Received: 7 October 2019
Revised: 29 April 2021
Accepted: 29 April 2021
DOI: 10.1111/1460-6984.12635
RESEARCH REPORT
Language acquisition of early sequentially bilingual
children is moderated by short-term memory for order in
developmental language disorder: Findings from the HelSLI
study
Pekka Lahti-Nuuttila1,2
Sari Kunnari4
Marja Laasonen1,2,3
Eva Arkkila1
1 Department of Otorhinolaryngology and
Phoniatrics, Head and Neck Surgery,
Helsinki University Hospital and
University of Helsinki, Helsinki, Finland
2
Department of Psychology and
Logopedics, Faculty of Medicine,
University of Helsinki, Helsinki, Finland
3
Logopedics, School of Humanities,
Philosophical Faculty, University of
Eastern Finland, Joensuu, Finland
4
Research Unit of Logopedics, University
of Oulu, Oulu, Finland
5
Centre for Advanced Research in
Experimental and Applied Linguistics
(ARiEAL), Department of Linguistics and
Languages, McMaster University,
Hamilton, Canada
Correspondence
Pekka Lahti-Nuuttila, Department of
Otorhinolaryngology and Phoniatrics,
Head and Neck Surgery, Helsinki University Hospital and University of Helsinki,
Helsinki, Finland.
Email: pekka.lahti-nuuttila@helsinki.fi
Sini Smolander1,4
Elisabet Service2,5
Abstract
Background: The role of domain-general short-term memory (STM) in language development remains controversial. A previous finding from the HelSLI
study on children with developmental language disorder (DLD) suggested that
not only verbal but also non-verbal STM for temporal order is related to language
acquisition in monolingual children with DLD.
Aims: To investigate if a similar relationship could be replicated in a sample of
sequentially bilingual children with DLD. In addition to the effect of age, the
effect of cumulative second language (L2) exposure was studied.
Methods & Procedures: Sixty-one 4–6-year-old bilingual children with DLD
and 63 typically developing (TD) bilingual children participated in a crosssectional study conducted in their L2. Children completed novel game-like tests
of visual and auditory non-verbal serial STM, as well as tests of cognitive functioning and language. Interactions of STM for order with age and exposure to L2
(Finnish) were explored as explanatory variables.
Outcomes & Results: First, the improvement of non-verbal serial STM with age
was faster in sequentially bilingual TD children than in bilingual children with
DLD. A similar effect was observed for L2 exposure. However, when both age and
exposure were considered simultaneously, only age was related to the differential
growth of non-verbal STM for order in the groups. Second, only in children with
DLD was better non-verbal serial STM capacity related to an improvement in
language scores with age and exposure.
Conclusions & Implications: The results suggest that, as previously found
in Finnish monolingual children, domain-general serial STM processing is also
compromised in bilingual children with DLD. Further, similar to the monolingual findings, better non-verbal serial STM was associated with greater language
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the
original work is properly cited.
© 2021 The Authors. International Journal of Language & Communication Disorders published by John Wiley & Sons Ltd on behalf of Royal College of Speech and Language
Therapists
Int J Lang Commun Disord. 2021;1–20.
wileyonlinelibrary.com/journal/jlcd
1
2
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
improvement with age and exposure, but only in children with DLD, in the age
range studied here. Thus, in clinical settings, assessing non-verbal serial STM of
bilingual children could improve the detection of DLD and understanding of its
non-linguistic symptoms.
KEYWORDS
non-verbal serial short-term memory, language, vocabulary, developmental language disorder,
specific language impairment, second language acquisition, sequentially bilingual, multilingual, memory for order
What this paper adds
What is already known on the subject
∙ Both phonological and non-verbal STM have been associated with DLD in
monolingual and sequentially bilingual children. Monolingual children with
DLD have also shown slower non-verbal serial STM development than TD
children.
What this study adds to existing knowledge
∙ Sequentially bilingual TD children’s non-verbal serial STM improves more
between ages 4 and 7 years than that of their peers with DLD, replicating a
finding for monolingual children with DLD. Better non-verbal serial STM was
especially associated with early receptive language development in sequentially bilingual children with DLD. L2 exposure showed largely comparable
effects with age. These results support the hypothesis that a domain-general
serial STM deficit is linked to DLD.
What are the potential or actual clinical implications of this work?
∙ Non-verbal assessment of STM for serial order in sequentially bilingual children with DLD could benefit the development of better tailored therapeutic
interventions.
INTRODUCTION
The term developmental language disorder (DLD) has been
proposed for ‘children who are likely to have language
problems enduring into middle childhood and beyond,
with a significant impact on everyday social interactions or
educational progress’ (Bishop et al. 2017: 1070) but whose
difficulties are not part of an identified biomedical condition. In contrast to the previously used label specific language impairment (SLI), DLD does not exclude deficits in
non-verbal abilities and ‘children with low non-verbal ability who do not meet criteria for intellectual disability can
be included as cases of DLD’ (Bishop et al. 2017: 1072). The
present study introduces two non-linguistic tasks designed
to tap short-term memory (STM) for order in time (also
referred to as serial STM). These tasks are used to explore
whether a domain-general mechanism for recording tem-
poral serial order may be compromised in DLD. The earlier
HelSLI study of monolingual 4–6-year-old children (LahtiNuuttila et al. 2021) suggested this was the case. This article
presents data from a parallel sample of children acquiring
a second language. Typically developing (TD) sequentially
bilingual children and sequentially bilingual children
with DLD were compared in a cross-sectional design to
probe for differences in STM and language developmental
patterns.
A number of impairments in non-linguistic cognitive
processes have been associated with DLD, for example, in
processing speed (Leonard et al. 2007), procedural learning
(Ullman and Pierpont 2005) and sustained attention (Ebert
and Kohnert 2011, Ebert et al. 2019, Finneran et al. 2009).
Also, and more specifically, several recent studies and
reviews have examined non-linguistic working memory
(WM) as well as STM and how these are affected in DLD
LAHTI-NUUTTILA et al.
(Archibald 2017, Henry and Botting 2017, Leonard et al.
2007, Montgomery et al. 2010, Vugs et al. 2013). Previous
research on the relationship between STM and/or WM and
DLD has mainly addressed two hypotheses identified by
Vugs et al. (2013). The phonological storage deficit hypothesis of DLD (Archibald and Gathercole 2006a, 2006b,
Baddeley et al. 1998, Gathercole and Baddeley 1990) suggests that mainly phonological WM is impaired in DLD.
The alternative domain-general hypothesis of DLD asserts
that general and non-verbal factors are involved in addition to phonological memory. A meta-analysis that examined the association between DLD and visuospatial WM
found that children with DLD have deficits in both complex visuospatial WM tasks, in which information has to
be actively processed as well as maintained, and simple
storage tasks (Vugs et al. 2013). Most previous studies (e.g.,
Arslan et al. 2020) have contrasted performance on verbal
tasks (e.g., forwards and backwards digit span) and visuospatial tasks (e.g., forwards and backwards Corsi Blocks,
which probes memory for ordered tapping of spatially distributed blocks or screen locations). In Corsi Blocks and
similar tasks, temporal and spatial order are confounded
as both memory for spatial and serial patterns affect performance. In the present study, the specific focus is on memory for temporal order. The question is whether a domaingeneral STM mechanism for order in time (as opposed to
space) is related to DLD, and if it plays a role in atypical
language development.
STM for verbal serial order, sometimes referred to as
phonological STM, has been shown to predict typical
vocabulary and grammar acquisition (e.g., Clark and Lum
2017, Hsu and Bishop 2011, Majerus and Boukebza 2013,
Majerus et al. 2006b). A relation between phonological
STM and DLD has also been reported in many investigations (Archibald 2017, Archibald and Gathercole 2006a,
Baddeley 2003, Gathercole and Baddeley 1990, 1993, Montgomery et al. 2010, Verhagen and Leseman 2016). Furthermore, studies of monolingual TD children that examined
STM for verbal items and their order separately found that
these were independently linked to vocabulary acquisition
(Attout et al. 2020, Leclercq and Majerus 2010, Majerus
and Boukebza 2013, Majerus et al. 2006a, 2006b, Ordonez
Magro et al. 2018). For example, in a recent study, a link
between order STM and both receptive vocabulary and
expressive vocabulary was found in 4–6-year-old TD children (Attout et al. 2020). In addition, in a study of 6–7-yearold TD children, better serial order reconstruction performance was related to faster novel word learning (Majerus
and Boukebza 2013). In theories of STM, the nature of order
coding mechanisms remains controversial. It has been suggested that order coding could be domain-general (Hurlstone et al. 2014), but recent research also points to the
possibility of partly shared, partly domain-specific, or com-
3
pletely domain-specific mechanisms for verbal and nonverbal material (Hartley et al. 2016, Hurlstone 2019, Hurlstone and Hitch 2018).
