artificial language learning experiments using a novel game-like computer interface. The model introduces a novel mathematical treatment of the nonlinear decision process linking input to output in language learning. Together, the results indicate that:
  • The nonlinearity involved in regularization is sufficiently weak that it can be detected at the micro level (the level of individual experiments) only with very good statistical power.
  • Individual differences in the degree and direction of regularization are considerable.
  • Individual differences, as they interact with social connections,  play a major role in determining which patterns become entrenched as linguistic norms  and which don't in the course of language change.

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