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Abstract The nonword repetition (NWR) test has been shown to be a good predictor of children's vocabulary size. NWR performance has been explained using phonological working memory, which is seen as a critical component in the learning of new words. However, no detailed specification of the link between phonological working memory and long‐term memory (LTM) has been proposed. In this paper, we present a computational model of children's vocabulary acquisition (EPAM‐VOC) that specifies how phonological working memory and LTM interact. The model learns phoneme sequences, which are stored in LTM and mediate how much information can be held in working memory. The model's behaviour is compared with that of children in a new study of NWR, conducted in order to ensure the same nonword stimuli and methodology across ages. EPAM‐VOC shows a pattern of results similar to that of children: performance is better for shorter nonwords and for wordlike nonwords, and performance improves with age. EPAM‐VOC also simulates the superior performance for single consonant nonwords over clustered consonant nonwords found in previous NWR studies. EPAM‐VOC provides a simple and elegant computational account of some of the key processes involved in the learning of new words: it specifies how phonological working memory and LTM interact; makes testable predictions; and suggests that developmental changes in NWR performance may reflect differences in the amount of information that has been encoded in LTM rather than developmental changes in working memory capacity.

AbstractTests 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 Sub‐lexical 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 are 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.