Few studies have investigated STM for order in DLD.
In a study including dyslexic children with or without
DLD, Cowan et al. (2017) reported that children who had
both DLD and dyslexia performed more poorly in serial
order memory tasks than TD children. As the authors
suggested, this might be explained by a deficit in general
order memory. Because of the dearth of studies, the role
of domain-general serial order STM in language acquisition remains unresolved. In a recent cross-sectional study
in the HelSLI project (Lahti-Nuuttila et al. 2021), the associations between non-verbal serial STM and composite
language measures of expressive language, receptive language and language reasoning were investigated in fiftyone 4–6-year-old monolingual Finnish children with DLD
and 66 TD children. Non-verbal serial STM was found to
improve more rapidly with age in the TD children than
in the children with DLD. Furthermore, non-verbal serial
STM (measured similarly as in the present study, that is,
as a composite variable of non-verbal visual and auditory
serial STM tasks) moderated the development of receptive
language with age in the children with DLD but not in
the TD group. Only in the children with DLD was better
non-verbal serial STM related to better receptive language
scores. Other studies that have found verbal or non-verbal
STM impairment in children with DLD have mainly compared monolingual children with DLD with monolingual
TD children. However, the relationship between memory
for order of verbal material and language acquisition has
also been found in bilingual children (e.g., Boerma et al.
2015, Engel de Abreu et al. 2014, Girbau and Schwartz 2008,
Windsor et al. 2010). For example, Girbau and Schwartz
(2008) compared children with DLD and TD children who
had Spanish as a first language (L1) and English as a second language (L2) with a task thought to rely on memory for phoneme order, that is, a non-word repetition task
with Spanish phonotactics. They found TD children to perform significantly better. Windsor et al. (2010) replicated
this finding for L2 non-words.
In monolingual children, the general non-linguistic processing weaknesses (e.g., WM, sustained attention, processing speed) are linked to language acquisition and to
DLD (Archibald 2017, Finneran et al. 2009, Ebert and
Kohnert 2011, Leonard et al. 2007, Vugs et al. 2013). There
has been much less research on bilingual children. However, a similar relationship has been suggested for bilingual children with language impairment (Kohnert et al.
2009, Kohnert 2010). A recent study replicated inferior
non-verbal sustained attention and attentional control in
bilingual as well as monolingual children with DLD compared with TD children (Ebert et al. 2019). Moreover, a
4
mediation analysis (Boerma et al. 2017) suggested an indirect role for sustained attention in the longitudinal development of vocabulary and morphology similarly in both
mono- and bilingual groups of children with DLD despite
different exposure rates to the tested language. To what
extent other subclinical cognitive weaknesses, such as
serial temporal order STM, interact with age and exposure
in bilingual language difficulties is currently unknown.
The effects of age and exposure on language development cannot be separated in regular monolingual samples,
but they are of interest for optimal targeting of interventions for specific groups with DLD. Studying L2 learners
makes it possible to ask whether serial STM differently
moderates the effects of age and cumulative L2 exposure
on TD and DLD language performance. The current crosssectional study investigates the specific hypothesis that
domain-general serial STM moderates the language development of 4–6-year-old sequentially bilingual TD children
and sequentially bilingual children with DLD. As in the earlier study of HelSLI (Lahti-Nuuttila et al. 2021), domaingeneral STM moderation effects in relation to different
aspects of language competence (expressive and receptive
language as well as a broader domain of language reasoning tasks) were explored. The participants were 4–6-yearold early sequentially bilingual children who had acquired
their L2, Finnish, between 0;1 and 5;10 years of age. If the
associations of age, DLD and non-verbal serial STM with
language turned out to be similar in bilingual children, as
found in the monolingual children of the earlier HelSLI
study, this would suggest that assessment of non-verbal
serial STM could be informative for identifying DLD in
young bilingual children.
The assessment of non-verbal serial STM in the current
study was designed to make minimal demands on proficiency in L2 as the task instructions were straightforward
and the child could respond non-verbally. When optimized
for sensitivity and specificity in young children, such a
task could also be helpful for testing children with limited
L2 exposure when testing in their L1 is not feasible. Furthermore, the functioning of non-verbal serial STM may
relate to specific limitations of information processing, particularly of building memory representations for structure
in time. Such temporal structure processing is central to
learning the phonological structures of words and combination of words to phrases and sentences. Better understanding for these processes could, thus, inform interventions for DLD.
Based on the conception that memory for order is necessary for language acquisition, it was hypothesized that
bilingual children with DLD have poorer and more slowly
improving non-verbal serial STM capacity than bilingual
TD children. Another hypothesis was that the development of language competence with age and L2 exposure
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
is moderated by the development of non-verbal serial STM
capacity. If serial STM capacity growth is different in children with DLD and TD children, cross-sectionally studied language development could also be differently moderated by STM in TD children compared with children with
DLD. Therefore, it is hypothesized that significant interactions between participant group (TD versus DLD), age or
L2 exposure, and serial STM in predicting composite language variables will be revealed.
METHODS
Participants
The group of sequentially bilingual children with DLD
consisted of 61 children (46 boys) and the sequentially
bilingual TD group of 63 children (47 boys). All children
were between the ages of 4;0 and 7;3 (mean = 5;7, SD =
0;10). All had only one language other than Finnish as
their L1, but there were 33 different L1s (see table S1 in the
additional supporting information). Finnish was the only
L2 with at least 7 months of exposure (mean = 3;0, SD =
1;3), and all but four children with DLD had more than 1
year of exposure. The mean age of onset for L2 was 2;7,
SD = 1;1. None of the children had any gross neurological difficulties (e.g., diagnoses of autism spectrum disorder (ASD), epilepsy or chromosomal abnormalities), hearing impairment, intellectual disability or oral anomalies.
Parental consent was obtained for each child participating in the study. Ethical approval for the study had been
granted by the ethical board of the Hospital District of
Helsinki and Uusimaa.
The children with DLD had been referred to the Audiophoniatric Ward for Children, Department of Phoniatrics,
Helsinki University Hospital for suspected DLD. They
were examined during their visits to the ward and were
diagnosed with ICD-10 (WHO 2010) as having a language disorder. Diagnoses of other developmental disorders (e.g., hearing impairment, intellectual disability, ASD,
oral anomalies, or a diagnosed neurological impairment
or disability) were used as exclusion criteria. Non-verbal
intelligence was also part of the exclusion criteria, and a
performance intelligence quotient (PIQ) of at least 70 was
a requisite for inclusion for children with DLD. A total of
21 of the recruited children with DLD had a PIQ between
70 and 84 based on the Wechsler Preschool and Primary
Scale of Intelligence—Third Edition (WPPSI-III) (Wechsler 2009). In the final sample, the PIQ of TD children
(mean = 101.0, SD = 11.5) was statistically significantly
higher than the PIQ of children with DLD (mean = 92.5,
SD = 14.8) (p < 0.001, d = 0.64), which is in line with
the results of the meta-analysis of Gallinat and Spaulding
5
LAHTI-NUUTTILA et al.
(2014). The present sample is representative of the DLD
children generally assessed in the ward, and their inclusion
follows statement 8 of the recent Criteria and Terminology
Applied to Language Impairments: Synthesising the Evidence (CATALISE) consensus report (Bishop et al. 2017),
which acknowledges that children with DLD can have low
levels of non-verbal ability.
The bilingual TD children were voluntary participants
from kindergartens in the metropolitan area of Helsinki.
They were required not to have any diagnosed or suspected language difficulties except possible minor articulation impediments. TD children were required to have a
PIQ of at least 85. Before CATALISE (Bishop et al. 2017), the
initial plan had been to split the DLD group into two subgroups at PIQ = 85. After the CATALISE consensus process, the terminology and criteria were revised. It was also
found that splitting the DLD group would have resulted
in unacceptably small samples for the planned analyses.
Consequently, the children with DLD were included as
one group, and non-verbal reasoning was statistically controlled. It can be noted that in the initial screening of
data the relationships between non-verbal subtests (see the
section ‘Language and cognitive tests’) were very similar
throughout the whole PIQ range.
