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Contribution of Nonverbal Cognitive Skills on Bilingual Children’s Grammatical Performance: Influence of Exposure, Task Type, and Language of Assessment

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Abstract: This study explores the contribution of nonverbal working memory and processing speed on bilingual children’s morphosyntactic knowledge, after controlling for language exposure. Participants include 307 Spanish-English bilinguals in Kindergarten, second, and fourth grade (mean age=7;8, SD=18 months). Morphosyntactic knowledge in English and Spanish was measured using two separate language tasks: a cloze task and a narrative language task. In a series of four hierarchical linear regressions predicting cloze and narrative performance in English and Spanish, we evaluate the proportion of variance explained after adding (a) English exposure, (b) processing speed and working memory, and (c) interaction terms to the model. Results reveal differential contribution of nonverbal cognitive skills on cloze versus narrative tasks. Cloze tasks in both languages tap working memory; cloze and narrative tasks in English pose an additional processing cost. Findings suggest that cognitive demands vary for bilinguals based on the language of assessment and the task. Keywords: Morphosyntax, nonverbal cognition, bilinguals, processing speed, working memory
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Article
Contribution of Nonverbal Cognitive Skills on Bilingual
Children’s Grammatical Performance: Influence of Exposure,
Task Type, and Language of Assessment
Taffeta Wood 1, *, Amy S. Pratt 1, Kathleen Durant 2, Stephanie McMillen 3, Elizabeth D. Peña 1
and Lisa M. Bedore 4


Citation: Wood, Taffeta, Amy S.
Pratt, Kathleen Durant, Stephanie
McMillen, Elizabeth D. Peña, and
Lisa M. Bedore. 2021. Contribution of
Nonverbal Cognitive Skills on
Bilingual Children’s Grammatical
Performance: Influence of Exposure,
Task Type, and Language of
Assessment. Languages 6: 36.
https://doi.org/10.3390/
languages6010036
Received: 1 December 2020
Accepted: 7 February 2021
Published: 27 February 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1School of Education, University of California Irvine, Irvine, CA 92697, USA; apratt1@uci.edu (A.S.P.);
edpena@uci.edu (E.D.P.)
2Speech Pathology and Audiology Program, School of Health Sciences, Kent State University,
Kent, OH 44242, USA; kdurant@kent.edu
3Department of Communications Sciences & Disorders, College of Arts & Sciences, Syracuse University,
Syracuse, NY 13244, USA; smcmille@syr.edu
4Department of Communication Sciences and Disorders, Temple University, Philadelphia, PA 19122, USA;
lisa.bedore@temple.edu
*Correspondence: tswood@uci.edu
Abstract:
This study explores the contribution of nonverbal working memory and processing speed
on bilingual children’s morphosyntactic knowledge, after controlling for language exposure. Par-
ticipants include 307 Spanish–English bilinguals in Kindergarten, second, and fourth grade (mean
age = 7;8, SD = 18 months). Morphosyntactic knowledge in English and Spanish was measured
using two separate language tasks: a cloze task and a narrative language task. In a series of four
hierarchical linear regressions predicting cloze and narrative performance in English and Spanish,
we evaluate the proportion of variance explained after adding (a) English exposure, (b) processing
speed and working memory, and (c) interaction terms to the model. The results reveal the differential
contribution of nonverbal cognitive skills across English and Spanish. Cognition was not significantly
related to performance on either grammatical cloze or narrative tasks in Spanish. Narrative tasks
in English were significantly predicted by processing speed, after controlling for age and exposure.
Grammatical cloze tasks in English posed an additional cognitive demand on working memory. The
findings suggest that cognitive demands vary for bilinguals based on the language of assessment
and the task.
Keywords: morphosyntax; nonverbal cognition; bilinguals; processing speed; working memory
1. Introduction
Cognition is intricately linked to bilingual development and to language development
more broadly (Barac and Bialystok 2012). However, the extent to which distinct aspects
of cognition—such as processing speed and working memory—explain bilinguals’ lan-
guage representations (e.g., morphosyntactic knowledge) in their first (L1) and second
(L2) languages is not well understood. Our study asks: What is the nature of the relation-
ship between nonverbal cognition and morphosyntactic knowledge, and how does this
relationship change across languages and tasks?
1.1. The Role of Exposure on Bilingual Language Development
Bilingual language development is a dynamic process influenced by multiple factors,
including the amount of a child’s exposure to their L2, the age of a child’s first L2 exposure,
and the complexity of structures in the input (Bedore et al. 2012). Young Spanish-English
bilinguals in the United States often begin to learn a second language while their first
language is still being acquired, which can lead to highly variable performance across
Languages 2021,6, 36. https://doi.org/10.3390/languages6010036 https://www.mdpi.com/journal/languages
Languages 2021,6, 36 2 of 21
the L1 and L2 (Sagarra and Herschensohn 2010). For many of these bilinguals in the
U.S., L2 English learning occurs within the context of an immersive L2 environment, in
either an early education or elementary school setting Oppenheim et al. 2020). A common
pattern is that children receive support in their first language in early elementary school
that is gradually withdrawn. As a consequence, many children shift dominance from
one language to another over time (Bedore et al. 2012;Paradis 2010). Often, the L2 input
that bilinguals encounter in their classrooms is more academic in nature. Academic
language is characterized by decontextualized discourse, and by longer, more complex
morphosyntactic structures (e.g., subordinate clauses) as compared to the more social
and routinized discourse of the home language or English used in the wider community
(Valdés 2004)
. Thus, for these learners, their first L2 exposure is different in terms of both
language as well as discourse type.
Earlier age of L2 exposure has been frequently associated with higher L2 proficiency
(Festman 2021). However, recent research informs the question of how exposure affects
language development. For instance, one study shows that differential cross-linguistic
performance is associated with both current exposure as well as linguistic domain (e.g.,
semantics, morphosyntax, and narrative) (Oppenheim et al. 2020). Others have found that
opportunities to hear and use each language differentially predict language outcomes across
domains. For instance, among Spanish–English school-age bilinguals, opportunities to hear
each language have been associated with semantic performance, whereas opportunities to
use each language have been associated with grammatical performance (Bedore et al. 2012;
Ribot et al. 2018).
These domain-related differences can be observed within individuals, as well. Re-
search by our lab, and others, has shown that bilinguals can be stronger in one area of
language (i.e., semantics) in one of their two languages, while being dominant in another
area (i.e., morphosyntax) in the other language (Bedore et al. 2012;Uccelli and Páez 2007;
Paradis et al. 2003). While exposure may partially explain these “mixed dominance profiles”
(Festman 2021), it is also possible that different areas of language tap different cognitive
processes, such as working memory or metacognitive strategies (Duncan and Owen 2000).
It remains an open question as to how different aspects of cognition interact with expo-
sure to explain the language performance, including mixed dominance, of school-aged
bilingual children.
1.2. The Role of Working Memory and Processing Speed in Bilingual Development
In educational contexts, cognition refers to the discrete and interactive mental pro-
cesses that occur between the presentation of a sensory stimulus (e.g., visual or acoustic sig-
nal) and the production of a behavioral response (e.g., motor or spoken response). Bilingual
language acquisition depends in part on verbal cognition, or the processing of information
through the representation of concepts in morphemes (the smallest units of meaning)
encoded by phonemes (the smallest contrastive units of sound)
(Summers et al. 2010).
Con-
versely, nonverbal cognition does not require the representation of linguistic information,
such as morphemes or phonemes, and is generally thought to be independent of language
(Bracken and McCallum 1998).
The theory of cognitive abilities by Cattell–Horn–Carroll (Cattell 1963;Flanagan and
McGrew 1998) proposes that cognition is multidimensional, featuring domain-general
aspects of cognition as well as domain-specific aspects of cognition. In this model, domain-
general cognitive ability supports the more narrow, specified functions of cognition. For
example, how we process incoming acoustic information and then store that information
in working memory is domain-general, while the retrieval of information from the lexicon
and the integration of semantic and morphosyntactic information is domain-specific. Two
domain-general aspects of cognition are working memory and processing speed.
Working memory. Working memory refers to the ability to temporarily hold information
for active use and manipulation. Working memory encompasses both domain-general (e.g.,
capacity limitations) and domain-specific processes (e.g., task-dependent coordination and
Languages 2021,6, 36 3 of 21
transformation of active information in working memory) (Oberauer 2004). According
to Baddeley’s seminal model (Baddeley 1992,2012), working memory has two domain-
specific components: visuospatial working memory, which stores and processes visual
(nonverbal) information, and phonological working memory, which stores and processes
speech (verbal) information. Both components of working memory support the retention
of new information in long-term memory, where information is stored for later retrieval
and use (Baddeley 1992,2012). There is abundant evidence that verbal working memory
impacts language performance (Gathercole and Baddeley 2014;Summers et al. 2010). In
bilingual adults, greater verbal working memory is positively associated with the parsing
of morphosyntactically complex sentences (e.g., Hopp 2015;Cunnings 2017). Evidence with
bilingual children also shows that verbal working memory supports the short-term pro-
cessing of the linguistic information often presented on language assessments (Oppenheim
et al. 2020;Talli and Stavrakaki 2020).
