The effects of dual language exposure on executive function in Spanish–English bilingual children with different language abilities

Journal of Experimental Child Psychology 188 (2019) 104663

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The effects of dual language exposure on executive function in Spanish–English bilingual children with different language abilities Kimberly Crespo ⇑, Megan Gross 1, Margarita Kaushanskaya Department of Communication Sciences and Disorders, University of Wisconsin–Madison, Madison, WI 53706, USA

a r t i c l e

i n f o

Article history: Received 26 October 2018 Revised 8 July 2019

Keywords: Bilingualism Dual language exposure Executive function Language skills Adaptive control hypothesis Dimensional change card sort task

a b s t r a c t The current study examined the effects of dual language exposure on executive function in 5- to 11-year-old Spanish–English bilingual children with different language skills. Dual language exposure was measured via parent report and was operationalized as the proportion of time spent in an environment where both English and Spanish were present. Executive function was measured via the Dimensional Change Card Sort (DCCS) task. Shifting costs, switching costs, and mixing costs were derived to index executive function performance. A significant interaction between extent of dual language exposure and language skills was observed such that children showed smaller shifting and mixing costs with increased dual language input as their language skills increased. The results suggest a graded effect of dual language exposure on executive function, where a robust language system may be required for dual language exposure to influence executive function. Ó 2019 Elsevier Inc. All rights reserved.

Introduction Despite more than 60 years of study, the effects of bilingualism on cognition remain contentious. To date, there are mixed findings regarding whether bilingualism enhances the development of

⇑ Corresponding author. 1

E-mail address: [email protected] (K. Crespo). Current address: School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA.

https://doi.org/10.1016/j.jecp.2019.104663 0022-0965/Ó 2019 Elsevier Inc. All rights reserved.

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executive function (EF)—cognitive processes involved in goal-driven behavior (Best & Miller, 2010; Diamond, 2013; Miller & Cohen, 2001). One reason behind the mixed findings may be the variability of language exposure and use experienced by bilinguals, with some types of exposure/use leading to changes in cognition but not others. Theories have been proposed that consider how different interactional contexts (e.g., single language vs. dual language) may place contrasting demands on EF processes (e.g., Green & Abutalebi, 2013; Yang, Hartanto, & Yang, 2016). However, there is little empirical work linking fluctuations in dual language exposure to EF. Do differences in dual language exposure influence the degree to which the EF processes are enhanced in bilingual children? This was the question asked in the current study, where we tested the relationship between dual language exposure and EF performance in a large diverse sample of Spanish–English bilingual school-aged children. The sample varied not only in exposure to dual language contexts but also in levels of language skills. Thus, another contribution of our study is that we examined whether language skills would moderate the effects of dual language exposure on EF.

Bilingualism and EF Bilingualism has been theorized to influence the development of EF via recruitment of domaingeneral cognitive processes to select, maintain, and process multiple languages (e.g., Abutalebi & Green, 2007, 2008, 2016; Bialystok, 2011; Bialystok, Craik, & Luk, 2012; Carlson & Meltzoff, 2008; Colzato et al., 2008; Green, 1998). Bilingualism has been found to confer cognitive benefits on behavioral tasks in infants (e.g., Kovács & Mehler, 2009a, 2009b; Mattock, Polka, Rvachew, & Krehm, 2010; Sebastián-Gallés, Albareda-Castellot, Weikum, & Werker, 2012; Singh et al., 2015) and children (e.g., Bialystok & Martin, 2004; Poulin-Dubois, Blaye, Coutya, & Bialystok, 2011). Recent neuroimaging work has also provided support for this broad theorizing, revealing shared regions in the prefrontal and parietal cortices of bilingual speakers associated with language switching, dual language processing, and domain-general cognitive processes (e.g., Abutalebi & Green, 2008; Abutalebi et al., 2013; Garbin et al., 2011; Guo, Liu, Misra, & Kroll, 2011; Ma et al., 2014). Performance on task shifting measures such as the Dimensional Change Card Sort (DCCS) task has been widely used to investigate dimensions of EF in children (e.g., Barac & Bialystok, 2012; Bialystok & Martin, 2004; Diamond, Carlson, & Beck, 2005; Morton, Bosma, & Ansari, 2009; Okanda, Moriguchi, & Itakura, 2010; Park, Ellis Weismer, & Kaushanskaya, 2018; Wiseheart, Viswanathan, & Bialystok, 2016; Zelazo, 2006; Zelazo, Müller, Frye, & Marcovitch, 2003; for a review, see Doebel & Zelazo, 2015). On this complex task, children first sort stimuli by a single dimension (i.e., pre-switch phase). Then, children are required to sort the same stimuli by a different dimension (i.e., post-switch phase). In some versions of the task, a mixing phase is then introduced, where children are required to switch between the two sorting rules within a single block. Performance in each of these phases has been argued to index EF skills related to mental set shifting, inhibition of prepotent responses, and information updating and monitoring (e.g., Miyake et al., 2000; Monsell, 2003; Rubinstein, Meyer, & Evans, 2001). Three performance cost indices are traditionally computed to index these EF skills: shifting cost (indexing shifting skills), switching cost (indexing inhibition skills), and mixing cost (indexing monitoring skills) (e.g., Monsell, 2003; for a review, see Kiesel et al., 2010). Shifting refers to the ability to shift between multiple tasks, operations, or mental sets (Monsell, 2003), and on a task-shifting measure such as the DCCS shifting costs are derived to index shifting ability. A decrease in accuracy and an increase in reaction time in the post-switch phase, compared with the pre-switch phase, have been used to indicate shifting costs. A developmental pattern of shifting abilities has been documented where 3-year-olds have been observed to systematically perseverate in the post-switch phase, but by 5 years of age children can easily sort picture stimuli first by one rule and then by another (e.g., Carlson, 2005; Hongwanishkul, Happaney, Lee, & Zelazo, 2005; Kirkham, Cruess, & Diamond, 2003; Munakata & Yerys, 2001; Zelazo, 2006; Zelazo & Müller, 2002; Zelazo et al., 2003). This improvement in shifting ability has been linked to the development of working memory and inhibitory control during the preschool years (e.g., Blakey, Visser, & Carroll, 2016; Diamond et al., 2005; Garon, Bryson, & Smith, 2008; Müller, Steven Dick, Gela, Overton, & Zelazo, 2006).