Estimates of exposure to L2 were obtained from the
Finnish version of the Alberta Language Environment
Questionnaire (ALEQ) (Paradis 2011, Smolander et al.
2021). First, the number of months between the age at
which the child began to have regular kindergarten exposure to Finnish and the age at which they participated in
the present study was calculated. Based on the questions
of ALEQ addressing the proportion of L1 and L2 languages
in the child’s life, a cumulative L2 exposure score was
then calculated as a product of L2 proportion and L2 exposure (Smolander et al. 2021). According to the information
gained with ALEQ, the most important source of L2 exposure was the Finnish kindergarten, but also interaction
with family members and peers, hobbies and other activities were taken into account. For an even more detailed
description of the participants and more precise criteria
related to exposure and other inclusion/exclusion criteria,
see Laasonen et al. (2018); for a more comprehensive report
about the estimate of L2 exposure, see Smolander et al.
(2021).
Descriptive statistics for both groups are shown in
table 1. The clinical context and the young age of the participants resulted in missing values in some language and
cognitive tests. A total of 11 children had one missing value
and five children had two. Missing value frequencies are
reported in table 1. The groups’ ages did not significantly
differ. Neither did L2 exposure differ significantly between
TD children and children with DLD. Children in the TD
group had significantly higher scores than the DLD group
in all language tests with large effect sizes in 15 of 17 comparisons. They also had higher absolute scores in the nonverbal tests, most differences being statistically significant,
although the effect sizes were smaller than in the verbal
tests. To control potential confounds, age, L2 exposure and
non-verbal test differences were adjusted using a propensity score method. Propensity-score adjusted standardized
mean differences are presented in table 1.
Language and cognitive tests
The children had 33 different first languages. Since the
focus of the present study was L2 acquisition, children
were assessed in Finnish, their L2. Finnish is a morphologically complex agglutinating language in which most
tokens of nouns, verbs and adjectives are inflected forms,
consisting of two or multiple morphemes. Finnish is not
closely related to other major languages except Estonian
(and more distantly Hungarian). The choice of testing
measures was limited to those that have been standardized
for use in Finland. Picture Naming, Receptive Vocabulary,
Information, Vocabulary, Word Reasoning, Block Design
and Matrix Reasoning were selected from the WPPSIIII (Wechsler 2009). The Comprehension of Instructions,
Imitating Hand Positions, Theory of Mind (Contextual
Task) and Design Copying subtests were selected from the
Nepsy-II (Korkman et al. 2008). In addition, the Comprehension and Expressive Scales subtests of the Reynell
Developmental Language Scales III (Edwards et al. 1997)
were administered. Children were also assessed using the
Expressive (Martin and Brownell 2011) and Receptive OneWord Picture Vocabulary Tests (Martin and Brownell 2010)
as well as the Boston Naming Test (Kaplan et al. 1983).
The raw scores of these variables, sample-centred transformations of the raw scores and sample-standardized ztransformations of raw scores were used when appropriate
in the particular analyses (for a description of the roles of
the variables, see the section ‘Statistical analyses’).
Serial STM tasks
Two serial STM tasks were developed to test immediate
memory for temporal order in non-verbal sequences. The
STM tasks were presented to the child as tablet computer
games. Pictures of four barns were shown on the screen.
Two opposing upper barns were described as belonging to
Matt and two lower barns to Mary. In both auditory and
visual STM tasks, lengthening pairs of stimulus sequences
were presented for comparison of order. In both modalities, the participants had to bind the stimuli, presented one
at a time, to a temporal sequence in their WM.
6
TA B L E 1
Descriptive statistics of fundamental variables by group and the results for mean comparisons
Variable
Group
TD (n = 63)
Mean (SD)
N of missing
Range
DLD (n = 61)
Mean (SD)
N of missing
Range
Age (months)
67.0 (10.6)
–
49–87
66.7 (9.3)
–
48–83
Cumulative L2 exposure
(months)
18.5 (8.3)
–
7–38
17.2 (8.1)
–
Non-verbal Reasoningc,d
0.2 (0.9)
–
(−2.0)–2.2
–0.2 (0.9)
Matrix Reas.
16.7 (4.3)
–
5–25
Block Designc
27.3 (4.7)
–
20–38
Imit. Hand Pos.e
15.2 (4.4)
1
5–23
10.7 (5.0)
Theory of Minde
4.9 (1.7)
–
0–8
4.1 (1.5)
–
1–8
0.009
0.47
0.00
Design Copyinge
7.2 (2.5)
1
3–14
5.8 (1.8)
–
2–11
<0.001
0.63
0.00
Vocab.c
14.4 (6.7)
–
3–33
6.4 (4.0)
–
0–18
<0.001
1.45
1.06
Inform.c
21.8 (4.3)
–
4–30
13.8 (6.4)
–
0–23
<0.001
1.47
0.99
Word Reas.c
12.8 (7.1)
1
1–23
3.5 (4.5)
–
0–16
<0.001
1.54
1.14
Compr. Instr.e
17.3 (4.7)
–
5–28
11.6 (4.6)
–
0–23
<0.001
1.23
0.85
c
da
PS adjusted db
0.850
0.03
0.00
3–40
0.426
0.14
0.00
–
(−1.7)–2.4
0.033
0.38
0.00
14.5 (4.6)
–
4–24
0.006
0.49
0.00
26.4 (3.9)
–
20–40
0.255
0.20
0.00
2
2–20
<0.001
0.93
0.00
p
16.0 (6.4)
–
2–30
8.7 (5.9)
6
0–29
<0.001
1.15
0.83
31.0 (12.2)
–
5–48
15.8 (8.7)
7
0–37
<0.001
1.35
1.01
EOWPVT
48.0 (15.6)
–
15–83
29.6 (15.3)
1
0–74
<0.001
1.18
0.86
Pict. Namingc
15.5 (4.0)
–
6–22
9.4 (5.6)
–
0–20
<0.001
1.25
0.93
Receptive Voc.c
23.9 (5.4)
–
11–32
19.4 (7.6)
–
1–33
<0.001
0.68
0.36
RDLS Compr.
50.7 (8.0)
–
16–61
38.3 (14.8)
–
0–57
<0.001
1.05
0.68
ROWPVT
77.8 (30.0)
–
28–158
43.8 (18.1)
2
8–88
<0.001
1.34
0.80
Note: TD, typically developing children; DLD, children with developmental language disorder; d, Cohen’s d, effect size; PS adjusted d, propensity score adjusted Cohen’s d, effect size; Matrix Reas., Matrix Reasoning; Imit.
Hand Pos., Imitating Hand Positions; Vocab., Vocabulary; Inform., Information; Word Reas., Word Reasoning; Compr. Instr., Comprehension of Instructions; BNT, Boston Naming Test; RDLS, Reynell Developmental
Language Scales III; Expr., Expressive Scale; Compr., Comprehension Scale; EOWPVT, Expressive One Word Picture Vocabulary Test; Pict. Naming, picture naming; Receptive Voc., receptive vocabulary; and ROWPVT,
Receptive One Word Picture Vocabulary Test.
a
In the case of missing values, p- and d-values were pooled from the independent samples t-tests in 20 multiple imputations.
b
Propensity scores were based on age, cumulative L2 exposure and the raw scores of Matrix Reasoning, Block Design, Imitating Hand Positions, Theory of Mind and Design Copying.
c
Wechsler Preschool and Primary Scale of Intelligence, Third edition (Wechsler 2009).
d
Non-verbal reasoning score is the mean of sample standardized z-scores of Matrix Reasoning and Block Design raw scores.
e
Nepsy-II (Korkman et al. 2008).
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
BNT
RDLS Expr.
7
LAHTI-NUUTTILA et al.
