Acquisition of morphosyntax: A pattern-learning approach
Heidi Getz
April 2020

Natural languages contain complex grammatical patterns. For example, in German, embedded clauses have subjects first and verbs final, while main clauses have the verb second and allow any topicalized constituent to be first (V2 word order). Children acquire V2 patterns remarkably early, motivating claims that they fully represent the abstract structure of their language (e.g. Poeppel & Wexler, 1993). However, in order to represent patterns in an abstract way, learners must first notice the patterns, raising the possibility that these patterns are—at least initially—all the learner knows. I explore the possibility that learners begin acquiring morphosyntax by identifying and correlating concrete surface patterns. On this account, statistical patterns—not abstract tree structures—are the main content of learners’ early morphosyntactic representations. I use a miniature language paradigm, where learners’ knowledge can be carefully probed to distinguish different types of representations. In Experiment 1, I show that adults easily learn V2 patterns without representing the language’s full structure, refuting claims that V2 patterns are too complex to learn and demonstrating that knowledge of patterns is possible without abstract structural representations. In Experiments 2-4, I explore constraints on this pattern-learning mechanism by manipulating the miniature language’s morphology and measuring changes in learning of V2 patterns. V2 patterns were learned only when the language had V2 morphology, a universal property of natural V2 languages. Taken together, these results suggest that adults have access to a robust pattern-learning mechanism that operates quickly, does not require full linguistic representations, and is most effective under typologically natural conditions. This mechanism has the right properties to play an important role in the early stages of child language acquisition.
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Reference: lingbuzz/006322
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keywords: language acquisition, statistical learning, morphology, syntax
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