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Switching costs have been proposed to reflect local inhibitory control mechanisms (e.g., Koch, Gade, Schuch, & Philipp, 2010). Switching costs are computed by comparing performance on switch trials with that on non-switch trials in a mixed block. During a mixed block, trial-specific rules are believed to persist in memory, enhancing performance when the trial type repeats (i.e., non-switch trials) but creating interference when the trial type switches (i.e., switch trials) (Altmann & Gray, 2008; Monsell, 2003; Waszak, Hommel, & Allport, 2003; Wylie & Allport, 2000). In contrast to switching costs, mixing costs have been hypothesized to reflect global control mechanisms (Braver, Reynolds, & Donaldson, 2003; Koch, Prinz, & Allport, 2005). Sometimes referred to as global switching costs, mixing costs are computed by comparing performance between non-switch trials from the mixed phase and trials from the pre-switch single-dimension phase. Mixing costs are believed to index the ability to update and maintain multiple tasks, implicating working memory and monitoring skills (e.g., Kiesel et al., 2010; Mayr, 2001; Reimers & Maylor, 2005; Rubin & Meiran, 2005). Positive effects of bilingualism on these three indices derived from the DCCS (i.e., shifting costs, switching costs, and mixing costs) have been documented in some studies (e.g., Bialystok & Barac, 2012; Bialystok & Martin, 2004; Bialystok & Shapero, 2005; Diamond et al., 2005; Hernández, Martin, Barceló, & Costa, 2013; Prior & Gollan, 2011; Prior & MacWhinney, 2010; Soveri, RodriguezFornells, & Laine, 2011) but not others (e.g., Carlson & Meltzoff, 2008; Hernández et al., 2013; Kaushanskaya, Gross, & Buac, 2014; Paap & Greenberg, 2013), reflecting the more broad bilingual EF literature that has yielded inconsistent findings (e.g., Gathercole et al., 2014; Hilchey & Klein, 2011; Kousaie, Sheppard, Lemieux, Monetta, & Taler, 2014; Lehtonen et al., 2018; Paap, Johnson, & Sawi, 2016; Paap & Sawi, 2014; Tse & Altarriba, 2015). It remains unclear what specific aspects of the bilingual experience may be responsible for inconsistent findings across studies and what specific factors may be modulating performance on tasks of nonlinguistic executive functioning. Interactional context factors that place different constraints on the language control system have been suggested to be an important contributor to whether bilingualism leads to enhanced executive functioning (e.g., Blanco-Elorrieta & Pylkkänen, 2017; Green & Abutalebi, 2013; Green & Wei, 2014). Interactional context and EF Green and Abutalebi (2013) proposed an adaptive control hypothesis (ACH), where the extent to which bilinguals experience adaptive changes in the neural regions and circuits associated with EF is modulated by the interactional context in which bilinguals use their languages. In their model, single language, dual language, and dense code switching contexts impose different demands on cognitive control processes required to maintain language choice. In a single language context, a bilingual speaker uses each language in a distinct environment. For example, a Spanish–English bilingual child may speak English exclusively at school but speak Spanish exclusively at home. Crucially, there is no switching between languages in a single language context. Conversely, a dual language context is characterized by an interactional environment where both languages are used but typically with different speakers. In a dual language context, a speaker may switch between languages within a conversation. For example, a bilingual child may interact with siblings or playmates in both English and Spanish but use Spanish exclusively with a parent. The dual language context is hypothesized to impose the highest demands on cognitive control processes because interactions in a dual language context require the speaker to manage a myriad of multimodal sociolinguistic cues within the interaction as well as in the immediate environment (e.g., Hernández et al., 2013). There is a growing body of behavioral evidence showing that presence of two languages in bilingual children’s homes is associated with enhanced EF skills (e.g., Bosma, Hoekstra, Versloot, & Blom, 2017; Carlson & Meltzoff, 2008; Gathercole et al., 2010; Hartanto & Yang, 2016; Verhagen, Mulder, & Leseman, 2017). For example, Verhagen et al. (2017) found that bilingual children whose parents spoke more than one language at home performed significantly better on tasks of inhibitory control than bilingual children who were exposed to only one language at home. Similarly, Gathercole et al. (2010) found that bilingual children who were exposed to both languages at home outperformed bilingual children who were exposed to only one language at home as well as monolingual children. Although dual language input was inferred rather than measured in these studies, the findings nevertheless suggest that advantages on EF tasks may be specific to bilinguals who interact in dual language contexts.

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To date, no studies have investigated whether the effects of dual language exposure on EF in bilinguals may be graded and whether higher levels of exposure to dual language contexts would be associated with enhanced EF skills. However, a few studies have examined other dimensions of bilingualism such as age of acquisition (e.g., Carlson & Meltzoff, 2008; Garbin et al., 2011; Pelham & Abrams, 2014; Sebastián-Gallés, Echeverría, & Bosch, 2005), absolute levels of language exposure (e.g., Bialystok & Barac, 2012; Bialystok, Peets, & Moreno, 2014; Kaushanskaya et al., 2014; Luk, De Sa, & Bialystok, 2011), and proficiency (e.g., Blumenfeld & Marian, 2007; Gathercole et al., 2010; Iluz-Cohen & Armon-Lotem, 2013; Poarch & van Hell, 2012) in relation to EF skills. The findings generally suggest a possibility of graded relationships among these bilingual experience metrics and EF performance. Because bilingualism is a multidimensional construct, it is very likely that fluctuations in the many aspects of bilingual experience may affect the development of EF skills. In the current study, we focused specifically on the effects of dual language exposure because of its importance in Green and Abutalebi (2013) ACH, the theoretical framework for this work. In designing a study that would answer this question, we also considered the possibility that children’s language skills may be an important moderator of the relationship between dual language exposure and cognitive control. EF and language skills The relationship between language skills and EF skills has been studied extensively in children with specific language impairment (SLI), with findings demonstrating that children with weak language skills also have weaker shifting skills (e.g., Marton, Campanelli, Scheuer, Yoon, & Eichorn, 2012; Vissers, Koolen, Hermans, Scheper, & Knoors, 2015) and inhibitory control skills (e.g., Marton, Kelmenson, & Pinkhasova, 2007; Spaulding, 2010; Vissers et al., 2015) relative to children with more robust linguistic systems (for a review, see Kapa & Plante, 2015). At the same time, studies have also shown that language skills and EF skills interact in typically developing populations (e.g., Fuhs & Day, 2011; Fuhs, Nesbitt, Farran, & Dong, 2014; Kaushanskaya, Park, Gangopadhyay, Davidson, & Ellis Weismer, 2017; White, Alexander, & Greenfield, 2017; Woodard, Pozzan, & Trueswell, 2016). For example, Kaushanskaya et al. (2017) found that nonverbal updating skills were associated with performance on a standardized measure of receptive language in typically developing school-aged children. In the same study, nonverbal inhibition skills, but not shifting skills or updating skills, were found to predict children’s syntactic abilities. Similarly, Fuhs et al. (2014) found that after controlling for preschool gains in both EF and achievement, EF skills continued to predict children’s language gains in kindergarten. Interestingly, in the bilingual EF literature, language skills are rarely viewed as a possible contributor to individual differences in bilingual children’s EF performance. Although there is a sizable literature linking language-specific proficiency to bilinguals’ performance on tasks of nonlinguistic executive functioning (e.g., Gathercole et al., 2010; Poarch & van Hell, 2012), only one study has examined the relationship between broad language skills and executive functioning within a sample of bilingual children (Iluz-Cohen & Armon-Lotem, 2013). The findings are consistent with the results observed in monolinguals, suggesting a positive relationship between language skills and EF in bilingual children, where children with lower language skills in both languages demonstrated significantly lower performance on tasks of inhibition and shifting. In the current study, we examined whether exposure to dual language contexts would affect EF skills and whether this relationship would be moderated by language skills. The current study In the current study, we examined the relation between dual language exposure and EF in a highly diverse sample of Spanish–English bilingual school-aged children, whose language environment was characterized by significant variability for both home language exposure and school language exposure. When defining dual language exposure, we were interested in the total amount of time children were exposed to both languages in the same environment independent of the specific setting where such exposure has occurred. The great variability in the specific settings where children in our sample were exposed to English and Spanish is precisely what enabled us to derive a graded measure of dual language exposure. EF was measured by performance on the DCCS task (e.g., Zelazo, 2006; Zelazo et al., 2003). In

