Available online at www.sciencedirect.com

www.elsevier.com/locate/cogsys

Cognitive Systems Research 12 (2011) 113–121

A computational simulation of children’s performance acrossthree nonword repetition tests

Action editor: David Peebles

Gary Jones ⇑

Division of Psychology, Nottingham Trent University, Chaucer Building, Goldsmith Street, Nottingham NG1 5LT, UK

Received 11 December 2009; accepted 2 July 2010Available online 13 August 2010

Abstract

The nonword repetition test has been regularly used to examine children’s vocabulary acquisition, and yet there is no clear explana-tion of all of the effects seen in nonword repetition. This paper presents a study of 5–6 year-old children’s repetition performance on threenonword repetition tests that vary in the degree of their lexicality. A model of children’s vocabulary acquisition is then presented thatcaptures the children’s performance in all three repetition tests. The model represents a clear explanation of how working memory andlong-term lexical and sub-lexical knowledge interact in a way that is able to simulate repetition performance across three nonword testswithin the same model and without requiring test specific parameter settings.� 2010 Elsevier B.V. All rights reserved.

Keywords: Computational modelling; Nonword repetition; Phonological working memory; Lexical knowledge; Chunking

1. Introduction

Language learning is a complex procedure that involvesat least the following processes. First, the learner mustidentify where words begin and end from speech that isoften continuous. Second, the learner must store the newlyidentified words in their long-term lexicon. Finally, thelearner must acquire rules of syntax that govern the wayin which their lexicon words can be combined. It is the sec-ond of these three processes that is the focus of this paper:the process of vocabulary learning.

Research that examines vocabulary learning is prolifer-ated with tests of nonword repetition – a test that involvesnonsense words being spoken aloud to the language lear-ner, who must repeat them accurately. Nonwords are usedsince one can be certain that the child has never encoun-tered the sequence of sounds before, hence providing a truetest of vocabulary learning. Studies of nonword repetitionhave found clear links between repetition performance and

1389-0417/$ - see front matter � 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.cogsys.2010.07.008

⇑ Tel.: +44 115 848 2422; fax: +44 115 848 6829.E-mail address: Gary.Jones@NTU.ac.uk.

vocabulary learning. For example, Gathercole and Badde-ley (1989) measured repetition performance at four years ofage and found it to be the best predictor of vocabularyknowledge at five years of age – over and above other lan-guage-related tests such as sound mimicry. Repetition per-formance also directly predicts children’s ability to learnnew words. Children with high scores in repetition testsare more able to learn new names than children with lowscores in repetition tests (Gathercole & Baddeley, 1990).Finally, nonword repetition is used as a key marker of lan-guage impairment (e.g. Bishop, North, & Donlan, 1996;Conti-Ramsden, Botting, & Faragher, 2001; Ellis Weismeret al., 2000), with language impaired children showing adeficit in their repetition performance that is often com-bined with a smaller vocabulary than their age matchedcounterparts (e.g. Dollaghan & Campbell, 1998; Munson,Kurtz & Windsor, 2005).

1.1. Nonword repetition research

Nonword repetition tests were originally developed toexamine the influence of phonological working memory

114 G. Jones / Cognitive Systems Research 12 (2011) 113–121

on the vocabulary learning process. For example, Gather-cole and Baddeley (1989) showed that repetition accuracyimproved between the ages of 4 and 5 years, and perfor-mance declined as nonword length increased for both ages.Both of these findings were interpreted in terms of phono-logical working memory: an improvement with age couldbe explained by an increase in memory capacity; and adecrease in performance as nonword length increased couldbe explained by the decay of items in working memory.

However, subsequent research has shown that children’sexisting lexical and sub-lexical knowledge plays a majorrole in their nonword repetition ability. Gathercole (1995)re-analysed the nonwords in their original test (Gathercole& Baddeley, 1989) by separating them into “wordlike” and“non-wordlike” nonwords based on adult ratings of word-likeness. She found that children performed significantlybetter for nonwords that were wordlike. Although word-likeness is a subjective measure, even when more objectivemeasures are used, there are still clear differences betweennonwords that share substantial lexical and sub-lexical fea-tures with words compared to those that do not. For exam-ple, if one actively distinguishes nonwords based on theirconstituent phoneme combinations – having one set thatcontain highly frequent combinations of sounds versusone set containing relatively infrequent combinations –there are clear performance differences, with children regu-larly finding high frequency nonwords easier to repeat (e.g.Edwards, Beckman, & Munson, 2004; Vitevitch, Luce,Charles-Luce, & Kemmerer, 1997).

It would therefore seem that nonword repetition, and inturn vocabulary acquisition, can be affected by both pho-nological working memory and long-term lexical andsub-lexical knowledge. There are at least two prominentexplanations of vocabulary acquisition that explain repeti-tion performance in terms of both of these processes.

1.2. Explanations of nonword repetition performance

Since nonword repetition performance is affected by aninteraction between working memory and long-term mem-ory, any explanation of performance must provide somedetail of how these two processes interact. Gathercoleand colleagues (e.g. Gathercole, 2006; Gathercole &Adams, 1993; Gathercole, Frankish, Pickering, & Peaker,1999) explained this interaction using the idea of phonolog-ical frames. Phonological working memory is used to storelinguistic stimuli (e.g. nonwords in the repetition test) andwhen these items decay, long-term lexical and sub-lexicalknowledge is relied upon to help bolster the decaying rep-resentations in working memory. Since nonwords that arewordlike, or that contain highly-frequent sounds, will sharemore information with lexical items in long-term memory,it is these items that gain more help from existing vocabu-lary knowledge. That is, the support provided by the pho-nological frames of existing vocabulary items increases asthe amount of overlap in shared features (to nonwords)increases.