While these studies show that working memory predicts morphosyntactic perfor-
mance, all of them have targeted verbal working memory. Relevant to the present study,
emerging research suggests that nonverbal working memory may also be implicated in
bilinguals’ language performance. Cognitive processing demands inherent to nonverbal
tasks moderate success in retrieving information for a short time span (Jiao et al. 2019). For
example, adult bilinguals have been observed to score higher on behavioral measures of
nonverbal working memory after completing intensive interpreter training (Macnamara
and Conway 2014). Evidence from bilingual children with language disorders has shown
that nonverbal visuospatial working memory tasks are more difficult than processing speed
tasks for children with language disorders (Durant et al. 2019). However, one explanation
for this may be that bilingual children use self-talk as a language-based strategy to recall
and recreate a sequence of pictures even though the task is elicited and performed non-
verbally. Additional evidence links nonverbal working memory with morphosyntactic
performance. In a study of 47 Spanish–English bilingual children with language disor-
ders, Ebert (2014) showed that nonverbal working memory predicted unique variance
in morphosyntactic performance in both languages, as measured by sentence repetition,
after accounting for chronological age and verbal working memory. Another study that
examined the differential contributions of non-verbal working memory to morphosyn-
tactic processing using a grammaticality judgment task in school-aged monolingual and
simultaneous bilingual children found that monolinguals were more sensitive to errors on
the grammaticality judgement task than were their simultaneous bilingual counterparts
(Gangopadhyay et al. 2016).
Here, greater working memory was associated with perform-
ing the grammaticality judgment task only in bilinguals. The contribution of working
memory to syntactic processing was found only in children with lower language skills.
The contribution of nonverbal working memory on distinct types of language tasks
used to measure morphosyntactic knowledge, however, remains relatively unknown. As
language tasks become more complex, the cognitive and linguistic task demands are likely
to increase, necessitating multiple retrievals from working memory within a short time
frame (Archibald 2017). It is possible that a sentence repetition task, similar to the one
used in Ebert (2014), may tap nonverbal working memory differently than a sentence
completion (cloze) task or narrative recall or generation task. Additional empirical research
is needed to understand how nonverbal working memory is associated across different
tasks of morphosyntax administered to young bilingual children.
Processing speed. Processing speed refers to the automaticity in the access, retrieval,
and integration of information (Bracken and McCallum 1998). It is generally measured
by how quickly an individual is able to successfully complete a task. As with working
memory, processing speed is both domain-general and domain-specific (and verbal or
nonverbal), depending on its reliance on language knowledge. Examples of processing
speed tasks that interact with language include lexical naming tasks, such as rapid naming
tasks (McMillen et al. 2020). Examples of processing tasks that purport to be nonverbal
limit the amount of language used in the presentation and administration of the task and
Languages 2021,6, 36 4 of 21
are designed to elicit a nonverbal (motor) response, such as orienting a shape to complete a
puzzle (Bracken and McCallum 1998).
It is generally understood that processing speed is related to the efficiency with
which bilinguals comprehend and produce language (Clahsen and Felser 2006). The
processing of morphosyntactic information may be less automatic for bilinguals with
reduced L2 proficiency (see Sabourin et al. 2003;Weber-Fox and Neville 1996, for examples
of ERP studies with adult bilinguals). However, this may be due to differences in the
task type, as there may be costs to processing speed when tasks obligate production
versus comprehension of language (Paradis 2010). In online processing tasks that require
children to judge the grammaticality of sentences, bilingual children demonstrate longer
processing times than their monolingual peers (Chondrogianni et al. 2015a). They are
also more accurate in these types of comprehension tasks in comparison to production
tasks. This gap between accuracy of performance on comprehension tasks in comparison to
accuracy of performance on production tasks is larger for bilinguals than for monolinguals.
This asymmetrical performance, where superior performance is found on comprehension,
suggests that bilingual children have the morphosyntactic representation, but may have
difficulty with the retrieval process necessary for speedy production (Chondrogianni and
Marinis 2012;Chondrogianni et al. 2015a,2015b;Paradis 2010). Nonetheless, sensitivity
to L2 grammaticality is dependent upon the grammatical structure being tested and the
L2 learner’s proficiency (for an example with adults, see Van Patten et al. 2012). In a
comparison of simultaneous and sequential bilingual children, Lemmerth and Hopp (2019)
demonstrated that sequential bilinguals may rely on their L1 morphosyntactic knowledge
to processes morphosyntactic information in their L2. This indirect processing route may
slow the processing of morphosyntax.
1.3. Morphosyntactic Task Complexity and Cognitive Demands
In educational contexts, language assessments are used to measure morphosyntactic
knowledge using a variety of tasks. These tasks can elicit responses both at the sentence
level (e.g., aural/oral cloze tasks) as well as at the level of discourse (e.g., narratives). Cloze
tasks are perhaps the most commonly used task for evaluating morphosyntactic knowledge
(Levenston et al. 1984). During a cloze task, children are presented with a picture and asked
to respond to a prompt with the morphosyntactic form that is obligated by the picture
and prompt. For example, the Bilingual English Spanish Assessment (BESA; Peña et al.
2018) probes use of past tense -ed by prompting, “Today, he is walking the dog. Yesterday,
he did it, too. What did he do yesterday? Yesterday, he
. . .
” The expected answer is
“walk
ed
the dog”. In order to complete a cloze task, children must integrate the semantic
and morphosyntactic information presented in the prompt, anticipate and produce a
semantically related word to complete the sentence, and include any obligatory inflectional
morphemes. In adult L2 learners, anticipatory language processing, which is needed to
generate the targeted word form in the cloze task, is supported by high levels of working
memory capacity as well as exposure (Hopp 2015;Sagarra and Herschensohn 2010).
Narrative tasks present a more integrated semantic, morphosyntactic, and pragmatic
task. Such tasks can include the retelling of a story previously heard using one’s own words
or the telling of an original story, often using picture cues. Children have the freedom to
respond using self-determined words and sentences, with little structure provided by the
assessor or imposed by the task itself. Children’s narratives are often mined for information
about their morphosyntax knowledge and lexical-semantic knowledge (microstructure)
as well as use of story structure (macrostructure). Elements of microstructure include
grammaticality, mean length of utterance, and clausal density (Hipfner-Boucher et al. 2015;
Gagarina et al. 2015).
Performance on grammatical cloze and narrative tasks can reflect a wide range of
morphosyntactic abilities. On one end of the spectrum are cloze tasks, which are highly
constrained so as to elicit a single, inflexible response (i.e., walked, marked for past tense -ed).
On the other end are narrative tasks, which are open-ended and virtually unconstrained
Languages 2021,6, 36 5 of 21
as they allow the speaker to select from grammatically accurate options to express the
narrative content. The two types of tasks also differ in their contextualization. Whereas
cloze tasks offer limited context (often only a picture and a brief prompt), narrative tasks
tend to be highly contextualized, with elaborate pictures and/or plots.
Given these differences, it is reasonable to expect that the cognitive demands across
these two different morphosyntactic tasks may differ (Field 2011). Neuroimaging studies
have observed higher levels of neural activity for cloze tasks than for multiple choice items
targeting the same information (Mizumoto et al. 2016). This is hypothesized to reflect both
domain-general (e.g., attention, processing speed) and domain-specific cognitive processes
(e.g., grammatical inferencing), as well as working memory. Furthermore, research with
bilinguals suggests that assessments in the L1 and L2 may pose distinct processing demands.
Evidence from both sentence repetition tasks (Pratt et al. 2020) and narratives (Gutiérrez-
Clellen and Peña 2001) show there is an additional processing cost associated with language
tasks when administered in L2 English to sequential Spanish–English bilingual children.
1.4. Present Study
Previous literature shows that the extent to which bilinguals’ nonverbal working
memory and processing speed contribute to performance on tasks of morphosyntax may
be influenced by the age of the learner, their language exposure, and the type of task being
performed. The present study focuses on nonverbal cognition, as nonverbal cognition is
thought to be most independent of language and consequently demonstrative of domain-
general cognitive abilities (Bracken and McCallum 1998;Cattell 1963). Specifically, we pose
the following research questions:
(1) To what extent does exposure, working memory, and processing speed explain vari-
ability in school-age Spanish–English bilinguals’ performance on cloze and narrative tasks?
(2) To what extent does exposure, working memory, and processing speed explain
variability in school-age Spanish–English bilinguals’ performance on these tasks when
administered in the first versus second language?
We hypothesize that students’ nonverbal abilities will differentially interact with their
language exposure as they complete two kinds of tasks: grammatical cloze tasks and
narrative tasks.
More specifically, we hypothesize that there are distinct nonverbal demands on pro-
cessing speed and working memory. In this study, we ask (a) whether a grammatical cloze
task and a narrative task pose distinct nonverbal cognitive demands and (b) whether these
cognitive demands differ across children’s first (L1) and second (L2) languages.
2. Materials and Methods
2.1. Participants
Data for this study were obtained from a larger longitudinal investigation evaluating
spoken language trajectories for young school-age Spanish–English bilingual children,
Cross-Language Outcomes of Typical and Atypical Development in Bilinguals (Peña et al. 2010).
Of the 360 children who originally participated in the study, children were excluded from
this study if they were missing language exposure data (n= 44), missing data on the
UNIT (n= 8), missing morphosyntax cloze and/or TNL data (English n= 1; Spanish n
= 53). Our participants were Spanish–English bilingual children (163 girls, 144 boys) in
kindergarten through fourth grade recruited from the Central Texas area. Children in this
age range were included in the current investigation because their profiles of language
skills and shifting language dominance are representative of the broader population of
young school-age bilinguals in the United States (for an illustration with an overlapping
sample of children, see Oppenheim et al. 2020). All children were learning Spanish in
the home environment as their first language (age of English acquisition M= 2.3 years,
SD = 1.92) and had a range of English exposure (M= 56.84%, SD = 26.85). Children
attended a variety of educational programs, including one and two-way dual immersion
programs, as well as English as a second language programs, and some children were in
Languages 2021,6, 36 6 of 21
English-only instruction. Thus, children had between 0 and 100% exposure to Spanish and
English at school. For this study, we had complete data for 307 children for the English
analyses and 254 children from the Spanish analyses. See Table 1for demographic data.
