Our premise was that the two clusters would cut across the clinical status, since our test variables (LITMUS NWRT and SRT) have been proposed to be sensitive to the presence or absence of language impairment, see Section "The Language Impairment Testing in Multilingual Settings (LITMUS) Tools for Crosslinguistic Research." The cutoff is a reference value ascertained after the cluster memberships are determined. Since we have uni-dimensional data (using just one variable per cluster analysis), the cutoff is on the same scale as the score of the dependent measure. The cutoff is an imaginary line separating the two clusters. It is calculated as the mean of the maximum score in

10A ROC analysis is currently being prepared for the performance of the bilingual groups on NWRT and in SRT-Id.

11We thank Istvan Fekete for drawing our attention to this method and his support with statistics in the following analysis.

#### Table 6 | Summary of bilingualism factors in the bilingual groups [mean (SD) and range].a

*a When applicable.*

the "lower" cluster and the minimum score of the "higher" cluster. Individual data points (here scores) allotted to the participants can then be ordered by group, which in turn allows calculation of sensitivity and specificity of the test.

### RESULTS

### Background Comparisons on Bilingualism Measures

In Section "Participants and Procedure for Verification of Clinical Status," we established that the bilingual groups were comparable in terms of the LI variables "age, non-verbal intelligence and SES." We further compared the bilingual groups for language background information obtained *via* the PaBiQ as displayed in **Table 6**. Group comparisons using a non-parametric Kruskal– Wallis test revealed no significant differences between the bilingual typically developing children according to L1 group on AoO, LoE, early L1 exposure, early L2 exposure, current L1 richness, and current L2 richness as well as the degree of L2 dominance as indicated by the LDI. Likewise, there were no significant differences between the BiSLI and BiTD groups on the aforementioned bilingualism measures.

Following the procedure and using the calculations described in Section "The LITMUS-PaBiQ," we established language dominance in our groups of bilingual participants. **Table 7** summarizes these classifications by L1 and by final status. Note that in the Turkish/German typical children we find the highest rate of L1-dominant children. Among the BiSLI children, balanced or German dominant children are the majority. This might be a reflex of the traditional advice given to parents of bilingual children with language difficulties that they should use the majority language at home or with the child.

### Overall Results on the LITMUS NWRT and SRT

We first ran omnibus Kruskal–Wallis tests using scores on NWRT, SRT "identical repetition," henceforth SRT\_Id, and SRT "target structure," henceforth SRT\_Tar, as dependent variables to determine if clinical group has an effect. All three tests yielded significant results [χ2 (3, *N* = 76) = 33.394, *p* < 0.001 for NWRT, χ2 (3, *N* = 76) = 38.926, *p* < 0.001 for SRT\_Id, and

Table 7 | Language dominance in bilingual children per L1.

χ2 (3, *N* = 76) = 38.126, *p* < 0.001 for SRT\_Tar]. In a next step, *post hoc* Mann–Whitney *U* comparisons were carried out on the dependent measures applying Bonferroni-adjustment of *p*-values to reduce Type I error that can arise due to multiple comparisons.

The overall performance of the different groups defined in **Table 2** in the NWRT and SRT is given in **Figure 1**. The NWRT significantly distinguishes the MoSLIs from the MoTDs (*U* = 5.5, *p* < 0.001, *r* = 0.767) and the BiSLIs from the BiTDs (*U* = 24.5, *p* < 0.001, *r* = 0.528). Moreover, BiTDs perform significantly different from the MoSLIs in the NWRT (*U* = 38.0, *p* < 0.001, *r* = 0.600). This means that the LITMUS NWRT can identify SLI across populations. In addition, performance in NWRT does not statistically differ in BiTDs and MoTDs.

**Figure 1** further shows that the SRT can well discriminate SLI from TD children in monolingual and bilingual populations with both scoring methods. The score of SRT\_Id distinguishes the MoSLIs from the MoTDs (*U* = 0.000, *p* < 0.001, *r* = 0.846) and the BiSLIs from the BiTDs (*U* = 36.00, *p* < 0.001, *r* = 0.490). Here as well, BiTDs perform significantly better than the MoSLIs (*U* = 46.5, *p* < 0.001, *r* = 0.578). If SRT is rated with the measure of SRT\_Tar, bilingual children perform better. Again, MoTDs are significantly different from MoSLIs (*U* = 0.000, *p* < 0.001, *r* = 0.844), BiTDs perform significantly better than BiSLIs (*U* = *32.5*, *p* < 0.001, *r* = 0.539), and also BiTDs perform significantly better than MoSLIs (*U* = 40.0, *p* < 0.001, *r* = 0.595). However, the MoTDs and BiTDs do not perform alike in the SRT by score SRT\_Id: (*U* = 76.5, *p* = 0.006, *r* = 0.438) and SRT\_Tar: (*U* = 102.5, *p* = 0.036, *r* = 0.364). Outliers in the SRT scored by

SRT\_Id are **29**12 and **71**, where the latter is also the outlier in the NWRT. These two children perform within or even below the BiSLI range. The outlier in the MoSLI group, **11**, is an older child (9;4). Bilingual children performing below the group range in the mastery of SRT\_Tar are **29** and **71**, but also **27** and **70**. 13

For further analyses, we first present results from NWRT and SRT\_Id and single out SRT\_Tar for closer analysis. We first run a ROC curve analysis on MoTD and MoSLI to determine the optimal monolingual cutoff score and the diagnostic accuracy for each of the tasks. As can be seen in **Table 8**, both LITMUS tests have excellent diagnostic accuracy in monolingual children. When looking at the individual scores of the monolingual children, it emerges that a cutoff of 59.85% for the NWRT and 63.33% on SRT\_Id sharply group the children with 100% sensitivity and specificity for SRT\_Id and 91.7% sensitivity and 90% specificity for NWRT. Applying the measure SRT\_Tar to the monolingual data allows a cutoff of 77.78%, still with 100% sensitivity and specificity.

Comparison of the bilingual groups with the monolingual groups points to the fact that other factors than language impairment could lead to poor performance. To address this problem, we performed a *k*-means cluster analysis of the performance of all bilinguals on NWRT, SRT\_Id and SRT\_Tar. The *k-*clustering, unbiased as to any given classification of participants, renders two clusters, participants who are performing well on the task (cluster A "higher cluster") and those performing poorly (cluster B "lower cluster"), the cutoff line between the two clusters was determined for each of the measures as outlined in Section "Data Analysis."

Table 8 | Diagnostic accuracy of the German Language Impairment Testing in Multilingual Settings sentence repetition task (SRT) and non-word repetition task (NWRT) among monolingual children.

*a If specificity* = *100% then LR+ ratios are undefined.*

For the NWRT, the *k*-means cluster analysis rendered two clusters separated by a *k-*means cutoff of 63.5%: 34 children performing above cutoff and 20 children scoring below. In the SRT\_Id, the analysis rendered a 41.25% cutoff separating the two clusters. On this measure, 35 children, cluster A, scored above the cutoff, whereas 19 children, cluster B, performed below cutoff score. To complete the analysis and calculate the sensitivity and specificity levels, the individual values in the clusters for each task were identified as scores of individual BiTD or BiSLI children. **Figures 2** and **3** depict the performance of cluster A and cluster B in NWRT and SRT\_Id, respectively. All of the eight children assigned to the BiSLI group based on standardized test procedures belonged to the lower cluster on both measures, which yields a sensitivity of 100% with an LR− of 0.0. However, the specificity levels and the corresponding LR+ values for ruling out language impairment were only suggestive as can be seen in **Table 9**. This is ascribed to the fact that 12 children with the final status BiTD scored below cutoff on NWRT and 11 children scored below cutoff on SRT\_Id, and thus belonged to cluster B.

For the measure of SRT\_Tar, *k*-clustering resulted in two clusters separated by a 52.2% cutoff: 39 children, cluster A, performing above cutoff and 15 children, cluster B, performing

12We use case numbers for the identification of individual participants. 13The outliers are included in the group analysis.

Table 9 | Diagnostic accuracy of the German Language Impairment Testing in Multilingual Settings sentence repetition task (SRT) and non-word repetition task (NWRT) among bilingual children for individual measures and for test combinations.

below this cutoff. **Figure 4** shows the individual performance of members of cluster A and cluster B in the above measure. The children classified as BiSLI all belonged to cluster B, except **26** who is 9;1 years old and does not seem to be impaired in German morphosyntax. However, eight children in cluster B, some with extremely low scores, had received the final status of BiTD. These children are in particular: **70**, **71**, **44**, **45**, **27**, **76**, **28**, and **29**. Interestingly, most of these children except for **44** and **45** performed below *k*-means cutoff on both SRT\_Id and NWRT. It remains to be investigated why these children scored low.

The measure SRT\_Tar, which gives more weight to mastery of syntactically complex structures than to lexical abilities, gave lower sensitivity but better specificity levels than SRT\_Id or NWRT, see **Table 9**. We also investigated whether combining the NWRT with SRT raises diagnostic accuracy. The results in **Table 9** indicate that a combination of NWRT and SRT\_Id or NWRT and SRT\_Tar indeed results in better specificity and thus overall diagnostic accuracy.

### Dominance As a Factor for the Performance of Bilingual Typically Developing Children

To examine whether language dominance affects the performance of bilingual children without language impairment, we plotted the children's individual scores on SRT\_Tar and NWRT against

their LDI.14 As illustrated in **Figure 5**, it emerges that language dominance strongly influences performance of the typical bilingual children in SRT\_Tar. On the other hand, just as Almeida et al. (2017) show for the French SRT, among the 20 L1-dominant children the majority, here 70% (14/20) score over 60% correct in SRT-Tar and 75% (15/20) score over 52.2% (see **Figure 5**). The five L1-dominant children who perform below a 52.2% cutoff are identified as **70**, **44**, **45**, **28**, and **29**.

At first glance (see **Figure 6**), language dominance may seem to influence performance on NWRT to the same extent as in SRT\_Tar: 6 out of 20 L1-dominant children perform below the *k*-means cutoff score of 63.5%. However, unlike in the SRT\_Tar, four of the latter six children perform almost at cutoff (≥61% correct) and all children perform above cutoff on the LD part of the task.15 **29**, who performs below cutoff on NWRT, scores above cutoff in the LD part. Only two L1-dominant children **50** and **28** had an overall score on NWRT < 61% due to poor performance on the LD part of the NWRT (**50**: 27.78% correct, **28**: 44%

14We chose only the measure with higher specificity for the SRT.

15 In this study, the results of both LI and LD parts of the NWRT are collapsed together. However, in cases of L1-dominant children, we verified that their scores on the LD part were above cutoff to exclude the potential effect of L1 dominance.

correct). This allows the conclusion that performance on the NWRT is less independent of language dominance than the SRT. Note also that among the balanced and German dominant children, only three score below the cutoff both in SRT\_Tar and in NWRT and these children are **71**, **76**, and **27**, whose status might have to be reanalyzed as will be discussed in Section "Discussion."

### DISCUSSION

### Summary

This study investigated the accuracy of two German LITMUS tasks, an NWRT and an SRT in the identification of language impairment in bilingual children. Both NWRT and SRT prove to have good sensitivity and specificity in monolinguals: NWRT (sensitivity = 91.7%, specificity = 90%) and SRT show 100% sensitivity and specificity for both scoring methods SRT\_Id and SRT\_Tar. The results for monolinguals clearly show that the tasks are well constructed and reliably identify SLI. The same can be said for bilingual settings. Especially, the fact that the results for the NWRT are more or less independent of language dominance makes it a valuable new tool for language assessment. The reduced specificity of the SRT for bilinguals is due to several factors that will emerge more clearly in a detailed discussion of the individual cases we highlighted in Sections "Overall Results on the LITMUS NWRT and SRT" and "Dominance As a Factor for the Performance of Bilingual Typically Developing Children." It was noteworthy that the same children were identified in several types of analyses as either being "underdiagnosed" by the SRT (**26**) or of being "overdiagnosed" (**71**, **27**, and **76**) by both SRT and NWRT. Moreover, L1-dominant children such as **70**, **44**, **45**, **28**, and **29** also performed under cutoff 52.2% in the SRT\_Tar.