An alternative explanation of vocabulary learningshares many features with Gathercole’s idea of phonologi-cal frames, yet is more explicit in its detail. Jones, Gobetand Pine’s (2007, 2008) EPAM-VOC is a computationalmodel of vocabulary learning that concretely specifieshow phonological working memory and long-term phono-logical knowledge interact. Long-term knowledge is repre-sented by “chunks” of phoneme sequences – as the model issubjected to more and more spoken (phonemic) input,these chunks of phonemes become larger and larger. Pho-nological working memory is represented by a fixed num-ber of chunks that can be stored. Hence, early on in themodel’s learning, EPAM-VOC is able to store only a lim-ited amount of information in phonological working mem-ory since the chunks at this point in time will not be largesequences of phonological information. Later on in learn-ing, the phoneme sequences within chunks will be relativelylarge and so an increased amount of information can bestored in phonological working memory, even if the num-ber of chunks remained the same. This explanation ofvocabulary learning puts forward the idea that improvedperformance with age arises due to an increased amountof phonological knowledge. However, the model alsoexplains wordlikeness and frequency effects quite easily.Phoneme sequences that appear regularly in the child’s lan-guage will be represented within the model as relativelylarge chunks, whereas low frequency sequences will not –even when the sequences are matched for phonemic length.Therefore nonwords that contain high frequency sequencescan be stored in phonological working memory using fewerchunks, giving rise to an increase in the likelihood of theircorrect repetition over nonwords containing low frequencysequences. A similar explanation can be used for wordlikeversus non-wordlike nonwords. The former, since they beargreat resemblance to words, are likely to be representedwithin the model using fewer chunks than non-wordlikenonwords.

1.3. The current paper

EPAM-VOC has thus far been used to successfully sim-ulate the nonword results of Gathercole and Baddeley(1989) and a nonword set devised for children between 2and 5 years of age. Using the interaction between phono-logical working memory and long-term lexical and sub-lex-ical knowledge outlined above, the model showed a goodmatch to children’s nonword repetition performance. How-ever, the interaction itself was not tested in detail becauseneither of the nonword tests were specifically designed tovary in their lexicality. Since research has shown that non-word repetition is strongly influenced by the similaritybetween nonwords and lexical and sub-lexical knowledge,testing children on nonwords that vary in their lexicalitywill increase our knowledge of the interplay between pho-nological working memory and long-term lexical andsub-lexical knowledge. By comparing the children’s datato the results of EPAM-VOC, this research will also pro-

Fig. 1. Graphical representation of EPAM-VOC when beginning withknowledge about individual phonemes only, and having been presentedwith the phoneme sequence “Toys” (T OY Z) as input two times. Chunksare represented by ellipses and links are represented by arrows. Note thatalthough only five phonemes are represented as single phoneme chunks(K, OY, T, Z and P) the model knows all phonemes in English asindividual chunks.

G. Jones / Cognitive Systems Research 12 (2011) 113–121 115

vide a test of the interaction mechanisms employed withinthe model. The current paper therefore examines both childand model repetition performance across three sets of non-words that vary in the degree of their lexicality.

The predictions from both the phonological frameaccount and EPAM-VOC should be that as nonwordsincrease in their lexicality, repetition accuracy increases.However, since repetition performance in the model is sub-ject to the interplay between naturalistic input, phonologi-cal working memory and long-term lexical and sub-lexicalknowledge, accurate predictions will arise when the simula-tions are performed.

The remainder of this paper is as follows. First, themodel itself is detailed so that the reader has more exten-sive knowledge of its workings. Second, a new study of5–6 year-old children’s repetition is described that presentsthree different nonword repetition tests that vary in theextent of their lexicality. Third, the results of the childrenare compared to the results of the model. Finally, theresults are discussed in light of the findings presented.

2. EPAM-VOC: a model of vocabulary learning

EPAM-VOC is a model of vocabulary learning that isbased on the EPAM modelling architecture (Feigenbaum& Simon, 1984). This architecture has been used to success-fully simulate human behaviour in a range of psychologicaldomains (see Gobet et al., 2001). Furthermore, EPAMmodels with a similar set of assumptions to EPAM-VOChave successfully been applied to syntax acquisition (e.g.Freudenthal, Pine, Aguado-Orea, & Gobet, 2007; Freuden-thal, Pine, & Gobet, 2006). The model presented here is anupdated version of that described by Jones, Gobet, andPine (2007, 2008), since that model did not have an explicitmethod by which phoneme sequences were articulated (n.b.the method of articulation is outside of the EPAMarchitecture).

