When Unbiased Probabilistic Learning Is Not Enough: Acquiring a Parametric System of Metrical Phonology

Abstract

Parametric systems have been proposed as models of how humans represent knowledge about language, motivated in part as a way to explain children's rapid acquisition of linguistic knowledge. Given this, it seems reasonable to examine if children with knowledge of parameters could in fact acquire the adult system from the data available to them. That is, we explore an argument from acquisition for this knowledge representation. We use the English metrical phonology system as a nontrivial case study and test several computational models of unbiased probabilistic learners. Special attention is given to the modeled learners' input and the psychological plausibility of the model components in order to consider the learning problem from the perspective of children acquiring their native language. We find that such cognitively inspired unbiased probabilistic learners uniformly fail to acquire the English grammar proposed in recent metrical studies from English child-directed speech, suggesting that probabilistic learning alone is insufficient to acquire the correct grammar when using this parametric knowledge representation. Several potential sources of this failure are discussed, along with their implications for the parametric knowledge representation and the trajectory of acquisition for English metrical phonology.