In Section "Dominance As a Factor for the Performance of Bilingual Typically Developing Children," we already identified one factor of possible misdiagnosis in L2 tasks, namely, L1 dominance, see **Figure 5**. L1 dominance will then interact with other factors, which we discuss in the following. One possibility for a reduced diagnostic accuracy is that our final status assignment might have been too strict, see also Bossuyt et al. (2015) on the impact of clinical group definition on accuracy measures. Note that Armon-Lotem and Meir (2016) use L1 and L2 tests with global scores, but additionally rely on parental or teacher concern. Boerma et al. (2015) and Boerma and Blom (2017) rely on clinical referral, i.e., on L2 testing exclusively. Thordardottir (2015) recommends including measures from samples of spontaneous production in addition to norm-referenced L1 and L2 tests. Given these different methods for identifying the clinical population, we will discuss cases of possible misclassification by our procedure, drawing also on impressions from the samples of narratives we have at our disposal. Alternatively, and given that the SRT, and SRT\_Tar in particular, targets morphosyntactic skills, misclassification could arise because our procedure did not take into account selective impairments such as grammatical/syntactic SLI. This would mean that an individual child has been classified as BiTD, but is syntactically impaired, which arguably leads to poor performance in SRT\_Tar. Children who show impairments in phonology and lexicon, but not in morphosyntax, would have been classified as BiSLI, but will not necessarily perform poorly in the SRT. Finally, misclassification could arise if standardized tests are not reliable in certain constellations of bilingualism, such as heritage situations.16 In the discussion, we specifically address the problems arising from our strict procedure and the (non)-applicability of standardized L1 tests in heritage situations.

### Subgroups of SLI

Since our classifying procedure did not isolate subgroups of SLI, but clearly aimed at a broader definition, we first address this problem by discussing the cases revealed by the clustering for SRT\_Tar, see **Figure 4.** The BiSLI child in cluster A, **26**, was classified as BiSLI because of her scores in the L1 test, ELO-L, and because her lexical and phonological abilities were below norm in L2. Note that she was 9;1 years at the time of testing but she performed well below the norms for younger children (7;11) in the L1 test, in which her sentence production showed a slight impairment whereas her phonological production showed great deficits. For L2 testing, the lexical test is normed till 9;11, for phonology she was below norm of younger children and the LiSe-DaZ norms could be age adjusted as described in Section "Standardized L2 and L1 Tests." Her spontaneous L2-language sample did not evidence any of the characteristic markers of SLI. This indicates that she might not be syntactically impaired. This seems to be confirmed by her good score in SRT\_Tar. Not surprisingly, her score in SRT\_Id was below 41.25%, despite her age, since it involves recollection of vocabulary.

The same problem, namely, that the impairment might be selective, is exemplified by **27**, who is BiTD because of reasonable scores in L2 lexicon and phonology, but has clear problems in some areas of morphosyntax identified by the LiSe-DaZ, among them SVA. Since this domain does not receive a *t* value in the test, it is not included in our final evaluation procedure. **27** is a simultaneous bilingual, L2 dominant, clearly impaired in L1, and her spontaneous production in both languages confirms problems with morphosyntax. **27** performs low in the SRT in both measures as well as in the NWRT and hence may be selectively impaired, i.e., the final status allotted may be misleading.

To see whether we might have missed bilingual children with grammatical SLI, we first consider children who show poor L2 performance only in the LiSe-DaZ. These are **24**, **27**, and **28**. **27** was already discussed as a possible case of grammatical SLI. **28** is below cutoff in SRT\_Tar and in the NWRT. She is also language impaired by her L1 status and her parents voiced concern. In other words she would be a BiSLI child if we had included selective impairments. **24** does not show problems in any of the experimental tasks and is not L1 impaired.

To summarize: **27** and **28** might be cases of grammatical SLI who show poor performance in LiSe-DaZ, but also in the SRT. On the other hand, **26** might be a case of lexical/phonological impairment with good performance in the LiSe-DaZ and in SRT\_Tar.

Reevaluating L1-dominant children below cutoff (52.2%) by taking into account the possibility of selective impairments, leads to the following picture: **70**, **45**, and **29**17 have a very high L1-language index and score as typical children in their L1 tests. **45** also performs well in the NWRT, and **70** performs almost at cutoff on the task. This implies that L1 dominance explains the performance of **70** and **45**. **28** and **44**, however, score as language impaired in their L1 and are classified as impaired in one of the L2 tests applied here: **28** has morphosyntactic problems, **44** performs low in the lexical assessment. These children might therefore be selectively impaired.18 However, only **28** also performs low in the NWRT, whereas **44** performs above cutoff. **29** remains a problem since she performs low in all L2 tests as well as the experimental tasks whereas her L1 test puts her firmly among the typical children.

Interestingly, most of the children discussed above perform below cutoff not only in SRT, but also in NWRT: **71**, **76**, and **27** among the balanced and L2-dominant children, and **28** and **29** among the L1-dominant children.

### L1 Assessment in Heritage Situations

It is not surprising that some children who perform below cutoffs in the standardized L2 tasks (and also in the SRT and NWRT), nevertheless have a final status as BiTD because they did well in the L1 tests. Five of the 46 typical bilinguals would be diagnosed as language impaired by the L2 tests but are doing well in L1. Especially for L1-dominant children this might be expected. **29** is a case in point: all three German tests classified the child as impaired, so would NWRT and SRT. The child did perfectly well in the TEDIL, however. **71**, who is balanced according to the PaBiQ, seems to be a similar case but turns out to be a child whose final status might be reconsidered: **71** scored above the norms in lexical reception in the GOL-E, but would have been below the norm in the assessment of the lexicon provided by the PALPA-P, which exists for her age range. Recall that we decided to use GOL-E as lexical assessment for all Portuguese children because in this area there were age gaps in the norms for the PALPA-P, which does not apply to **71**. More surprising is the fact that even German dominant children who scored as impaired in the German tests sometimes do well in the L1 tasks as is the case of **76**. However, **76** performed only minimally above cutoff in the TEDIL. Both **76** and **71** would be classified as language impaired if their (L1 and L2) samples of spontaneous production had been included in the initial decision about final status.

If the results of the L1 tests are examined more closely, it is rather striking that 16 of the 46 bilingual typical children have an L1 diagnosis of impairment, whereas only 5 are so diagnosed by the L2 tests. Examining these numbers by dominance we see that among the 20 L1-dominant children, 6 would have been diagnosed as impaired by the L1 test (1 Arabic child, 3 Portuguese children, and 2 Turkish children). Among the 11 balanced

16Our full test battery included WM tasks and tasks measuring executive function. We measured forward digit span (FDS) and backwards digit span as WM measures. Preliminary regression analyses showed that FDS only explains a small portion of the variance in SR performance in typical bilinguals. Therefore, the possible influence of WM on the performance in these tasks is not further pursued here.

17With a 60% cutoff (**Figure 5**), **47** would also be below cutoff. This child is L1 dominant and performs below norms in L2-lexical skills but within norms in the L1 assessments and in the NWRT.

18**73** and **26** were classified as BiSLI by the strict criteria, and like **44**, do not show a morphosyntactic impairment, but are impaired in the lexicon in particular.

children, 2 would have been diagnosed as L1 impaired. Among the 15 German dominant children, 8 would have been diagnosed as L1 impaired (2 Arabic children, 5 Portuguese children, and 1 Turkish child). These figures point to problems with the applicability of the L1 tests, which, in turn, call in question the final BiSLI status.

There are multiple reasons for this situation. Heritage speakers growing up as simultaneous bilinguals often show differences to monolingual speakers in their adult performance. This seems to concern morphosyntax and lexicon more than phonology (Montrul, 2010; Rinke and Flores, 2014). Reasons for this situation have been sought in the fact that children who have been exposed to their L2 early or are simultaneous bilinguals are often subject to language attrition in their L1 or could be claimed to suffer from incomplete acquisition (Köpke et al., 2004; Montrul, 2008, 2010; Benmamoun et al., 2013). Moreover, the language of children growing up as Turkish/German bilinguals in Germany is special from several perspectives: They are often third generation heritage speakers and Immigrant Turkish in Germany has features (Schroeder and Dollnick, 2013) which count as clinical markers for SLI in Standard Turkish (see also Chilla and San, 2017). Finally, the L1 tests we chose might have other inherent problems: The TEDIL only has two global scores, which do not allow identifying language domains as specifically problematic. The version of the Portuguese PALPA-P that we used has been normed with only few children for some ages and subtests. It is a linguistically well-controlled test but the lack of norms in receptive vocabulary in crucial age ranges made it necessary to use a different test for assessment of the lexicon, the GOL-E. Some of the misdiagnosis may therefore be due to the specific language tests chosen here. The more fundamental problems seem to be the heritage situation and language attrition of L1 which has been shown to be particularly noticeable when L2 exposure is early, see Lein et al. (2017) for an analysis of heritage effects in the Portuguese bilinguals also investigated in this study.

If the 15 children dominant in their L2 German are considered, more than half of them (8) would have been classified as SLI if only tested in their L1, which is not surprising. In contrast, overdiagnosis due to the L2 tests did not occur. German dominant children with a final classification of BiTD (also considering L1) were all correctly classified as BiTD by the combination of the three norm-referenced tests used for classification. Of the 46 children with a final classification of BiTD there are only three children who would have been BiSLI if only the L2 had been considered. This seems to indicate that in heritage situations L2 tests are more reliable than L1 tests, which may have multiple reasons: the contact situation and the existence of immigrant varieties, language attrition, and possibly properties of the L1 tests. Incidentally, **29**, who remains a problematic case also after we reconsidered the status of the bilingual children, might highlight the problems with the Turkish test, see also Almeida et al. (2017).

### Reconsidering the Status of the Bilingual Children

Following the argumentation about (a) selective impairments and (b) possible problems with L1 tests in heritage situations, we suggest different criteria for the identification of the bilingual clinical group: We consider children as BiSLI if they have a selective L2 impairment, and score below norms in their L1 tests or show poor spontaneous production in both languages.19 These criteria still require an impairment in both languages of a bilingual child but would classify **71**, **76**, **27**, **28**, and **44** as BiSLI. Incidentally, two of these children had been in SLT when recruited (**27** and **28**) and the remaining three might be cases of underdiagnosis. Given the clustering shown by **Figures 2**–**4**, and the foregoing discussion of these particular cases, such a regrouping would clearly raise diagnostic accuracy for all measures (SRT\_Id, SRT\_Tar and NWRT).

Given that the grouping of children we presented in **Table 2** takes into account language dominance by adjusting the norms in standardized L1 and L2 tests, the discussion points raised above show that especially selective impairments should be taken into account when deciding on the status of a bilingual child and when considering the accuracy of a particular test, which might be targeting one language domain more than others. The heritage situation adds to the difficulty and the cases discussed suggest that norm adjustments for L1 might have to be reconsidered for heritage speakers.

### CONCLUSION

Our investigation of the German LITMUS NWRT and SRT has shown that both are well suited as tools for the identification of SLI in bilinguals. Both tasks clearly identify SLI in German monolinguals demonstrating that they target crucial phonological and syntactic areas and structures. In addition, both tasks can identify SLI in bilingual contexts. Since the construction of both tasks was guided by linguistic notions such as phonological or syntactic complexity and neither task primarily measures WM, this is a result relevant on the theoretical and the practical level.

Both tasks clearly measure linguistic abilities, the NWRT on the phonological side, the SRT in morphosyntax. The SRT was scored in two different ways: SRT\_Id as a measure includes all morphosyntactic but also all lexical errors, not cumulating them. SRT\_Tar scores only morphosyntax and, by concentrating on syntactic structures and not counting morphological errors such as case or gender if they do not change the structure aimed at, does not penalize bilingual children and seems a good measure of (morpho)syntactic abilities. From the practical point of view, the possibility of using both or one of these scoring methods allows fine-grained diagnosis of the impaired domains. Concentrating on certain structures such as those involving Wh-movement with and without embeddings or intervention (see The German LITMUS SRT) would give an even more detailed picture but was not the focus of this study.

Our evaluation of the LITMUS tools started with rather strict criteria as to the status of a bilingual child as typical or language impaired. We classified a child as BiSLI only if the

19We do not apply any formal measure here but judge production by certain markers: correct SVA, sentence bracket or V2 and presence of embeddings.

child scored below (adjusted) norms in two domains of both her L1 and her L2. For this categorization, and also for further evaluation of our results, see Section "Dominance As a Factor for the Performance of Bilingual Typically Developing Children," the parental questionnaire, the PaBiQ, was an indispensable tool. We concentrated on the language dominance value, which allowed adjusting the norms for standardized tests and helped us in the interpretation of our results on the performance in the LITMUS NWRT and SRT. It emerged that performance in the NWRT is largely independent of language dominance, whereas it influences performance in the SRT. However, 75% of the L1-dominant children performed above the cutoff in the SRT when scored as SRT\_Tar, so that accuracy remains satisfactory. Similar findings are reported in Almeida et al. (2017) for the corresponding French tasks and in Grimm and Hübner (in press) for the German NWRT. Interestingly, LoE does not influence performance in the NWRT either, as reported in Grimm and Hübner (in press). 20

Considering individual cases and their performance in these new tasks revealed that the grouping we chose on the basis of standardized L1 and L2 tests might have missed cases of language impairment, which would not be surprising giving that this classification cut in half the group of children in SLT. We attributed such missed cases to either the problems of using and interpreting L1 tests (even with adjusted norms) in heritage contexts or to cases of selective language impairments. Clearly, interpretation of individual results for bilinguals is impossible without background information as provided by parental questionnaires.

