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Learning structure-dependent agreement in a hierarchical artificial grammar

Published in Journal of memory and language. 2016, vol. 87, p. 84-104
Abstract We present a novel way to implement hierarchical structure and test its learnability in an artificial language involving structure-dependent, long-distance agreement relations. In Experiment 1, the grammar was exclusively cued by phonological and prosodic markers similar to those found in natural languages. Experiment 2 contained additional semantic cues in the form of a reference world. At the group level, successful generalization of the phrase structure rules to new words was found in both experiments. Analyses of individual profiles show that a subset of participants also generalized their knowledge to novel phrase structure rules, instantiating a natural extension of the training grammar, based on recursion of coordination. Rule induction improves across-the-board in the presence of semantic cues. It is concluded that adults are able to develop, to some extent, abstract knowledge of hierarchical, structure-dependent representations despite impoverished input data and minimal training.
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Research groups Développement du langage et cognition
Perception et production de la parole
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FRANCK, Julie, ROTONDI, Irène, FRAUENFELDER, Ulrich Hans. Learning structure-dependent agreement in a hierarchical artificial grammar. In: Journal of memory and language, 2016, vol. 87, p. 84-104. doi: 10.1016/j.jml.2015.11.003 https://archive-ouverte.unige.ch/unige:80961

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Deposited on : 2016-02-25

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