Why computational models are better than verbal theories: the case of nonword repetition

Jones, G. ORCID: 0000-0003-3867-9947, Gobet, F., Freudenthal, D., Watson, S.E. and Pine, J.M., 2014. Why computational models are better than verbal theories: the case of nonword repetition. Developmental Science, 17 (2), pp. 298-310. ISSN 1467-7687

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Abstract

Tests of nonword repetition (NWR) have often been used to examine children’s phonological knowledge and word learning abilities. However, theories of NWR primarily explain performance either in terms of phonological working memory or long-term knowledge, with little consideration of how these processes interact. One theoretical account that focuses specifically on the interaction between short-term and long-term memory is the chunking hypothesis. Chunking occurs because of repeated exposure to meaningful stimulus items, resulting in the items becoming grouped (or chunked); once chunked, the items can be represented in short-term memory using one chunk rather than one chunk per item. We tested several predictions of the chunking hypothesis by presenting 5-6 year-old children with three tests of NWR that were either high, medium, or low in wordlikeness. The results did not show strong support for the chunking hypothesis, suggesting that chunking fails to fully explain children’s NWR behavior. However, simulations using a computational implementation of chunking (namely CLASSIC, or Chunking Lexical And Sublexical Sequences In Children) show that, when the linguistic input to 5-6 year old children is estimated in a reasonable way, the children’s data is matched across all three NWR tests. These results have three implications for the field: (a) a chunking account can explain key NWR phenomena in 5-6 year old children; (b) tests of chunking accounts require a detailed specification both of the chunking mechanism itself and of the input on which the chunking mechanism operates; and (c) verbal theories emphasizing the role of long-term knowledge (such as chunking) are not precise enough to make detailed predictions about experimental data, but computational implementations of the theories can bridge the gap.

Item Type: Journal article
Publication Title: Developmental Science
Creators: Jones, G., Gobet, F., Freudenthal, D., Watson, S.E. and Pine, J.M.
Publisher: Wiley
Date: 2014
Volume: 17
Number: 2
ISSN: 1467-7687
Identifiers:
NumberType
10.1111/desc.12111DOI
Divisions: Schools > School of Social Sciences
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:03
Last Modified: 09 Jun 2017 13:17
URI: http://irep.ntu.ac.uk/id/eprint/7066

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