On the practical level, this leads us to conclude that the LITMUS NWRT and SRT are indeed reliable tools that can be used as a first evaluation of a child's language abilities, singly, but better in combination. Since their administration takes only a fraction of the time that has to be invested for standardized tests, this is a good overall result. On the theoretical level, we have shown that L2 tasks, if linguistically well controlled and targeting complex structures, clearly identify language impairment in bilingual contexts.

### REFERENCES

### ETHICS STATEMENT

This study was carried out with the recommendation of the "Kommission für Forschungsfolgenabschätzung und Ethik" (Commission for the Evaluation of Research Consequences and Ethics) of the Carl-von-Ossietzky University of Oldenburg (ref. Drs. 21/16/2013) with written informed consent from all subjects. All subjects (or their parents) were informed that audio recordings were made and gave written consent in accordance with the Declaration of Helsinki. The protocol was approved by the Kommission für Forschungsfolgenabschätzung und Ethik of the Carl-von-Ossietzky University of Oldenburg.

### AUTHOR CONTRIBUTIONS

Both the authors CH and LA are fully responsible for all parts of the text and the analyses. LA collected most of the data and conducted its analysis.

### ACKNOWLEDGMENTS

This study was funded by DFG (German Science Foundation) grant HA 2335/6-1 to Cornelia Hamann. It is part of the BiLaD project with additional DFG grants to Monika Rothweiler and Solveig Chilla as well as an ANR (French Science Agency) grant to Laurice Tuller and her team. The authors thank all the investigators involved in the project for their continued advice and support, with special thanks to Tatjana Lein and Hilal San for collecting and analyzing the Portuguese/German and the Turkish/German data. Special thanks also go to Angela Grimm for sharing her German LITMUS NWRT and to Istvan Fekete for theoretical and practical support with the statistical analysis. The authors thank all the parents, educators, teachers, and speech language therapists for their cooperation and, last but not least, the authors particularly thank the children for their participation and their patience with us in completing the tasks.

### FUNDING

This work was funded by DFG (German Science Foundation) grant HA 2335/6-1 to CH, supplying funding for the PhD work of LA.

20 Since this factor contributes to our dominance calculation, we did not consider it separately.

Hamann, C., and Tuller, L. (2014). Genuine versus superficial relatives in French: the depth of embedding factor. *Revisita di Grammatica Generativa* 36, 146–181.

IBM SPSS 22. (2013). *IBM SPSS Statistics for Windows, Version 22.0*. Armonk, NY: IBM Corp.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Hamann and Abed Ibrahim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Verbal Working Memory Is Related to the Acquisition of Cross-Linguistic Phonological Regularities

Evelyn Bosma1,2,3 \*, Wilbert Heeringa1 , Eric Hoekstra1 , Arjen Versloot1,4 and Elma Blom5

1 Fryske Akademy, Leeuwarden, Netherlands, 2 Amsterdam Center for Language and Communication, University of Amsterdam, Amsterdam, Netherlands, 3 Leiden University Centre for Linguistics, Leiden University, Leiden, Netherlands, 4 Department of Modern Foreign Languages and Cultures, University of Amsterdam, Amsterdam, Netherlands, 5 Special Education Cognitive and Motor Disabilities, Department of Education and Pedagogy, Utrecht University, Utrecht, Netherlands

Closely related languages share cross-linguistic phonological regularities, such as Frisian -âld [O:t] and Dutch -oud [Aut], as in the cognate pairs kâld [kO:t] – koud [kAut] 'cold' and wâld [wO:t] – woud [wAut] 'forest'. Within Bybee's (1995, 2001, 2008, 2010) network model, these regularities are, just like grammatical rules within a language, generalizations that emerge from schemas of phonologically and semantically related words. Previous research has shown that verbal working memory is related to the acquisition of grammar, but not vocabulary. This suggests that verbal working memory supports the acquisition of linguistic regularities. In order to test this hypothesis we investigated whether verbal working memory is also related to the acquisition of cross-linguistic phonological regularities. For three consecutive years, 5- to 8-yearold Frisian-Dutch bilingual children (n = 120) were tested annually on verbal working memory and a Frisian receptive vocabulary task that comprised four cognate categories: (1) identical cognates, (2) non-identical cognates that either do or (3) do not exhibit a phonological regularity between Frisian and Dutch, and (4) non-cognates. The results showed that verbal working memory had a significantly stronger effect on cognate category (2) than on the other three cognate categories. This suggests that verbal working memory is related to the acquisition of cross-linguistic phonological regularities. More generally, it confirms the hypothesis that verbal working memory plays a role in the acquisition of linguistic regularities.

Keywords: bilingualism, cognates, verbal working memory, cross-linguistic phonological regularities, minority language

### INTRODUCTION

Closely related languages such as Frisian and Dutch share cross-linguistic phonological regularities (Sjölin, 1976; Rys, 2009; Taeldeman, 2013). These regularities connect a fixed sequence of phonemes in one language to another fixed sequence of phonemes in the other language. An example of such a regularity is Frisian -âld [O:t] and Dutch -oud [Aut], as in the cognate pairs kâld [kO:t] – koud [kAut] 'cold' and wâld [wO:t] – woud [wAut] 'forest'. However, not all cognate pairs follow a cross-linguistic regularity. For example, it is not the case that Frisian a- [a] as in amer [am@r] always corresponds to Dutch e- [ε] as in emmer [εm@r] 'bucket'. It is thought that

#### Edited by:

Maria Garraffa, Heriot-Watt University, United Kingdom

#### Reviewed by:

Jing Zhao, Capital Normal University, China Josh S. Payne, Bangor University, United Kingdom

> \*Correspondence: Evelyn Bosma<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 20 April 2017 Accepted: 17 August 2017 Published: 12 September 2017

#### Citation:

Bosma E, Heeringa W, Hoekstra E, Versloot A and Blom E (2017) Verbal Working Memory Is Related to the Acquisition of Cross-Linguistic Phonological Regularities. Front. Psychol. 8:1487. doi: 10.3389/fpsyg.2017.01487

bilingual speakers make use of cross-linguistic phonological regularities to relate the vocabulary of one language to the other and to quickly switch between languages (Sjölin, 1976; Rys, 2009; Taeldeman, 2013). However, as far as we know, there is no psycholinguistic evidence for this claim. Recent research, though, suggests that cross-linguistic phonological regularities do have a mental reality, as children seem to start using them as they grow older (Bosma et al., 2016).

In the present study, we investigated whether the acquisition of cross-linguistic phonological regularities is related to verbal working memory. This could not only give us more insight into the acquisition of these regularities themselves. As we will explain, it may also shed more light on the mechanisms that support language acquisition in general. In what follows, we will first describe our previous study (Bosma et al., 2016) in more detail, followed by a description of how the acquisition of crosslinguistic phonological regularities could be explained within Bybee's (1995, 2001, 2008, 2010) usage-based network model. It was not our intention to test this model or to make theoretical statements. Rather, we used the model as a framework to describe and interpret regularities within the lexicon in a comprehensible way. Within the network model, applied to a bilingual learning context, phonological regularities across languages are similar to grammatical rules within a language. As the acquisition of grammar, but not vocabulary is supported by verbal working memory (Gottardo et al., 1996; McDonald, 2008; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016), this suggests that verbal working memory supports the acquisition of linguistic regularities. If this is the case, then we would expect verbal working memory to be related to the acquisition of cognates with a cross-linguistic phonological regularity, but not to the acquisition of other types of cognates and non-cognates.

In a longitudinal study with three consecutive annual measurements, Bosma et al. (2016) tested 5- to 8-year-old Frisian-Dutch bilingual children on a Frisian receptive vocabulary task that comprised four cognate categories: (1) identical cognates, (2) non-identical cognates with a simple crosslinguistic phonological regularity (3) non-identical cognates without or with a more complex cross-linguistic phonological regularity, and (4) non-cognates. The results showed a gradual cognate facilitation effect for children with a low intensity of exposure to Frisian at home: the higher the degree of crosslanguage similarity, the better their performance. Furthermore, over time, the children with a low intensity of exposure to Frisian at home improved the most on non-identical cognates with a cross-linguistic phonological regularity. In the first and second year of the study, their performance on this type of cognates was comparable to their performance on non-identical cognates without such a regularity, whereas in the third year of the study, it was similar to their performance on identical cognates. This suggests that as they grow older, children become better at recognizing regularities between the Frisian and Dutch phonological systems.

The graduality of the cognate facilitation effect shows that a word in the input co-activates semantically and phonologically similar words in the other language depending on their degree of similarity. In fact, the spreading of activation in the bilingual lexicon is probably no different from the spreading of activation in the monolingual lexicon (Costa et al., 2005), which has also been shown to depend on the degree of phonological and semantic similarity between words (Gonnerman et al., 2007). This spreading of lexical activation as a function of similarity is the basis of Bybee's (1995, 2001, 2008, 2010) network model, which proposes that the lexicon is a complex network of linguistic items in which phonologically and semantically related words are stored as spatially proximate. In this model, it is argued that similarity-based categorization and analogy are two of the domain-general mechanisms that support language acquisition. As speakers categorize linguistic items for storage, so-called schemas arise. These are organizational patterns in the lexicon that capture phonological and semantic generalizations about linguistic items. For example, English past tense verbs with the allomorph /d/ are stored together because they have the same final consonant and share past-tense meaning. The connections between these past tense forms lead to the identification of the suffix. When a speaker creates novel items based on analogy to this schema, the past tense suffix becomes productive. In contrast to what is traditionally thought of as grammar, the generalizations that arise from schemas in the lexicon do not necessarily have a cognitive representation that is independent of the individual linguistic items that together form the schema. This means that there is no separate storage of the rule. Within Bybee's network model, grammar is not seen as a system that is separate from the lexicon [as in Pinker's (1991) dual-processing model or Ullman's (2004) declarative/procedural model], but rather as the structure that arises from the complex network of phonological and semantic relations within the lexicon.

As similarity-based activation of lexical items occurs both within (Gonnerman et al., 2007) and across languages (Dijkstra et al., 2010; Bosma et al., 2016), it can be assumed that phonologically and semantically similar words are stored closely together, regardless of whether they belong to the same or to a different language. Thus, the network model is not only able to account for regularities within a language, but also for regularities across languages. This suggests that cross-linguistic phonological regularities resemble grammatical rules, as they can both be thought of as generalizations that arise from schemas of phonologically and semantically related words.

Previous research has shown that grammar acquisition is related to verbal working memory (Gottardo et al., 1996; McDonald, 2008; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016). The precise cognitive architecture of the verbal working memory system is still under debate, but although different researchers work with different definitions (for an overview, see Cowan, 2016), most views support that it is used for both the temporary storage, also referred to as verbal short-term memory, and the processing of verbal information. Following Baddeley and Hitch (1974) and Baddeley (1986), verbal short-term is thus considered to be part of the larger verbal working memory system. Verbal short-term memory has been shown to play a role in children's first (L1) (Gathercole et al., 1992; Gathercole et al., 1997; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016) and second language (L2)

vocabulary acquisition (Cheung, 1996; Masoura and Gathercole, 2005; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016) as well as in children's L1 (Montgomery, 1995) and L2 grammar acquisition (French and O'Brien, 2008; Verhagen et al., 2015; Verhagen and Leseman, 2016). The processing component of verbal working memory is also argued to be important for children's L1 (Gottardo et al., 1996; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016) and L2 grammar acquisition (McDonald, 2008; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016), as has been shown by studies involving receptive grammar (Engel de Abreu and Gathercole, 2012), sentence repetition (Verhagen and Leseman, 2016), grammaticality judgment (Gottardo et al., 1996; McDonald, 2008) and inflectional morphology (Verhagen and Leseman, 2016). However, no relationship has been found between verbal working memory and vocabulary acquisition (Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016). This suggests that verbal short-term memory and verbal working memory are differentially associated with language learning. As both vocabulary and grammar are related to verbal short-term memory, it is argued that the storage component of verbal working memory is important for the development of stable phonological representations in long-term memory (Baddeley et al., 1998). After all, children can only transfer words and multiword units to long-term memory after they have first stored them in short-term memory (Speidel, 1993).

The observation that verbal working memory is related to the acquisition of grammar, but not vocabulary suggests that verbal working memory is important for the processing of linguistic regularities. In terms of Bybee's network model, this suggests that it plays a role in the formation of linguistic schemas through categorization and/or their productive use through analogy, a view that is supported by the finding that verbal working memory also plays a role in the categorization of non-linguistic items (Lewandowsky, 2011; Lewandowsky et al., 2012) and in non-linguistic analogical reasoning (Waltz et al., 2000).

