Remembering and generalizing from examples of words

Janet B. Pierrehumbert
University of Oxford, Oxford e-Research Centre

People learn words from experienced examples of them. The words in turn provide the statistical foundation for learning word-formation patterns. What information about words is encoded and remembered? How are generalisations formed from stored examples? When people encounter variable input, do they simply remember and reproduce this variation, or do they systematise it in their own outputs?

In this talk, I will present results from corpus analyses and online game-like experiments that address these questions. These indicate that mental representations of words include much detail, including contextual detail such as social-indexical associations. At the same time, there is a great amount of individual variation in what information is encoded and the way abstract generalizations are formed from remembered examples. The basic mechanism for learning word formation is not probability-matching, as assumed in recent Bayesian models. People have a propensity to systematise the input, but differ in what contextual associations they notice, how open they are to unexpected input, and how aggressively they systematise. These results support claims that heterogeneity in the speech community plays a strong role in language variation and change.

If you would like to meet with the speaker, please contact Frank Zimmerer.