TA B L E 2
Serial STM tasks and language composite scores: Descriptive statistics by group and the results for mean comparisons
Variable
Group
TD (n = 63)
Mean (SD)
Range
DLD (n = 61)
Mean (SD)
Range
da
p
Serial short-term memory
Visual serial STMb
6.9 (6.2)
0–24
4.6 (3.3)
1–18
0.010
0.46
Auditory serial STMb
8.0 (7.2)
1–24
4.1 (3.5)
0–19
<0.001
0.69
Serial STM composite
0.3 (1.0)
(−0.8)–3.6
−0.3 (0.4)
(−0.8)–0.9
<0.001
0.69
General language composite
0.5 (0.7)
(−1.1)–1.8
−0.5 (0.7)
(−2.1)–0.9
<0.001
1.46
Expressive language composite
0.5 (0.8)
(−1.2)–1.8
−0.5 (0.8)
(−2.0)–1.6
<0.001
1.38
Receptive language composite
0.4 (0.7)
(−1.2)–1.7
−0.5 (0.8)
(−2.6)–1.1
<0.001
1.17
Language reasoning composite
0.6 (0.8)
(−1.1)–2.2
−0.6 (0.7)
(−1.9)–0.9
<0.001
1.63
Language
Notes: TD, typically developing children; DLD, children with developmental language disorder; d, Cohen’s d, effect size; and STM, short-term memory.
a
P- and d-values are pooled from the independent samples t-tests in 20 multiple imputations.
b
One TD and one DLD child had visual serial STM task missing. One other TD child had a missing value for the auditory serial STM task. Three high score values
in both STM tasks were winsorized in the TD group.
In the visual task, a first sequence of fantasy animals
travelled one by one from Matt’s left barn to his right barn.
After a short pause, a second sequence of animals moved
from Mary’s left barn to her right barn. Each sequence
consisted of tokens of two different animals sampled from
the pool of five possible animals. Matt’s and Mary’s paired
sequences always had the same two animals. After each
pair of sequences, the child had to touch a green circle with
a tick mark on the screen if Mary’s animals had moved in
the same order as Matt’s, and a red circle with a cross if
they had appeared in a different order.
In the auditory task, tokens of two different back-tofront animal calls sampled from the pool of five possible calls were used on each trial. In this task, Matt’s and
Mary’s barns were seen as in the visual task, but now
it was evening dusk. No animals were visible, but their
calls could be heard. Matt’s right-side barn was lit during
each call in the first sequence of sounds as invisible animals moved in and said good night. Mary’s right-side barn
was lit during each call in the second sequence. Again,
the child was asked to check whether the sequences were
identical.
In half the comparisons at each sequence length, Matt’s
and Mary’s sequences were the same, and in the other
half, they were different. First, five practice comparisons
were presented to make sure that the child had understood
the task. In the actual task, six comparisons per sequence
length were presented. The initial sequence length was
two, and it increased only if the child responded correctly
on at least four out of six trials of the current length. If the
child responded correctly on the first four trials, the last
two trials of that sequence length were not presented but
were credited. The children’s score in each task was the
number of actual correct answers and these credits. The
maximum sequence length was seven, so the theoretical
maximum score was 36. Half the children were presented
with the auditory task first, whereas the other half was first
presented with the visual task.
For practical reasons, only a limited number of trials
could be included in the tasks. For a more reliable STM
measure, the visual and auditory scores were, therefore,
standardized, and a composite STM score was calculated
as the average of the standard scores. The combination
of visual and auditory tasks also served to control for
modality-specific strategies. The descriptive statistics for
the STM and the language composite variables used in the
main analyses are presented in table 2.
STATISTICAL ANALYSIS
The main goal of the present study was to examine the
relationship between non-verbal STM for order and language development in bilingual TD children and bilingual
children with DLD as a function of age and exposure.
From the 11 observed language variables, composites were
formed for receptive language, expressive language and
language reasoning (cf., Lahti-Nuuttila et al. 2021), as well
as a second-order composite variable for general language.
A receptive language composite was formed as a mean of
sample standardized values of the Reynell III Comprehension Scale, the Receptive One-Word Picture Vocabulary
Test and Receptive Vocabulary of WPPSI-III. The expressive language composite included sample standardized
values of the Reynell III Expressive Scale, the Expressive
One-Word Picture Vocabulary Test, the Boston Naming
Test and Picture Naming from WPPSI-III. The remainder
of the language tests, that is, Information, Vocabulary
8
and Word Reasoning from WPPSI-III and the Comprehension of Instructions from the Nepsy-II, formed the
language reasoning composite. A general language
composite was formed as an average of receptive and
expressive language and language reasoning composites.
The initial screening of the data revealed that there were
slightly fewer than expected raw scores at the high end of
the distribution on some cognitive measures among the
children with DLD who were over 5;6 years of age: 5.5-yearolds had equivalent raw scores to many 4-year-old children. The local regression (loess) curves confirmed that
some older children with DLD performed relatively worse
than the younger children with DLD. To correct for this
possible slight bias resulting from an unequal age distribution of cognitive skills in the DLD group, and to adjust
for the group difference in non-verbal reasoning, propensity scores were used (Rosenbaum and Rubin 1983, Schafer
and Kang 2008).
A propensity score is a balance score (Austin 2011) that
can control for possible confounding that results from
unintended group differences. The good thing about the
propensity score method is that it can control for many
possible confounders at the same time. One way to create a propensity score for a measure is to employ logistic regression to predict group membership with a set of
explanatory variables that may need to be controlled. A
propensity score estimate is based on the predicted probability of group membership found in this analysis. The
propensity score can be used to create propensity score
classes (Schafer and Kang 2008). In the current study, a
propensity score analysis was conducted with binary logistic regression, using the group as the outcome variable, and
age, cumulative L2 exposure, as well as the raw scores of
the non-verbal measures Matrix Reasoning, Block Design,
Imitating Hand Positions, Theory of Mind and Design
Copying as predictor variables. The propensity scores were
estimated as the predicted group membership probabilities. Balance checking between groups was conducted with
these propensity scores. For the regression analyses of nonverbal serial STM and the language composite variables,
a procedure proposed by Schafer and Kang (2008) was
used. Subjects were classified into five propensity score
classes, and four dummy variables that distinguished these
classes were used as covariates in the main analyses. The
dummy variables were constructed so that all observations
that were classified as belonging to the first propensity
score class received a value of 1 and all other observations
received a value of 0 for the first dummy variable. The three
other dummy variables were coded similarly. These variables were used as covariates in the regression models of
interest.1
For the missing values in the data (table 1), the multiple imputation (MI) procedure of SPSS 25 with 20 imputed
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
data sets was used. The results are reported pooled and
based on small-sample degrees of freedom (Reiter 2007,
van Ginkel and Kroonenberg 2014). The MI was performed
for the raw scores of the language, cognitive and STM variables before centring or standardizing with gender, group
status, age, L2 exposure and cumulative L2 exposure also
in the MI model. In the TD group, three high outlier values
in both the visual and auditory STM tasks were detected.
These raw values were winsorized before calculating the
non-verbal serial STM composite so that they would not
disproportionately influence the results.
The main interest was moderator effects, in other
words, interactions. To make the interpretation of the
effect estimates more comprehensible, the predictor variables were mean-centred for estimating unstandardized
effects, following common practice in moderation analyses
(Hayes and Rockwood 2017). The standardized effects (βcoefficients) were estimated with sample standardized (ztransformed) variables using the GLM procedure of SPSS
25.0.0.2. In the analyses where the STM composite was the
dependent variable, the statistically significant interaction
of age and group was further cross-checked using the PROCESS macro (Hayes 2018), and tests of conditional effects
(effects of age within the groups) were estimated with the
macro-procedures separately for each multiply imputed
sample. Again, the results from all 20 samples were pooled
using small-sample degrees of freedom (Reiter 2007, van
Ginkel and Kroonenberg 2014). Two-tailed statistical significance tests were used, and the significance level was
originally set as α = 0.05.
RESULTS
Balance check of propensity scores
Standardized mean differences between the groups using
the propensity score as a covariate in the analyses are presented in table 1. These show that for age, for L2 cumulative
exposure and for the non-verbal reasoning and its subtests
(Matrix Reasoning and Block Design), the groups were
balanced, all d’s being near 0 and the group differences
nonsignificant. In the classification of propensity scores
(Schafer and Kang 2008), the numbers of TD children in
the five bins were 22, 20, 9, 10 and 1, while the numbers of
children with DLD were 2, 5, 16, 15 and 24, respectively.
Relationship of age and cumulative L2
exposure with serial STM
Correlations between age, L2 cumulative exposure, nonverbal reasoning composite, serial STM composite and
9
LAHTI-NUUTTILA et al.