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line with previous work (e.g., Barac & Bialystok, 2012; Park et al., 2018; Wiseheart et al., 2016), cost indices were derived to index shifting ability (shifting costs), local inhibitory control (switching costs), and monitoring ability (mixing costs). Based on the ACH (Green & Abutalebi, 2013), we hypothesized that higher levels of dual language exposure would be associated with enhanced performance on the DCCS. Furthermore, given the evidence linking language skills and EF skills in children (e.g., Iluz-Cohen & Armon-Lotem, 2013; Kaushanskaya et al., 2017; White et al., 2017; Woodard et al., 2016), we examined whether language skills would moderate the relationship between dual language exposure and EF. We hypothesized that dual language exposure may be associated with enhanced EF skills in children with stronger language skills but might not play a role (or might even be associated with reduced EF performance) in children with weaker language skills. We based this prediction on the literature indicating that dual language input may be more challenging than single language input (e.g., Green & Abutalebi, 2013; Hernández et al., 2013; Yang et al., 2016) and that, therefore, robust language skills may be necessary to accommodate to such input and benefit from it.

Method Participants Informed consent was obtained for experimentation with human participants. The current study included 156 typically developing Spanish–English bilingual participants (80 boys) aged 5–11 years. This age range was selected because the cognitive system experiences a critical period of development in this age range where EF performance is observed to be relatively mature by 12 years (e.g., Anderson, 2002; Welsh, Pennington, & Groisser, 2009). Inclusionary criteria included normal or corrected-tonormal vision, normal hearing per parent report, and a nonverbal IQ of at least 80 on the Visual Matrices subtest of the Kaufman Brief Intelligence Test (KBIT-2; Kaufman & Kaufman, 2004). All participants also passed a hearing screening. Participants were exposed to English and Spanish at the time of the study with no significant exposure to a language other than English and Spanish (defined as <5%). The participant sample subsumed three distinct subgroups of bilingual children with distinct language experiences, language acquisition timelines, and exposure patterns. Specifically, 38% of participants (n = 60) were exposed to English and Spanish before their third birthday, 25% of participants (n = 39) were native Spanish speakers who acquired English on entry into formal schooling; and 37% of participants (n = 57) were native English speakers who acquired Spanish via dual immersion programs where at least 50% of their school instruction was in Spanish. The pattern of school language by language in the home also varied across children (see Table S1 in the online supplementary material). The great variability among children’s language acquisition and exposure profiles enabled us to model the effects of dual language exposure in a graded manner. Participants with a history of developmental language delay, participants who were receiving language therapy services, and children with an organic medical diagnosis were excluded. However, because we were interested in whether language skills moderated the relationship between exposure to dual language input and EF skills, participants with below average language scores, but without a formal diagnosis of language impairment, were included. Information about primary caregivers’ language use, language proficiency, and socioeconomic status (operationalized as primary caregivers’ total years of education) was collected through the Language Experience and Proficiency Questionnaire (LEAP-Q; Marian, Blumenfeld, & Kaushanskaya, 2007). Information about participants’ language history, current language exposure, language dominance, and language use was collected through parent questionnaires and a parent interview. See Table 1 for participant characteristics.

Procedure Participants completed standardized assessments of language and cognition and the DCCS task over the course of three sessions.

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K. Crespo et al. / Journal of Experimental Child Psychology 188 (2019) 104663 Table 1 Participant characteristics. M (SD) N Gender Age (years) Primary caregiver’s years of education Nonverbal IQa Current English exposureb (%) English Core Languagec English receptive languagec English expressive languagec Current Spanish exposureb (%) Spanish Core Languaged Spanish receptive languaged Spanish expressive languaged Age at first exposure to English (months) Age at first exposure to Spanish (months) Proportion exposure to dual language (%)e Language skillsf

156 80 boys 8.03 (1.56) 15.11 (4.14) 108.47 (14.45) 58 (20) 96.99 (17.55) 102.90 (14.10) 96.15 (18.12) 42 (20) 87.40 (13.30) 99.16 (11.99) 83.31 (14.27) 12.03 (20.86) 24.28 (29.33) 50 (30) 102.18 (13.51) n

Language indexing language skillsf English Spanish Language heard at school English only Spanish at least 50% of time Language heard at home Mostly English Mostly Spanish Both English and Spanish Language spoken at home Mostly English Mostly Spanish Both English and Spanish

100 56 44 112 83 49 24 83 48 24

a

Matrices subtest of Kaufmann Brief Intelligence Test-2. Parental report of exposure to language during waking hours in a typical week. c Standard scores of subtests of Clinical Evaluation of Language Fundamentals-4 English. d Standard scores of subtests of Clinical Evaluation of Language Fundamentals-4 Spanish. e Proportion of dual language exposure was calculated by dividing the number of hours spent in a context where both English and Spanish were present by the total waking hours in a typical week. f Highest Core Language Index score from either CELF-4 English or CELF-4 Spanish. b

Dual language exposure During the parent interview, parents filled out a timetable indicating the waking hours during which their children were exposed to English, Spanish, or both languages throughout their usual activities on a typical weekday, Saturday, and Sunday. In the current study, dual language exposure was defined as the amount of time that children spent in a context where both languages were present independent of the exact ratio of English-to-Spanish use. Proportion of dual language exposure was calculated by dividing the number of hours spent in a context where both English and Spanish were present by the total waking hours in a typical week. Two research assistants completed these calculations. Then, 10% of the dual language exposure data were recalculated by the first and second authors for reliability. The recalculations revealed discrepancies on four calculations. Discrepancies

