Skewing the evidence: the effect of input structure on child and adult learning of lexically based patterns in an artificial language

Wonnacott, E, Brown, H ORCID: https://orcid.org/0000-0001-9404-9515 and Nation, K, 2017. Skewing the evidence: the effect of input structure on child and adult learning of lexically based patterns in an artificial language. Journal of Memory and Language, 95, pp. 36-48. ISSN 0749-596X

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Abstract

Successful language acquisition requires both generalization and lexically based learning. Previous research suggests that this is achieved, at least in part, by tracking distributional statistics at and above the level of lexical items. We explored this learning using a semi-artificial language learning paradigm with 6-year-olds and adults, looking at learning of co-occurrence relationships between (meaningless) particles and English nouns. Both age groups showed stronger lexical learning (and less generalization) given "skewed" languages where a majority particle co-occurred with most nouns. In addition, adults, but not children, were affected by overall lexicality, showing weaker lexical learning (more generalization) when some input nouns were seen to alternate (i.e. occur with both particles). The results suggest that restricting generalization is affected by distributional statistics above the level of words/bigrams. Findings are discussed within the framework offered by models capturing generalization as rational inference, namely hierarchical-Bayesian and simplicity-based models.

Item Type:	Journal article
Publication Title:	Journal of Memory and Language
Creators:	Wonnacott, E., Brown, H. and Nation, K.
Publisher:	Elsevier
Date:	August 2017
Volume:	95
ISSN:	0749-596X
Identifiers:	Number Type 10.1016/j.jml.2017.01.005 DOI S0749596X16301139 Publisher Item Identifier 658086 Other
Divisions:	Schools > School of Social Sciences
Record created by:	Linda Sullivan
Date Added:	30 Jan 2018 14:47
Last Modified:	05 Mar 2020 16:28
URI:	https://irep.ntu.ac.uk/id/eprint/32583

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