2.1. Representing long-term knowledge

Knowledge within EPAM-VOC is represented as a hier-archy of chunks that contain phoneme sequences. Chunksthat are lower down in the hierarchy contain largersequences, and hence long-term knowledge in EPAM-VOC is represented as a tree-like structure. As we will seein Section 2.3, this type of structure is used so that the tem-poral sequence of phonemes is maintained – chunks furtherdown the hierarchy contain the phoneme sequence of theirparent chunk plus an additional phoneme(s) that occurafter the parent phoneme sequence in the input. The modelbegins with knowledge of all of the constituent phonemesin English, since there is good reason to believe that evenat an early age, children have knowledge of the phonemesof their language (Bailey & Plunkett, 2002).

An example hierarchy of chunks is given in Fig. 1. Hereit can be seen that the model knows the phoneme sequencefor the word “Toys” (T OY Z). We represent phonemes

using the phoneme set in the CMU Lexicon Database(available at http://www.speech.cs.cmu.edu/cgi-bin/cmu-dict) rather than the International Phonetic Alphabet(IPA). This is chiefly because the database allows thesemi-automatic conversion of spoken utterances into theirphonemic equivalent (this will be detailed later when theinput regime for the model is covered). Although theCMU Lexicon Database has occasional differences toIPA (and has 39 phonemes compared to 44 in IPA), manyof the phonemic representations of words are similar acrossthe two. Since any differences are relatively minimal, usingthe CMU Lexicon Database rather than IPA would not beexpected to affect any of the results presented below.

2.2. Representing phonological working memory

The EPAM architecture is chiefly focused on the repre-sentation of long-term knowledge and we therefore appealto previous literature for guidance on how to implement aphonological working memory component to the model.We decided to implement a time-based phonological work-ing memory in EPAM-VOC, since: (a) there is research toindicate that items in working memory have a temporalduration of 2000 ms unless rehearsed (e.g. Baddeley,Thomson, & Buchanan, 1975); and (b) there is alsoresearch that places timing estimates on accessing a chunkand accessing its constituent phonemes (Zhang & Simon,1985). Any given input can therefore be assigned an accesstime based on the number of chunks that are required torepresent the input, and the number of constituent pho-nemes that appear in each chunk. However, in the previousversion of EPAM-VOC, if the time limit of 2000 ms wasexceeded the remainder of the chunked input was ignored.This mechanism gave a strong bias towards rememberingchunks that represent information at the beginning of theinput at the expense of chunks that represent informationoccurring later in the input. This view of memory did notfit with existing memory data such as free recall (e.g. Bhat-arah, Ward, & Tan, 2008) where even for a long list ofitems, each item had at least a 40% chance of being cor-rectly recalled. As we will see later, the current version of

116 G. Jones / Cognitive Systems Research 12 (2011) 113–121

EPAM-VOC addresses this problem by placing a probabil-ity on correctly accessing a chunk that varies based on theextent to which the 2000 ms time limit of phonologicalworking memory has been compromised. This account isconsistent with more recent accounts of working memory(e.g. Cowan, 1997) that place all of the input in phonolog-ical working memory but assign different probabilities (e.g.via activation) of subsequently accessing the input.

For any given input, EPAM-VOC’s long-term knowl-edge is accessed in order to convert the input into a seriesof chunks (i.e. representing the input sequence in as fewchunks as possible when examining the input from left-to-right). This is accomplished by: (a) traversing the hierar-chy of chunks as far as possible for a given input; (b)removing the contents of the traversed chunk from theinput; and (c) beginning traversal afresh with the remainderof the input. For example, if the input was “T OY” withlong-term knowledge as shown in Fig. 1, EPAM-VOCwould begin at the null top chunk and examine all linksbelow this for a match to “T”. Since there is a match, tra-versal would proceed to the “T” chunk and the phoneme“T” would be removed from the input. Subsequent tra-versal would examine all links below the “T” chunk for amatch to the “OY” phoneme. Once again, a link can betaken and traversal proceeds to the “T OY” chunk. Sincethere is no further input, the “T OY” chunk would bereturned.

Once an input has been converted into chunks, a pointerto each chunk is placed in phonological working memory.Each pointer is then assigned a time for the subsequentaccess of its associated chunk based on timings from Zhangand Simon (1985) – 400 ms to access the chunk and a fur-ther 30 ms to access each constituent phoneme excludingthe first. For example, given the long-term knowledge ofFig. 1, the pointer to the chunk “Toys” (T OY Z) wouldrequire 460 ms to access the chunk, whereas a pointer tothe single phoneme chunk “K” would require 400 ms.The total time to access all chunks from their associatedpointers is calculated by summing the access times for allpointers. When this total exceeds 2000 ms, then there is aprobability of less than 1.0 that a chunk can be correctlyaccessed from its pointer (when learning or articulating,the model requires the chunk to be accessed from its poin-ter in working memory).

Let us take the input “My toys are here” as an example(phonemic representation: “M AY T OY Z AE R H IYR”) and the knowledge in Fig. 1. Only “T OY Z” existsas a multi-phoneme chunk, and the pointer associated withthis chunk is assigned an access time of 460 ms. All pointersto other chunks (“M”, “AY”, “AE”, etc.) are assigned anaccess time of 400 ms – there are a total of 8 chunksrequired to represent the input, giving a total access timeof (1 � 460 ms) + (7 � 400 ms) = 2560 ms. The probabilityof subsequently accessing a chunk from its pointer is thetemporal duration of working memory divided by the totaltime required to access all of the chunks: 2000/2560 = .78125.

