Modeling the Acquisition of Phonological Interactions: Biases and Generalization

Abstract

In this paper we computationally implement four different theories for representing opaque and transparent phonological interactions: Harmonic Serialism, Stratal OT, Two-Level Constraints, and Indexed Constraints. We then show that these theories make unique predictions on two tasks: (1) a learning-bias task, based on previous experimental work with humans and (2) a novel generalization task that no human data exists for. Our results in (1) show that serial models predict that transparent languages should be easier to acquire, while parallel models do not. Furthermore, the results for (2) show that all four of the theories we test make unique predictions for how humans should generalize to novel phonological interaction types.