Approximately 14% of the children participating in the larger longitudinal study were
diagnosed with developmental language disorder (DLD), based on converging evidence
about language ability from licensed speech-language pathologists. Aligned with recent
criteria for diagnosis of DLD (i.e., Bishop et al. 2017, we did not exclude children who
performed 1.5 standard deviations below the mean on our nonverbal intelligence measure
(see Table 1for descriptive information on nonverbal IQ scores for our sample). Based
on these criteria, this prevalence rate is slightly elevated from recent population studies,
evaluating the prevalence of DLD (Norbury et al. 2016). In the larger study from which
these data are derived, we sampled children at risk for DLD, based on their screening scores
in year 0. For the current study, we did not exclude these children as they are representative
of an educational sample featuring the full range of cognitive and linguistic abilities.
Table 1. Descriptive demographic information.
Demographic Information
Range Mean (SD)
Age (months) 61–138 92.22 (18.67)
English exposure (% week) 0–100 56.84 (26.85)
Age of 1st English exposure (years) 0–6 2.30 (1.92)
SES a0–7 3.01 (1.73)
Nonverbal IQ b60–132 100.48 (14.57)
Gender 47% male
Ethnicity 99% Hispanic
Note:
a
= Hollingshead score (Hollingshead 1973);
b
= scores derived from the abbreviated nonverbal IQ composite on the Universal
Nonverbal Intelligence Test (UNIT; Bracken and McCallum 1998).
2.2. Procedures
All data were collected during the first year of the longitudinal study. Testing occurred
in quiet locations within the children’s schools over 4–5 sessions lasting 30 to 45 min each.
The order and language of testing was randomized across participants, and tests were ad-
ministered by trained bilingual examiners and certified speech language pathologists who
had experience working with bilingual school-age children. Measures of nonverbal ability
were originally given in additional to the comprehensive battery of language measures
to better inform how domain general and specific factors impact children’s performance
on language-based tasks, including tasks on the BESA. Descriptions of tasks that are of
interest for the current study are as follows. Procedures were approved by the Institutional
Review Board of The University of Texas at Austin, 2009-11-0110.
2.3. Language Exposure
Children’s language exposure was collected using the Bilingual Input–Output Survey
(BIOS; Peña et al. 2018). Parents reported on each child’s language input and output in
the home environment on an hour-by-hour basis during a typical weekday and a typical
weekend day. Language input and output was also obtained from teachers for children’s
typical school day. Language input at home and school was combined to create a composite
language exposure measure and averaged across the week for a percent exposure for each
language. For this study, we used percentage of English exposure as the variable in our
statistical analyses.
2.4. Measures of Nonverbal Cognitive Ability
Nonverbal cognitive ability was measured using the Universal Nonverbal Intelligence
Test (UNIT; Bracken and McCallum 1998), which is a measure of general intelligence for
children 5 to 17 years of age. Critically, the administration of this test does not require
Languages 2021,6, 36 7 of 21
language, and children are not required to respond verbally. Nonverbal working memory
and processing speed are the two cognitive constructs measured by the subtests in the
UNIT. The two subtests on the UNIT in the abbreviated IQ composite are (1) symbolic
memory, which assesses working memory via short-term visual recall of content, location,
and sequence, and (2) cube design, which assesses processing speed via pattern processing,
problem solving, understanding of relationships, and planning abilities.
Symbolic Memory. This subtest measures nonverbal working memory. The child is
presented with a series of simple pictures and asked to replicate the sequence. The child
replicates the sequence of drawings using cards featuring the drawings.
Cube Design. This subtest is a timed measure of processing speed. The child uses
colored blocks to replicate a pictured model. The child sees pictures of green and white
blocks arranged in a specific structure. Then, the child must use green and white blocks
to replicate the model structure in the picture within a limited amount of time (Bracken
and McCallum 1998). See Durant et al. (2019) for additional information concerning
administration and reliability with Spanish–English bilingual children.
2.5. Morphosyntactic Ability
Cloze. Cloze items were derived from children’s performance on the morphosyntax
subtests of the Bilingual English Spanish Assessment (BESA; Peña et al. 2018) and the Bilingual
English Spanish Assessment—Middle Extension Field Test Version (BESA-ME; Peña et al. 2016).
The BESA was used for children ages 4;0 to 6;11 while the BESA-ME was used with children
7;0 to 11;0. The BESA presents 18 grammatical cloze items in English and 19 grammatical
cloze items in Spanish; the BESA-ME contains 37 grammatical cloze items in English and
38 grammatical cloze items in Spanish. Differences in the number and type of cloze items
were included for each language to account for cultural- and language-based variations in
children’s knowledge. Morphosyntactic constructions in each language that are known
to be challenging for bilingual children with language impairment (e.g., past tense -ed,
third person present tense -s, and copula in English; direct object clitics, passives, and
subjunctive in Spanish) and serve as clinical markers for DLD (see Bedore et al. 2018, for
additional discussion of clinical markers) are specifically targeted within these tests to
maximize differences by language ability. An example of a cloze item targeting irregular
past tense in English includes “Today, she is eating a banana. And yesterday, she did it too.
What did she do yesterday? Yesterday she______” (response: ate the banana). Children’s
raw scores across all items were summed and divided by the total possible score, yielding
a percent accuracy for the cloze task.
Narratives. Children also completed the Test of Narrative Language in English (TNL;
Gillam and Pearson 2004) and in Spanish (TNL-S; Gillam et al. Forthcoming, in develop-
ment). The TNL is a standardized measure used to assess multiple aspects of children’s
narrative production and comprehension, including listening comprehension, sequencing,
short-term memory, and story cohesion and coherence. The TNL-S is an experimental
measure comprising stories and narrative tasks that are as nearly identical in structure
and type to those on the TNL English version. The present study compiled test items,
specifically targeting morphosyntactic forms which capture children’s morphosyntactic
knowledge; these items comprise a morphosyntactic composite.
The morphosyntactic composite, described in Table A1 in Appendix A, is derived
from children’s performance on two narrative production tasks in each language. In the
first task, children tell a story based on a sequence of a pictures; in the second task, children
tell a story about a single picture. The composite includes nine items per language: two
items assessing temporal relationships, two items assessing causal relationships, two items
assessing grammaticality, two items assessing use of tense, and one item assessing the
consistent use of references/pronouns. All items are scored on a 3-point scale, such that a
0 indicates absent/poor performance, a 1 indicates incomplete/inconsistent performance,
and a 2 indicates complete/accurate performance. For instance, the item evaluating
grammaticality reads, “0 = three or more grammatical errors; 1 = one or more grammatical
Languages 2021,6, 36 8 of 21
errors; 2 = no grammatical errors”. Children’s raw scores ranged from 0 to 18 in each
language. Scores were summed and divided by 18 to yield a percentage accuracy for each
language. (For further information see Table A2 in Appendix B).
2.6. Analytical Strategy
Less than 20% of data for each variable of interest was missing and missing data
were deleted listwise. Prior to exploring concurrent relationships between morphosyn-
tactic variables and predictor variables, we conducted zero-order correlations to explore
the bivariate relationships between morphosyntactic scores on grammatical cloze tasks
and narrative tasks in both languages with each of the predictors: English input/output,
working memory, and processing speed. Four hierarchical linear regressions were con-
ducted as a follow-up analysis to the correlation analysis, as this analysis allows us to
assess the unique contribution of each independent variable to morphosyntax, relative to
other predictors. After controlling for age, we entered in subsequent blocks (a) English
exposure, (b) processing speed and working memory, and (c) interactions between ex-
posure and cognition, to find the combination of variables most highly associated with
morphosyntax scores in each language. We then considered the role of language input
and cognition on morphosyntax using children’s scores from their stronger vs. weaker
language performance.
3. Results
The present study aimed to explore the effects of exposure and cognition on bilingual
children’s performance on two tasks measuring morphosyntax: a grammatical cloze task
and a narrative task. Prior to the main analyses, we conducted a preliminary examination
of central tendencies of the data. Histograms and Q-Q plots showed normal distribution of
scores on all measures in both languages, skewness and kurtosis fell within the acceptable
range of
2 to 2, and Shapiro–Wilk statistical tests of normality were non-significant at an
alpha of 0.05. Ranges, means, and standard deviations for all child-direct measures are
presented in Table 2. On average, participants scored near 50% on both morphosyntax
tasks, with large standard deviations. The range of scores spanned floor (0%) to ceiling
(100%), as is characteristic of school-based bilingual population.
Table 2. Morphosyntactic performance (by language) and nonverbal cognitive performance.
Morphosyntax English: Spanish: Best Language:
Range Mean (SD) Mean (SD) Mean (SD)
Cloze a(% correct) 0–100 53.36 (29.28) 48.42 (25.52) 66.19 (20.70)
Narrative b(% correct) 0–100 43.23 (23.89) 54.68 (23.67) 61.78 (18.47)
Cognition
Range Mean (SD)
Working memory c0–25 9.28 4.53
Processing speed c0–43 16.22 8.08
Note:
a
= scores derived from BESA (Peña et al. 2018) or BESA-ME (Peña et al. 2016);
b
= scores derived from TNL
(Gillam and Pearson 2004)
and TNL-S (Gillam et al. Forthcoming; in development);
c
= scores derived from the Symbolic Memory Symbolic Memory (working
memory) and Cube Design (processing speed) subtests on the UNIT (Bracken and McCallum 1998).
Bivariate correlations between age, English exposure, and all observed morphosyntac-
tic and cognitive variables are presented in Table 3. Age was significantly and positively
related to all measures, with the exception of the Spanish cloze task, with effect sizes
ranging from moderate (age; r= 0.39, p< 0.01) to large (processing speed; r= 0.60, p< 0.01).