In the current study, we investigated the hypothesis that verbal working memory is related to the acquisition of linguistic regularities. Although previous studies did not find a relationship between verbal working memory and the acquisition of vocabulary (Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016), we expected to find this relationship when the words follow a particular pattern. To this end, we investigated children's vocabulary acquisition in a bilingual context with two closely related languages that share cross-linguistic phonological regularities. We hypothesized that verbal working memory would support the acquisition of cognates that follow a cross-linguistic phonological regularity, but not the acquisition of other types of cognates and non-cognates. In order to answer this question, we used the longitudinal data from the 5- to 8-year-old children in our previous cognate study (Bosma et al., 2016) and investigated associations with verbal working memory, thereby controlling for verbal short-term memory (Engel de Abreu and Gathercole, 2012), SES (Rice and Hoffman, 2015), exposure (Pearson et al., 1997), non-verbal IQ (Rice and Hoffman, 2015) and age, which have previously been shown to be related to vocabulary learning.

### MATERIALS AND METHODS

### Participants

Participants were recruited by contacting primary schools in the countryside of the Dutch province of Fryslân. A total of 122 children from 14 different schools took part in the first year of our study (61 girls and 61 boys). Two children dropped out after the first wave of data collection, leaving 120 children in the second and third year of the study (61 girls and 59 boys). They were 5- or 6-years-old at time 1, 6- or 7-years-old at time 2 and 7- or 8-yearsold at time 3. **Table 1** provides an overview of participants' age, non-verbal IQ scores, socioeconomic status (SES) and intensity of exposure to Frisian at home. Non-verbal IQ was measured with the subsets Matrices and Recognition of the Wechsler Nonverbal Scale of Ability (WNV; Wechsler and Naglieri, 2006). Information about SES and intensity of exposure to Frisian at home were obtained through a parental questionnaire, based on the Questionnaire for Parents of Bilingual Children (PaBiQ) (Cost Action ISO804, 2011; Tuller, 2015). SES was calculated as the mean educational level of the father and the mother of the child, which was measured on a 1 to 9 scale, ranging from no education (1) to university degree (9). Intensity of exposure to Frisian was measured as the mean percentage of Frisian input the child received from his mother, father, siblings and other adults who looked after the child at least once per week. For each of these people the question had to be answered how often (s)he spoke Frisian to the child: 'never' (0%), 'seldom' (25%), 'sometimes' (50%), 'usually' (75%) and 'always' (100%). Intensity of exposure to Dutch at home was 100% minus intensity of exposure to Frisian at home. As SES and IQ (Rice and Hoffman, 2015) and exposure (Pearson et al., 1997) have been shown to be related to vocabulary learning we included these as control variables.

### Measurement Instruments Frisian Receptive Vocabulary

Frisian receptive vocabulary was measured with a task that was based on the Peabody Picture Vocabulary Test-III-NL (PPVT-III-NL; Schlichting, 2005), which is the Dutch version of the PPVT-III (Dunn and Dunn, 1997). Permission was obtained from the publisher to use this Frisian adaptation for research purposes. In this Frisian adaptation [see Bosma et al. (2016) for more details], only the first 144 words of the Dutch PPVT were used. These items suffice to test the vocabulary knowledge of

TABLE 1 | Descriptive characteristics of the participants.

Age, age in months; IQ, intelligence quotient; SES, socioeconomic status; % FR, intensity of exposure to Frisian at home.

the children in our age range. To make sure that all children completed all items, we did not use basal and ceiling criteria.

Words were assigned to four different cognate categories that differed with respect to degree of cross-language similarity: (1) identical cognates, such as Frisian poes [pus] and Dutch poes [pus] 'cat, (2) non-identical cognates that exhibit a simple phonological regularity between Frisian and Dutch, such as wâld [wO:t] – woud [wAut] 'forest', (3) non-identical cognates that do not exhibit a simple phonological regularity between Frisian and Dutch, such as Frisian amer [am@r] and Dutch emmer [εm@r] 'bucket' and (4) non-cognates, such as Frisian bern [bε:n] and Dutch kind [kInt] 'child'.

Category (2) comprised items that exhibit a regularity of one, two or three phonemes. An overview of all cross-linguistic phonological regularities of category 2 and some examples can be found in **Table 2**. The vast majority of the items in category (3) were cognates without a cross-linguistic regularity (34 items). Two items followed a more complex cross-linguistic regularity that involves four phonemes. In order to check if the outcomes, in particular differences between category 2 and category 3, were affected by these two items, analyses were run both with and without these items.

As a consequence of how we defined the cognate categories, there was a significant difference between the four categories regarding the number of phoneme differences between the Frisian and Dutch translation equivalents. F(3,140) = 93.47, p < 0.001, η 2 p = 0.67 (category 1: M = 0.00, SD = 0.00; category 2: M = 1.86, SD = 0.99; category 3: M = 2.92, SD = 1.25; category 4: M = 5.72, SD = 2.50). Pairwise comparisons showed that all differences between categories were significant at the p < 0.01 level. There were, however, no significant differences between the four cognate categories with respect to the number of phonemes per word, F(3,140) = 0.95, p = 0.42, η 2 p = 0.02 (category 1: M = 6.17, SD = 2.06; category 2: M = 6.75, SD = 2.63; category 3: M = 5.83, SD = 2.04; category 4: M = 6.17, SD = 2.60).

Furthermore, it was ensured that there were no word frequency differences between the four categories. The only available corpus for Frisian is a non-lemmatized database of standardized written language, which is not representative of the language that is spoken by speakers of Frisian (Breuker, 1993). Therefore, we used frequencies per million words from two Dutch corpora instead: CELEX (Center for Lexical Information, 1993), which is a corpus of written Dutch that was also used for the PPVT-III-NL, and Corpus Gesproken Nederlands ("Corpus Spoken Dutch"; CGN; Nederlandse Taalunie, 2004), which is a corpus of spoken Dutch. As Frisian and Dutch are closely related languages, the Dutch frequencies were thought to be representative of the Frisian frequencies. As frequency is perceived logarithmically, we calculated Zipf scores (Van Heuven et al., 2014), which are based on logarithmic (10-log) instead of absolute frequencies.

The four cognate categories each had about the same frequencies in CELEX and CGN, which was also confirmed by the high correlation between the CELEX and the CGN frequencies, r = 0.75, p < 0.001. A One-Way ANOVA with category as the independent variable and CELEX frequencies as the dependent variable showed that there was no significant effect of CELEX frequency, F(3,140) = 0.24, p = 0.87, and that the CELEX frequencies of category 1 (M = 3.82, SD = 0.92), category 2 (M = 3.85, SD = 1.39), category 3 (M = 4.04, SD = 1.22) and category 4 (M = 3.96, SD = 1.37) could be assumed to be the same. A One-Way ANOVA with category as the independent variable and CGN frequencies as the dependent variable showed that there was also no significant effect of CGN frequency, F(3,140) = 0.40, p = 0.76, and that the CGN frequencies of category 1 (M = 3.71, SD = 0.66), category 2 (M = 3.79, SD = 0.86), category 3 (M = 3.93, SD = 1.05) and category 4 (M = 3.85, SD = 0.99) could be assumed to be the same. Furthermore, Cronbach's alpha, as calculated at time 1, showed that the internal consistency of the items in the test was sufficient, α = 0.76.

#### Verbal Memory

Both verbal short-term memory and verbal working memory were measured, as this allowed us to separate the storage component of verbal working memory from the processing

component. Verbal short-term memory was measured with the Forward Digit Span and verbal working memory with the Backward Digit Span. These tasks were based on the Alloway Working Memory Assessment (AWMA; Alloway, 2012) and translated to Dutch. It was assumed that all children were able to count to 10 in Dutch, since Dutch is the main language of education and all children had spent at least 1 year in education at the first time of testing. In the forward version of the task, children had to repeat sequences of digits in the same order, whereas in the Backward Digit Span, they had to repeat them in reversed order. The Forward Digit Span is considered a measure of verbal short-term memory, because it only requires the storage of the digits. The Backward Digit Span, in contrast, is considered a measure of verbal working memory, because the added requirement to recall the digits in reversed order imposes a substantial processing load on the child (Alloway et al., 2008).

The task started with sequences of one digit, after which the sequences became increasingly longer. Per block, there were six trials and after three incorrect trials within one block the task stopped. When the child repeated the first four trials within one block correctly, he or she automatically continued with the next block and received a score of six. When the child repeated four out of the first five trials correctly, he or she also automatically continued with the next block and received a score of five. The AWMA procedure (Alloway, 2012) was applied for scoring. Trials were scored as incorrect if (part of) the sequence was incorrect, if children recalled one or more digits incorrectly, or if they omitted one or more digits. There were seven blocks for both the Forward and the Backward Digit Span, so the scores could range from 0 to 42.

### Procedure

The schools distributed consent forms and folders providing information about the experiment among the parents of the children. Children whose parents had signed the consent form were tested individually in a quiet room at school, except for one child at time 1, four children at time 2 and five children at time 3, who were tested at home. The children were tested by the first author and two research assistants, who all had a native level command of both Frisian and Dutch. The tasks in this study were part of a larger test battery that included language and cognitive tasks that are not reported on in the current study. Children were tested on all tasks at all three time points.

## RESULTS

### Descriptive Statistics

Means and standard deviations for the Forward Digit Span, the Backward Digit Span and the four cognate categories are given in **Table 3**. Repeated measures ANOVAs showed that over time, children improved on all measures, p < 0.001. Bivariate correlations among all variables at time 1, 2 and 3 are reported in the **Appendix**.

### Mixed Models Analysis

The research question of the current study was whether verbal working memory is related to the acquisition of cross-linguistic phonological regularities. We investigated this research question by examining whether the Backward Digit Span (verbal working memory) had a stronger effect on vocabulary items from cognate category (2) than on vocabulary items from cognate category (1), (3) and (4). In order to answer the research question we used a cumulative link mixed model. The mixed model was run, using the clmm function as implemented in the R package ordinal (Christensen, 2015). We entered Frisian receptive vocabulary accuracy as the ordered dependent variable, with 1 indicating a correct answer and 0 indicating an incorrect answer. We included random intercepts for subject and item, as both of these variables had repeated values. Including random intercepts would allow us to generalize the outcomes to the larger population of Frisian-Dutch bilingual children and to other items. A manual stepwise model selection procedure was carried out in which factors were added in such a way that the Akaike Information Criterion (AIC) was minimized. This procedure was applied with Category and Backward Digit Span as the main predictors of our study. In addition, the following predictors were added as control variables: Time, Frisian exposure at home, SES, IQ, Age and Forward Digit Span (verbal short-term memory). Time was added as an ordered factor, with 1 < 2 < 3. All of the predictors, except for Category, improved the model fit and were thus included in

Category 1 = identical cognates; category 2 = cognates with a simple rule; category 3 = cognates without a simple rule; category 4 = non-cognates.

the final model. As expected, higher scores on exposure, SES, non-verbal IQ, age and Backward Digit Span were related to better performance on Frisian receptive vocabulary. Time was not a significant predictor, but was added to the final model, as the AIC showed that it did improve the fit. Furthermore, it must be noted that the Forward Digit Span was only significant when the Backward Digit Span was not included in the model.

The model was further refined in an exploratory way by adding potential interactions between the predictors, including Category. This was done in order to increase the amount of explained variance, which would give a better focus on the variables of interest. Interactions between Category and Exposure, Category and Forward Digit Span, and Category and Backward Digit Span significantly improved the model fit and were therefore included in the final model. Models with threeway interactions including Time did not converge. In order to examine the interaction effects in more detail, the model was run four times with different reference levels for Category (1, 2, 3, and 4). We will first discuss the control interactions (Category × Exposure, Category × Forward Digit Span), followed by the interaction of interest (Category × Backward Digit Span). The interaction effect between Category and Exposure showed that the effect of Exposure on Frisian vocabulary was strongest for category (4), followed by category (3), category (2), and category (1) (4 > 3 > 2 > 1). The interaction effect between Category and Forward Digit Span showed that the effect of Forward Digit Span on Frisian vocabulary was significantly stronger for items from category (1) than for items from category (3) and (4), and stronger for items from category (2) than for items from category (4) (1 > 3, 4; 2 > 4). This shows that the effect of Forward Digit Span was stronger for items with a high degree of overlap across Frisian and Dutch than for items with a low degree of overlap, although the effect of Forward Digit Span on two adjacent categories was never significantly different. Finally, we examined the interaction effect between Category and Backward Digit Span, which was the focus of the current study. The results showed that the Backward Digit Span had a significantly stronger effect on vocabulary items from category (2) than on vocabulary items from category (1), (3) and (4). The differences between categories (1), (3) and (4) were statistically non-significant (2 > 1, 3, 4). The results of the final model are reported in **Table 4**, with category (2) as the reference level, as this category was the focus of our study. **Figure 1** shows the interaction effect between Category and Backward Digit Span. In this figure, it can be seen that the slope of category (2) is steeper than the slope of the other three categories.

