UCLA

A major focus of linguistic research is characterizing adult knowledge of language and detailing how it is acquired. Language change, to the extent that it is driven by learners in response to observed adult data, is a valuable source of data for pursuing this topic. The shift from one language to another is only possible if the analytic preferences of language learners lead them to adopt a different analysis than that of their parents. A particularly noteworthy type of change is paradigm levelling, where some allomorphs of a morpheme are replaced by another allomorph.

This dissertation proposes a learning algorithm that replicates historically attested paradigm

levellings. Previous attempts have restricted the inputs of phonological computation to be identical to a surface allomorph, so that paradigm levelling is triggered whenever a derived allomorph is not predictable from the base allomorph. Such a restriction is unnecessary. In the system proposed here, the absence of a grammar to explain the observed language is the trigger for levelling. In this case, the learner privileges the generation of a subset of a paradigm, and selects inputs that are appropriate to that task. The observed replacement of an allomorph by another in paradigm levelling is achieved by using an input that the grammar maps to the replacing allomorph, but which cannot be mapped to the replaced allomorph.

The learning algorithm proposed here makes accurate predictions for language change. Languages that are straightforwardly described in the assumed grammatical framework (parallel OT), are diachronically stable. This stands in contrast to previous theories of levelling, which predicted diachronic instability for some paradigms in Russian. Furthermore, other languages are correctly predicted to undergo levelling, including a particularly dramatic case from Odawa. Ultimately, this helps substantiate the link between learning theory and language change.