Thus, partial correlations between English exposure and all observed morphosyntactic and
cognitive variables, controlling for age, are also presented in Table 3below the diagonal.
Languages 2021,6, 36 9 of 21
Table 3. Bivariate correlations above the diagonal; partial correlations, controlling for age, below the diagonal.
2.
Exp
3.
WM
4.
PS
5.
Eng. cl
6.
Sp cl
7.
Eng. narr
8.
Sp. narr.
1. Age 0.36 ** 0.57 ** 0.60 ** 0.57 ** 0.04 0.53 ** 0.39 **
2. Exposure 0.14 * 0.26 ** 0.63 ** 0.44 ** 0.46 ** 0.00
3. Working memory 0.02 0.51 ** 0.40 ** 0.10 0.39 ** 0.27 **
4. Processing speed 0.06 0.25 ** 0.46 ** 0.02 0.46 ** 0.23 **
5. English cloze 0.35 ** 0.20 ** 0.21 ** 0.05 0.68 ** 0.28 **
6. Spanish cloze 0.24 ** 0.10 0.03 0.15 * 0.03 0.52 **
7. English narrative 0.25 ** 0.12 0.20 ** 0.53 ** 0.10 0.26 **
8. Spanish narrative 0.28 0.06 0.00 0.04 0.60 ** 0.04
* = p< 0.05; ** = p< 0.01.
3.1. Hierarchical Multiple Regressions
A series of hierarchical multiple regression models were computed to predict gram-
matical cloze and narrative morphosyntax scores in both English and Spanish. Predictor
variables were added one block at a time in order to evaluate the unique contribution of
each additional predictor to the model. In all models, age was entered first to control for
developmental effects in subsequent blocks. All models featured the same forced entry of
predictors across four blocks: (1) at block one, age was entered; (2) at block two, English ex-
posure was entered; (3) at block three, the two nonverbal cognitive variables were entered
(working memory and processing speed); (4) finally, at block four, the two interaction terms
between input and each of the cognitive variables were entered. We centered all variables
in the analyses. Centered variables were calculated by subtracting the mean
(a constant)
from each score, X. Centering is an important step when testing interaction effects of XY in
multiple regression, as using uncentered scores can affect collinearity and the interpretation
of interaction results in hierarchical multiple regression (Aiken et al. 1991).
We tested for multicollinearity upon running the hierarchical regression models,
using variance inflation factors (VIF) and tolerance. As recommended, VIF was computed
only after first centering variables (Freund et al. 2003). When VIF values exceed 4.00 or
tolerance levels are less than 0.20, the assumptions of non-collinearity may be violated
(Hair et al. 2013).
For our data, all VIF values were less than 2.28 and all tolerance values
were greater than 0.44.
3.2. Predicting Performance on Cloze Tasks in English and Spanish
English cloze results are shown in the upper half of Table 4. The addition of predictor
variables at all four blocks yielded significant improvement in variance explained. At block
one, age accounted for 33% (adjusted R
2
= 0.32) of the variation in children’s English cloze
scores, F(1, 302) = 145.35, p< 0.01. At block two, English exposure explained an additional
21% of variance (adjusted R
2
= 0.53), F(1, 301) = 133.10, p< 0.01. At block three, working
memory and processing speed accounted for 3% of additional variance (adjusted R
2
= 0.56),
F(2, 299) = 10.07, p< 0.01. At block four, the interaction terms of exposure-by-working
memory and exposure-by-processing speed accounted for an additional 1% of variance, F
(2, 297) = 4.55, p= 0.01. However, examination of univariate t-tests reveals that exposure-by-
processing speed is the only interaction term with univariate significance (part correlation
=
0.08, see Figure 1). After four blocks, the resulting model accounted for approximately
57% of variability in children’s English cloze scores, with age, exposure, working memory,
processing speed, and exposure-by-processing speed retaining individual significance.
Languages 2021,6, 36 10 of 21
Table 4. Model summaries of regressions predicting morphosyntax using cloze tasks.
Block 1 Block 2 Block 3 Block 4
βtβtβtβt
English cloze
1. Age 0.57 11.99 ** 0.39 9.25 ** 0.24 4.35 ** 0.24 4.54 **
2. Exposure to English 0.49 11.53 ** 0.49 11.93 ** 0.47 11.14 **
3. Working memory 0.13 2.76 ** 0.14 2.86 **
Processing speed 0.13 2.62 ** 0.13 2.69 **
4.
Exp. * Working mem.
– – – 0.03 0.74
Exp. * Processing sp. 0.09 2.11 *
R20.32 0.53 0.56 0.57
R2change 0.32 ** 0.21 ** 0.03 ** 0.01 *
Spanish cloze
1. Age 0.04 0.66 0.18 3.06 0.09 1.22 0.10 1.27
2. Exposure to English 0.5 8.77 ** 0.51 8.58 ** 0.52 8.20 **
3. Working memory – – 0.13 1.90 0.13 1.85
Processing speed 0.02 0.24 0.00 0.01
4.
Exp. * Working mem.
– – 0.02 0.35
Exp. * Processing sp. 0.12 1.97 *
R20.00 0.22 0.24 0.25
R2change 0.00 0.22 ** 0.01 0.01
* = p< 0.05; ** = p< 0.01.
Languages 2021, 6, x FOR PEER REVIEW 10 of 23
cloze scores, F (1, 302) = 145.35, p < 0.01. At block two, English exposure explained an
additional 21% of variance (adjusted R2 = 0.53), F (1, 301) = 133.10, p < 0.01. At block three,
working memory and processing speed accounted for 3% of additional variance (adjusted
R2 = 0.56), F (2, 299) = 10.07, p < 0.01. At block four, the interaction terms of exposure-by-
working memory and exposure-by-processing speed accounted for an additional 1% of
variance, F (2, 297) = 4.55, p = 0.01. However, examination of univariate t-tests reveals that
exposure-by-processing speed is the only interaction term with univariate significance
(part correlation = −0.08, see Figure 1). After four blocks, the resulting model accounted
for approximately 57% of variability in children’s English cloze scores, with age, exposure,
working memory, processing speed, and exposure-by-processing speed retaining individ-
ual significance.
Figure 1. Scatterplot of interaction between English exposure (divided into three levels) and pro-
cessing speed on English cloze performance.
Spanish cloze results are shown in the bottom half of Table 4. In contrast to English
cloze results, only the addition of English exposure at block 2 explained significant varia-
bility. At block one, age accounted for just 0.2% of variability, F (1, 269) = 0.412, p = 0.521.
At block two, English exposure accounted for 22% of variability (adjusted R2 = 0.22), F (1,
268) = 77.02, p < 0.01. At block three, the addition of working memory and processing
speed did not significantly improve variance accounted for, F (2, 266) = 2.08, p = 0.127. Part
correlations for working memory and processing speed, respectively, totaled 0.10 and
0.01, indicating that less than 1% of variance in Spanish cloze scores was uniquely ex-
plained by cognition. At block four, the addition of the interaction terms between expo-
sure and cognition was not significant, F (2, 264) = 2.10, p = 0.124. The resulting model
accounted for approximately 25% of variability in children’s English SR scores (adjusted
R2 = 0.23), with English exposure as the only significant predictor.
Table 4. Model summaries of regressions predicting morphosyntax using cloze tasks.
Block 1
Block 2
Block 3
β
t
β
t
β
t
β
t
English cloze
1.
Age
0.57
11.99 **
0.39
9.25 **
0.24
4.35 **
0.24
4.54 **
2.
Exposure to English
--
--
0.49
11.53 **
0.49
11.93 **
0.47
11.14 **
3.
Working memory
--
--
--
--
0.13
2.76 **
0.14
2.86 **
Processing speed
--
--
--
--
0.13
2.62 **
0.13
2.69 **
4.
Exp. * Working mem.
--
--
--
--
--
--
−0.03
0.74
Figure 1.
Scatterplot of interaction between English exposure (divided into three levels) and process-
ing speed on English cloze performance.
Spanish cloze results are shown in the bottom half of Table 4. In contrast to English
cloze results, only the addition of English exposure at block 2 explained significant vari-
ability. At block one, age accounted for just 0.2% of variability, F(1, 269) = 0.412, p= 0.521.
At block two, English exposure accounted for 22% of variability (adjusted R
2
= 0.22), F
(1, 268) = 77.02, p< 0.01. At block three, the addition of working memory and processing
speed did not significantly improve variance accounted for, F(2, 266) = 2.08, p= 0.127. Part
correlations for working memory and processing speed, respectively, totaled 0.10 and 0.01,
indicating that less than 1% of variance in Spanish cloze scores was uniquely explained
by cognition. At block four, the addition of the interaction terms between exposure and
cognition was not significant, F(2, 264) = 2.10, p= 0.124. The resulting model accounted for
approximately 25% of variability in children’s English SR scores (adjusted R
2
= 0.23), with
English exposure as the only significant predictor.
Languages 2021,6, 36 11 of 21
3.3. Predicting Performance on Narrative Tasks in English and Spanish
Next, we explored the contribution of exposure and cognition on morphosyntactic
scores collected using a more naturalistic narrative language task. Table 5features English
narrative results. As with the English cloze task, the addition of age, exposure, and the
two cognitive variables explained significant variance. At block one, age accounted for
29% (adjusted R
2
= 0.28) of the variation in children’s English cloze, F(1, 302) = 120.57,
p< 0.01.
At block two, English exposure significantly improved the overall variance and
accounted for 9% additional variance (adjusted R
2
= 0.37), F(1, 301) = 41.71, p< 0.01. At
block three, working memory and processing accounted for 3% of additional variance
(adjusted R
2
= 0.56), F(2, 299) = 8.32, p< 0.01. However, only processing speed retained
univariate significance, t(1, 299) = 3.02, p= 0.003. At block four, the interaction terms of
exposure-by-working memory and exposure-by-processing speed were not significant.
