Janet Pierrehumbert - Northwestern; New Zealand Institute of Language, Brain and Behavior
Regularization in Language Learning and Change
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.
The Cognition & Language Workshop is a Geballe Workshop sponsored by the Stanford Humanities Center. We gratefully acknowledge the Humanities Center's support, and additional support from the Center for the Study of Language and Information.