TA B L E 3
Predicting non-verbal serial short-term memory: Results of the multiple regression analyses
B
SE
95% CI
pa
R2
β
Model 1
Age
0.45
0.03
0.006
[0.02, 0.04]
<0.001
0.37
DLD
−0.49
0.154
[−0.80, −0.19]
0.002
−0.29
Age × DLD
−0.05
0.012
[−0.08, −0.03]
<0.001
−0.33
Model 2
Cumulative L2 exposure
0.32
0.03
0.008
[0.02, 0.05]
<0.001
0.31
DLD
−0.47
0.170
[−0.81, −0.13]
0.007
−0.28
Cumulative L2 exposure × DLD
−0.04
0.016
[−0.08, −0.01]
0.007
0.22
Model 3
0.50
Age
0.03
0.007
[0.01, 0.04]
<0.001
0.32
Cumulative L2 exposure
0.01
0.008
[0.00, 0.03]
0.099
0.13
DLD
−0.46
0.161
[−0.78, −0.14]
0.005
−0.28
Age × DLD
−0.05
0.014
[−0.08, −0.03]
<0.001
−0.32
Cumulative L2 exposure × DLD
−0.01
0.017
[−0.04, 0.02]
0.617
−0.04
0.00
0.001
[0.00, 0.00]
0.129
0.12
−0.00
0.002
[0.00, 0.00]
0.302
−0.08
Age × Cumulative L2 exposure
Age × Cumulative L2 exposure × DLD
Note: DLD, dummy variable, which before centring had 0 = typically developing children and 1 = children with developmental language disorder and after centring
−0.49 and 0.51, respectively; STM, short-term memory. Also, four propensity score class dummy variables were included in the model to adjust TD and DLD group
differences, but reporting their coefficients and that of the intercept is not relevant.
a
P-values were calculated using small-sample degrees of freedom for multiple imputations (Reiter 2007; Van Ginkel and Kroonenberg 2014).
F I G U R E 1 Serial STM composite score by age × group (left) and by cumulative L2 exposure × group (right). The thin dashed lines show
regression of the x-variable only, while the thick solid lines present estimations of models 1 and 2 from table 3 where possibly confounding
group differences were controlled via propensity score groups. Thick solid lines are for classified propensity score = 3
language composites are presented in table S2 in the additional supporting information. The model with age, group,
age × group and propensity score class dummies predicting serial STM was statistically significant (F7,114 = 13.4,
p < 0.001) and explained 45% of the variance in the nonverbal serial STM composite. The regression coefficients
for age, group and age × group are presented in table 3.
The four propensity score class dummy variables as a combination also had a significant effect (p = 0.009). The
age × group interaction (figure 1) was statistically signif-
icant, suggesting that the cross-sectionally obtained effect
of age on serial STM was different in children with DLD
than TD children, only TD children showing serial STM
improvement with age. A follow-up analysis of the interaction in model 1 showed that conditionally for group
(bcond. Age = bAge + bAge × group × centred group value) the
age effect in the TD group (b = 0.058, p < 0.001) was significant while in the DLD group it was not (b = 0.003,
p = 0.710). When cumulative L2 exposure was used as a
predictor in the model instead of age, the model was also
10
significant (F7,114 = 7.7, p < 0.001) but not as good as model
1 (table 3).
Finally, a model with both age and cumulative L2 exposure and their interactions with group (table 3, model 3)
had a slightly higher coefficient of determination (F11,110 =
10.0, p < 0.001). In this model, the only significant interaction was age × group, with a comparable β-coefficient to
model 1. Therefore, it seems that the age × group interaction was similar but stronger than the cumulative L2 exposure × group interaction. The STM composite score of TD
children increased with age and exposure, while that of
children with DLD did not show significant improvement
with either predictor.
Age, DLD and serial STM as predictors of
language composites
The results from the regression analyses of the models
where each language composite was regressed on age,
group, serial STM and their interactions are summarized
in table 4.
When predicting the general language composite with
the model including propensity score class, age, group status, age × group, serial STM, age × serial STM, group ×
serial STM and age × group × serial STM, the model was
statistically significant (F11,110 = 24.1, p < 0.001). The main
effects of age and group were statistically significant, as
were the two-way interactions of age × group and age ×
serial STM. Most importantly, the three-way interaction of
age × group × serial STM on general language was significant. Thus, the role of STM appears different for children with DLD than for TD children. This pattern was also
seen in the separate analyses of the three first-order language composites that are presented below and pictured
in figure 2, showing estimated developmental projections
in the 20th, 50th and 80th percentiles of STM performance.
Children with DLD who had higher STM composite scores
were found to have a steeper language growth than children with DLD and lower STM composite scores. In TD
children, higher or lower STM performance does not seem
to associate with language development.
The model for the expressive language composite was statistically significant (F11,110 = 17.5, p < 0.001) with significant main effects of age and group, significant two-way
interactions of age × group and age × serial STM, and a significant three-way interaction of age × group × serial STM
(table 4).
Similarly, the model for the receptive language composite was statistically significant (F11,110 = 18.2, p < 0.001),
including significant main effects of age and group, significant two-way interactions of age × group and age × serial
STM, two-way interaction of age × serial STM and a sig-
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
nificant three-way interaction of age × group × serial STM
(table 4).
Lastly, the model predicting the language reasoning composite was statistically significant (F11,110 = 28.0, p < 0.001),
including the significant main effects of age and group,
and a marginally significant three-way interaction of age ×
group × serial STM (p = 0.051). However, the age × group
interaction was not significant (table 4).
The models for the three language composites are illustrated in figure 2. For probing the interactions, three percentile values (20th, 50th and 80th) of the serial STM composite were chosen for the figure. The lines in the figure are
for the middle (third) propensity score class. These suggest
that those children with DLD who have better STM show
greater language improvement with age than children with
DLD who have poorer STM capacities, whereas serial STM
capacity does not seem to predict language development
for TD children in this age/skill range.
Cumulative L2 exposure, DLD and serial
STM as predictors of language composites
Exposure to language is perfectly confounded with age
in monolingual children. However, bilingual children’s
L2 exposure is somewhat separate from age. This allows
the examination of exposure as a predictor variable. The
results from the regression analyses of the models where
each language composite was regressed on cumulative
L2 exposure, group, serial STM and their interactions are
shown in table 5. Here, all the models significantly predicted the language composites (general language composite: F11,110 = 15.3, p < 0.001; expressive language composite:
F11,110 = 14.3, p < 0.001; receptive language composite: F11,110
= 10.7, p < 0.001; language reasoning composite: F11,110 =
16.2, p < 0.001). The main effects of cumulative L2 exposure
and group were significant in every model. In all of these
models, except in the model for receptive language, the
three-way interaction L2 exposure × group × serial STM
effect was also significant.
Age, cumulative L2 exposure, DLD, serial
STM and language composites
Finally, it was attempted to deconfound age and cumulative exposure by analysing two sets of models where both
age and cumulative L2 exposure were regressors, but interactions for only one of these variables were included. For
balanced comparison, standardized variables were analysed. Results are shown in table 6. All the models that
included interactions with age were significant (general
language composite: F12,109 = 28.0, p < 0.001; expressive
11
LAHTI-NUUTTILA et al.
