Is Regularization Uniform across Linguistic Levels? Comparing Learning and Production of Unconditioned Probabilistic Variation in Morphology and Word Order

Bibliographic Data

ID21746402
AuthorsCarmen Saldana (0009-0001-2816-0829, University of Edinburgh, corresponding author), Kenny Smith (0000-0002-4530-6914, University of Edinburgh), Simon Kirby (0000-0002-6496-1340, University of Edinburgh), Jennifer Culbertson (0000-0002-1737-6296, University of Edinburgh)
Year2021
Volume17
Issue2
Pages158-188
Publication date2021-04-03
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueLanguage Learning and Development (JOURNAL)
Journal identifiersISSN: 1547-3341 • E-ISSN: 1547-5441
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15475441.2021.1876697
OpenAlexW2948817987
LanguageEN
Citations received6
References cited66

Languages exhibit variation at all linguistic levels, from phonology, to the lexicon, to syntax. Importantly, that variation tends to be (at least partially) conditioned on some aspect of the social or linguistic context. When variation is unconditioned, language learners regularize it – removing some or all variants, or conditioning variant use on context. Previous studies using artificial language learning experiments have documented regularizing behavior in the learning of lexical, morphological, and syntactic variation. These studies implicitly assume that regularization reflects uniform mechanisms and processes across linguistic levels. However, studies on natural language learning and pidgin/creole formation suggest that morphological and syntactic variation may be treated differently. In particular, there is evidence that morphological variation may be more susceptible to regularization. Here we provide the first systematic comparison of the strength of regularization across these two linguistic levels. In line with previous studies, we find that the presence of a favored variant can induce different degrees of regularization. However, when input languages are carefully matched – with comparable initial variability, and no variant-specific biases – regularization can be comparable across morphology and word order. This is the case regardless of whether the task is explicitly communicative. Overall, our findings suggest an overarching regularizing mechanism at work, with apparent differences among levels likely due to differences in inherent complexity or variant-specific biases. Differences between production and encoding in our tasks further suggest this overarching mechanism is driven by production

Biology · Linguistics · Natural language processing · Probabilistic logic · Word order · Computer Science · Natural Language Processing Techniques · Philosophy · Phonetics and Phonology Research · Syntax, Semantics, Linguistic Variation · Artificial Intelligence

Tools

Open DOIOpen Access