The resulting model accounted for approximately 40% of variability in children’s English
cloze scores, with age, exposure, and processing speed retaining individual significance.
Table 5. Model summaries of regressions predicting morphosyntax using narrative tasks.
Block 1 Block 2 Block 3 Block 4
βtβtβtβt
English narrative
1. Age 0.53 10.90 ** 0.42 8.51 ** 0.25 3.92 ** 0.25 3.96 **
2. Exposure to English 0.31 6.42 ** 0.31 6.57 ** 0.31 6.17 **
3. Working memory 0.11 1.95 0.11 1.95
Processing speed 0.18 3.12 ** 0.18 3.12 **
4. Exp. * Working mem. 0.01 0.14
Exp. * Processing sp. 0.04 0.84
R20.29 0.37 0.41 0.41
R2change 0.29 ** 0.09 ** 0.04 ** 0.00
Spanish narrative
1. Age 0.39 6.71 ** 0.55 8.32 ** 0.52 6.15 ** 0.54 6.37 **
2. Exposure to English 0.30 4.56 ** 0.30 4.52 ** 0.32 4.71 **
3. Working memory 0.06 0.90 0.03 0.36
Processing speed 0.00 0.02 0.03 0.40
4. Exp. * Working mem. 0.10 1.51
Exp. * Processing sp. 0.06 0.80
R20.15 0.22 0.22 0.23
R2change 0.15 ** 0.07 ** 0.00 0.02
* = p< 0.05; ** = p< 0.01.
Spanish narrative results are shown in the bottom half of Table 5. As with English
narrative results, age and exposure explained significant variability. At block one, age
accounted for 15% of variability, F(1, 248) = 45.51, p< 0.01 and at block two, English
exposure accounted for 6% of additional variance (adjusted R
2
= 0.21), F(1, 268) = 77.02,
p< 0.01. At block three, the addition of working memory and processing speed did not
significantly improve variance accounted for, F(2, 245) = 0.38, p= 0.69, nor did the addition
of interaction terms at block four, F(2, 243) = 2.343, p= 0.09. After four blocks, the resulting
model accounted for approximately 24% of variability in children’s Spanish narrative
scores (adjusted R
2
= 0.21), with age and English exposure the only significant predictors.
For the sake of thoroughness, interaction terms with age were also tested and entered into
all models. None of the interactions were significant and, for parsimony, we excluded them
from all models.
3.4. Predicting Performance on Cloze Tasks Based on Language Dominance
Given the differential predictors of morphosyntactic performance across English and
Spanish, we further explored the role of input and cognition on morphosyntax using
children’s scores from their stronger vs. weaker language performance. Stronger language
performance was determined by taking the higher of the two cloze scores across both
languages, whereas weaker language performance was determined by taking the lower of
Languages 2021,6, 36 12 of 21
the cloze two scores. Only children with complete testing data in both languages for the
cloze task (n= 270) and the narrative task (n= 249) were included in follow-up language
dominance analyses. Table 6reports the number and percentage of children who scored
better in English or Spanish on cloze and narrative tasks, among those without any missing
data. Note that a majority of children (65%) were dominant in the same language on both
tasks; however, 35% of children showed mixed dominance across tasks, scoring better in
one language for one task and the other language on the other task.
Table 6. Cross-tabulation of “stronger language” frequency counts across morphosyntax tasks.
Narrative:
Stronger Language
Cloze:
Stronger
language
English: Spanish: Total:
English: n= 51 74 125 (50%)
Spanish: 13 112 125 (50%)
Total: 64 (26%) 186 (74%) 250
Results for weaker and stronger cloze performance are displayed in the upper and
lower halves of Table 7, respectively. We consider the weaker language performance first.
After controlling for age at block one F(1, 268) = 60.47, p< 0.001, English exposure explained
6% additional variance at block two, F(1, 267) = 20.97, p< 0.001. The
β
coefficient was
negative, indicating, with higher exposure to English, that the cloze scores in their weaker
language were lower (recall that for half of participants the weaker cloze performance
occurred in English and for the other half of participants the weaker cloze performance
occurred in Spanish). At block three, working memory and processing speed explained
4% additional variance (adjusted R
2
= 0.27), F(2, 265) = 6.63, p= 0.002; yet, only working
memory retained univariate significance. At block four, interaction terms explained an
additional 5% of variability, F(2, 263) = 10.18, p< 0.001; and only exposure-by-processing
speed retained univariate significance. Again, the
β
coefficient was negative, meaning
that processing speed was more strongly associated with cloze performance in the weaker
language among children with low exposure to English. The resulting model accounted
for approximately 33% of variability in children’s cloze scores in their weaker language,
with age, exposure, working memory, and exposure-by-processing speed all retaining
individual significance.
Table 7.
Model summaries of regressions predicting morphosyntax using cloze tasks in children’s weaker and stronger
languages.
Block 1 Block 2 Block 3 Block 4
βtβtβtβt
Child’s weaker language
1. Age 0.43 7.78 ** 0.54 9.24 ** 0.38 5.04 ** 0.39 5.28 **
2. Exposure to English 0.27 4.58 ** 0.26 4.45 ** 0.27 4.56 **
3. Working memory 0.20 3.03 ** 0.19 2.90 **
Processing speed 0.13 1.16 0.06 0.86
4. Exp. * Working mem. 0.02 0.32
Exp. * Processing sp. 0.24 4.06 **
R20.18 0.24 0.28 0.33
R2change 0.18 ** 0.06 ** 0.04 ** 0.05 **
Child’s stronger language
1. Age 0.32 5.52 ** 0.22 3.49 0.09 1.16 0.10 1.17
2. Exposure to English 0.24 3.89 ** 0.25 3.96 ** 0.25 3.70 **
3. Working memory 0.13 1.79 0.13 1.73
Processing speed 0.08 1.14 0.08 1.06
4. Exp. * Working mem. 0.01 0.10
Exp. * Processing sp. 0.05 0.77
R20.10 0.15 0.17 0.17
R2change 0.10 ** 0.05 ** 0.02 0.00
* = p< 0.05; ** = p< 0.01.
Languages 2021,6, 36 13 of 21
In contrast, exposure was the only predictor that significantly explained children’s
morphosyntactic scores in their stronger language. At block one, age accounted for 10%
of variance in scores. At block two, English exposure significantly improved the variance
accounted for to 15%, F(1, 267) = 15.13, p< 0.001. The addition of cognitive variables and
interaction terms were not significantly related to morphosyntactic outcomes at blocks
three and four.
3.5. Predicting Performance on Narrative Tasks Based on Language Dominance
Table 8depicts children’s narrative scores in their weaker and stronger language. In
the weaker language, only the addition of age at block one yielded significant change in
variance accounted for, F(1, 247) = 108.91, p< 0.001, explaining 31% of variability. An
analysis of all predictors present in the model at block four revealed that the interaction
of exposure-by-processing speed was also significant (t=
2.13, p= 0.026). The part
correlation of the interaction was weak (r=
0.11), explaining 1.25% of unique variability
in the final model.
Table 8.
Model summaries of regressions predicting morphosyntax performance using narrative tasks in children’s weaker
and stronger languages.
Block 1 Block 2 Block 3 Block 4
βtβtβtβt
Child’s weaker language
1. Age 0.55 10.44 ** 0.58 9.21 ** 0.49 6.17 ** 0.50 6.32 **
2. Exposure to English 0.05 0.83 0.06 0.92 0.06 0.88
3. Working memory – – 0.06 0.85 0.05 0.73
Processing speed 0.10 1.52 0.05 0.65
4.
Exp. * Working mem.
– – 0.01 0.13
Exp. * Processing sp. 0.14 2.13 *
R20.31 0.31 0.32 0.33
R2change 0.31 ** 0.00 0.01 0.02
Child’s stronger language
1. Age 0.56 10.64 ** 0.56 8.91 ** 0.44 5.59 ** 0.46 5.86 **
2. Exposure to English 0.00 0.01 0.00 0.06 0.03 0.46
3. Working memory – – 0.11 1.62 0.06 0.88
Processing speed 0.10 1.52 0.10 1.45
4.
Exp. * Working mem.
– – – 0.15 2.33 *
Exp. * Processing sp. 0.03 0.47
R20.31 0.31 0.33 0.35
R2change 0.31 ** 0.00 0.02 * 0.02
* = p< 0.05; ** = p< 0.01, =p= 0.056.
Similarly, the model predicting narrative scores in the stronger language also showed
a significant change in variance accounted for at block one (adjusted R
2
= 0.31) with the
addition of age, F(1, 247) = 113.10, p< 0.001. As with the weaker language, exposure
accounted for no additional significant variability at block two, F(1, 246) = 0.00, p= 0.993.
At block three, working memory and processing accounted for 2% of additional variance
(adjusted R
2
= 0.32), F(2, 244) = 3.28, p= 0.039. However, only age reached univariate
significance. At block four, the interaction terms of exposure-by-working memory and
exposure-by-processing speed were not significant, F(2, 242) = 2.91, p= 0.056. After four
blocks, the final model accounted for approximately 35% of variability in children’s stronger
narrative, with age and exposure-by-working memory retaining individual significance.
4. Discussion
The purpose of this paper was to examine potential differential contributions of
nonverbal cognitive processing to morphosyntactic task performance. We found that
nonverbal cognitive skills predicted children’s performance differently on grammatical
Languages 2021,6, 36 14 of 21
cloze versus narrative tasks. In children’s L1 (Spanish), cognition was not significantly
related to performance on either grammatical cloze or narrative tasks. Performance on
narrative tasks in children’s L2 (English) was significantly predicted by processing speed,
after controlling for age and exposure. Grammatical cloze tasks in children’s L2 (English)
posed an additional cognitive demand on working memory, as well as processing speed.