As explained in the Method section, there were two items from category (3) that followed a more complex cross-linguistic phonological regularity. In order to check if these items affected the outcomes, the analyses described above were rerun without these items. The results showed that excluding these two items did not affect the outcomes.

We considered that the effect of Time, Exposure, SES, IQ, Age, Forward Digit Span, Backward Digit Span and Category may be different per subject and per item. Therefore, we added several combinations of these variables as random slopes to subject and item. We found that adding Age as random slope to subject and the factors Age, Time and Forward Digit Span

TABLE 4 | Fixed effects from the final model with Frisian receptive vocabulary accuracy as dependent variable and category 2 as reference level.

Category 1 = identical cognates; category 2 = cognates with a simple rule; category 3 = cognates without a simple rule; category 4 = non-cognates; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

as random slopes to item improved the model fit and slightly changed the results, with Time now being a significant predictor, p = 0.016. However, when we tried to rerun this model with the same random slopes but without the Backward Digit Span as a predictor, the model did not converge. The same problem occurred when we tried to rerun the model with the same random slopes but without the two items from category (3) that followed a more complex cross-linguistic phonological regularity.

### DISCUSSION

Previous research has shown that verbal working memory is related to the acquisition of grammar, but not vocabulary (e.g., Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016). This suggests that verbal working memory supports the acquisition of linguistic regularities. In the present study, we investigated this hypothesis by examining whether verbal working memory is also related to the acquisition of crosslinguistic phonological regularities, such as Frisian -âld [O:t] and Dutch -oud [Aut], as in the cognate pairs kâld [kO:t] – koud [kAut] 'cold' and wâld [wO:t] – woud [wAut] 'forest'. In order to answer this question, 5- to 8-year-old Frisian-Dutch bilingual children were tested annually for a 3-year period on verbal working memory and a Frisian receptive vocabulary task with four cognate categories: (1) identical cognates, (2) non-identical cognates that either do or (3) do not exhibit a phonological regularity between Frisian and Dutch, and (4) non-cognates. As age, non-verbal IQ (Rice and Hoffman, 2015), exposure (Pearson et al., 1997), SES (Rice and Hoffman, 2015) and verbal short-term memory (Engel de Abreu and Gathercole, 2012) have previously been shown to be related to vocabulary acquisition, these were also measured and included as control variables.

In line with previous studies, the results showed significant main effects of age, SES, non-verbal IQ and exposure on Frisian receptive vocabulary, with higher scores on these variables resulting in better vocabulary scores. Verbal short-term memory was only significant when verbal working memory was not included in the model. When a model was run that included both verbal short-term memory and verbal working memory, only verbal working memory came out as a significant predictor. This is probably due to the fact that, according to some definitions (Baddeley and Hitch, 1974; Baddeley, 1986), verbal short-term memory is part of verbal working memory. In addition to these main effects, we found interaction effects between cognate category and exposure, cognate category and verbal short-term memory, and cognate category and verbal working memory. As the first two interactions were only added as control variables to improve the model, we will not discuss these here, but instead concentrate on the interaction between cognate category and verbal working memory, which was the focus of the current study. The interaction between cognate category and verbal working memory showed that verbal working memory had a significantly stronger effect on cognate category (2) than on cognate category (1), (3) and (4). This suggests that verbal working memory supports the acquisition of regularities across the Frisian and Dutch phonological systems.

The finding that verbal working memory supports the acquisition of cross-linguistic phonological regularities is noteworthy for the following reasons. First, it provides psycholinguistic evidence for the existence of cross-linguistic phonological regularities (Sjölin, 1976; Rys, 2009; Taeldeman, 2013). Second, it confirms that bilingual children learn these regularities (Bosma et al., 2016) by showing that they do so on the basis of a general cognitive capacity, namely verbal

working memory. Third, the results suggest that the acquisition of phonological regularities across languages shares important characteristics with the acquisition of grammatical rules within a language, which has previously been shown to be related to verbal working memory (Gottardo et al., 1996; McDonald, 2008; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016). Fourth, as both the acquisition of grammar and the acquisition of cross-linguistic phonological regularities are related to verbal working memory, this suggests that verbal working memory plays a role in the acquisition of linguistic regularities.

These results can well be explained within the framework of Bybee's (1995, 2001, 2008, 2010) network model, although we do not exclude the possibility that other models may also fit the data. As Costa et al. (2005) already mentioned, the spreading of activation within the bilingual lexicon (Dijkstra et al., 2010; Bosma et al., 2016) is similar to the spreading of activation within the monolingual lexicon (Gonnerman et al., 2007). Within the network model, this implies that related words are stored together, regardless of whether they belong to the same or to a different language. This suggests that the acquisition of phonological regularities across languages shares important characteristics with the acquisition of grammatical relations within a language, as they are both generalizations that emerge from schemas of phonologically and semantically related words. Our finding that the acquisition of cross-linguistic phonological regularities is related to verbal working memory supports this suggestion, as previous research has shown that the acquisition of grammar is also related to verbal working memory (Gottardo et al., 1996; McDonald, 2008; Engel de Abreu and Gathercole, 2012; Verhagen and Leseman, 2016). In terms of Bybee's network model, this parallel between crosslinguistic regularities and grammar suggests that verbal working memory plays a role in the formation of linguistic schemas through categorization and/or their productive use through analogy, a view that is in line with previous evidence that verbal working memory also plays a role in the categorization of non-linguistic items (Lewandowsky, 2011; Lewandowsky et al., 2012) and in non-linguistic analogical reasoning (Waltz et al., 2000).

There are a number of limitations to the present study that are relevant to mention. First, although we only investigated the role of verbal working memory in the acquisition of crosslinguistic phonological regularities, other cognitive skills might play a role as well. An example of another skill that may influence the acquisition of cross-linguistic phonological regularities is phonological awareness, which is the conscious ability to detect and differentiate between the sounds of a word and to manipulate phonemes to create new words. Previous research has shown that phonological awareness positively influences reading and spelling acquisition, because children with high phonological awareness skills are better able to identify and use lettersound correspondences (Ehri et al., 2001). In the same way, phonological awareness might help children to identify and use correspondences between the phonological systems of two languages.

A second limitation of the current study is that we investigated the acquisition of cross-linguistic phonological regularities in general, without zooming in on differences that might exist between different types of regularities. Within the network model, it is argued that the productivity of a regularity is to a large extent determined by its type frequency, that is, the number of items that follow that regularity. The more items a schema encompasses, the stronger it is, and the higher the likelihood that the pattern will be extended to novel items. Type frequency interacts with degree of schematicity, that is, the degree of dissimilarity of the members of a class. Highly schematic classes include a wide range of dissimilar items. For example, the English past tense has a high degree of schematicity, as it can be applied to all verbs, no matter their phonological form. In the network model, it is argued that a high type frequency in combination with a high degree of schematicity results in a maximally productive construction. For future research, it would be interesting to examine to what extent the acquisition of cross-linguistic phonological regularities depends on type frequency and degree of schematicity and whether type frequency and schematicity interact with verbal working memory.

Taken together, the main finding of this study is that verbal working memory is related to the acquisition of cross-linguistic phonological regularities. This supports the hypothesis that verbal working memory plays a role in the acquisition of linguistic regularities, thus providing more insight into the mechanisms that facilitate language acquisition.

### ETHICS STATEMENT

All the parents of the participating children gave their written informed consent, as was stated in the Section "Materials and Methods" of our paper. Unfortunately, the study was not officially evaluated by an ethics committee before the start of the study due to a miscommunication. In hindsight, the ethics committee of the University of Amsterdam evaluated the information folder and the informed consent form that we used and came to the conclusion that the research had been conducted with the wellbeing of the participants in mind.

### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

### FUNDING

This work was supported by the Province of Fryslân and by the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 613465.

### REFERENCES

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longitudinal study. Dev. Psychol. 28, 887–898. doi: 10.1037/0012-1649.28. 5.887

learners. J. Exp. Child Psychol. 141, 65–82. doi: 10.1016/j.jecp.2015. 06.015

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Bosma, Heeringa, Hoekstra, Versloot and Blom. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## APPENDIX

TABLE | Bivariate correlations among all variables at Time 1.

fpsyg-08-01487 September 8, 2017 Time: 19:40 # 11

Cat1 = identical cognates; cat2 = cognates with a simple rule; cat3 = cognates without a simple rule; cat4 = non-cognates; % FR = intensity of exposure to Frisian at home; FW DS, Forward Digit Span; BW DS, Backward Digit Span; ∗p ≤ 0.05; ∗∗p ≤ 0.01; ∗∗∗p ≤ 0.001.

TABLE | Bivariate correlations among all variables at Time 2.

Cat1 = identical cognates; cat2 = cognates with a simple rule; cat3 = cognates without a simple rule; cat4 = non-cognates; % FR = intensity of exposure to Frisian at home; FW DS, Forward Digit Span; BW DS, Backward Digit Span; ∗p ≤ 0.05; ∗∗p ≤ 0.01; ∗∗∗p ≤ 0.001.

Cat1 = identical cognates; cat2 = cognates with a simple rule; cat3 = cognates without a simple rule; cat4 = non-cognates; % FR = intensity of exposure to Frisian at home; FW DS, Forward Digit Span; BW DS, Backward Digit Span; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

# How Does L1 and L2 Exposure Impact L1 Performance in Bilingual Children? Evidence from Polish-English Migrants to the United Kingdom

Ewa Haman1 \*, Zofia Wodniecka2 \*, Marta Marecka2 , Jakub Szewczyk2 , Marta Białecka-Pikul3 , Agnieszka Otwinowska4 , Karolina Mieszkowska1 , Magdalena Łuniewska1 , Joanna Kołak1 , Aneta Mi ˛ekisz1 , Agnieszka Kacprzak1 , Natalia Banasik1 and Małgorzata Forys-Nogala ´ 1

#### Edited by:

Maria Teresa Guasti, University of Milano-Bicocca, Italy

#### Reviewed by:

Maja Roch, University of Padua, Italy Vicky Chondrogianni, University of Edinburgh, United Kingdom

#### \*Correspondence:

Ewa Haman<EMAIL_ADDRESS>Zofia Wodniecka<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

Received: 20 April 2017 Accepted: 09 August 2017 Published: 04 September 2017

#### Citation:

Haman E, Wodniecka Z, Marecka M, Szewczyk J, Białecka-Pikul M, Otwinowska A, Mieszkowska K, Łuniewska M, Kołak J, Mi ˛ekisz A, Kacprzak A, Banasik N and Forys-Nogala M (2017) How Does L1 ´ and L2 Exposure Impact L1 Performance in Bilingual Children? Evidence from Polish-English Migrants to the United Kingdom. Front. Psychol. 8:1444. doi: 10.3389/fpsyg.2017.01444 1 Psycholinguistics Lab, Faculty of Psychology, University of Warsaw, Warsaw, Poland, 2 Psychology of Language and Bilingualism Lab, Institute of Psychology, Jagiellonian University, Krakow, Poland, 3 Early Child Development Psychology Laboratory, Institute of Psychology, Jagiellonian University, Krakow, Poland, 4 Institute of English Studies, University of Warsaw, Warsaw, Poland

Most studies on bilingual language development focus on children's second language (L2). Here, we investigated first language (L1) development of Polish-English early migrant bilinguals in four domains: vocabulary, grammar, phonological processing, and discourse. We first compared Polish language skills between bilinguals and their Polish non-migrant monolingual peers, and then investigated the influence of the cumulative exposure to L1 and L2 on bilinguals' performance. We then examined whether high exposure to L1 could possibly minimize the gap between monolinguals and bilinguals. We analyzed data from 233 typically developing children (88 bilingual and 145 monolingual) aged 4;0 to 7;5 (years;months) on six language measures in Polish: receptive vocabulary, productive vocabulary, receptive grammar, productive grammar (sentence repetition), phonological processing (non-word repetition), and discourse abilities (narration). Information about language exposure was obtained via parental questionnaires. For each language task, we analyzed the data from the subsample of bilinguals who had completed all the tasks in question and from monolinguals matched one-on-one to the bilingual group on age, SES (measured by years of mother's education), gender, non-verbal IQ, and short-term memory. The bilingual children scored lower than monolinguals in all language domains, except discourse. The group differences were more pronounced on the productive tasks (vocabulary, grammar, and phonological processing) and moderate on the receptive tasks (vocabulary and grammar). L1 exposure correlated positively with the vocabulary size and phonological processing. Grammar scores were not related to the levels of L1 exposure, but were predicted by general cognitive abilities. L2 exposure negatively influenced productive grammar in L1, suggesting possible L2 transfer effects on L1 grammatical performance. Children's narrative skills benefitted from exposure to two languages: both L1 and

**138**

L2 exposure influenced story structure scores in L1. Importantly, we did not find any evidence (in any of the tasks in which the gap was present) that the performance gap between monolinguals and bilinguals could be fully closed with high amounts of L1 input.