To summarise, any given input is converted into as fewchunks as possible using EPAM-VOC’s long-term knowl-edge of phoneme sequences. This matching process takesa certain amount of time, and the result of the process isthat a pointer to each chunk is placed in working memory.Since working memory only contains pointers to chunks,any process that requires the actual information in thechunk (e.g. when learning or articulating items in workingmemory) must access the chunk itself. The accurate accessof information in a chunk is probabilistic, dependent uponwhether the total access time for all chunks exceeds the2000 ms time limit of working memory.

At this point, it is worth noting that the following sim-ulations of the children’s data maintain the 2000 ms timelimit for phonological working memory. There is ampledevelopmental data to illustrate that working memorycapacity increases with age, even for nonword repetition(e.g. Gathercole & Baddeley, 1989; Jones et al., 2007).However, the underlying causes of developmental increasesin memory performance are less clear. EPAM-VOC pro-vides a specification for how the amount of informationin phonological working memory can increase based onincreases in long-term knowledge, without the need forany change to phonological working memory capacity. Asimilar position is held by Case (1985) who suggests thatchildren have a functional memory capacity wherebyincreases in task experience lead to more complex knowl-edge structures being held in memory, leading to improvedtask performance. EPAM-VOC can therefore be seen as anoperationalised version of the Case theory that is focusedon the task of language learning. A fixed working memorycapacity is also supported in the developmental literature.For example, in children’s chess playing, Chi (1978) andSchneider, Gruber, Gold, and Opwis (1993) found thatworking memory capacity for chess-based informationincreased as a function of expertise, yet for other tasks,such as digit span, no difference was found between thechess players and controls. Furthermore, Jones et al.(2008) examined developmental increases in both long-termknowledge and phonological working memory, findingthat increases in long-term knowledge provided the bestfit to the children’s data. Note that keeping the capacityof phonological working memory consistent with adultsalso means that the model has one less parameter.

2.3. Learning phoneme sequences

Once the input has been encoded as pointers to chunksin long-term knowledge, the learning process then exam-ines each pair of pointers to see if a phoneme sequencecan be learnt that combines the information within eachchunk pairing. This can only be done if each chunk is cor-rectly accessed from its pointer, but if this is the case, a newchunk is learnt whose contents are the combination of bothchunks.

Let us use the input “Toys” (“T OY Z”) as an example.When EPAM-VOC first begins its learning, it only knows

G. Jones / Cognitive Systems Research 12 (2011) 113–121 117

single phonemes as chunks, and therefore “T OY Z” wouldbe represented in working memory using three pointers tothree chunks (one pointer to each of “T”, “OY” and “Z”).Since the time to encode the three chunks is 400 ms for each(totalling 1200 ms and therefore within the 2000 ms limit)then the subsequent accessing of the information withinthe chunks will be completely accurate. EPAM-VOC takeseach pair of pointers in turn and tries to learn somethingfrom them. The first pair are “T” and “OY”. The “T” chunkis accessed, and then a link to a new chunk is placed below the“T” chunk. The link will specify the additional informationthat is being learnt (“OY”) and the new chunk contains thejoint set of information (“T OY”). The next pair of chunks(“OY” and “Z”) are then examined, and in a similar vein, anew chunk “OY Z” is learnt. Should “T OY Z” be presentedto EPAM-VOC a second time, it can now be represented astwo pointers to the chunks “T OY” and “Z”. The learningfrom this pair of pointers would result in a new chunk “TOY Z” being added below the “T OY” chunk, and the result-ing network would be that shown in Fig. 1. Note that EPAM-VOC only learns in left-to-right order, and so all chunks con-tain information about what succeeds phonemes in a giveninput, not what precedes phonemes (e.g. the “T OY” chunkappears below “T” only, and not also below “OY”).Although learning in EPAM-VOC may proceed ratherquickly, one must remember that the model is presented withonly a small subset of the language input a child hears.Reducing the learning rate has been successful for other vari-ants of EPAM models (e.g. Croker, Pine, & Gobet, 2003).

Let us now see how learning progresses when the accesstime exceeds the 2000 ms limit. Take the previous examplesequence “My toys are here” (“M AY T OY Z AE R H IYR”) and the long-term knowledge of Fig. 1. It was alreadydetermined that there was a .78125 probability of accessinga chunk that is related to a pointer for this input. Since thepointers in working memory are analysed in a pairwisefashion, then if one pointer cannot access its associatedchunk, no learning can be accomplished for that pointer.For example, if the pointer to the chunk “AY” failed, thenEPAM-VOC could not learn the sequence “M AY” or thesequence “AY T OY Z”.

2.4. Articulating phoneme sequences

In order for a phoneme sequence to be articulated, itmust first be represented in working memory as a seriesof pointers to chunks (as described above). In the sameway as for learning, each chunk needs to be correctlyaccessed from its pointer, otherwise an incorrect articula-tion takes place. However, even if each chunk is correctlyaccessed, the chunk may still be incorrectly articulated.We assume that the articulation of phonemes in a chunkis based on the frequency of that chunk – with thosechunks that are low in frequency attracting more errorsof articulation than chunks that are high frequency.EPAM-VOC maintains a frequency for each chunk basedon the number of times that the chunk has been accessed.