T A B L E 4 Predicting language composites: Results of the multiple regression analyses with centred age (months), group status,
non-verbal serial short-term memory and their interactions as predictors
B
SE
95% CI
pa
R2
β
General language composite
Age
DLD
Age × DLD
Non-verbal serial STM
Age × Non-verbal serial STM
DLD × Non-verbal serial STM
Age × DLD × Non-verbal serial STM
0.72
0.06
0.006
[0.05, 0.07]
<0.001
0.68
−0.97
0.135
[−1.24, −0.70]
<0.001
−0.56
0.03
0.013
[0.00, 0.05]
0.041
0.16
−0.06
0.099
[−0.25, 0.14]
0.569
−0.05
0.03
0.012
[0.01, 0.05]
0.008
0.30
−0.19
0.198
[−0.58, 0.20]
0.335
−0.09
0.06
0.024
[0.01, 0.11]
0.017
0.28
Expressive language composite
Age
DLD
Age × DLD
Non-verbal serial STM
Age × Non-verbal serial STM
DLD × Non-verbal serial STM
Age × DLD × Non-verbal serial STM
0.65
0.06
0.008
[0.04, 0.07]
<0.001
0.63
−0.96
0.159
[−1.28, −0.65]
<0.001
−0.53
0.03
0.016
[0.00, 0.06]
0.038
0.18
−0.00
0.116
[−0.23, 0.23]
0.974
−0.00
0.04
0.014
[0.01, 0.06]
0.010
0.32
−0.09
0.233
[−0.55, 0.38]
0.713
−0.04
0.06
0.028
[0.01, 0.12]
0.031
0.28
Receptive language composite
Age
DLD
Age × DLD
Non-verbal serial STM
Age × Non-verbal serial STM
DLD × Non-verbal serial STM
Age × DLD × Non-verbal serial STM
0.65
0.06
0.007
[0.05, 0.08]
<0.001
0.69
−0.82
0.152
[−1.12, −0.52]
<0.001
−0.46
0.03
0.015
[0.01, 0.06]
0.021
0.20
−0.07
0.111
[−0.29, 0.15]
0.545
−0.06
0.03
0.013
[0.00, 0.05]
0.040
0.25
−0.30
0.222
[−0.74, 0.14]
0.184
−0.14
0.07
0.027
[0.01, 0.12]
0.015
0.31
Language reasoning composite
Age
DLD
Age × DLD
Non-verbal serial STM
Age × Non-verbal serial STM
DLD × Non-verbal serial STM
Age × DLD × Non-verbal serial STM
0.74
0.06
0.006
[0.05, 0.07]
<0.001
0.63
−1.12
0.134
[−1.39, −0.86]
<0.001
−0.62
0.01
0.013
[−0.01, 0.04]
0.260
0.08
−0.10
0.098
[−0.29, 0.10]
0.318
−0.09
0.03
0.011
[0.01, 0.05]
0.007
0.29
−0.19
0.196
[−0.58, 0.20]
0.329
−0.09
0.05
0.024
[0.00, 0.09]
0.051
0.21
Note: DLD, dummy variable, which before centring had 0 = typically developing children and 1 = children with developmental language disorder and after centring
−0.49 and 0.51, respectively; STM, short-term memory. Also, four propensity score class dummy variables were included in the model to adjust TD and DLD group
differences but reporting their coefficients is irrelevant.
a
P-values were calculated using small-sample degrees of freedom for multiple imputations (Reiter 2007; Van Ginkel and Kroonenberg 2014).
language composite: F12,109 = 21.6, p < 0.001; receptive language composite: F12,109 = 19.8, p < 0.001; language reasoning composite: F12,109 = 29.8, p < 0.001). The three-way
interaction effect of age × group × serial STM was significant only when predicting general language and receptive
language, although it showed non-significant trends also
for the other two composites (p = 0.054 for expressive language and p = 0.085 for language reasoning).
The models with cumulative L2 exposure interactions
were likewise significant (general language composite:
F12,109 = 28.9, p < 0.001; expressive language composite:
F12,109 = 21.9, p < 0.001; receptive language composite: F12,109
= 19.9, p < 0.001; language reasoning composite: F12,109 =
30.2, p < 0.001). The three-way interaction effect of cumulative L2 exposure × group × serial STM was significant in
all but in the model for receptive language (p = 0.097).
Taken together, these results show that for children
with DLD, the language boosting effect of better nonverbal STM was reliably detectable on receptive language as a function of age. For expressive language and
12
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
F I G U R E 2 Language composites by age × group × serial STM interaction (left) and by cumulative L2 exposure × group × serial STM
interaction (right). The classified propensity score = 3
language reasoning, the boosting effect was detectable for
language improvement as a function of cumulative L2
exposure. However, if non-significant trends are considered, the results for age and cumulative L2 exposure were
similar. For TD children, who had better STM than children with DLD, the boosting effect was not found, but their
language scores improved simply as a function of age and
cumulative L2 exposure.
Discussion
Studies on the role of STM and WM in DLD have mainly
concentrated on phonological STM and verbal WM as
possible causes of DLD (e.g., Archibald and Gathercole
2006a, Gathercole and Baddeley 1990; for a review, see
Archibald 2017). Investigations of non-verbal memory have
reported findings of deficient visuo-spatial executive WM
13
LAHTI-NUUTTILA et al.
T A B L E 5 Predicting language composites: Results of the multiple regression analyses with centred L2 cumulative exposure, group
status, non-verbal serial short-term memory and their interactions as predictors
B
SE
95% CI
pa
R2
β
General language composite
Cumulative L2 exposure
DLD
0.61
0.05
0.007
[0.04, 0.07]
<0.001
0.47
−1.02
0.154
[−1.32, −0.71]
<0.001
−0.58
Cumulative L2 exposure × DLD
0.01
0.015
[−0.02, 0.04]
0.726
0.02
Non-verbal serial STM
0.04
0.111
[−0.18, 0.26]
0.724
0.04
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
Cumulative L2 exposure × DLD × Non-verbal serial STM
0.02
0.012
[0.00, 0.04]
0.121
0.14
−0.36
0.223
[−0.80, 0.08]
0.110
−0.17
0.05
0.024
[0.00, 0.10]
0.041
0.20
Expressive language composite
Cumulative L2 exposure
0.60
0.06
0.008
[0.04, 0.07]
<0.001
0.50
−1.04
0.166
[−1.37, −0.71]
<0.001
−0.57
Cumulative L2 exposure × DLD
0.01
0.016
[−0.03, 0.04]
0.744
0.02
Non-verbal serial STM
0.07
0.120
[−0.17, 0.31]
0.568
0.06
DLD
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
Cumulative L2 exposure × DLD × Non-verbal serial STM
0.02
0.013
[0.00, 0.05]
0.097
0.16
−0.25
0.240
[−0.72, 0.23]
0.302
−0.11
0.05
0.026
[0.00, 0.11]
0.036
0.20
Receptive language composite
Cumulative L2 exposure
DLD
0.52
0.05
0.008
[0.03, 0.07]
<0.001
0.46
−0.82
0.174
[−1.17, −0.48]
<0.001
−0.47
Cumulative L2 exposure × DLD
0.01
0.017
[−0.03, 0.04]
0.754
0.02
Non-verbal serial STM
0.02
0.126
[−0.23, 0.26]
0.904
0.01
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
Cumulative L2 exposure × DLD × Non-verbal serial STM
0.01
0.013
[−0.01, 0.04]
0.290
0.11
−0.36
0.251
[−0.86, 0.13]
0.150
−0.17
0.04
0.027
[−0.01, 0.10]
0.124
0.16
Language reasoning composite
Cumulative L2 exposure
0.63
0.05
0.008
[0.03, 0.06]
<0.001
0.41
−1.19
0.158
[−1.50, −0.87]
<0.001
−0.65
Cumulative L2 exposure × DLD
0.01
0.015
[−0.03, 0.04]
0.735
0.02
Non-verbal serial STM
0.03
0.114
[−0.19, 0.26]
0.761
0.03
DLD
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
Cumulative L2 exposure × DLD × Non-verbal serial STM
0.02
0.012
[0.00, 0.04]
0.103
0.15
−0.47
0.229
[−0.92, −0.01]
0.045
−0.23
0.05
0.025
[0.00, 0.10]
0.035
0.21
Note: DLD, dummy variable, which before centring had 0 = typically developing children and 1 = children with developmental language disorder and after centring
−0.49 and 0.51, respectively; STM, short-term memory. Also, four propensity score class dummy variables were included in the model to adjust TD and DLD group
differences but reporting their coefficients is irrelevant.
a
P-values were calculated using small-sample degrees of freedom for multiple imputations (Reiter 2007; Van Ginkel and Kroonenberg 2014).
in DLD (Arslan et al. 2020, Vugs et al. 2013) but often
failed to find impairments in simple visuo-spatial storage tasks (e.g., Arslan et al. 2020, Engel de Abreu et al.
2014). The present study did not aim to contrast verbal
with visuo-spatial STM or WM. Instead, the interest was
in STM for order. Based on previous research on the role
of order memory in vocabulary acquisition (Cowan et al.
2017, Majerus and Boukebza 2013, Majerus et al. 2006b), it
was hypothesized that the development of domain-general
STM for temporal order would be atypical in DLD. For
this purpose, two serial STM tasks were designed in the
visual and auditory modality, respectively. A composite
variable of these tasks (the average of their z-standardized
scores) should control for modality-specific strategies, as
in the composite, common variation is greater. In an earlier study (Lahti-Nuuttila et al. 2021), monolingual children with DLD were compared with their TD peers using
this non-verbal serial STM composite variable. A pattern of effects was found suggesting that storing temporal
order is difficult for children with DLD. Within the group
14
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
T A B L E 6 Disentangling age and cumulative L2 exposure. Comparison of two sets of multiple regression models differing in the included
interactions. The shared predictors of each of the language composites were standardized age, cumulative L2 exposure, group status,
non-verbal serial short-term memory and their interactions. Model 1 also included interactions for age but not cumulative L2 exposure,
whereas model 2 also included interactions for cumulative L2 exposure but not age
Model 1
β
pa
General language composite
R2
Model 2
β
pa
0.76
0.77
Age
0.57
<0.001
0.49
<0.001
Cumulative L2 exposure
0.25
<0.001
0.31
<0.001
−0.57
<0.001
−0.59
<0.001
0.10
0.146
n.a.
n.a.
n.a.
n.a.