Keywords: bilingual children, L1 acquisition, migrant children, Polish-English bilinguals, home language, minority language, language exposure, language input

### INTRODUCTION

fpsyg-08-01444 August 31, 2017 Time: 17:9 # 2

Many studies examining early bilingualism in migrant populations focus on the development of the majority language1 (i.e., L2, e.g., Gutiérrez-Clellen et al., 2008; Paradis, 2009; Chondrogianni and Marinis, 2011, 2012; Verhoeven et al., 2011; Hoff et al., 2012). This is because proficiency in the majority language is a prerequisite of success in education (e.g., Strand et al., 2015) and on the job market in the new country (e.g., Shields and Price, 2004; Guven and Islam, 2015). One exception to the predominance of studies on L2 is research on heritage language speakers, conducted mostly in the North American context (e.g., Montrul, 2008; Rothman, 2009; Montrul and Ionin, 2010). A heritage language is understood as "a language spoken at home or otherwise readily available to young children," but not dominant in the larger society (Rothman, 2009, pp. 156), i.e., it is defined in the same way as we define a minority language in the current paper. While there are many studies on grammatical performance of heritage speakers in L1 (e.g., Polinsky, 2008; Rothman, 2009), there are only a few studies on other aspects of heritage language such as vocabulary and phonology (e.g., Montrul, 2010). Moreover, few of the heritage speaker studies concentrated on the language acquisition process in children (e.g., Montrul, 2008; Polinsky, 2011), but rather on its outcomes in adulthood (for discussion see Rothman, 2009; Rothman and Treffers-Daller, 2014). Overall, although research shows that maintaining the minority language (L1) is of great importance for both well-being of an individual (Portes and Hao, 1998; Yu, 2013; De Houwer, 2015) and for language preservation at the community level (Potowski, 2013), only a few studies have thoroughly examined the development and maintenance of children's L1 (Rodríguez et al., 1995; Winsler et al., 1999; Gathercole and Thomas, 2009).

We aim to fill this gap by investigating L1 developmental patterns in migrant children raised bilingually. We focus on 4–7 year old Polish-English migrant children living in the United Kingdom. The choice of this particular language group was driven by an unprecedented influx of Poles to the United Kingdom since Poland joined the EU in 2004. The Polish community in the United Kingdom has now reached one million (White, 2011; Kułakowska, 2013), and each year c.a. 25,000 children are born to Polish families (Office for National Statistics [ONS], 2014). This offers an opportunity for systematic and large-scale research on bilingual language development in Polish children, a community that, to our knowledge, has not been thoroughly addressed in the existing research. Although migrant communities of similar sizes exist in other countries, this one seemed especially appropriate for the purpose of studying bilingual language development because of the characteristics of this wave of Polish migration to the United Kingdom. The group, unlike most migrant communities studied so far, does not comprise exclusively unskilled workers with low socio-economic status (SES), which might bias the result. A majority of postaccession migrants from Poland to the United Kingdom were people with secondary education, many of them also holding academic degrees. Also, they were mostly young adults, often bringing young children with them or having children while staying in the United Kingdom (Okólski and Salt, 2014). For this reason, in the current paper, we take a "snapshot" of this new bilingual population and compare the bilinguals' home language performance to that of their Polish-speaking monolingual peers raised in Poland. We also looked at the age-related differences in the two groups. We aimed to establish to what extent the bilingual migrant children and their monolingual peers in the home country differ in their L1 abilities across four domains of language, i.e., vocabulary, grammar, phonological processing, and discourse. Moreover, our goal was to examine how language experience (in both L1 and L2) influences L1 performance of bilinguals, while controlling the sources of variance related to their general cognitive abilities and socioeconomic status.

### Bilingual vs. Monolingual Language Development

Studies focusing on L2 development in bilinguals demonstrate that bilingual children lag behind their monolingual peers in most aspects of language processing, often scoring similarly to monolinguals with specific language impairment (SLI; Kohnert et al., 2009; Ebert and Kohnert, 2016). Studies investigating L1 in bilinguals offer less conclusive results (e.g., Umbel and Oller, 1994; Winsler et al., 1999), but many indicate a performance gap between bilinguals and their monolingual peers (e.g., Fabiano-Smith and Barlow, 2010). Indeed, research on adult heritage speakers indicates that literacy and formal education in the majority language (L2) often results in the incomplete heritage (L1) language acquisition (Montrul, 2008). As Sorace (2005) points out, this is because the language input heritage speakers receive varies in terms of quality, as heritage speakers are exposed to the input in the minority language mostly from their parents, whose language may have already attrited. However, the differences between monolingual and bilingual children should not conceal similarities between the two developmental paths.

1Throughout the paper, we use the term, "majority language" or L2 for the language of the country where the bilingual children of the migrants live (in the case of this study – English). We use the term "minority language," "home language" or "L1" for the language used by at least one the child's parents (in the case of this study – Polish).

Several studies suggest that bilinguals achieve the developmental milestones (defined as the age when the child begins acquiring a particular language skill) roughly at the same time as monolinguals do. This is true for lexical development (Pearson et al., 1993; Hoff et al., 2012), grammatical development (Paradis and Genesee, 1996; De Houwer, 2005; Genesee and Nicoladis, 2007; Paradis, 2009) and phonological development (Fabiano-Smith and Barlow, 2010). For example, both bilinguals and monolinguals utter their first words around the age of one, and have similarly sized vocabulary and phonological inventory, when both languages of the bilingual are taken into consideration (Fabiano-Smith and Barlow, 2010; Hoff et al., 2012). There is also evidence that the abilities to produce coherent discourse do not differ between bilinguals and monolinguals of a comparable age (e.g., Paradis and Kirova, 2014). In other words, there are both similarities and differences between monolingual and bilingual developmental paths. Research findings suggest that the bilingual development has its own specificity, and that monolingual norms should not be applied to bilingual speakers (Gathercole, 2013a,b; Armon-Lotem et al., 2015).

In the subsequent sections, we briefly review the literature related to the bilingual development in the four language domains that are the focus of our study: vocabulary, grammar, phonology, and discourse. For each language domain, we address two critical issues: the differences between bilingual children and their monolingual peers, and the impact of language exposure on performance in each of the language domains in L1 and L2.

### Vocabulary

Studies examining L2 vocabulary in bilingual children consistently report that bilinguals lag behind their monolingual peers on both receptive tasks (Bialystok et al., 2010; Verhoeven et al., 2011) and productive tasks (Uccelli and Páez, 2007). Some studies even find typically developing bilingual children to have smaller receptive vocabularies in L2 than monolinguals with SLI (Verhoeven et al., 2011).

In terms of L1 vocabulary size, some studies suggest that bilingual children raised in the migrant setting are disadvantaged (e.g., Pearson et al., 1997; Uccelli and Páez, 2007). Other studies indicate that L1 vocabulary in bilinguals is not affected negatively, either in the receptive tasks (Umbel and Oller, 1994; Winsler et al., 1999; Leseman, 2010), or in the productive tasks (Leseman, 2010). Thus, the results are inconclusive and they should be treated with caution, since the majority of L1 vocabulary studies compared children's lexical acquisition between the two languages of bilinguals, and did not compare bilinguals' L1 scores to the vocabulary scores of a matched monolingual group.

The observed discrepancy in the results on L1 vocabulary in bilinguals may stem from methodological issues (e.g., the lack of well-matched control groups), but also from the variability in exposure to languages. Previous research indicates that L1 vocabulary size is closely connected to the reported amount of L1 exposure, while L2 vocabulary size is related to exposure to L2 (Pearson et al., 1997; Vermeer, 2001; Patterson, 2002; De Houwer, 2007; Thordardottir, 2011; Hoff et al., 2012; Hoff and Core, 2013). This relationship is especially strong for the productive vocabulary. For example, in a study on English-French bilingual children in Canada, the participants with equal amounts of exposure to L1 and L2 had native-like scores in a receptive vocabulary task, but not in a productive vocabulary task. To perform on par with the monolinguals in the productive vocabulary task, the children needed to have more exposure in the language tested (Thordardottir, 2011). Moreover, Pearson et al. (1997) established the 20% threshold hypothesis – they claim that children who hear less than 20% of their input in a given language are often unwilling to speak that language. In line with this hypothesis, Hoff et al. (2012) suggests that 20% is an absolute minimum of input for a child to be able to use a language. Studies on heritage speakers also suggest that vocabulary in L1 is affected by both the amount and quality of input in L1 (Schwartz, 2008).

Overall, the current literature indicates that bilingual children have significantly lower vocabulary scores in L2, compared to their monolingual peers, while the findings regarding L1 vocabulary are inconclusive. In general, the amount of exposure seems to be crucially linked to vocabulary performance of the bilingual children, especially in language production.

#### Grammar

The studies examining specific areas of grammar in bilingual development show mixed results. On the one hand, some reported that bilinguals acquire certain structures in L2 (e.g., such as finite verb forms, Paradis and Genesee, 1996) just like their monolingual peers, especially when L2 is their dominant language (see De Houwer, 2005; Conboy and Thal, 2006; Genesee and Nicoladis, 2007; Parra et al., 2011). Still, many studies suggested that bilingual children perform worse than monolinguals on L2 grammar tasks, for example the ones examining the application of tense morphology (see Hoff et al., 2012). The bilingual disadvantage seems to be smaller for the receptive than productive tasks (Verhoeven et al., 2011; Chondrogianni and Marinis, 2012). Moreover, the majority of grammatical errors reported in studies on bilingual acquisition appear to be developmental errors (for review see Paradis, 2009). As for global L2 grammar measures, including the Sentence Repetition task (SRep; see Marinis and Armon-Lotem, 2015), which involves verbatim repetitions of sentences with various grammatical structures in the target language, bilingual children usually score lower than monolinguals (Verhoeven et al., 2011; Komeili and Marshall, 2013; Thordardottir and Brandeker, 2013). When it comes to grammatical systems of L1 in the minority speakers, they are often simplified as regards the development of certain grammatical structures (see Benmamoun et al., 2013; Scontras et al., 2015). Bilingual children can also score lower on L1 holistic grammatical assessment tasks such as the SRep, especially if they did not have much exposure to that language (Thordardottir and Brandeker, 2013). The areas of L1 grammar that appear to be particularly problematic include agreement morphology (e.g., Bolonyai, 2007; Montrul and Potowski, 2007; Polinsky, 2008; Gathercole and Thomas, 2009), overusing rigid word order patterns (e.g., Isurin and Ivanova-Sullivan, 2008), or applying and interpreting longdistance binding (e.g., Polinsky, 2006; Kim et al., 2009). However, since many of these accounts come from studies on older participants than preschool children, it is necessary to further

investigate at which point in development those alternations in syntax emerge (Polinsky, 2016). In a study focused specifically on child minority language, Montrul and Potowski (2007) investigated the acquisition of Spanish gender agreement in school-aged heritage speakers of Spanish enrolled in a dual Spanish-English immersion program. As evidenced by the data coming from an oral narrative task and a picture matching task, the heritage speakers scored lower than Spanish monolinguals but higher than the L2 learners in applying gender agreement rules to determiners and adjectives.

Overall, research indicates that poorer performance on L1 grammatical tasks might be related to impoverished or altered exposure to L1 or to the influence of the dominant community language (see Rothman, 2007; Gathercole and Thomas, 2009; Benmamoun et al., 2013; Scontras et al., 2015; Hoff et al., 2017). For example, in Spanish-English bilingual children, L1 exposure at home has been found to be related to scores in L1 (Spanish) grammaticality judgment task targeting the knowledge of gender marking and that-trace structures (Gathercole, 2002a,b). In Welsh-English bilinguals, home and school exposure to the L1 minority language (Welsh) correlated with children's receptive command of the syntactic patterns of Welsh gender marking and the use of word order cues in identifying subjects (Gathercole and Thomas, 2009). Montrul and Potowski (2007) observed that sequential bilinguals, who were first exposed exclusively to Spanish as an L1, and thus received more overall exposure in that language, outperformed simultaneous bilinguals in applying gender agreement rules to adjectives. The results showed that the development of certain aspects of L2 grammar may be affected by reduced exposure in early childhood.

There is also evidence suggesting that structures from the dominant language might be incorporated into the weaker language more often than the other way around (Döpke, 1998; Yip and Matthews, 2000). For instance, the effects of L2 exposure on the L1 minority language may affect some specific areas of L1 grammar, such as the use of overt versus null subjects (e.g., Paradis and Navarro, 2003), determiners (e.g. Kupisch, 2007; Montrul and Ionin, 2010) or inflectional morphology (see Benmamoun et al., 2013). However, it is often hard to disentangle the effects of L2 transfer from the effects of the reduced input in L1 (Scontras et al., 2015).