Correct articulation of an input sequence (e.g. a nonword)is only achieved when all of the relevant chunks are cor-rectly encoded into phonological working memory, andall of the phonemes are correctly articulated from eachchunk based on the frequency of the chunk.

2.5. Training the model

The model uses naturalistic speech input based on thematernal input from 12 sets of mother–child interactionsto 2–3 year-old children (Theakston, Lieven, Pine, & Row-land, 2001). The average number of utterances for eachmother was 25,519 (range 17,474–33,452). All input is con-verted into the phonetic alphabet of the CMU LexiconDatabase, as discussed previously. Twelve simulations arecarried out, one for each set of mother’s input. However,since comparisons are going to be made to 5–6 year-oldchildren, additional input was sought from paternal inter-actions with 5 year-old children plus input from readingmaterial for children of this age group (e.g. Snow White).

During training, the model was presented with the sameamount of input as per the original maternal input. How-ever, as the input presented to the model increased, moreand more of the maternal input was replaced with the addi-tional input outlined above (reflecting the input a 5–6 year-old child would receive). The gradual change in the type oflanguage input presented to the model is intended to reflectthe change in language input that occurs between the agesof 2 and 6. This form of input has been applied successfullyto simulate the repetition performance of 2–3 and 4–5 year-old children (Jones et al., 2007).

Since the input to the model can vary based on whichutterances from the mother were chosen for replacement,and which input from the 5–6 year-old input set was chosenas the replacement, the model was run ten times for each“mother”. This ensures that the results from the modelare replicable and are not simply based on an advantageousinput set.

3. Study of 5–6 year-old children’s repetition performance

3.1. Participants

Eighteen 5–6 year-old children (5;4–6;6, M = 6;0; 7male, 11 female) participated in this study. All childrenscored within normal ranges on the British Picture Vocabu-lary Scale (Dunn, Dunn, Whetton, & Burley, 1997). Allchildren were English monolinguals and had no hearingdifficulties, as reported by their school teacher.

3.2. Materials

Three nonword tests were used as follows:

1. The Children’s Test of Nonword Repetition (CNRep,Gathercole, Willis, Baddeley, & Emslie, 1994), whichcontained nonwords with singleton consonants only

Fig. 2. Nonword repetition performance (%) for the high lexicalitynonwords for both the children and the two sets of model runs. Thenumber on the x-axis denotes the number of syllables (2, 3, or 4) and theletter denotes the nonword type (S = single consonant, C = clusteredconsonant). For example, 2S = 2 syllable single consonant nonwords.Error bars indicate standard error.

118 G. Jones / Cognitive Systems Research 12 (2011) 113–121

between each vowel (e.g. rubid) and nonwords contain-ing at least one consonant cluster (e.g. glistow). Therewere five nonwords of each type at each of three lengths(2–4 syllables). 5-syllable nonwords were excludedbecause children at this age had difficulty in repeatingnonwords of this length. This test was considered highin lexicality since many of the nonwords included sylla-bles that were either lexical items (e.g. thickery) or mor-phemes (e.g. glistering).

2. The nonword test of Dollaghan, Biber, and Campbell(1995) that contained twelve 3-syllable nonwords. Sixnonwords contained a syllable that was a lexical item(e.g. bathesis) and six changed one phoneme in the non-word so that it was entirely non-lexical (e.g. fathesis).Children show greater repetition accuracy for nonwordscontaining a lexical item than nonwords that do notcontain a lexical item (Dollaghan et al., 1995). This testwas considered medium in lexicality since half of thenonwords contained lexical items and half did not.

3. A new nonword repetition test that was entirely non-lex-ical and non-morphological that contained two sets of 3-syllable nonwords: eight that contained phonemesequences that were low in frequency (e.g. latmunoz)and eight that contained phoneme sequences that werevery low in frequency (e.g. wegnerterk). The nonwordsin each set were matched for syllable structure, numberof phonemes, age of acquisition of the phonemes, andnumber of consonant clusters. The average log fre-quency was lower for nonwords containing very low fre-quency phoneme sequences than nonwords containinglow frequency phoneme sequences (.51 versus .44,t(7) = 3.92, p = .01) using a procedure for measuringbi-phone frequency similar to that of Luce and col-leagues (e.g. Jusczyk, Luce, & Charles-Luce, 1994;Vitevitch et al., 1997). This test was considered to below in lexicality since nonwords were carefully con-structed so as not to contain lexical items or morphemes.

3.3. Design

The high lexicality nonword set had two independentvariables: nonword-type (single or clustered) and nonwordlength (2, 3, or 4 syllables). The medium lexicality non-words had one independent variable (lexical-item: presentor absent). The new nonword test also had one indepen-dent variable (frequency: low or very low). The dependentvariable in all cases was the percentage of nonwords accu-rately recalled.

3.4. Procedure

All children were tested individually on a one-to-onebasis in a quiet area of their school. Each nonword repeti-tion test was carried out on a separate day. For the highlexicality nonwords, the original recordings were main-tained (to be comparable to other studies using these non-

words); for the medium and low lexicality tests, thenonwords were recorded by a speaker native to Notting-ham, UK. The instructions for each set of nonwords aregiven below, and were the same for each nonword test.Children’s responses were recorded onto a Sony ICD-MX20 digital voice dictaphone for later analysis.

“Hello, in a few seconds you will hear a funny made upword. Please say the word aloud yourself as soon as youhear it.”