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were due to differences in rounding conventions and interpretation of waking hours, and the average discrepancy between the two sets of calculations was only .025. Dimensional Change Card Sort task The DCCS task is a widely used global measure of EF where participants need to shift, switch, and monitor sorting rules. The version of the DCCS task administered in the current study integrated components of the ‘‘color–shape game” used by Bialystok and Martin (2004) and the DCCS task created for the National Institutes of Health (NIH) toolbox (Zelazo & Bauer, 2013). The DCCS task was presented on a desktop computer monitor using E-Prime 2.0. Given our interest in domain-general EF skills and the diversity of language experience in our sample, an effort was made to minimize language demands of the task. Initial verbal instructions were presented in children’s preferred language along with the visual illustrations of each step of the task. The gray response buckets marked with a red square and a blue circle that were present at the left and right bottom corners of the screen were highlighted. Participants were taught to press buttons on a corresponding response box with left and right buttons marked with a red square and a blue circle. Attention was also drawn to the top of the screen where sorting cues were presented on each trial. Participants were taught to associate a row of amorphous color patches as the cue to sort by color and a row of gray circles and squares as the cue to short by shape. To reduce working memory demands, the cues remained on the screen throughout the task. During each trial, a sorting cue appeared at the top of the screen. After 500 ms, a red square or blue circle appeared in the center of the screen. Participants were instructed to press the button corresponding to the bucket to sort the target stimuli. Participants were instructed to respond as quickly as possible without making mistakes. The sorting cue, target stimulus, and response buckets remained on the screen until children responded. Participants had 10 s to respond before the trial was terminated. The intertrial interval was 800 ms. The trials were organized into three phases: pre-switch (i.e., before presenting a switch in sorting rules), post-switch (i.e., after presenting a switch in sorting rules), and mixed (i.e., switching back and forth between sorting rules). During the pre-switch phase, participants were introduced to the ‘‘color game” where they were taught to sort the blue square into the response bucket marked with the blue circle. Participants were also taught to sort the red circle into the response bucket marked with the red square. Each child completed 4 practice trials with feedback. To make sure that participants understood the task, they needed to correctly sort at least on 3 practice trials; otherwise, the instructions and practice were repeated. Immediately following practice trials, participants completed 5 preswitch trials with no feedback. In the post-switch phase, participants were introduced to the ‘‘shape game.” In the shape game, participants were explicitly taught to sort the red circle into the response bucket with the blue circle and to sort the blue square into the response bucket with the red square (see Fig. 1). There was no practice phase before the 5 post-switch trials because we wanted the postswitch phase to capture participants’ responses immediately after the change in sorting rules. Following the post-switch phase, participants were introduced to the mixed phase (30 trials). During the mixing game, participants played both the color game and the shape game and were instructed to look at the sorting cues at the top of the screen during each trial to know which game to play. The 30 trials in the mixed phase were presented in a fixed pseudorandomized sequence based on the NIH toolbox version (Zelazo & Bauer, 2013) such that 7 color trials were interspersed among 23 shape trials with 2–5 shape trials between each color trial. As a result, there were 13 switch trials, where participants were cued to switch from the shape game to the color game or from the color game back to the shape game. There were 17 non-switch trials, where participants continued to sort by shape. Accuracy and reaction time (RT) data were collected for each trial. Standardized measures Participants completed standardized measures of expressive and receptive abilities in English and Spanish as well as nonverbal intelligence. The Clinical Evaluation of Language Fundamentals-Fourth Edition (CELF-4 English; Semel, Wiig, & Secord, 2003) was used to evaluate each participant’s expressive and receptive language abilities in English. The CELF-4 Spanish Edition (CELF-4 Spanish; Wiig,

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Fig. 1. The Dimensional Change Card Sort task consisted of a color game A, a shape game B, and a mixing game that interleaved color and shape trials. During each trial, a sorting cue appeared at the top of the screen. Participants were instructed to press the button corresponding to the bucket to sort the target stimuli. Below are training trials from each game.

Semel, & Secord, 2006) was used to evaluate participants’ expressive and receptive language abilities in Spanish. Language skills Language skills were indexed by participants’ highest Core Language Index score from either CELF4 English or CELF-4 Spanish (see ‘‘Standardized measures” section). The Core Language Index score is computed by summing scaled scores from subtests that measure overall receptive and expressive language skills. Because we were not interested in the effects of relative proficiency on executive control performance but rather were interested in the strength of children’s linguistic system, we used children’s best Core Language Index score to index language skills (either English or Spanish). The CELF-4 English standard score was used as the index of language skills for 97 children, and the CELF-4 Spanish standard score was used as the index of language skills for 55 children. It is, of course, very likely that this measure of language skills is influenced by language-specific exposure. We used this measure precisely because some children in our sample presented with unbalanced language exposure/language score profiles. Using dominant language scores as the indicator of language skills appeared to us to be the least problematic and least biased procedure for accommodating exposure-based low language scores in the nondominant language in children with unbalanced language exposure and language score profiles. Analyses Descriptive data for DCCS performance are presented in Table 2. RT data were analyzed for correct responses only and were trimmed by excluding RTs that fell beyond 2.5 standard deviations of each individual child’s mean RT for all conditions. Approximately 20% of the trials (1275 of 6240 total) were removed from the RT analysis for the task as a whole due to incorrect responses.

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Table 2 Dimensional Change Card Sort task performance. Condition

Accuracy

Reaction time (ms)

Pre-switch (color game) Post-switch (shape game) Mixed phase (stay trials) Mixed phase (switch trials)

.95 .82 .82 .68

649 (221) 987 (699) 1243 (503) 1401 (522)

(.11) (.25) (.20) (.17)

Note. Standard deviations are in parentheses.

Three accuracy cost indices and three RT cost indices from the DCCS task were calculated and served as the dependent variables. First, a shifting cost measure was calculated to index shifting skills. Shifting costs compared overall performance (i.e., mean accuracy or mean RT) on the color game with overall performance on the shape game. Larger numbers on the shifting cost index would indicate greater difficulty in shifting from sorting by color to sorting by shape. Second, a switching cost measure was calculated from the mixing game where performance on non-switch trials (i.e., accuracy or RT) was compared with performance on switch trials to index local inhibitory control skills. Larger switching costs would indicate greater difficulty in switching back and forth between sorting rules. Third, a mixing cost measure was calculated to index monitoring skills. Mixing costs were calculated by comparing performance (i.e., accuracy or RT) for non-switch trials during the mixed phase, where participants were required to monitor the sorting rule, with overall performance in the pre-switch phase, where participants sorted by a single dimension throughout. Therefore, higher mixing costs indicated the extent to which monitoring the rule of each trial in the mixing phase reduced accuracy or increased RTs. See Table S2 in supplementary material for a correlation matrix among all variables of interest. Because RT data were not normally distributed, all RTs were log transformed to log10 values to reduce skewness. A difference score was then calculated to create each RT cost index. Four participants were excluded from the analyses to satisfy model assumptions. These participants were identified as outliers based on their performance of more than 3 standard deviations from the mean. Age was used as a covariate in the final model because of the broad age range of the children included in the study. We estimated three separate regression models in R (R Core Team, 2015), where we regressed each cost measure on age (mean centered), the proportion of dual language exposure (mean centered), language skills (mean centered), and the interaction between proportion of dual language exposure and language skills. Variance inflation factors were less than 1.10 for all predictors in each model, suggesting no presence of multicollinearity. Stepwise model comparisons were conducted that included different background variables as covariates in the analyses. Results revealed that language group membership (i.e., native English, simultaneous, or native Spanish), F(2, 143) = 0.18, p = .84, primary caregiver’s years of education, F (1, 144) = 1.27, p = .26, nonverbal IQ, F(1, 144) = 0.01, p = .94, exposure to English, F(1, 144) = 3.59, p = .06, and age and dual language exposure interaction, F(3, 142) = 0.56, p = .64, did not explain significant amounts of variance in the model over and above age, dual language exposure, language skills, and the interaction between dual language exposure and language skills.