Overall findings suggest the cognitive demands of assessment vary for bilinguals based
upon the language and type of assessment.
In order to approach morphosyntactic tasks in a broadly informative way, we used
language tasks that target sentence-level (grammatical cloze tasks) and discourse-level
(narrative tasks) language. We temper this by being very clear that a grammatical cloze
task may, in some ways, prime children to complete the task (Shin and Kiel 2009). This is
due to the nature of grammatical cloze tasks, which elicit a target word that is very similar
to words embedded earlier in the test item. For example, a grammatical cloze item may
read: “The boys swim every day. Yesterday they _____”. Because the word “swim” appears
within the test item, the child may be cognitively primed to choose the word “swam”. The
narrative tasks on the TNL asked children to both recount stories from their own experience
and to retell stories they had heard. These grammatical cloze and narrative tasks can be
conceptualized as being on opposite ends of a continuum, in regard to both their contextual
support and formality. The grammatical cloze tasks were closed-ended, more formal in
nature, and lower in contextualization when compared to narrative tasks. Thus, during
cloze tasks, children are not asked to use language in a naturalistic way, as one would when
recounting an experience. In contrast, the narrative tasks were open-ended, less formal,
and featured images for contextualization. Given these differences, we hypothesized that
the distinct morphosyntactic tasks may tap different cognitive processing capacities. We
also hypothesized that, for our sample of children, who represent a bilingual population,
tasks in English and Spanish may tap cognitive skills differently.
We found a differential contribution of nonverbal cognitive skills on children’s perfor-
mance on grammatical cloze versus narrative tasks in English, suggesting that, for bilingual
students, domain general nonverbal cognitive skills are tapped differently across distinct
tasks. Specifically, an additional 3% of variability in cloze performance in English was
explained by the addition of working memory and processing speed (after controlling for
age and exposure). An additional 4% of variability in narrative performance in English
was explained by the addition of working memory and processing speed, although only
processing speed retained univariate significance (p< 0.05). These results may reflect the
priming effect that exists in grammatical cloze tasks (Bock 1986;Shin and Kiel 2009;Shin
and Kiel 2012) but is less apparent in narrative tasks. Priming and automaticity may go
hand in hand with the automaticity of cloze responses, particularly as children become
more familiar with their second language and are able to process the task more quickly (for
example, as seen in gating tasks) (Marshall and van der Lely 2008).
Our results also found that these cognitive processes are differentially taxed across
children’s first and second language. Cognition was not significantly related to performance
in the L1 on any tasks. One explanation for this finding may be that a bilingual performing
morphosyntactic tasks in their L2 utilizes nonverbal cognitive skills to a greater degree than
a bilingual performing identical tasks in their L1, because verbal abilities are less robust in
their L2. Bilinguals may use their nonverbal cognitive skills in a compensatory way when
completing language tasks in the L2, to support verbal skills that are still in development.
These findings are consistent with those from previous investigations on cognitive
processing among bilinguals, showing a differential taxation of cognitive processes, as
indicated by performance on grammatical tasks (Da Fontoura and Siegel 1995;Abu-Rabia
and Siegel 2002). The additional processing cost in the L2 may stem from the cognitive
demands inherent in cloze tasks: interpreting the cloze sentence’s meaning, choosing the
missing word, and producing it in its grammatically correct form, all in the L2. We posit
that grammar in an L2 may not be as automatic as it is in an L1, in alignment with our
hypothesis. Given these findings, students completing the grammatical cloze task in the
Languages 2021,6, 36 15 of 21
L2 must utilize their cognitive skills to a greater extent than they would in their L1, in order
to produce language in the L2.
Narrative tasks in the L2 also taxed children’s processing speed more than narratives
produced in the L1, implicating larger task demands for L2. Importantly, this finding
implies that, while bilingual students are often able to complete language-based narrative
assessments in their L2, the processing demands required to successfully perform these
tasks are differentially impacted according to the language (L1 or L2) of the task. Narrative
tasks completed in the L2 may be more difficult for bilingual students than narrative tasks
in the L1. The relative difficulty of an assessment task, based upon the test taker’s language
status, is an important consideration for practitioners and creators of assessments.
4.1. A Balance of Tasks
Given that bilingual children are not two monolinguals in one cognitive system, but
rather, bilinguals are learners of multiple languages which develop over time, we wanted
to consider whether there were patterns of cognitive contribution which were reflective
of the child’s weaker or stronger language. Additionally, we wanted to consider weaker
versus stronger language and task demands in light of recent findings on how a child’s
dominant language is not always their first language (Oppenheim et al. 2020). Therefore,
language strength is not as simple as “L1” and “L2”. Weaker versus stronger may be a
more useful conceptualization of bilinguals. For our Texas-based sample, in particular,
children do not simply learn another language, such as English, in a formal language class
later in schooling. They live in bilingual realities. This balance of languages via exposure
in their environments is such that the language that is not their L1 may become more
dominant as they are exposed to things such as more formal schooling, older siblings,
media, and friends.
Thus, we considered a child’s stronger and weaker performance on each of the lan-
guage tasks (see Table 6). Of children with complete data in both languages, (n= 250),
we conducted follow-up analyses to look at patterns across the language in which they
performed more accurately (i.e., stronger language) vs. less accurately (i.e., weaker). Not
surprisingly, children’s performance in their weaker language on cloze tasks was associated
with a higher processing cost. This was not true for stronger language performance cloze
tasks. There was no significant relationship between our study’s cognitive factors and
performance on narrative tasks in either strong or weaker language. That is, we did not
find a significant relationship between morphosyntactic skills when measured by narrative
tasks and working memory or processing speed. This null relationship was observed in
both the weaker and stronger language. Recall that the narrative language tasks are less
structured, more contextualized tasks. In these ways they are a more ecologically valid
language task. So, it may be that when completing narrative tasks, children are more
relaxed and/or more practiced or habituated to thinking in and talking in the manner of
telling narratives. There are also more ways to be correct when telling a narrative versus
the relatively narrowly correct way to answer the grammatical cloze task items. These
provide a possible explanation for why we did not find a cognitive contribution in either
strong or weak language (regardless of the language being English or Spanish) on the
grammatical narrative task, though this merits further research.
There were interaction effects when we considered language exposure and perfor-
mance on tasks in children’s weaker or stronger language. There was a negative association
between Spanish cloze task performance when effects were interacted with higher English
language exposure for children who performed weaker on the cloze task in Spanish. This
was logical, given that increased exposure to English will negatively impact grammatical
cloze performance in Spanish (See Table 7). This is possibly because of the higher process-
ing demands of having to complete tasks in Spanish when students are being exposed to
English at higher rates. As exposure to English increases, children theoretically become
further and further away from a “balanced” profile of exposure. In fact, children have been
shown to switch in language dominance from their L1 to their L2 (Oppenheim et al. 2020).
Languages 2021,6, 36 16 of 21
An additional explanation for this phenomenon is that, among children who have
low cumulative English exposure and whose weaker language is English, high current
exposure to English may not necessarily result in immediate increased accuracy on an
English grammatical task. It is possible there is U-shaped learning—that is, children who
are early learners of English may perform in a less standardized grammatical way before
they perform in a more standardized grammatical way (i.e., overgeneralization of rules
governing regular and irregular cases) (Kuczaj 1977).
For children who have high English exposure, and whose weaker of two languages is,
nonetheless, English, it might be the case that these children represent the bottom 15% of
learners, who are low in both English and Spanish, regardless of exposure. This could also
be an effect of the nature of the children’s English exposure. We only collected data on the
quantity of language that children were exposed to via self-report. We do not collect any
direct data on the quality/nature of this English exposure. It is possible that this measure
is missing some essential aspects of English language exposure that might explain this
negative relationship.
4.2. Findings in Light of the Theory of Cognitive Abilities
There are different potential reasons for our various findings that can best be un-
derstood via our theoretical framework of the theory of cognitive abilities. Recall that
our study and hypotheses were grounded in the theory of cognitive abilities by Cattell–
Horn–Carroll (Cattell 1963;Flanagan and McGrew 1998). This framework posits that
domain general factors we tested (working memory and processing speed) may support
the completion of formal educational assessment tasks. Cattell–Horn–Carroll (Cattell 1963;
Flanagan and McGrew 1998) posited that intelligence is made up of abilities at both the
domain-general level and at the domain-specific level. It is important to note that domain-
specific abilities are often supported by domain-general abilities. An example would be
working memory capacity (domain general) supporting the domain-specific abilities of
language that are used to manipulate language on an assessment task. We can see this type
of functioning in using language to complete a grammatical cloze task. We must bear in
mind that as domain-general factors support domain-specific tasks, these mechanisms are
not always cleanly delineated. For example, students may use auditory working memory
to hold phonological sequence representations in order to complete grammatical cloze
tasks
(Park et al. 2015).
Such complex relationships may explain the joint contributions of
cognitive factors on English grammatical cloze tasks.
4.3. Implications
Our study and findings suggest implications for bilingual language assessment best
practice. The ways in which nonverbal cognitive skills support language task performance
may inform supports for remediating possible linguistic deficits. This is one reason why it
is important to test a child in both languages, document their exposure, and understand
that up to 10% of children’s performance on certain tasks (such as grammatical cloze
tasks) may be explained by cognition and not language. While grammatical narrative
tasks did not tap cognitive factors in a statistically significant way (for either language,
whether weak or strong) it may be that language tasks which are closed-ended and low
in contextualization are, by their nature, tapping different skills based upon the child’s
language status. Additionally, an assessment which does not take into account a child’s
amount of exposure to the language being measured may lack clarity around the cognitive
processes at work behind a bilingual child’s performance.