Overall, many studies suggest that bilingual children may experience developmental difficulties in the domain of morphosyntax in their non-dominant language, whether L1 or L2. Crucially, however, the gap between the performance of mono- and bilingual groups has been found to depend on the amount and type of exposure to the target language.

#### Phonology

Bilingual children can differ from their monolingual peers in terms of phonological development in L1 and L2 in three ways: delay, acceleration, and transfer. First, bilinguals might learn to produce some speech patterns (e.g., vowels, Kehoe, 2002; consonants, Goldstein and Washington, 2001; prosody, Lleó, 2002) later than monolinguals. Moreover, when tested in L2 on generalized phonological assessment measures such as English Diagnostic Evaluation of Articulation and Phonology (Dodd et al., 2002), bilingual children might obtain low scores, which in monolinguals would be typical for phonological delay (En et al., 2014). The delay in the acquisition of phonological features of L1 has also been reported (Goldstein and Washington, 2001), but not in all studies (Kehoe, 2002).

Secondly, bilinguals might acquire some phonological features in L2 faster than their monolingual peers. For instance, Polish-English bilinguals and Welsh-English bilinguals acquire complex consonantal clusters in English faster than their monolingual peers, most likely due to the fact that their L1 is rich in complex consonant clusters (Mayr et al., 2014; Tamburelli et al., 2015). To our knowledge, there have been no studies showing a similar effect for L1 in bilingual speech.

Thirdly, bilinguals might exhibit phonological transfer, i.e., pronounce the sounds in one language with the phonetic features of their other language. Phonological transfer between bilinguals' two languages may affect both prosodic patterns (Paradis, 2001) and segmental features (Fabiano-Smith and Barlow, 2010; Barlow, 2014) and can take both directions, i.e., from L1 to L2 and from L2 to L1 (Fabiano-Smith and Barlow, 2010; Fabiano-Smith and Goldstein, 2010; Marecka et al., 2016). Overall, while bilingual children do not have smaller phonological inventories than monolinguals, they tend to mix the phonological features of both languages (Fabiano-Smith and Barlow, 2010). Heritage language studies suggest that these tendencies might carry into adulthood of the bilingual speakers. L1 phonological features in the speech of adult heritage speakers such as vowel quality or VOT can shift toward L2-like values (Godson, 2004; Nagy and Kochetov, 2013), even though the L1 accent of these adult heritage speakers is reported to be more native-like than the accent of L2 learners of a particular language (Au et al., 2002; Oh et al., 2003).

Apart from testing for the ability to produce appropriate phonemes in the target language, several studies used the nonword repetition (NWR) task to study phonological processing in bilingual children. When the non-words used in the test are highly L1- or L2-like, they tend to measure the inventory of phonological representations of a child (Jones et al., 2010; Jones, 2011). Bilinguals perform worse than monolinguals on the NWR with L2-like non-words (Kohnert et al., 2006), sometimes even on par with monolinguals with SLI (Windsor et al., 2010). When tested in their L1 (and not L2) bilinguals tend to perform better (Gutiérrez-Clellen and Simon-Cereijido, 2010; Summers et al., 2010). When non-words are quasi language-universal, bilinguals perform similarly to their monolingual peers (Boerma et al., 2015).

Both phonological development and processing are influenced by the cumulative language exposure. Many studies of phonological development have reported that children who started acquiring L2 earlier (i.e., cumulatively had more exposure to L2) sound more native-like than children who started acquiring the language later (Asher and García, 1969; Snow and Hoefnagel-Höhle, 1977; Flege and Fletcher, 1992; Flege, 1995; Aoyama et al., 2008). Moreover, the phonological performance in both L2 and L1 is directly proportional to the exposure and use of a particular language (Flege, 2002). Phonological processing

(as measured with NWR) is also connected to the amount of exposure that bilinguals receive in the tested language (Summers et al., 2010), although to a smaller degree than vocabulary (Thordardottir and Brandeker, 2013).

### Discourse

In studies of discursive abilities, children are usually asked to narrate a story, often based on pictorial stimuli. Narrative data support the results from standardized tests by providing additional performance measures across the languages of the bilingual child (Iluz-Cohen and Walters, 2012). A measure usually taken into consideration here is the structural coherence of narratives, i.e., the story structure, which is subsequently assessed in terms of how well the child refers to the goals of the characters, the attempts to reach these goals and their outcomes (Gagarina et al., 2016; see also Stein and Glenn, 1979). Story structure scores go beyond the assessment of single words or sentences, but instead indicate the level of more complex cognitive and pragmatic abilities (Gagarina, 2016). Studies comparing the story structure of bilinguals in L2 or L1 with that of their monolingual peers are infrequent and their results are mixed. One study comparing L1 structural coherence in bilingual Finnish-Swedish children with that of Finnish monolinguals found no differences between the two groups of children (Kunnari et al., 2016). On the other hand, in a study comparing the performance in L1 Russian of Russian-Norwegian children to Russian monolinguals, the bilinguals scored lower on the story structure in their L1 (Rodina, 2016). The same pattern has also been observed in the studies on heritage speakers. In a case study by Polinsky (2008), two heritage speakers of Russian (a 9-year-old and a college student) were found to produce significantly shorter utterances and narrate at a slower pace than monolingual Russian speakers.

The effect of language exposure on children's narrative abilities is a complex issue. On the one hand, some findings suggest that the exposure to a particular language might not be crucial to narrating in that language. Most studies comparing bilingual children's narrative abilities in L1 and L2 indicate that the structure of narratives is relatively invariant across languages and that the measures of the story coherence in the child's two languages tend to be highly correlated (Muñoz et al., 2003; Fiestas and Peña, 2004; Uccelli and Páez, 2007; Gagarina, 2016; Kunnari et al., 2016). In general, children produce equally coherent stories in both languages, even if the child's linguistic abilities in terms of vocabulary or grammar in one of the languages are weaker (Gagarina, 2016). The finding that the story structure does not differ across the languages of a bilingual is probably related to the fact that the ability to tell coherent stories taps into the child's general knowledge about the world and thus seems to be relatively language-independent (Gagarina, 2016; Gagarina et al., 2016). This would indicate that language-specific exposure might not be crucial for developing narrative skills.

On the other hand, several studies point to the importance of language exposure, showing that the narrative structure in bilinguals might be better in L1 than in L2 (Kapalková et al., 2016; Roch et al., 2016). A study on L1 Russian narratives in Russian-Norwegian preschoolers suggests that the L1 story structure might be dependent on the amount of exposure to L1 (e.g., Rodina, 2016). Further, as indicated by Gagarina (2016), the strong positive correlations between the story structure in L1 and L2 cease to occur after several years of schooling in the majority language. Then, the stories told in the language of schooling become more coherent than those in the home language. This result suggests that the story structure, rather invariant across languages in young bilinguals, might be sensitive to explicit narrative teaching at school and to receiving large amounts of structured input and modeling in the majority language. Finally, several studies showed that older bilingual children produce more coherent stories than younger children (Bohnacker, 2016; Mavi¸s et al., 2016). This might be attributable to children's cognitive maturity, but also to the differences in language exposure.

To conclude, bilingual children's discursive abilities are rather under-researched in comparison with other aspects of language use, and the results of studies are not clear-cut. Some suggest that the narrative abilities of bilinguals might be influenced by exposure and modeling, especially at the later stages of education. However, the results of studies on the narrative abilities in bilingual preschool children suggest that producing coherent stories is an area where bilinguals and monolinguals might perform similarly, regardless of the L1 exposure.

### The Current Study

The literature review presented above reveals a rich body of research devoted to language acquisition in bilingual children. However, it is clear that despite the wealth of studies, many facets of bilingual language acquisition are still under-researched. The majority of studies focused on the L2 of bilinguals and only few examined their L1 and benchmarked it against a monolingual control group (e.g., Umbel and Oller, 1994; Thordardottir, 2011; Thordardottir and Brandeker, 2013). Moreover, only few studies investigated several different language measures on the same group of participants (Uccelli and Páez, 2007; Verhoeven et al., 2011; Thordardottir and Brandeker, 2013). Thus, there is certainly a need for large-scale investigations that would allow to obtain a comprehensive picture of differences in the linguistic performance between monolinguals and bilinguals by comparing them in different areas of language use. Also, a certain limitation of many previous studies is that they seldom controlled for language exposure in the bilingual group, despite the fact that this single variable can potentially explain many differences between monolinguals and bilinguals (Pearson et al., 1997; Thordardottir, 2011; Thordardottir and Brandeker, 2013). Finally, to our best knowledge, there are no studies which would examine the effect of language exposure on different language domains in child bilingual speakers, while controlling for potentially confounding variables such as short-term memory (STM) capacity, non-verbal IQ, or SES. Controlling these variables seems important, since research consistently indicates their crucial role in language development. STM capacity has been linked to the development of vocabulary (Gathercole et al., 1992) and both vocabulary and grammar (Verhagen and Leseman, 2016) in preschool children. Moreover, deficits in non-verbal IQ might be linked to language deficits (Botting, 2005) and SES might determine the overall language development (see Hoff, 2006 for a review; Hoff, 2013).

#### Measuring Language Exposure

fpsyg-08-01444 August 31, 2017 Time: 17:9 # 6

Although it is generally agreed that language exposure plays an important role in language acquisition, the construct is a matter of much controversy (Carroll, 2017). The term "language exposure" lacks an accurate definition and is measured in various ways (see Armon-Lotem, 2016; Carroll, 2017 for discussion). In the present paper, we are following Carroll (2017) and we define exposure as an observable and measurable contact with a particular language.

The quantification of language exposure has been a challenging task. To estimate exposure several related factors can be used: the intensity of contact with a given language (also as a function of the number of interlocutors available for a given language), the age of the first contact with the language, and the time spent while exposed to a particular language. Indirectly, also chronological age might be a contributing factor, because older children tend to have greater length of exposure to a given language in their lifetime. Ideally, all these factors should be disentangled and their contribution measured independently. However, because these predictors are highly correlated, doing so would require testing huge participant samples, and to the best of our knowledge, no study has accomplished this so far. The existing studies that controlled for one of these factors conceded that the other ones were left uncontrolled (e.g., Bedore et al., 2016). One way of solving this problem is to eliminate at least one factor, for example the Age of Acquisition, by testing populations that are exposed to both languages from birth (e.g., testing English-French in bilingual families in Montreal; Thordardottir, 2017). But even then, the contribution of the three other highly correlated variables remains to be controlled. A better way of addressing the problem is to circumvent it by creating one cumulative index that encompasses all the related factors. Such an approach was taken in a few recent studies (Unsworth, 2013; Unsworth et al., 2014; Vender et al., 2016) and it is also chosen in the present study. Such an index typically reflects the length of exposure to a language (from the age of the first contact to the time of testing), obtained from parental questionnaires. Specific approaches to exposure may differ in how exactly this information is elicited via background questionnaires. For example, Unsworth (2013) estimates the percentage of waking hours during which children were exposed to a particular language, in each year of their life. In the present study, we estimated the intensity of contact with Polish and English. We multiplied this estimation by the time before and after migration, respectively. The estimate of intensity of contact was based on the number of speakers at home when the language was used. Hence, our index of language exposure simultaneously reflects both the quantity and quality of exposure (i.e., the number of different speakers). In the methods section, we describe how our index of cumulative language exposure was constructed in more detail.

### Research Questions

Here, we present a comprehensive analysis of L1 performance in bilingual migrant children, as compared with their monolingual peers, with a number of factors controlled. We used six direct language measures to test over 200 typically developing children (including more than 80 bilinguals) aged 4;0 to 7;5. The measures included receptive and productive vocabulary, receptive and productive grammar (SRep), phonological processing (NWR), and narrative skills. What is more, in the current analyses, we assess the impact of exposure to both L1 and L2 on bilinguals' performance in each of the language domains.

Our analyses focused on the three main research questions:

### MATERIALS AND METHODS

### Participants

Overall, 173 bilingual children and 311 monolingual participants took part in the study. However, the analyses presented in the current paper were based on subsamples from both groups. In the analyses, we considered only those participants for whom we had a full data set necessary to control for the non-verbal intelligence (Raven's Colored Progressive Matrices; Raven, 2003; Jaworowska and Szustrowa, 2003), STM (forward digit span, Wechsler, 1974), and SES (background questionnaires). We excluded the children who had hearing problems (6 bilinguals, 3.5% of the bilingual sample; 9 monolinguals; 2.9% of the monolingual sample). Additionally, from the bilingual group we excluded the children who were effectively trilingual (15 children; 8.7% of the bilingual sample; see also Mieszkowska et al., 2017), from the monolingual group those who occurred to be bilingual (living in Poland, 3 children, 1% of the monolingual sample) and those at risk of SLI, as indicated by parental concerns reported in the questionnaires (4 bilinguals; 2.3% of the bilingual sample, 3 monolinguals, 1% of the monolingual sample). Eventually, data from 233 children (88 bilingual and 145 monolingual) were considered for further analyses. Seventy of the bilingual children who took part in the study had both Polish-speaking parents. Eighteen children lived in families with a Polish-speaking mother and a father speaking English at home (11 native English speakers and 7 non-native English speakers). All the bilinguals lived in the United Kingdom, but they varied in terms of the age of their first contact with English (M = 13 months, SD = 16 months). Fifty-five of them were first exposed to English within the first year of life (36 just after birth). Others had their first contact with English later (up to 60th month of life).