Results Accuracy results The intercept for shifting costs in accuracy was not significant, F(1, 147) = 1.67, p = .20, suggesting that there was no overall cost in accuracy in the post-switch versus pre-switch condition. The intercept for switching costs in accuracy was also not significant, F(1, 147) = 3.28, p = .07, suggesting that there was no overall accuracy cost in switching between rules in the mixed phase. However, the intercept for mixing costs in accuracy was significant, F(1, 147) = 15.90, B = 0.33, p = .0001, reflecting a significant overall cost in accuracy in the mixed phase versus the single dimension phase (i.e., pre-switch

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phase). There was also a main effect of age, F(1, 147) = 7.42, B = 0.03, p < .01, such that younger children showed greater mixing costs (see Table 3). None of the other variables was a significant predictor in any of the three models. RT results The intercept for RT shifting costs was significant, F(1, 142) = 5.14, B = 0.13, p = .03, g2p = .04, reflecting a significant overall shifting cost where, on average, children slowed down in the post-switch phase relative to the pre-switch phase. A significant interaction between proportion of dual language exposure and language skills was observed for shifting costs, F(1, 142) = 4.92, B = 0.006, p = .03, g2p = .03, such that children showed smaller shifting costs with increased exposure to dual language input as their language skills increased. See Fig. 2 for a visual depiction of this interaction. Main effects of age, dual language exposure, and language skills were not significant (see Table 4). The degrees of freedom for the RT shifting costs are conditioned by the fact that in the post-switch phase 5 children obtained an accuracy score of 0, and consequently a raw RT of 0, because they perseverated and continued to sort the target by color and not shape. The intercept for RT switching costs was not significant, F(1, 147) = 1.20, B = 0.04, p = .27, reflecting that, on average, children did not slow down on the switch trials compared with the non-switch trials. Age was a significant predictor of switching costs, F(1, 147) = 6.68, B = 0.01, p = .01, g2p = .04, such that older children showed larger switching costs. Main effects and the interaction between dual language exposure and language skills were not significant (see Table 4). The intercept for RT mixing costs was significant, F(1, 147) = 15.69, B = 0.24, p < .001, g2p = .10, reflecting a significant overall cost from monitoring where, on average, children slowed down on non-switch trials in the mixed phase relative to the pre-switch phase. A significant interaction between proportion of dual language exposure and language skills was observed for mixing costs, F (1, 147) = 11.33, B = 0.01, p = .001, g2p = .07, such that children showed smaller mixing costs with increased exposure to dual language input as their language skills increased (see Fig. 2). Main effects of age, dual language exposure, and language skills were not significant (see Table 4). Discussion We examined the graded effects of dual language exposure on EF skills in bilingual children, testing whether language skills would moderate the effects of dual language exposure. Children showed smaller shifting costs and mixing costs on a DCCS task with increased exposure to dual language input as their language skills increased. These findings are largely consistent with Green and Abutalebi (2013) ACH, providing experimental evidence for a possible mechanistic connection between dual language exposure and EF skills in bilinguals. The findings also offer an important insight into the role of language skills in possibly moderating the effects of experience in dual language contexts on EF. Table 3 Regression models for accuracy cost indices from the Dimensional Change Card Sort task. Shifting cost

Switching cost

Mixing cost

Intercept Age Dual language exposure Language skills Dual Language * Language Skills

0.15 (0.12) 0.004 (0.01) 0.06 (0.07) 0.001 (0.002) 0.005 (0.005)

0.14 (0.08) 0.001 (0.01) 0.02 (0.05) 0.001 (0.004) 0.002 (0.004)

0.33 (0.08)*** 0.03 (0.01)** 0.09 (0.05) 0.001 (0.001) 0.005 (0.004)

Observations R2 Adjusted R2 F statistic (df = 4, 147)

152 .01 .02 0.38

152 .01 .02 0.40

152 .09 .06 3.60**

Note. Values in upper panel of table are B (and standard error). ** p < .01. *** p < .001.

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Fig. 2. Language skills moderated the relationship between dual language exposure and executive function skills on shifting and monitoring such that children with higher language skills were more likely to show smaller costs with greater dual language exposure. We measured language skills as a continuous variable, but for graphing purposes we divided language skills into thirds: low (language skills scores = 71–97), mid (language skills scores = 98–109), and high (language skills scores = 110– 136). RT, reaction time.

Table 4 Regression models for reaction time cost indices from the Dimensional Change Card Sort task. Shifting cost

Switching cost

Mixing cost

Intercept Age Dual language exposure Language skills Dual Language * Language Skills

0.13 (0.06)* 0.004 (0.007) 0.03 (0.04) 0.0005 (0.001) 0.006 (0.003)*

0.04 (0.04) 0.01 (0.005)* 0.04 (0.02) 0.00003 (0.0006) 0.003 (0.002)

0.24 (0.06)*** 0.004 (0.01) 0.02 (0.04) 0.0004 (0.001) 0.01 (0.003)***

Observations R2 Adjusted R2 F statistic

147 .06 .03 2.06 (df = 4, 142)

152 .07 .04 2.65* (df = 4, 147)

152 .08 .05 2.99* (df = 4, 147)

Note. Values in upper panel of table are B (and standard error). * p < .05. *** p < .001.

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Dual language input and different cost indices yielded by DCCS Children in our study exhibited decreased shifting and mixing costs with increased exposure to dual language input as their language skills increased, suggesting that dual language exposure may facilitate the development of cognitive flexibility skills in children with robust linguistic skills. Interestingly, we did not observe such a relationship for switching costs. The ACH (Green & Abutalebi, 2013) does not make differential predictions with respect to the various control processes and their sensitivity to dual language input. Instead, the ACH broadly posits that increased dual language input puts pressures on multiple control processes, including goal maintenance, interference control, response inhibition, and task engagement/disengagement—all the processes that correspond to the three cost indices we derived from DCCS performance. What, then, might be the reasons behind the differential effect of dual language input on the different cost indices yielded by the DCCS? One possibility is a task-specific one rather than a mechanistic one. For instance, it is possible that the children in our study had used a particular strategy in the mixed phase of the DCCS such that they slowed down overall, yielding higher mixing costs but smaller switching costs. Indeed, in our dataset, there was an inverse relationship between switching costs and mixing costs, suggesting just such a strategy (see also Barac & Bialystok, 2012). An alternative possibility is that mixing costs and switching costs are, in fact, differentially sensitive to the joint effects of dual language input and language skills. Such an interpretation is consistent with emerging evidence suggesting that mixing costs and switching costs may reflect different control processes that rely on different neural regions (e.g., Braver et al., 2003; Goffaux, Phillips, Sinai, & Pushkar, 2006; Rubin & Meiran, 2005). Mixing costs have been hypothesized to reflect global control mechanisms involved in sustained attention and monitoring to detect task-switching cues (Braver et al., 2003; Koch et al., 2005), whereas switching costs appear to be related to local control mechanisms involved in managing stimulus–response actions (Braver et al., 2003; Koch et al., 2005). It is possible that dual language input modulates bilingual children’s monitoring skills in a graded manner, whereas the local control mechanisms indexed by the switching costs are less sensitive to such fluctuations in the bilingual environment. It will be important for future studies to attempt to replicate these findings, perhaps with different measures of the different control processes. Here, it is important to note that the strength of the results for the shifting costs must be interpreted with caution given that the overall model for shifting costs was not significant. It is possible that the combination of covariates or missing data rendered the overall model nonsignificant. ACH and language skills In their model, Green and Abutalebi (2013) suggested that different linguistic contexts place distinct demands on the bilingual control system. The ACH proposes that language task schemas are in competition in both the single language and dual language contexts. However, the demands on cognitive control processes are more complex in a dual language context, and consequently the adaptive response is more robust. Why would cognitive control processes adapt in response to demands of interactional contexts? Green and Abutalebi (2013) hypothesized that the need for effective communication creates interactional costs that motivate the adaptation. That is, using a language that is not understood by the conversation partner or that is unexpected for a particular context or topic will interfere with the ability to communicate. As a result, individuals are motivated to align their language choice with the context. This alignment becomes more complex for dual language speakers, who must identify and select the appropriate linguistic system to deliver and communicate their message to the target listener. In addition to maintaining the language goal and suppressing interference, which are also necessary in a single language context, speakers in a dual language context must be adept at switching languages when appropriate (i.e., when addressed by someone with whom they converse in their other language). However, the sensitivity required to detect the external cues that prompt language switching is in conflict with the goal of reducing interference from the non-target language. This cost that is unique to dual language contexts creates an additional demand over and above the demands imposed on goal maintenance, conflict monitoring, and interference suppression in a single