−0.03
0.594
0.183
−0.12
0.178
DLD
Age × DLD
Cumulative L2 exposure × DLD
Non-verbal serial STM
−0.12
Age × Non-verbal serial STM
0.28
0.008
n.a.
n.a.
Cumulative L2 exposure × Non-verbal serial STM
n.a.
n.a.
0.19
0.012
DLD × Non-verbal serial STM
−0.08
0.368
−0.06
Age × DLD × Non-verbal serial STM
0.23
0.029
n.a.
n.a.
Cumulative L2 exposure × DLD × Non-verbal serial STM
n.a.
n.a.
0.18
0.019
Expressive language composite
0.456
0.71
0.71
Age
0.51
<0.001
0.42
<0.001
Cumulative L2 exposure
0.30
<0.001
0.36
<0.001
−0.54
<0.001
−0.58
<0.001
0.11
0.145
n.a.
n.a.
n.a.
n.a.
−0.02
0.703
0.417
−0.07
0.458
DLD
Age × DLD
Cumulative L2 exposure × DLD
Non-verbal serial STM
−0.08
Age × Non-verbal serial STM
0.30
0.010
n.a.
n.a.
Cumulative L2 exposure × Non-verbal serial STM
n.a.
n.a.
0.19
0.018
DLD × Non-verbal serial STM
−0.02
0.807
−0.02
Age × DLD × Non-verbal serial STM
0.23
0.054
n.a.
n.a.
Cumulative L2 exposure × DLD × Non-verbal serial STM
n.a.
n.a.
0.19
0.024
Receptive language composite
0.842
0.69
0.69
Age
0.60
<0.001
0.51
<0.001
Cumulative L2 exposure
0.23
<0.001
0.29
<0.001
DLD
−0.48
<0.001
−0.48
<0.001
Age × DLD
0.15
0.071
n.a.
n.a.
Cumulative L2 exposure × DLD
n.a.
n.a.
−0.03
−0.12
0.226
−0.15
0.23
0.048
n.a.
n.a.
n.a.
n.a.
Non-verbal serial STM
Age × Non-verbal serial STM
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
−0.13
0.613
0.139
0.15
0.073
0.199
−0.06
0.552
Age × DLD × Non-verbal serial STM
0.27
0.025
n.a.
n.a.
Cumulative L2 exposure × DLD × Non-verbal serial STM
n.a.
n.a.
0.14
0.097
0.55
<0.001
0.48
<0.001
0.20
<0.001
0.24
<0.001
−0.63
<0.001
−0.66
<0.001
n.a.
n.a.
Language reasoning composite
Age
Cumulative L2 exposure
DLD
0.77
0.78
Age × DLD
0.04
0.566
Cumulative L2 exposure × DLD
n.a.
n.a.
−0.03
0.582
0.108
−0.12
0.158
Non-verbal serial STM
−0.14
R2
(Continues)
15
LAHTI-NUUTTILA et al.
TA B L E 6
(Continued)
Model 1
β
Age × Non-verbal serial STM
Cumulative L2 exposure × Non-verbal serial STM
DLD × Non-verbal serial STM
pa
0.27
0.008
n.a.
n.a.
−0.08
R2
Model 2
β
pa
n.a.
n.a.
0.19
0.009
0.363
−0.11
0.204
Age × DLD × Non-verbal serial STM
0.18
0.085
n.a.
n.a.
Cumulative L2 exposure × DLD × Non-verbal serial STM
n.a.
n.a.
0.18
0.015
R2
Note: DLD, dummy variable, which before centring had 0 = typically developing children and 1 = children with developmental language disorder and after
standardization −1 and 1, respectively; STM, short-term memory; n.a., not applicable as the effect was not included in the model. Also, four standardized propensity
score class dummy variables were included in the model to adjust TD and DLD group differences but reporting their coefficients is irrelevant.
a
P-values were calculated using small-sample degrees of freedom for multiple imputations (Reiter 2007; Van Ginkel and Kroonenberg 2014).
with DLD, good serial STM appeared to support language
acquisition.
In the current study, early sequential bilinguals with
DLD were compared with bilingual TD children. It was
hypothesized again that impairment of a domain-general
capacity to represent order would be associated with DLD
and atypical language development. Capacity for representing temporal order, reflected in serial STM performance, could have effects on language that might depend
on either age, language exposure, or both. These variables
could not be separated in the study of monolingual children. The present study was designed to examine the relationship between serial STM and DLD in children learning
L2. Separate consideration of the effects of age and cumulative L2 exposure in bilingual children should make it possible to disentangle these two variables in the language
acquisition of TD children and children with DLD.
To test the hypothesis that STM for order plays a special
role in DLD, the development of non-verbal serial STM as a
function of age was examined, on the one hand, and cumulative L2 exposure, on the other, comparing both TD children and children with DLD acquiring their second language. The results revealed that TD children’s serial STM
capacity, as probed by the non-verbal order STM tasks, was
greater than that of children with DLD as a function of both
age and cumulative L2 exposure. The results replicated the
previous findings in a sample of monolingual children (a
sample that similarly consisted of TD children and children with DLD) with respect to serial STM development
with age (Lahti-Nuuttila et al. 2021). These earlier results
suggested that a domain-general mechanism for presenting temporal order develops atypically in DLD. However,
there is no a priori reason to expect domain-general memory for order to develop with exposure to a second language. In the present study, evidence for a relationship
between cumulative L2 exposure and STM was also found.
The most likely explanation for the result lies in the moderate correlations between age and cumulative L2 exposure in this sample. This interpretation is supported by
the finding that the moderation effect for L2 exposure on
the effect of DLD on STM was no longer significant when
age was included in the same regression model (table 3,
model 3). In addition to age, the amount of experience with
Finnish daycare pedagogics covaries with L2 exposure in
the present data set. Potentially these structured but unspecific organized activities can also contribute to STM development, showing up in the effect of the operationalization
of cumulative language exposure in this study.
To test the second hypothesis that serial STM is related to
language development in DLD, moderation by non-verbal
serial STM of the effects of age and cumulative L2 exposure on different language composites in the two groups
of children was studied. Both age and L2 exposure had
stronger effects in TD children compared with children
with DLD, reflecting faster language acquisition with both
age and exposure in TD. The moderation of the effect of
age by serial STM was found only in the children with
DLD, especially robustly in expressive and receptive language. For them, better non-verbal serial STM was associated with greater improvements in language measures
with increasing age. Similar, but perhaps smaller, moderation effects were also found for cumulative L2 exposure on the expressive language and the language reasoning composites, whereas receptive language only showed
a non-significant trend. The moderation effects on measures of language development with age suggest that memory for serial order could play a role in language acquisition in DLD. There was a very similar pattern of serial
STM moderation of the relationship between exposure and
the language composites. The differences between effects
on different language components have to be treated with
caution, as the psychometric test items that account for
individual variation are different at different ages (moving
from word level to sentence level in some tests) and possibly differently sensitive to the amount of language exposure. Future studies with experimentally constructed tasks
will be able to probe in detail the different aspects of language development in relation to serial STM.
16
When moderation effects were studied for one of the
variables (age or cumulative exposure) while controlling
the main effect of the other variable, the moderation of
age was found to be statistically significant for receptive
language. However, for expressive language and language
reasoning, it was the moderation of cumulative exposure
that was significant. Here the effect sizes do not differ very
much, and in a complex model with moderate sample size,
the interpretation must be cautious because age and cumulative exposure covary, so this result could be a statistical
artefact. However, the result could also truly indicate that
with cumulative exposure controlled, serial STM moderation is somewhat different. This needs to be tested with a
targeted study design in the future.