For each of the language measures reported in this paper, we conducted separate analyses on a subsample of children. The subsamples consisted of all bilingual children for whom we had the data on the task of interest and a group of monolinguals matched one-to-one to the bilingual group on age, SES (years of mother's education), gender, non-verbal IQ (Raven scores), and STM (as measured by forward digit span).

TABLE 1 |

Demographic

information

 and descriptive

 statistics for background

 measures in the participant subsamples.

The matching procedure served to ensure that any differences between the groups can be attributed to language status (bilingual or monolingual), and not to other factors known to affect the performance in the tasks of interest, such as environmental differences related to SES (see Hoff, 2006; Qi et al., 2006; Hoff and Core, 2013), or children's cognitive abilities (see Kail, 2000). The characteristics of the overall sample and the task-specific subsamples are presented in **Table 1**.

### Materials and Procedures

#### Tasks

The testing battery included six published normed tests or their non-normed adaptations, six experimental tasks used in previous research, six language tasks designed as a part of the Bi-SLI-Poland project within the European COST Action IS0804, and three experimental tasks designed for the project. Below all the tasks are recounted and the tasks used in the current analysis which do not have standardized administration procedures described in the tests manuals are presented in more detail.

### **Receptive vocabulary (Obrazkowy Test Słownikowy – Rozumienie, OTSR)**

Children's receptive vocabulary was measured with Obrazkowy Test Słownikowy, OTSR (The Picture Vocabulary Test – Comprehension; Haman and Fronczyk, 2012). Each child was tested with two available versions of the test (A and B) to allow more data points in the assessment. The two versions of the test are fully comparable with each other and are used independently when testing for diagnostic purposes or when a retest is needed in a short period of time. Each version includes 88 items that are ordered from the least to the most difficult. The OTSR assesses the comprehension of nouns, verbs, and adjectives. Each test item is accompanied by four colored pictures. One picture depicts the target word and the three other pictures are foils, which consistently include one phonetic foil, one semantic foil, and one thematic foil.

The child is presented with one word at a time and has to point to one picture out of four that appropriately depicts the word. The child does both versions of the test, with the order of the versions counterbalanced. Depending on the child's age, the easier, initial items are skipped in each version. The procedure in each version is terminated after four consecutive errors.

Overall, a participant can receive a maximum of 88 points in each version – one point for each correct answer. For the purpose of this study, we considered only one of the test versions, for which a child obtained a higher score. We assumed that this score was more immune to the problems connected with test delivery, such as the child's boredom, or lack of concentration that led to the early termination of the test.

### **Productive vocabulary (Zadanie Nazywania Obrazków, ZNO)**

The productive vocabulary was measured with Zadanie Nazywania Obrazków, ZNO (Picture Naming Task; Haman et al., 2012; Haman and Smoczynska, 2010, unpublished). The ´ task consists of 53 color pictures depicting 32 nouns and 21 verbs presented in the order of ascending difficulty. Each child is presented with all 53 pictures one by one, and is asked to name each picture with one word. The task has to be administered to the last item, regardless of the number of errors made by the child. The child scores a point for each correct answer, which includes the target word, its close synonym, or a dialectal variant. The maximal number of points is 53.

### **Receptive grammar (TROG-2)**

We used the Test for the Reception of Grammar – TROG-2 (Bishop, 2003; the Polish translation by Smoczynska, 2008, ´ unpublished) as a measure of receptive grammar. TROG-2 tests the comprehension of 20 syntactic constructs, organized in blocks A–T with progressing order of difficulty, as established for the English version. Each grammatical construct is included in four test items. The structures tested by TROG-2 include, for example: negatives, singular and plural inflection, object and subject relative clauses, etc. (for the exhaustive list of TROG-2 structure blocks, see Bishop, 2003).

Each test item is presented in a multiple-choice format with four pictures presented on a single board. One of the pictures illustrates the target structure and three constitute the lexical and grammatical foils to this structure. The child is auditorily presented with the stimulus containing a particular grammatical structure. Then the experimenter asks the child to point to one of four pictures which best corresponds to what he/she has heard. For each correct answer the child scores one point, and the maximum number of points is 80. In the Polish version of TROG-2 all children were expected to complete the entire task.

### Productive Grammar (Sentence Repetition, LITMUS-SRep)

Productive grammar was examined with the Polish adaptation of Sentence Repetition task, LITMUS-SRep (henceforth: SRep, Banasik, Haman, and Smoczynska, 2012, unpublished), based on ´ the English task SASIT (Marinis et al., 2010). The adaptation is composed of 68 Polish sentences, with varying levels of grammatical complexity. The sentences contain a wide range of grammatical constructions, including negations, questions, passives, object and subject relative clauses, conditionals, object and subject clefts and noun complement clauses. The sentences are morphologically varied and controlled for length (between 5 and 9 words, no more than two clauses) and the properties of the content words used (lexical frequency, age of acquisition). All the sentences were recorded by two native speakers of Polish (male and female).

During task administration, children are asked to listen to the recorded sentences one by one and repeat them as accurately as possible. Each sentence is heard only once. The child is praised for repeating the sentences irrespective of accuracy, but no corrective feedback is given. The repetitions are recorded and then transcribed. The final score reflects the percent of correctly repeated words, relative to all the words in a given sentence (range 0–100).

### **Phonological processing (Non-word Repetition, NWR)**

We tested phonological processing with the Polish NWR task, NWR (Szewczyk and Wodniecka, 2012), consisting of 50 nonwords. All non-words, recorded by a female native speaker

of Polish, are between 2 and 4 syllables long, have a fixed stress pattern on the penultimate syllable (which is the default stress pattern in the Polish language) and are phonotactically legal. Most of the items are highly Polish-like, i.e., they contain consonant clusters and affixes typical for Polish morphology. Sometimes, they also contain lexical morphemes. The recordings of non-words are presented in the order of increasing difficulty. Participants listen to the recordings via headphones and repeat them. Subsequently, the recorded repetitions are transcribed by two independent judges. Based on their transcriptions, each non-word is categorized as either correct or incorrect. Developmental errors are disregarded and treated as correct productions. For each correctly repeated word the child receives one point. The maximal number of points for this task is 50.

### **Discourse (LITMUS-Multilingual Assessment Instrument for Narratives, LITMUS-MAIN)**

To assess children's discursive abilities we used the Polish adaptation of the LITMUS-Multilingual Assessment Instrument for Narratives, LITMUS-MAIN (henceforth: MAIN; Gagarina et al., 2012) by Kiebzak-Mandera et al. (2012). The MAIN consists of four parallel cross-culturally neutral picture stories, each comprising six pictures. Each story includes three episodes (two pictures per episode). The episodes can be described in terms of the GAO sequences: a Goal (i.e., the protagonist wanting something), an Attempt to reach this goal, and the Outcome (e.g., The cat wants to catch a butterfly – Goal; The cat jumps forward – Attempt; The cat falls into the bushes – Outcome). The testing procedure involved two modes, the Telling mode and the Retelling mode.

Each session starts with a warm-up conversation, followed by the Telling mode and the Retelling mode. In the Telling mode, the experimenter presents the child with three envelopes, containing the same picture story. The child is asked to choose one envelope, look at the pictures and tell a story based on the pictures without showing them to the experimenter (the non-shared attention paradigm). In the Retelling mode, the experimenter shows the child another picture story, tells the story to the child and asks the child to retell the story based on the pictures and the model story he/she has heard (the shared attention paradigm). The whole session is recorded and transcribed.

In this study, we assessed the story structure of each narrative (told and retold) in accordance with the MAIN (see Gagarina et al., 2012). The child could get the maximum of 2 points for the setting of the story and then 5 points for each episode including the GAO sequences (1 point for conveying the initial mental state of the character, 1 point for expressing the Goal, 1 point for the Attempt, 1 point for the Outcome, and 1 point for describing the character's reaction to the outcome), which gives the maximum of 17 points per story.

#### Procedure

All children were tested individually in a quiet room: the monolingual Polish children in their preschools or in their homes in Poland, the bilingual children in their schools or in their homes in the United Kingdom. Apart from the language tasks in Polish described above, each bilingual child was tested with a set of analogous language tasks in English, but these tasks are beyond the focus of the present report. Moreover, all children were tested with a battery of cognitive tasks, including the Digit Span (Wechsler, 1974) 2 and Raven's Colored Matrices (Jaworowska and Szustrowa, 2003). The bilingual children were tested on the cognitive tasks only in their dominant language, as declared by their parents. In the case of children whose parents declared that they could not indicate which language was dominant, it was assumed that the child was balanced in their knowledge of the two languages and the language in which the cognitive tasks were performed was randomly selected.

Each monolingual child was tested throughout 3–4 sessions and each bilingual child – throughout 5–7 testing sessions (2–3 sessions in the non-dominant language and 3–4 sessions in the dominant language). Each session lasted approximately 45–90 min including breaks between the tasks. The duration of the session depended on the child's pace of doing the tasks. The order of the tasks in the testing sessions was counterbalanced across participants. The tasks in Polish were administered by a native speaker of Polish, while the tasks in English (not included in the present report) were administered by a native speaker or a highly proficient user of English. Polish and English were never tested on the same day.

### Calculating the Index of Cumulative Exposure to L1 and L2

In order to statistically control for the language exposure of bilingual children, we calculated an index of cumulative language exposure in L1 and L2. First, we estimated to what extent a child was exposed to each language when living in the United Kingdom on the basis of the Questionnaire for Parents of Bilingual Children [PABIQ – Tuller, 2015; Polish adaptation by Ku´s, Otwinowska, Banasik, and Kiebzak-Mandera (2012, unpublished)]. In the questionnaire, we asked parents to estimate on a 5 point Likert scale how often the child was addressed in English and Polish in particular communicative situations such as parents talking to the child, other children talking to the child in the day-care, etc. (0 – not at all, 4 – exclusively in this language)3 . These scores were aggregated to obtain an estimate of the bilingual children's exposure to Polish and to English during their stay in the United Kingdom. The maximal score for each language was 91, the actual values for L1 (Polish) were in the 15– 67 range (M = 45.93, SD = 11.63), and for L2 (English) in the 15–61 range (M = 36.01, SD = 11.31). Because some of bilingual children (16 participants) in our group were born in Poland and only later immigrated to the United Kingdom, we assumed that when living in Poland the children had the maximal exposure to Polish (i.e., 91) and none to English. After immigrating to the United Kingdom, some children regularly spent a considerable amount of time in Poland (e.g., 3 months of summer holidays

2We slightly modified the original instruction to make it friendlier for children younger than 6-year-olds.

3The issues concerning the exposure to English and to Polish were not interdependent. More specifically, the parents could indicate that the child had a large exposure to both L1 and L2, or that the child had little exposure to both languages. In consequence, the estimates of exposure to L1 and L2 were only moderately correlated (r = −0.56, p < 0.001).

each year). Thus, we assumed that also during these periods of time the children had the maximal exposure to Polish and no exposure to English.

The final index of cumulative exposure reflected the time spent in Poland and in the United Kingdom in the lifetime of each child, as well as the amount of exposure the child received in each of these countries. The index of the cumulative exposure to Polish was calculated using the following formula: (time4 spent in Poland) ∗ 91 + (time spent in the United Kingdom) ∗ (exposure to Polish while in the United Kingdom). The actual unit of measurement used to calculate the index was the child's age in days represented as years (in decimals). The mean cumulative exposure to Polish was 316.45 (SD = 93.64, range: 70.83–515.86). The index of cumulative exposure to English was calculated as: (the time spent in Poland) ∗ 0 + (the time spent in the United Kingdom) ∗ (the exposure to English while in the United Kingdom). The mean cumulative exposure to English was 158.85 (SD = 81.34, range: 16.87–362.13). **Figure 1** shows different possible scenarios of how language exposure can change with age influencing values of the cumulative exposure index.

The index of exposure will be used only in the regression analyses focusing on the bilingual group, which is the main focus of the present paper. We could not directly compare the monolingual and the bilingual groups with regards to exposure, because only parents of the bilingual children filled in the questionnaire concerning exposure to both languages.

### Statistical Analyses