Doctoral Dissertation Research: The role of input reliability and shared variability on the acquisition of linguistic variation

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

To learn their first language, children need to learn variation in the language, as systematic variation is present at all levels of language structure and is an integral part of linguistic knowledge. To give an example in English, speakers sometimes pronounce words ending in -ing with -in’ (e.g., working vs. workin’) depending on language-internal factors (e.g., -in’ is used more frequently in participles than in nouns like ‘morning’) as well as external (e.g., -in’ is used more frequently in casual conversational context than formal). To learn such probabilistic patterns is a daunting challenge as children must figure out variants in alternation, factors relevant to the alternation, relative probabilistic rates associated with each variant and more. Accounting for this process is crucial to any account of language acquisition, yet only a handful of studies have directly investigated children’s acquisition of variation. While emergent evidence suggests that children do acquire variation, we also know they tend to change their language input and make it more regular — a process often referred to as regularization. Taken together, a paradox arises: children apparently learn and match probabilistic linguistic variation under some circumstances but regularize such variation under others. When would children match variation and when would they regularize? While previous research identified the presence of a conditioning factor, quantity of input and frequency of alternating context as important factors, this question remains largely under-explored. This dissertation investigates the effects of reliability of language input and whether variability is shared across speakers on learners' matching of probabilistic variation. Specifically, child learners are exposed to miniature languages designed to contain variation, and factors are manipulated one at a time to determine whether they provide learners with important cues with respect to whether to regularize or learn the variation. This research contributes to a further understanding of how children balance two important learning mechanisms to find regularity despite noisy signals while preserving meaningful probabilistic aspects of the language, thus yielding insight into cognitive mechanisms underlying language acquisition. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.