UCLA

Understanding how people learn the phonological patterns of their language is a major challenge facing the field of phonology. In this dissertation, I approach the issue of phonological learning by focusing on “saltatory” alternations, which occur when two alternating sounds “leap over” an intermediate, invariant sound (e.g., [p] becomes [v] between vowels, but [b] remains unchanged in that context). Saltation poses a theoretical challenge because it represents excessive modification: large perceptual changes (e.g., [p ∼ v]) are licensed where small changes (e.g., [b ∼ v] are not.

I present evidence from adult artificial language experiments that saltatory systems are dispreferred by learners. Specifically, participants who receive training data that are ambiguous between a saltatory system and a non-saltatory system are biased towards the non-saltatory system (Experiment 1). Moreover, when trained on a system that is explicitly saltatory, participants find the system difficult to learn (Experiment 2). An artificial language experiment with 12-month-old infants suggests that this anti-saltation bias is also present during early language acquisition.

On the basis of the experimental results, I argue that learners have an a priori substantive bias that causes them to consider alternations between similar sounds to be more likely than alternations between dissimilar sounds, consistent with the principles in Steriade's (2001/2008) theory of the P-map. This bias must be a “soft” bias, rather than an absolute bias, because it must be overturned in order to learn saltations. Because saltations are attested in real languages, they must be learnable.

To account for these observations, I propose a phonological framework with three components: (1) a set of *MAP faithfulness constraints (Zuraw, 2007) that makes it possible to penalize correspondences between specific pairs of segments, (2) a substantive bias making alternations more likely if they occur between perceptually similar sounds, and (3) a Maximum Entropy learning architecture, which allows the bias to be implemented computationally via the model's prior. The proposed learning model closely matches the pattern of experimental results and it makes the right general predictions: saltations are dispreferred, but learnable given sufficient training data. More broadly, the model represents a grammatical framework that can be used to make explicit, testable predictions for future research on phonological learning. I conclude by considering the potential implications of my analysis for phonological theory, phonological acquisition, and language change.