As the pattern of serial STM development seems similar
for mono- and bilingual children with DLD, the present
study added support for the hypothesis that domaingeneral serial STM development between the ages of four
and seven years is impaired in DLD. This study also replicated the findings from the study of monolingual children (Lahti-Nuuttila et al. 2021) that serial STM moderates
language development in children with DLD in this age
range but not in TD children. These findings can speculatively be explained by assuming that impaired STM for
order is part of the clinical picture of DLD. Further, when
serial STM is in the impaired range, it tends to be associated with slower than typical language development. In TD
children, individual differences in domain-general serial
STM are in a range that does not appear to be related to language development. The found moderation effect in children with DLD could also suggest that effective non-verbal
serial STM could be used as a compensation mechanism in
atypical language development. Since this study was crosssectional, causal (possibly reciprocal) relations between
serial STM and language need to be studied further with
a longitudinal design to rule out the possibility that it is
the language difficulties that affect non-verbal serial STM.
Several aspects of this study are problematic for the suggested interpretation. First, the range of PIQ values in the
group with DLD included values between 70 and 85. In the
original protocol, the plan was to treat these children separately. However, as the criteria for DLD were revised with
CATALISE (Bishop et al. 2017) and as the children with
DLD and PIQ of 70–84 formed roughly a third of the clinical sample, it was judged that excluding them would lead
to a misrepresentation of the clinical DLD population in
Finland. In studies of DLD, the non-verbal ability of the
DLD group is often somewhat lower than that of the TD
control group (e.g., Cowan et al. 2017). This may have been
accentuated in the present study because the PIQ score
in WPPSI-III is partly based on the subtest Picture Concepts, which has also been associated with the verbal ability (Peyre et al. 2016, Saar et al. 2018). This could have led to
STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
an underestimate of non-verbal reasoning skills, especially
in children with DLD tested in their L2. Also, among the
bilingual TD children, this possibly led to increased exclusions from the control group as in the additional subsequent analyses we noticed that bilingual TD children had
more often low standard scores specifically on this subtest compared with the other two PIQ components (Block
Design and Matrix Reasoning).
As the PIQ estimates were low for some of the children with DLD, it is possible that some of them may
later be given a different diagnosis, for example, general
learning or intellectual disability. However, in the present
study, these children did not show atypical adaptive reasoning capacity in their daily lives as reported by their parents or as observed by the multidisciplinary team in the
Audiophoniatric Ward. Furthermore, in the present study,
propensity scores were used to balance the two groups for
differences in non-verbal cognition.
A second limitation of the study is the reliability
of the serial STM measure among the younger children. Although this study showed a significant difference
between the two groups of children in STM improvement
with age, the question remains if, with better measures,
the difference could be found already at younger ages. One
possibility could be to increase the number of trials in the
STM tasks to improve sensitivity to impairment among the
youngest children. This would not necessarily make the
task much longer as the increase could be restricted to
short series lengths that are close to the youngest children’s
serial STM capacity limit.
A third consideration concerns the construct of serial
STM or STM for order. Other researchers have reported
impairments in sustained attention related to DLD
(Boerma et al. 2017, Ebert and Kohnert 2011, Ebert et al.
2019, Finneran et al. 2009) and sustained attention may be
one of the cognitive components enabling performance in
serial STM tasks. Related to this, some children, especially
in the DLD group, might later show even symptoms of
comorbid attention deficit hyperactivity disorder (ADHD)
as it is two to three times more likely for children with
language impairment to have ADHD than for TD children
(Mueller and Tomblin 2012). Unfortunately, the comorbidity of DLD and ADHD could not be taken into account in
the current study. According to the Finnish edition of ICD10 (WHO 2010), ADHD is difficult to detect in children
before school age, that is, under the age of seven years, due
to the wide normal variation, and the diagnosis should be
made only in extreme cases. The studied children had not
started school, were young to be evaluated for ADHD and
did not present extreme characteristics in the structured
clinical examination and interview including background
information and questionnaires. In addition to the possible role of attention in the STM tasks (Hakim et al. 2020),
17
LAHTI-NUUTTILA et al.
clinical or subclinical deficits in attention are likely to play
a role also in language development. A review of cognitive
skills supporting language comprehension and production
in adults (Federmeier et al. 2020) reveals how entangled
domain-general cognitive processes, such as attentiondependent executive processes, sustained attention, and
the ability to control information flow over time, are with
language. Future targeted research is needed to reveal
what role sustained or other attention plays in both representing serial order in STM and language development.
Another question for future research concerns the set of
specific processes in STM that operate in the kind of STM
task used here. To give just one example, it could be that
some children have better strategies for naming the stimuli
and perhaps subvocally rehearsing them also in non-verbal
serial STM tasks. The stimuli in this study were designed
to minimize naming in young children (Gathercole et al.
1994), but variation in strategic approaches to the task cannot be totally ruled out. Questions about the children’s use
of verbal and other strategies remain to be resolved in more
targeted research.
Also, an interesting subject for future research is the possible effect of speech and language therapy. In the DLD
group, some children had had speech and language therapy but, in this study, this could not be taken into account
because of the small sample size. To some extent, L2 therapy was included in the cumulative exposure, but certainly
a longitudinal intervention study would be more informative.
Finally, some background variables could introduce
confounds into the data. A potential problem might be the
unequal distribution of L1s in the DLD and TD groups (see
table S1 in the additional supporting information). Estonian is the only L1 in the sample that is closely related to
Finnish. There were seven more children with Estonian
as L1 in the TD group than in the DLD group. Although
the number of Estonian L1 children was small, to control
the possible effect of Estonian as L1, analyses with it as a
binary dummy variable were run. In these analyses, the
effects remained very similar to the ones reported. Another
interesting background variable whose impact should be
studied in the future is socioeconomic status. Inclusion of
mother’s education in years in the preliminary analyses did
not change the central results in the present data set.
CONCLUSIONS
This study was designed to explore whether deficits in nonverbal STM for order are associated with bilingual DLD. A
sample of 4–6-year-old bilingual children with DLD and
TD children was studied, assessed in their second language. The serial STM of children with DLD was found
to be poorer and to show less improvement with age than
that of TD children. Furthermore, the improvement of language performance as a function of age or L2 exposure,
detected by composite measures of receptive language,
expressive language, and language reasoning, was moderated by STM in children with DLD but not in TD children
of this age range. We conclude that STM for order, measured by simple non-verbal game-like tasks, can be helpful
in comprehending and planning interventions for DLD in
young children learning their second language.
AC K N OW L E D G E M E N T S
The authors are grateful to the participating children and
their families and to the speech language therapists, psychologists, phoniatricians, nurses and other personnel at
the Department of Phoniatrics, University of Helsinki, and
Helsinki University Hospital, as well as to the participating kindergartens and their personnel. For their invaluable
contributions to this work, we thank Miika Leminen MSc,
MPsych; software developer Iida Porokuokka MSc; Erkki
Vilkman MD, PhD; and Ahmed Geneid MD, PhD. This
study is part of a larger research project, the Helsinki longitudinal SLI study (Laasonen et al. 2018) and its cognitive
subproject.
D E C L A R AT I O N O F I N T E R E S T
The authors declare that they have no conflicts of interest.
D A T A AVA I L A B I L I T Y S T A T E M E N T
Data are available on request due to privacy/ethical restrictions. Requests to access the data sets should be directed to
Marja Laasonen.
ORCID
Pekka Lahti-Nuuttila https://orcid.org/0000-0001-54631738
Marja Laasonen https://orcid.org/0000-0002-4628-4251
Sini Smolander https://orcid.org/0000-0003-0517-0298
Sari Kunnari https://orcid.org/0000-0001-5290-4851
Eva Arkkila https://orcid.org/0000-0003-0067-3216
Elisabet Service https://orcid.org/0000-0002-7698-1189
NOTE
1
At most there were 12 regressor terms in the analyses. A priori
power analyses for adequate sample size were run as described by
Laasonen et al. (2018).
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STM FOR ORDER MODERATES L2 LANGUAGE IN DLD
S U P P O RT I N G I N F O R M AT I O N
Additional supporting information may be found online
in the Supporting Information section at the end of the
article.
How to cite this article: Lahti-Nuuttila P,
Laasonen M, Smolander S, Kunnari S, Arkkila E,
Service E. Language acquisition of early
sequentially bilingual children is moderated by
short-term memory for order in developmental
language disorder: Findings from the HelSLI study.
International Journal of Language &
Communication Disorders. 2021;1–20.
https://doi.org/10.1111/1460-6984.12635