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language environment. Our findings support this conceptualization, indicating that the increased exposure to dual language input is associated with better EFs. An important caveat is that a significant interaction between exposure to dual language input and language skills was observed where children showed smaller shifting costs and mixing costs with increased exposure to dual language input as their language skills increased. We interpret this finding to suggest that greater exposure to dual language input may enhance global control mechanisms involved in shifting and monitoring for children with superior language skills. Why might stronger language skills be required to benefit from dual language exposure? Although the direction of possible causal relationships between EF and language skills in children is uncertain, there is some evidence to suggest that early language skills have both direct and indirect effects on children’s development of EF skills (e.g., Brito & Barr, 2012, 2014; Fuhs & Day, 2011; Fuhs et al., 2014; Kaushanskaya et al., 2017; White et al., 2017; Woodard et al., 2016). It is likely that there is a dynamic bidirectional relationship between EF and language such that one system influences the other over time (e.g., Bohlmann & Downer, 2016; Slot & von Suchodoletz, 2018). Because dual language input may be both linguistically and cognitively demanding, it may require a higher level of linguistic ability and recruit more EF resources in the service of processing such input. At the same time, it is possible that the level of EF engagement in a weak language system is not sufficient to incur cognitive benefits from dual language input. Broad effects of dual language exposure on EF One surprising finding in the current study was an absence of a main effect of dual language exposure; that is, we did not observe an association between dual language exposure and EF measures that would not be moderated by language skills. This is in contrast to prior studies that have observed an effect of dual language exposure on EF skills in children (e.g., Verhagen et al., 2017). For instance, in Verhagen et al. (2017), 3-year-olds were split into two groups based on whether parents always or mostly addressed the children in the same language or in two different languages (i.e., one-parent– one-language approach). Post hoc comparisons showed that the ‘‘different languages” bilinguals significantly outperformed the monolinguals on the Stroop task, whereas the ‘‘same language” bilinguals did not. In addition, when the home language environment was taken into account, effects on inhibitory control tasks were observed within the bilingual group, albeit with small effect sizes. It is possible that the effects of dual language exposure are more robust at younger ages, when EF skills are beginning to emerge largely as a unitary system (e.g., Hughes, Ensor, Wilson, & Graham, 2010; Wiebe, Espy, & Charak, 2008; Wiebe et al., 2011), than at older ages, when EF skills begin to stabilize and separate into distinct components (e.g., Huizinga, Dolan, & van der Molen, 2006; Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003). Yet, age did not interact with dual language input in predicting EF performance in our dataset, perhaps because the age range of the children in our study was not wide enough. A challenge for future studies would be to examine the possibility of such an interaction through designing EF tasks that would be appropriate for both older and younger children. It is also possible that our approach to measuring dual language exposure influenced the pattern of findings. We calculated a proportion of dual language exposure that did not consider the exact ratio of English-to-Spanish use and did not specifically measure the type of dual language input (e.g., blocked vs. code-switched use of the two languages, produced by a single speaker vs. multiple speakers). Instead, our measure of dual language exposure collapsed across all the hours of the day when the two languages were used regardless of specific context (e.g., home vs. school). As a result, there is not a transparent link between this measure of dual language exposure and a particular child’s language environment. However, although our measure of dual language exposure may obscure the specifics of the bilingual environment, its benefit is that it enables inclusion and quantification of different types of bilingual environments within the same sample. Future avenues of research are needed to refine current measures of dual language exposure to more clearly and precisely index bilingual children’s language environment. It will be important for future studies to also attempt to distinguish the different aspects of dual language contexts and to assess their associations with EF performance separately. The ACH (Green & Abutalebi, 2013) hypothesizes demands on language control processes with a focus on language use in these distinct interactional contexts, but it is possible that processing of dual

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language input may place high demands on the cognitive system. In general, future work will need to consider whether language control mechanisms align differently for production versus comprehension and whether different interactional experiences may have distinct consequences for children with higher versus lower levels of language skills. Indeed, it is possible that accounting for language skills plays an important role in whether a global relationship is detected between dual language exposure and EF. For instance, in a recent study, Bosma et al. (2017) examined whether increased exposure to Frisian (i.e., the minority language) over a 3-year period predicted children’s language balance and whether there was a relationship between Frisian exposure at home and EF mediated by language balance in 5- to 8-year-olds. The significant relationship between intensity of exposure to Frisian at home and selective attention skills at Time 1 (when the children were 5 or 6 years of age) was no longer observed when children’s relative proficiency in Frisian and Dutch was taken into account. Language skills and EF Another surprising finding in the current study was an absence of a main effect of language. That is, we did not observe an association between children’s language skills and EF performance. This pattern of results is once again inconsistent with previous work linking language skills and EF skills in typically developing children (e.g., Iluz-Cohen & Armon-Lotem, 2013; Kaushanskaya et al., 2017; White et al., 2017; Woodard et al., 2016). Studies linking language skills and EF find relationships in younger preschool-age children (i.e., 3- to 5-year-olds) (e.g., Iluz-Cohen & Armon-Lotem, 2013; White et al., 2017; Woodard et al., 2016). It is possible that this relationship is less robust in older school-aged children. Another possibility is that our particular approach to measuring language skills weakened the relationship between language and EF skills. Our measure of language skills was a gestalt standardized measure that collapsed across lexical– semantic and morphosyntactic–syntactic skills in both expressive and receptive domains. Furthermore, because we were interested in the overall robustness of children’s linguistic system, and not in their proficiency in English and Spanish or in their degree of bilingualism, we indexed language skills via the highest standardized score independent of language. Both of these choices likely weakened the relationship between language and EF. In general, static broad standardized measures of language rely minimally on processes related to interference suppression, conflict monitoring, and local switching, especially when administered in children’s strongest language. Crucially, there remained sufficient fluctuation in children’s language skills, as measured here, to moderate the relationship between EF and dual language exposure. Limitations and conclusion It is important to carefully evaluate the approach we took to measuring dual language and the possibility that our findings are specific to this particular approach. Currently, there is no ‘‘gold standard” for measuring dual language input. The benefit of using a broader self-reported measure of dual language input that considers input from both home and community environments is that it may be more sensitive to fluctuations in exposure that children experience in the real world. Of course, the downside to a self-reported measure is that parents might not be reliable reporters of input experienced by their children. Yet, parents are routinely asked to report on children’s language exposure outside of the home in the bilingualism literature (e.g., Abbot-Smith, Morawska-Patera, Łuniewska, Spruce, & Haman, 2018; Bosma et al., 2017; Mieszkowska et al., 2017). In the current study, we asked parents to report on the presence of two languages not only in the home but also outside of the home, and this approach may have been especially problematic for a subset of bilingual children in our study who were exposed to both of their languages only in the school setting. However, direct measures of input, while likely yielding more reliable data, would also be necessarily constrained in providing only a narrow window into children’s overall pattern of language exposure. Future research is needed to develop more refined methodological approaches of capturing and indexing variability in bilingual children’s linguistic input.