4. Child and model results

Since there are probabilistic elements to both encodingand articulation within the model, ten nonword repetitiontests were carried out for each simulation to ensure thatthe results were reliable. There were therefore, for any non-word in a nonword repetition test: 12 mothers � 10 simula-tions runs � 10 attempts at each nonword = 1200repetitions of each nonword. For each repetition test, twosets of results are shown for the model: the average of allof the 1200 simulations and the average of 12 simulations(one from each mother). The 12 runs are included since sta-tistical analyses are based on these – the nature of the 1200simulations means that they show little variance, since the120 runs for each mother are all based on a similar set ofinput data. The selection of the 12 runs (i.e. one single sim-ulation run from each mother) was pseudo-random. Eachset of 120 simulation runs from each mother were reducedto include only those that approximated the average of all1200 runs when taken as a whole. One run from eachmother was randomly selected from this reduced set.

Fig. 2 shows the children’s results for the high lexicalitynonwords together with the results from EPAM-VOC.Correlation analyses show that the model has a very similarpattern of repetition performance to the children, for boththe 1200 simulations (r(4) = .94, p = .005) and the 12 sim-ulations (r(4) = .96, p = .003). Root Mean Square Error(RMSE) was also computed between the model and childdata. RMSE reveals the average percentage difference

G. Jones / Cognitive Systems Research 12 (2011) 113–121 119

between the model and the children. RMSE rates indicatedthat on average, the repetition accuracy of the 1200 simu-lations was within 4.23% of the children, and the repetitionaccuracy of the 12 simulations was within 5.74% of thechildren. Taken together, the correlations and RMSE fig-ures show that the model provides an extremely goodmatch to the children’s data for the high lexicalitynonwords.

A 2 (nonword-type: single or clustered) � 3 (nonwordlength: 2, 3, or 4 syllables) repeated-measures ANOVAwas performed on the children’s data. There was a signifi-cant effect of nonword-type (F(1, 17) = 39.62, p < .001,g2

p = .70), showing that performance was better for singleconsonant nonwords, and a significant effect of nonwordlength (F(2, 34) = 17.26, p < .001, g2

p = .50), showing thatperformance was better for short nonwords. There wasno interaction between the two (F(2, 34) = 1.53, p = .23,g2

p = .08). For the model, there was also the same effectof nonword-type (F(1, 11) = 5.50, p = .04, g2

p = .33) andnonword length (F(2, 22) = 9.80, p = .001, g2

p = .47), withno interaction between the two (F(2, 22) = .33, p = .72,g2

p = .03).Fig. 3 shows the children’s and model’s performance for

the medium and low lexicality nonwords. The children’sdata correlate extremely well with both the 1200 simula-tions based on the correlation co-efficients (r(2) = .99,p = .013) and the 12 simulations (r(2) = .92, p = .079).RMSE rates indicate that on average, the repetition accu-racy of the 1200 simulations was within 3.95% of the chil-dren, and the repetition accuracy of the 12 simulations waswithin 3.67% of the children. The correlations and RMSEfigures show that the model also provides an extremelygood match to the children’s data for the medium andlow lexicality nonwords.

For the medium lexicality nonwords, the childrenshowed no difference in their ability to repeat nonwordscontaining and not containing a lexical item (t(17) = .00,p > .99). The same was found in the model (t(11) = .48,

Fig. 3. Nonword repetition performance (%) for the medium (leftmosttwo blocks) and low lexicality (rightmost two blocks) nonwords, for thechildren and the two sets of model runs. LP = Medium lexicality, lexicalitem present; LA = Medium lexicality, lexical item absent; LF = Lowlexicality, low frequency; and VLF = Low lexicality, very low frequency.

p = .64). For the low lexicality nonwords, there was no dif-ference in children’s repetition accuracy between low andvery low frequency nonwords (t(24) = 1.44, p = .17).Again, the same result was found in the model(t(11) = .56, p = .59).

In order to examine whether there were any perfor-mance differences across nonwords that varied in their lex-icality, the 3 syllable high lexicality nonwords werecompared to the medium and low lexicality nonwords(both of these sets consisted of 3-syllable nonwords only).For the children, a repeated-measures one-way ANOVA(lexicality: high, medium, or low) showed no main effectof lexicality, although the results approached significance(F(2, 34) = 3.23, p = .052, g2

p = .16). For the model, therewas also no effect of lexicality (F(2, 22) = 1.38, p = .27,g2

p = .11).

5. Discussion

Children’s nonword repetition performance was exam-ined across three nonword tests varying in their lexicality.For high lexicality nonwords, results were consistent withthose found in 4 and 5 year-old children (e.g. Gathercole& Baddeley, 1989): performance improves for single conso-nant nonwords and for nonwords of fewer syllables. Infact, this set of findings is rather robust since they persistin older age groups also (e.g. Briscoe, Bishop, & Norbury,2001). For medium lexicality nonwords, there was no dif-ference in performance between nonwords that containeda lexical item and nonwords that did not, a finding thatis inconsistent with Dollaghan et al. (1995) who showedthat 10 year-old children are more able to repeat nonwordscontaining a lexical item. For low lexicality nonwords,there was no difference in performance between nonwordscontaining phoneme sequences that were low in frequencyand nonwords containing phoneme sequences that werevery low in frequency. Similar findings have been shownin the recall literature, where children’s recall rates showno difference for low and very low frequency items (Gath-ercole et al., 1999). It is perhaps the case, therefore, thatfrequency effects do not occur when all items for compari-son are low in frequency.