The implicit differential cognitive demands across children’s languages and language-
based tasks have implications for assessment and clinical practice. First, assessments
used to determine eligibility for language services or educational testing should consider
both of the child’s languages, not only the language that is considered to be dominate.
This is consistent with recommendations for clinical practice (Bedore and Peña 2008;
Bedore et al. 2012). While this is not always possible as standardized assessment measures
Languages 2021,6, 36 17 of 21
evaluating a child’s first language are not always available, our findings support previous
positions (i.e., Bedore et al. 2012) describing the importance of information on the child’s
development in their first language for acquiring the most accurate picture of their linguistic
ability. We additionally propose that—beyond the language of the task—examiners should
consider the demands of the language task, including the task type and morphosyntactic
structures embedded in the task, as this will differentially tax the child’s cognitive load.
This is particularly important as morphosyntactic forms are considered clinical markers for
identifying children with developmental language disorder (Bedore et al. 2018).
Additionally, the ways in which we consider and interpret studies of language profi-
ciency and/or disability versus difference that do not account for nonverbal factors may
shift somewhat if our findings are replicated and garner further attention. Additionally,
practitioners (including teachers and pediatricians), as well as parents, can be made further
aware of the ways in which potential difficulty with language and ultimately literacy
(Kim et al. 2015)
may be signaled by children’s ability or inability to perform certain kinds
of tasks, which may include nonverbal tasks.
Many of the current educational practices for bilingual instruction and assessment are
not yet evidence based. This is in part because of the ongoing debate about the validity of
studying bilingual’s cognition and the ways in which the fields of education, linguistics and
cognition generally consider the importance of studying bilingual cognition. As mentioned,
academic inquiry about bilingual’s cognition has generally sought to elucidate some added
cognitive benefit of bilingualism versus the elucidation of subtle differences in cognition in
a second language. For example, studies have focused on whether or not being bilingual
enhances such domain-general cognitive factors as executive function (Crivello et al. 2016;
Dick et al. 2019).
4.4. Limitations
We acknowledge that bilingualism as a whole involves languages other than En-
glish and Spanish. Additionally, we are reporting on the findings of language use in a
particular context and setting which may or may not generalize to other contexts and
settings. This includes, but is not limited to, the cultural and policy environments of Texas
and its complicated histories and attitudes towards bilingualism, particularly in public
school settings.
We acknowledge that our findings may not be fully generalizable to all bilingual
testing. Running these analyses with speakers of other languages (and in other areas)
might have yielded different results. Our study also examined kindergarteners through
fourth graders and these results may vary by age of participants.
4.5. Future Directions
Examining processing speed in typically developing (TD) and developmental lan-
guage disorder (DLD) populations of mono and bi-lingual students may provide insights
into the cross-linguistic differences of TD and DLD manifestations in cognition. These po-
tential differences in processing speeds may vary according to task demands and cognitive
domain-general and domain-specific processes. Additionally, implications for the findings
of our current study point toward possible ways in which nonverbal and verbal skills are
tapped according to child-level language exposure, which could potentially vary by not
only the child’s language proficiency status but also their typicality or atypicality (disabil-
ity). In the future, by exploring the ways in which nonverbal cognitive factors function
for typical and atypical bilinguals, we may better understand and evaluate students with
language difference, language disability, and those with both difference and disability.
Languages 2021,6, 36 18 of 21
Author Contributions:
Conceptualization, T.W., A.S.P., K.D., S.M., E.D.P., and L.M.B.; software,
A.S.P.; formal analysis, A.S.P.; investigation, T.W., A.S.P., K.D.; resources, E.D.P. and L.M.B.; data
curation, A.S.P.; writing—original draft preparation, T.W.; writing—review and editing, A.S.P., K.D.,
S.M., E.D.P., and L.M.B.; visualization, A.S.P.; supervision, E.D.P. and L.M.B.; project administration,
E.D.P. and L.M.B.; funding acquisition, E.D.P. and L.M.B. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by National Institute on Deafness and Other Communication
Disorders (NIDCD) Grant R01 DC010366.
Institutional Review Board Statement:
Protocol Number 2009-11-0110. “Research on individual or
group characteristics or behavior (including, but not limited to, research on perception, cognition,
motivation, identity, language, communication, cultural beliefs or practices, and social behavior) or
research employing survey, interview, oral history, focus group, program evaluation, human factors
evaluation, or quality assurance methodologies. Note: Some research in this category may be exempt
from the HHS regulations for the protection of human subjects. 45 CFR 46.101(b)(2) and (b)(3). This
listing refers only to research that is not exempt.”
Informed Consent Statement:
Parents signed consent forms indicating their permission for their
children to participate in the study. Children 7 and above signed an assent form affirming their
willingness to participate in the study.
Data Availability Statement:
The data are not publicly available because we are not yet done
analyzing and de-identifying the data.
Acknowledgments: We would like to thank all of our research participants.
Conflicts of Interest:
Elizabeth Peña and Lisa Bedore are authors of the BESA and BESAME.They
receive royalties for the sale of the BESA.
Appendix A
Table A1.
Morphosyntactic composites in English and Spanish derived from performance on Test of Narrative Language in
English and Spanish.
TNL Item Type English Items Spanish Items
Temporal relationships—uses one or more adverbial phrases or clauses
between actions or events
Task 4, #19
Task 6, #8
Task 4, #20
Task 6, #8
Causal relationships—indicates causal relationships between actions or events;
using (because, so that, since, in order to . . . )
Task 4, #20
Task 6, #9
Task 4, #21
Task 6, #9
Grammaticality—uses grammatically correct sentences without errors Task 4, #21
Task 6, #15
Task 4, #22
Task 6, #15
Tense—uses the same tense throughout the story Task 4, #22
Task 6, #14
Task 4, #23
Task 6, #14
Reference—consistent references to characters; appropriate pronoun use Task 6, #13 Task 6, #13
Total items: 18 18
Appendix B
Table A2.
Morphosyntactic cloze composites
a
, derived from Bilingual English Spanish Assessment
b
and Bilingual English Spanish Assessment—Middle Extension c.
English Forms Example # of items on
BESA
# of items on
BESA-ME
Progressive They are watching 3 –
Negative Doesn’t like 3 2
Plural Apples3 3
Possessive Duck’s eggs 3 3
Third-person singular
Plays3 4
Languages 2021,6, 36 19 of 21
Table A2. Cont.
English Forms Example # of items on
BESA
# of items on
BESA-ME
Copula Flower is pretty 3 2
Passive Dog was chased 3 4
Past Walked the dog 3 4
Irregular past Ate the banana 4
Question inversion What is it . . . ? 6
Prepositions On the plate 2
Relative clause Dog that has black fur – 3
Spanish forms Example # of items on
BESA
# of items on
BESA-ME
Progressive La niña estánadando 3 –
Article Unas/las manzanas 4 3
Direct object clitic Éllos abraza 4 3
Subjunctive Quiere que coman 4 6
Irregular past La niña fue – 4
Prepositions Encima de la mesa 2
Negative No tiene – 3
Relative clause Niño que juega con la pelota – 4
Adjective Zapato morado– 4
Conditional Iría– 5
Imperfect past Ella nadaba – 4
Note:
a
= scores on items in composites were summed and converted to percentages;
b
=Bilingual English Spanish
Assessment (BESA; Peña et al. 2018);
c
=Bilingual English Spanish Assessment—Middle Extension Field Test Version
(BESA-ME; Peña et al. 2016).
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Evidence is mixed regarding whether and why bilingual children might be advantaged in the development of executive functions. Five preregistered hypotheses regarding sources of a bilingual advantage were tested with data from 102 Spanish–English bilingual children and 25 English monolingual children who were administered a test of executive attention, the flanker task, at 7, 8, and 9 years of age. Measures of the children’s early and concurrent bilingual exposure and their concurrent English and Spanish skill were available from a larger longitudinal study in which these children participated. Tests of the preregistered hypotheses yielded null findings: The bilingual children’s executive attention abilities were unrelated to their amount of early exposure to mixed input, to balance in their early dual language exposure, to balance in their concurrent exposure, to their degree of bilingualism, or to their combined Spanish + English vocabulary score. English vocabulary score was a positive significant correlate of executive attention among the bilingual children, but those bilingual children above the group median in English vocabulary did not outperform the monolingual children when the comparison was adjusted for nonverbal IQ. These findings suggest that a language learning ability may explain the association between bilingualism and executive function. Because the best statistical approach to testing for effects on differences is a matter of dispute, all analyses were conducted with both a difference score and a residual gain score as the outcome variable. The central findings, but not all findings, were the same with both approaches.
Chapter
All normal children in normal environments acquire language. However, all normal children in normal bilingual environments do not acquire two languages. This chapter asks what makes the simultaneous acquisition of two languages more difficult than the acquisition of one. Focusing on children in immigrant families whose two languages are a minority language used more at home and a majority, societal language, this chapter describes common patterns and individual differences in bilingual development. The most frequently occurring outcome in that circumstance is strong skill in the majority language with more varied and weaker skills in the minority language. This chapter also reviews research that identifies factors that contribute to individual differences in order to identify the experiences and abilities that support bilingual development. Those factors include the quality and quantity of children's exposure to each language, children's use of each language, and the functional value of proficiency in each language. We conclude that two languages are more difficult to acquire than one because language acquisition requires substantial and continued environmental support. It is not easy for children to acquire strong and comparable skills levels in two languages because environments tend not to provide high and comparable levels of support for two languages.