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In addition, because of the correlational nature of the study, the directionality of the relationships we observed is not clear-cut. For instance, it is possible that the relationship between exposure to dual language input and the development of language skills is moderated by the robustness of the EF system rather than the other way around. Indeed, our own recent work has shown distinct relationships between exposure to code switching and language performance for children with different levels of working memory (Kaushanskaya & Crespo, 2019). In that study, greater exposure to code switching was associated with higher levels of language skills for children with high verbal working memory capacity. In contrast, for children with lower verbal working memory capacity, greater exposure to code switching was associated with lower levels of language skills. These findings suggest that children’s cognitive processing capacity may dictate whether exposure to dual language input promotes or negatively affects language growth. Finally, it is also important to note that the ACH makes predictions on how language control processes adapt to interactional context demands with a focus on speech production in adults. The effects of exposure to different interactional contexts on the development of EF skills are not considered. In addition, the ACH is not a developmental model. It is possible that interactional contexts may place different demands when the goal of the communicative exchange is to comprehend the message and when the language and cognitive system are still developing. Nevertheless, the results of the current study provide behavioral evidence in support of the ACH (Green & Abutalebi, 2013), where dual language exposure is viewed as a crucial factor promoting adaptive changes in cognitive control processes. Critically, the current study suggests that language skills moderate the extent to which dual language exposure influences EF performance in bilingual children. Children with stronger linguistic systems demonstrate stronger shifting and monitoring skills with increased exposure to dual language input than children with weaker linguistic systems. Future work is needed to identify why a certain level of language skills may be required to benefit from dual language exposure and how fluctuations in the linguistic environment may interact with other intrinsic characteristics to influence executive control. Acknowledgments This research was supported by National Institutes of Health Grants R03 DC010465 and R01 DC011750 to Margarita Kaushanskaya and by an F31 DC013920 to support Megan Gross. Support for the first author was provided by a T32 Training Grant (T32 DC05359-13, ‘‘Interdisciplinary Research Training in Speech–Language Disorders”) awarded to Susan Ellis Weismer. The authors thank all the members of the Language Acquisition and Bilingualism Lab for their assistance with data collection, scoring, and data coding. Finally, we deeply appreciate all of the children and parents who participated in the study. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.jecp.2019. 104663. References Abbot-Smith, K., Morawska-Patera, P., Łuniewska, M., Spruce, M., & Haman, E. (2018). Using parental questionnaires to investigate the heritage language proficiency of bilingual children. Child Language Teaching and Therapy, 34, 155–170. Abutalebi, J., Della Rosa, P. A., Ding, G., Weekes, B., Costa, A., & Green, D. W. (2013). Language proficiency modulates the engagement of cognitive control areas in multilinguals. Cortex, 49, 905–911. Abutalebi, J., & Green, D. W. (2007). Bilingual language production: The neurocognition of language representation and control. Journal of Neurolinguistics, 20, 242–275. Abutalebi, J., & Green, D. W. (2008). Control mechanisms in bilingual language production: Neural evidence from language switching studies. Language and Cognitive Processes, 23, 557–582. Abutalebi, J., & Green, D. W. (2016). Neuroimaging of language control in bilinguals: Neural adaptation and reserve. Bilingualism: Language and Cognition, 19, 689–698. Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. Psychological Review, 115, 602–639.

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Anderson, P. (2002). Assessment and development of executive function (EF) during childhood. Child Neuropsychology, 8, 71–82. Barac, R., & Bialystok, E. (2012). Bilingual effects on cognitive and linguistic development: Role of language, cultural background, and education. Child Development, 83, 413–422. Best, J. R., & Miller, P. H. (2010). A developmental perspective on executive function. Child Development, 81, 1641–1660. Bialystok, E. (2011). Reshaping the mind: The benefits of bilingualism. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Experimentale, 65, 229–235. Bialystok, E., & Barac, R. (2012). Emerging bilingualism: Dissociating advantages for metalinguistic awareness and executive control. Cognition, 122, 67–73. Bialystok, E., Craik, F. I. M., & Luk, G. (2012). Bilingualism: Consequences for mind and brain. Trends in Cognitive Sciences, 16, 240–250. Bialystok, E., & Martin, M. M. (2004). Attention and inhibition in bilingual children: Evidence from the dimensional change card sort task. Developmental Science, 7, 325–339. Bialystok, E., Peets, K. F., & Moreno, S. (2014). Producing bilinguals through immersion education: Development of metalinguistic awareness. Applied Psycholinguistics, 35, 177–191. Bialystok, E., & Shapero, D. (2005). Ambiguous benefits: The effect of bilingualism on reversing ambiguous figures. Developmental Science, 8, 595–604. Blakey, E., Visser, I., & Carroll, D. J. (2016). Different executive functions support different kinds of cognitive flexibility: Evidence from 2-, 3-, and 4-year-olds. Child Development, 87, 513–526. Blanco-Elorrieta, E., & Pylkkänen, L. (2017). Bilingual language switching in the laboratory versus in the wild: The spatiotemporal dynamics of adaptive language control. Journal of Neuroscience, 37, 9022–9036. Blumenfeld, H., & Marian, V. (2007). Constraints on parallel language activation in bilingual spoken language processing: Examining proficiency and lexical status using eye-tracking. Language and Cognitive Processes, 22, 633–660. Bohlmann, N. L., & Downer, J. T. (2016). Self-regulation and task engagement as predictors of emergent language and literacy skills. Journal of Early Education and Development, 27, 18–37. Bosma, E., Hoekstra, E., Versloot, A., & Blom, E. (2017). The minimal and short-lived effects of minority language exposure on the executive functions of Frisian-Dutch bilingual children. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01453. Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural mechanisms of transient and sustained cognitive control during task switching. Neuron, 39, 713–726. Brito, N., & Barr, R. (2012). Influence of bilingualism on memory generalization during infancy. Developmental Science, 15, 812–816. Brito, N., & Barr, R. (2014). Flexible memory retrieval in bilingual 6-month-old infants. Developmental Psychobiology, 56, 1156–1163. Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28, 595–616. Carlson, S. M., & Meltzoff, A. N. (2008). Bilingual experience and executive functioning in young children. Developmental Science, 11, 282–298. Colzato, L. S., Bajo, M. T., van den Wildenberg, W., Paolieri, D., Nieuwenhuis, S., La Heij, W., & Hommel, B. (2008). How does bilingualism improve executive control? A comparison of active and reactive inhibition mechanisms. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 302–312. Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168. Diamond, A., Carlson, S. M., & Beck, D. M. (2005). Preschool children’s performance in task switching on the Dimensional Change Card Sort task: Separating the dimensions aids the ability to switch. Developmental Neuropsychology, 28, 689–729. Doebel, S., & Zelazo, P. D. (2015). A meta-analysis of the Dimensional Change Card Sort: Implications for developmental theories and the measurement of executive function in children. Developmental Review, 38, 241–268. Fuhs, M. W., & Day, J. D. (2011). Verbal ability and executive functioning development in preschoolers at Head Start. Developmental Psychology, 47, 404–416. Fuhs, M. W., Nesbitt, K. T., Farran, D. C., & Dong, N. (2014). Longitudinal associations between executive functioning and academic skills across content areas. Developmental Psychology, 50, 1698–1709. Garbin, G., Costa, A., Sanjuan, A., Forn, C., Rodriguez-Pujadas, A., Ventura, N., ... Ávila, C. (2011). Neural bases of language switching in high and early proficient bilinguals. Brain and Language, 119, 129–135. Garon, N., Bryson, S. E., & Smith, I. M. (2008). Executive function in preschoolers: A review using an integrative framework. Psychological Bulletin, 134, 31–60. Gathercole, V. C., Thomas, E. M., Jones, L., Guasch, N. V., Young, N., & Hughes, E. K. (2010). Cognitive effects of bilingualism: Digging deeper for the contributions of language dominance, linguistic knowledge, socio-economic status and cognitive abilities. International Journal of Bilingual Education and Bilingualism, 13, 617–664. Gathercole, V. C. M., Thomas, E. M., Kennedy, I., Prys, C., Young, N., Viñas Guasch, N., ... Jones, L. (2014). Does language dominance affect cognitive performance in bilinguals? Lifespan evidence from preschoolers through older adults on card sorting, Simon, and metalinguistic tasks. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00011. Goffaux, P., Phillips, N. A., Sinai, M., & Pushkar, D. (2006). Behavioural and electrophysiological measures of task switching during single and mixed-task conditions. Biological Psychology, 72, 278–290. Green, D. W. (1998). Mental control of the bilingual lexico-semantic system. Bilingualism: Language & Cognition, 1, 67–81. Green, D. W., & Abutalebi, J. (2013). Language control in bilinguals: The adaptive control hypothesis. Journal of Cognitive Psychology (Hove), 25, 515–530. Green, D. W., & Wei, L. (2014). A control process model of code-switching. Language, Cognition and Neuroscience, 29, 499–511. Guo, T., Liu, H., Misra, M., & Kroll, J. F. (2011). Local and global inhibition in bilingual word production: fMRI evidence from Chinese-English bilinguals. NeuroImage, 56, 2300–2309. Hartanto, A., & Yang, H. (2016). Disparate bilingual experiences modulate task-switching advantages: A diffusion-model analysis of the effects of interactional context on switch costs. Cognition, 150, 10–19. Hernández, M., Martin, C. D., Barceló, F., & Costa, A. (2013). Where is the bilingual advantage in task-switching? Journal of Memory and Language, 69, 257–276.

