Constraining generalisation in language learning: a rational learning approach

DOI

Successful language acquisition relies on generalisation, yet many 'sensible' generalisations are actually ungrammatical (eg 'John carried me teddy.'). This grant explores how language learners balance generalisation and exception learning using the Artificial Language Learning (ALL) methodology, ie experiments where participants learn and are tested on novel experimenter-designed languages. Earlier research (Wonnacott et al. 2008) had used only adults - an important limitation given evidence for maturational differences in language learning (Newport, 1990). This grant therefore consists of a series of ALL experiments conducted with both child and adult participants, designed to address the following questions: (i) Do children, (like adults in previous studies) use distributional statistics eg word and construction frequency to determine which words should generalise/are exceptions? (ii) How do learners weigh such information against other sources of information such as semantics (eg if words with similar meanings tend to behave similarly). (iii) Do these processes differ across adults and children? (iv) Are there any factors that predict the extent of generalisation/exception learning for individual learners (eg working memory)? The long-term goal is to shed light on why language learning is generally more successful when it begins in childhood and the loci of individual differences in learning.

These are experimentally collected data. Full methods can be seen in publications - please contact E.A.Wonnacott@warwick.ac.uk for more details.

Identifier
DOI https://doi.org/10.5255/UKDA-SN-851081
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=3b5a2e9d146ac48829f957a871ff24c0a2dab2fef6876a2af5dfc638074dd8c6
Provenance
Creator Wonnacott, E, University of Warwick
Publisher UK Data Service
Publication Year 2013
Funding Reference Economic and Social Research Council
Rights Elizabeth Wonnacott, University of Warwick; The Data Collection is available for download to users registered with the UK Data Service.
OpenAccess true
Representation
Resource Type Numeric
Discipline Social Sciences
Spatial Coverage United Kingdom