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The present study analyzed lexical processing efficiency in Spanish-speaking English language learners (ELLs) and their monolingual English-speaking peers from kindergarten through second grade. Specifically, changes in the patterns of speed and accuracy on a rapid object-naming task were evaluated across languages for the ELL children and across the groups of children. Repeated measures analysis of variance demonstrated that ELL children have a rapid shift in language processing efficiency from Spanish to English by the end of kindergarten. Results also showed that by the end of kindergarten ELL children were slightly faster and more accurate in English compared with their monolingual peers. This work provides perspective on how lexical processing is impacted by the development of a dual lexical system. We discuss how lexical density, strength of lexical connections, and environmental constraints may influence this rapid shift in lexical processing efficiency for young Spanish-speaking ELL children.
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Theories of how language works have shifted from rule-like competence accounts to more skill-like incremental learning accounts. Under these, people acquire language incrementally, through practice, and may even lose it incrementally as they acquire competing mappings. Incremental learning implies that (1) a bilingual's abilities in their languages should depend on how much they practice each (not merely age of acquisition), and (2) using an L2 more could cause a bilingual to gradually 'unlearn' their L1. Using timed picture naming and vocabulary measures, we tracked 139 children for several years as they transitioned from mostly-Spanish homes to mostly-English schools. Following their increased English use, many became more proficient in English than Spanish around the third grade, demonstrating continual learning. But their Spanish also improved, showing that L1-attrition is not inevitable. Incremental learning explains both co-improvement and L1-attrition as consequences of experience-driven learning: improvement from continuing L1 use can offset competitive unlearning.
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This article investigates verbal short-term memory (vSTM) and verbal working memory (vWM) abilities and their relation to lexical and syntactic abilities in monolingual (mono-) and bilingual (bi-) children with Developmental Language Disorder (DLD) and typical development (TD). The authors employed the following tasks: vSTM (non-word repetition and forward digit span), vWM (backward digit span), receptive vocabulary, syntactic production (sentence repetition) and syntactic comprehension (relative clauses, reflexives and passives). While the mono-and bi-DLD groups underperformed the mono-and bi-TD groups respectively in all tasks, the two clinical groups differed only in receptive vocabulary. vSTM was a significant predictor of syntactic performance for both monolinguals and bilinguals, while vWM was a significant predictor of syntactic performance only for bilinguals. These findings suggest that impairments in vSTM, wVM and syntax are core clinical features in DLD, and that vWM makes a greater contribution to syntax in bilinguals than in monolinguals.
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Purpose This study investigates the interaction of language ability status, cultural experience, and nonverbal cognitive skill performance in Spanish–English bilinguals with typical development (TD) and developmental language disorder (DLD). Method One hundred sixty-nine Spanish–English bilingual kindergartener's scores on the Symbolic Memory and Cube Design subtests from the Universal Nonverbal Intelligence Test ( Bracken & McCallum, 1998 ) were analyzed by language ability (TD vs. DLD). Results t tests and analysis of variance showed bilingual children with TD and DLD performed comparably to the Universal Nonverbal Intelligence Test norming sample on the cube design task, while children with DLD had significantly lower performance on the symbolic memory task. Conclusion These results suggest that cultural experience minimally impacted performance for bilingual children with typically developing language. Bilingual children with DLD were differentially impacted on symbolic memory, a task that is verbally mediated despite nonverbal administration and performance. Findings are discussed within the Cattell–Horn–Carroll theory of cognitive abilities.
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Learning a second language in childhood is inherently advantageous for communication. However, parents, educators and scientists have been interested in determining whether there are additional cognitive advantages. One of the most exciting yet controversial¹ findings about bilinguals is a reported advantage for executive function. That is, several studies suggest that bilinguals perform better than monolinguals on tasks assessing cognitive abilities that are central to the voluntary control of thoughts and behaviours—the so-called ‘executive functions’ (for example, attention, inhibitory control, task switching and resolving conflict). Although a number of small-2–4 and large-sample5,6 studies have reported a bilingual executive function advantage (see refs. 7–9 for a review), there have been several failures to replicate these findings10–15, and recent meta-analyses have called into question the reliability of the original empirical claims8,9. Here we show, in a very large, demographically representative sample (n = 4,524) of 9- to 10-year-olds across the United States, that there is little evidence for a bilingual advantage for inhibitory control, attention and task switching, or cognitive flexibility, which are key aspects of executive function. We also replicate previously reported disadvantages in English vocabulary in bilinguals7,16,17. However, these English vocabulary differences are substantially mitigated when we account for individual differences in socioeconomic status or intelligence. In summary, notwithstanding the inherently positive benefits of learning a second language in childhood¹⁸, we found little evidence that it engenders additional benefits to executive function development.
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Purpose: This study examines English performance on a set of 11 grammatical forms in Spanish-English bilingual, school-age children in order to understand how item difficulty of grammatical constructions helps correctly classify language impairment (LI) from expected variability in second language acquisition when taking into account linguistic experience and exposure. Method: Three hundred seventy-eight children's scores on the Bilingual English-Spanish Assessment-Middle Extension (Peña, Bedore, Gutiérrez-Clellen, Iglesias, & Goldstein, 2008) morphosyntax cloze task were analyzed by bilingual experience groups (high Spanish experience, balanced English-Spanish experience, high English experience, ability (typically developing [TD] vs. LI), and grammatical form. Classification accuracy was calculated for the forms that best differentiated TD and LI groups. Results: Children with LI scored lower than TD children across all bilingual experience groups. There were differences by grammatical form across bilingual experience and ability groups. Children from high English experience and balanced English-Spanish experience groups could be accurately classified on the basis of all the English grammatical forms tested except for prepositions. For bilinguals with high Spanish experience, it was possible to rule out LI on the basis of grammatical production but not rule in LI. Conclusions: It is possible to accurately identify LI in English language learners once they use English 40% of the time or more. However, for children with high Spanish experience, more information about development and patterns of impairment is needed to positively identify LI.
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Aims The present study aimed to investigate the effect of task demand in working memory on bilingual cognitive advantage (interference suppression and response inhibition) in young bilinguals. Methodology Experiment 1 was performed with the flanker, Go/No-go, and modified flanker tasks, in which the first two tasks were involved in lower storage demand of working memory and the last task was involved in higher storage demand of working memory. Experiment 2 was performed with the Conditional-Go/No-go task, with a higher processing demand of working memory. Data and analysis Reaction time and accuracy data were analyzed using a repeated measures analysis of variance. Findings/Conclusions In Experiment 1, results showed that compared to monolinguals, the bilingual advantage in interference suppression occurred in the task with high storage demand (i.e., modified flanker task) and not in the low demand task (i.e., flanker task); however, this advantage effect was not observed in response inhibition. In Experiment 2, with the increasing working memory processing demand of tasks, the bilingual advantage in response inhibition was observed. Originality The current study firstly examined the effect of task working memory demand on the bilingual advantage and provided some restrictive conditions for the advantage. Significance/Implications Our results provide new evidence to support the effect of bilingual cognitive advantage.
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Purpose This exploratory study describes the emergent literacy skills of children with developmental language disorder (DLD) who speak Spanish, a language with a simple phonological structure and transparent orthography. We examine differences between children with DLD and their typically developing (TD) peers on a battery of emergent literacy measures. Method Participants included 15 monolingual Spanish-speaking children with DLD (who did not present with cognitive difficulties) and 15 TD controls matched for age, gender, and socioeconomic status, ranging in age from 3;10 to 6;6 (years;months; M age = 4;11). All children completed a battery of comprehension-related emergent literacy tasks (narrative retell, print concept knowledge) and code-related emergent literacy tasks (beginning sound, rhyming awareness, alphabet knowledge, and name-writing ability). Results On average, children with DLD performed significantly worse than TD controls on a battery of comprehension- and code-related emergent literacy measures. On all code-related skills except rhyming, children with DLD were more likely than their TD peers to score “at risk.” Conclusions The results suggest some universality in the effect of DLD on reading development. Difficulties with emergent literacy that are widely documented in English-speaking children with DLD were similarly observed in Spanish-speaking children with DLD. Future research should explore long-term reading outcomes in Spanish for children with DLD.
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This review scrutinizes the evidence concerning the factors that affect the ease with which multilinguals learn additional languages. First, I focus on language learning experiences that could help multilinguals acquire new languages (e.g., consequences of exposure, use of prior knowledge, biliteracy). I then discuss how multilinguals manage multiple languages and struggle with language control problems. By finally shedding more light onto effects of learning on the brain and the ways it adapts to the higher processing demands when having to manage multiple languages, it becomes clear that the key to understanding learning and processing of multiple languages lies in understanding the adaptive and dynamic nature of the brain. Although the brain is striving for efficient processing, environmental influences, communicative demands and genetic predispositions influence the learning and processing of multiple languages. I therefore suggest five specific effects related to multilingualism which may ease subsequent learning of multiple languages.
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To investigate cross-linguistic lexical and syntactic influences in grammatical gender in early bilingualism, we tested twelve simultaneous, twelve early successive bilingual Russian-German children, and fifteen monolingual German children aged 8-9 years. An elicited production task in German shows that all bilingual children assign target gender to nouns, irrespective of whether nouns belong to the same (gender-congruent) or different gender class in German and Russian. In visual-world eye tracking, we test whether children use gender marking on German articles or adjectives to anticipate upcoming nouns. Like monolingual children, simultaneous bilingual children make predictive use of gender irrespective of gender congruency. In contrast, the successive bilingual children show predictive gender processing only for lexically congruent nouns. We argue that the asynchronous acquisition of the L2 in successive bilinguals implicates that L2 gender is first accessed through the L1 lexicon. In contrast, syntactic differences between Russian and German gender do not affect early bilingual processing.