Homophony avoidance

Previous research has suggested that homophony avoidance plays a role in constraining language change; in particular, phonological contrasts are less likely to be neutralized if doing so would greatly increase the amount of homophony. Most of the research on homophony avoidance has focused on the history of real languages, comparing attested and unattested (hypothetical) phonological changes in various languages. In this joint project with James White (UCL), we take a novel approach by focusing on the language learner. Using an artificial language learning paradigm, we show that learners are less likely to acquire neutralizing phonological rules compared to non-neutralizing rules, but only if the neutralizing rules create homophony between lexical items encountered during learning. The results indicate that learners are biased against phonological patterns that create homophony, which could have an influence on language change. The results also suggest that lexical learning and phonological learning are highly integrated.

Similarity bias in the learning of palatalization

In a pilot artificial grammar learning experiment, participants spontaneously palatalized velars more often than labials, which is consistent with the typology of palatalization: cross-linguistically, full palatalization of velars before high vowels is common, whereas that of labials is rare and idiosyncratic. This founding suggests a similarity bias against alternations between dissimilar sounds, as proposed in Steriade (2001/2008), Wilson (2006), and White (2014). I'm currently running a follow-up study, supervised by Gillian Gallagher (NYU), by looking into the following two questions: first, among the many similarities learners would have access to during phonological generalization, e.g., similarity between segments of exposure/test alternations, which ones should have an effect on the learnability of the output alternation and how; second, do learners adopt perceptual similarity, in addition to featural similarity, as a metric for measuring similarities between segments?