A model of vocabulary acquisition, EPAM-VOC, waspresented that explicitly defined the interaction betweenphonological working memory and long-term lexical andsub-lexical knowledge during the learning of phonemesequences. Performance of the model showed a remarkablyclose match to the children for all three nonword tests. Notonly were there high correlations and low RMSE ratesacross the data, the model also showed the same statisticaleffects as the children. EPAM-VOC shows how a theoreti-cally supported account of the interaction between phono-logical working memory and long-term knowledge is ableto capture the effects seen across three different nonwordrepetition tests within the same version of the model and

without the need for any developmental changes in phonolog-

ical working memory capacity. The model shows that

120 G. Jones / Cognitive Systems Research 12 (2011) 113–121

children’s performance on three nonword sets that variedin their lexicality can be achieved solely because of theinterplay between phonological working memory andlong-term phonological chunks. There is no need for devel-opmental changes in phonological working memory or anyneed to alter parameter settings when simulating the differ-ent nonword sets.

Let us now consider two surprising findings within boththe child and model data: the lack of any effect of lexicalityand the lack of any difference in performance for the twotypes of medium lexicality nonwords (i.e. nonwords thatcontain a lexical item or do not). The phonological frameaccount predicted that repetition accuracy would increaseas the lexicality of the nonwords increased. Similarly, theaccount would also predict better performance for non-words containing a lexical item over those that do not.EPAM-VOC was able to simulate both of the surprise find-ings seen in the children. Why is this the case? An examina-tion of the model’s performance for the medium lexicalitynonwords shows that the lexical items within nonwords(e.g. ‘bath’ in bathesis) either do not exist as whole chunksor when they do, they are of low frequency. For example,‘bath’ only exists as a chunk in 78% of the model runs,and those chunks only have an average frequency of 103(i.e. the chunk has only been accessed, on average, 103times per model run). The lexical items are therefore notsufficiently well-practiced (based on their frequency ofuse) to cause improved performance for nonwords contain-ing a lexical item. The simple reason for the lack of effectsacross the medium lexicality nonwords, therefore, is thatchildren of 5 and 6 years of age have not had enough expo-sure to the lexical items within the nonwords. A similaraccount could explain why the children and the model donot show performance differences between the high lexical-ity nonwords and the two other nonword sets: the childrenand the model have not yet had enough exposure to the lex-ical items within the high lexicality nonwords. EPAM-VOCis therefore not only able to simulate the performance ofthe children, but is also able to put forward explanationsof the children’s behaviour. It would be interesting to pres-ent the model with further input (e.g. based on the type ofinput received by older children) to see at what point lexicaleffects emerge.

In summary, EPAM-VOC replicates the findings of 5–6 year-old children on three different nonword repetitiontests varying in the degree of their lexicality, using one sol-itary version of the model. It now needs to be seen whetherthe errors made in children’s repetitions are also mirroredby the model – if this is the case, then EPAM-VOC mayprove to be a very strong explanation of the way in whichchildren are learning vocabulary.

Acknowledgments

The author would like to thank Sarah Watson for herhelp in the data collection phase of this study, Marco Tam-burelli for his help in the construction of the new set of

nonwords outlined, Gabrielle Le Geyt for the spoken ver-sions of the nonwords, Fernand Gobet and Julian Pinefor helpful discussions on the model, and the schools andchildren who participated in the nonword repetition study.This research was funded by The Leverhulme Trust undergrant reference F/01 374/G.

References

Baddeley, A. D., Thomson, N., & Buchanan, M. (1975). Word length andthe structure of short-term memory. Journal of Verbal Learning and

Verbal Behavior, 14, 575–589.Bailey, T. M., & Plunkett, K. (2002). Phonological specificity in early

words. Cognitive Development, 17, 1265–1282.Bhatarah, P., Ward, G., & Tan, L. (2008). Examining the relationship

between free recall and serial recall: The serial nature of recall and theeffect of test expectancy. Memory & Cognition, 36, 20–34.

Bishop, D. V. M., North, T., & Donlan, C. (1996). Nonword repetition asa behavioural marker for inherited language impairment: Evidencefrom a twin study. Journal of Child Psychology and Psychiatry, 37,391–403.

Briscoe, J., Bishop, D. V. M., & Norbury, C. F. (2001). Phonologicalprocessing, language, and literacy: A comparison of children withmild-to-moderate sensorineural hearing loss and those with specificlanguage impairment. Journal of Child Psychology and Psychiatry, 42,329–340.

Case, R. (1985). Intellectual development: Birth to adulthood. Orlando,Florida: Academic Press.

Chi, M. T. H. (1978). Knowledge structures and memory development. InR. S. Siegler (Ed.), Children’s thinking: What develops? Hillsdale.Erlbaum: N.J.

Conti-Ramsden, G., Botting, N., & Faragher, B. (2001). Psycholinguisticmarkers for specific language impairment (SLI). Journal of Child

Psychology and Psychiatry, 42, 741–748.Cowan, N. (1997). The development of working memory. In N. Cowan &

C. Hulme (Eds.), The development of memory in childhood

(pp. 163–199). Hove, UK: Psychology Press.Croker, S., Pine, J. M., & Gobet, F. (2003). Modelling children’s negation

errors using probabilistic learning in MOSAIC. In F. Detje, D. Dorner,& H. Schaub (Eds.), Proceedings of the fifth international conference on

cognitive modeling (pp. 69–74). Bamberg: Universitats-Verlag.Dollaghan, C. A., Biber, M. E., & Campbell, T. F. (1995). Lexical

influences on nonword repetition. Applied Psycholinguistics, 16,211–222.

Dollaghan, C. A., & Campbell, T. F. (1998). Nonword repetition andchild language impairment. Journal of Speech, Language, and Hearing

Research, 41, 1136–1146.Dunn, L. M., Dunn, L. M., Whetton, C., & Burley, J. (1997). The British

picture vocabulary scale (2nd ed.). Windsor, UK: NFER-Nelson.Edwards, J., Beckman, M. E., & Munson, B. (2004). The interaction

between vocabulary size and phonotactic probability effects onchildren’s production accuracy and fluency in nonword repetition.Journal of Speech, Language, and Hearing Research, 47, 421–436.

Ellis Weismer, S., Tomblin, J. B., Zhang, X., Buckwalter, P., Chynoweth,J. G., & Jones, M. (2000). Nonword repetition performance in school-age children with and without language impairment. Journal of Speech,

Hearing, and Language Research, 43, 865–878.Feigenbaum, E. A., & Simon, H. A. (1984). EPAM-like models of

recognition and learning. Cognitive Science, 8, 305–336.Freudenthal, D., Pine, J. M., Aguado-Orea, J., & Gobet, F. (2007).

Modelling the developmental patterning of finiteness marking inEnglish, Dutch, German and Spanish using MOSAIC. Cognitive

Science, 31, 311–341.Freudenthal, D., Pine, J. M., & Gobet, F. (2006). Modelling the

development of children’s use of optional infinitives in English andDutch using MOSAIC. Cognitive Science, 30, 277–310.

G. Jones / Cognitive Systems Research 12 (2011) 113–121 121

Gathercole, S. E. (2006). Nonword repetition and word learning: Thenature of the relationship. Applied Psycholinguistics, 27, 513–543.

Gathercole, S. E. (1995). Is nonword repetition a test of phonologicalmemory or long-term knowledge? It all depends on the nonwords.Memory & Cognition, 23, 83–94.

Gathercole, S. E., & Adams, A.-M. (1993). Phonological working memoryin very young children. Developmental Psychology, 29, 770–778.

Gathercole, S. E., & Baddeley, A. D. (1989). Evaluation of the role ofphonological STM in the development of vocabulary in children: Alongitudinal study. Journal of Memory and Language, 28, 200–213.

Gathercole, S. E., & Baddeley, A. D. (1990). The role of phonologicalmemory in vocabulary acquisition: A study of young children learningnew names. British Journal of Psychology, 81, 439–454.

Gathercole, S. E., Frankish, C. R., Pickering, S. J., & Peaker, S. (1999).Phonotactic influences on short-term memory. Journal of Experimental

Psychology: Learning, Memory, and Cognition, 25, 84–95.Gathercole, S. E., Willis, C. S., Baddeley, A. D., & Emslie, H. (1994). The

children’s test of nonword repetition: A test of phonological workingmemory. Memory, 2, 103–127.

Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C.-H., Jones, G., Oliver,I., et al. (2001). Chunking mechanisms in human learning. Trends in

Cognitive Sciences, 5, 236–243.Jones, G., Gobet, F., & Pine, J. M. (2007). Linking working memory and

long-term memory: A computational model of the learning of novelsound patterns. Developmental Science, 10, 853–873.

Jones, G., Gobet, F., & Pine, J. M. (2008). Computer simulations ofdevelopmental change: The contributions of working memorycapacity and long-term knowledge. Cognitive Science, 32, 1148–1176.

Jusczyk, P. W., Luce, P. A., & Charles-Luce, J. (1994). Infants’ sensitivityto phonotactic patterns in the native language. Journal of Memory and

Language, 33, 630–645.Munson, B., Kurtz, B. A., & Windsor, J. (2005). The influence of

vocabulary size, phonotactic probability, and wordlikeness on non-word repetitions of children with and without specific languageimpairment. Journal of Speech, Language, and Hearing Research, 48,1033–1047.

Schneider, W., Gruber, H., Gold, A., & Opwis, K. (1993). Chess expertiseand memory for chess positions in children and adults. Journal of

Experimental Child Psychology, 56, 328–349.Theakston, A. L., Lieven, E. V. M., Pine, J. M., & Rowland, C. F. (2001).

The role of performance limitations in the acquisition of verb-argument structure: An alternative account. Journal of Child Lan-

guage, 28, 127–152.Vitevitch, M. S., Luce, P. A., Charles-Luce, J., & Kemmerer, D. (1997).

Phonotactics and syllable stress: Implications for the processing ofspoken nonsense words. Language and Speech, 40, 47–62.

Zhang, G., & Simon, H. A. (1985). STM capacity for Chinese words andidioms: Chunking and acoustical loop hypotheses. Memory & Cogni-

tion, 13, 193–201.