K. Crespo et al. / Journal of Experimental Child Psychology 188 (2019) 104663

17

Hilchey, M. D., & Klein, R. M. (2011). Are there bilingual advantages on nonlinguistic interference tasks? Implications for the plasticity of executive control processes. Psychonomic Bulletin & Review, 18, 625–658. Hongwanishkul, D., Happaney, K. R., Lee, W. S. C., & Zelazo, P. D. (2005). Assessment of hot and cool executive function in young children: Age-related changes and individual differences. Developmental Neuropsychology, 28, 617–644. Hughes, C., Ensor, R., Wilson, A., & Graham, A. (2010). Tracking executive function across the transition to school: A latent variable approach. Developmental Neuropsychology, 35, 20–36. Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017–2036. Iluz-Cohen, P., & Armon-Lotem, S. (2013). Language proficiency and executive control in bilingual children. Bilingualism: Language and Cognition, 16, 884–899. Kapa, L. L., & Plante, E. (2015). Executive function in SLI: Recent advances and future directions. Current Developmental Disorders Reports, 2, 245–252. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman brief intelligence test (2nd ed.). Bloomington, MN: Pearson. Kaushanskaya, M., & Crespo, K. (2019). Does exposure to code-switching influence language performance in bilingual children? Child Development, 90, 708–718. Kaushanskaya, M., Gross, M., & Buac, M. (2014). Effects of classroom bilingualism on task-shifting, verbal memory, and word learning in children. Developmental Science, 17, 564–583. Kaushanskaya, M., Park, J., Gangopadhyay, I., Davidson, M., & Ellis Weismer, S. (2017). The relationship between executive functions and language abilities in children: A latent variables approach. Journal of Speech, Language, and Hearing Research, 60, 912–923. Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost, K., Philipp, A. M., & Koch, I. (2010). Control and interference in task switching—A review. Psychological Bulletin, 136, 849–874. Kirkham, N. Z., Cruess, L., & Diamond, A. (2003). Helping children apply their knowledge to their behavior on a dimensionswitching task. Developmental Science, 6, 449–476. Koch, I., Gade, M., Schuch, S., & Philipp, A. M. (2010). The role of inhibition in task switching: A review. Psychonomic Bulletin & Review, 17, 1–14. Koch, I., Prinz, W., & Allport, A. (2005). Involuntary retrieval in alphabet–arithmetic tasks: Task-mixing and task-switching costs. Psychological Research, 69, 252–261. Kousaie, S., Sheppard, C., Lemieux, M., Monetta, L., & Taler, V. (2014). Executive function and bilingualism in young and older adults. Frontiers in Behavioral Neuroscience, 8. https://doi.org/10.3389/fnbeh.2014.00250. Kovács, A., & Mehler, J. (2009). Flexible learning of multiple speech structures in bilingual infants. Science, 325, 611–612. Kovács, A. M., & Mehler, J. (2009). Cognitive gains in 7-month-old bilingual infants. Proceedings of the National Academy of Sciences of the United States of America, 106, 6556–6560. Lehto, J. E., Juujärvi, P., Kooistra, L., & Pulkkinen, L. (2003). Dimensions of executive functioning: Evidence from children. British Journal of Developmental Psychology, 21, 59–80. Lehtonen, M., Soveri, A., Laine, A., Järvenpää, J., de Bruin, A., & Antfolk, J. (2018). Is bilingualism associated with enhanced executive functioning in adults? A meta-analytic review. Psychological Bulletin, 144, 394–425. Luk, G., De Sa, E., & Bialystok, E. (2011). Is there a relation between onset age of bilingualism and enhancement of cognitive control? Bilingualism: Language and Cognition, 14, 588–595. Ma, H., Hu, J., Xi, J., Shen, W., Ge, J., Geng, F., ... Yao, D. (2014). Bilingual cognitive control in language switching: An fMRI study of English-Chinese late bilinguals. PLoS One, 9(9), e106468. Marian, V., Blumenfeld, H. K., & Kaushanskaya, M. (2007). The Language Experience and Proficiency Questionnaire (LEAP-Q): Assessing language profiles in bilinguals and multilinguals. Journal of Speech, Language, and Hearing Research, 50, 940–967. Marton, K., Campanelli, L., Scheuer, J., Yoon, J., & Eichorn, N. (2012). Executive function profiles in children with and without specific language impairment. Rivista Di Psicolinguistica Applicata, 12(3), 57–73. Marton, K., Kelmenson, L., & Pinkhasova, M. (2007). Inhibition control and working memory capacity in children with SLI. Psychologia, 50, 110–121. Mattock, K., Polka, L., Rvachew, S., & Krehm, M. (2010). The first steps in word learning are easier when the shoes fit: Comparing monolingual and bilingual infants. Developmental Science, 13, 229–243. Mayr, U. (2001). Age differences in the selection of mental sets: The role of inhibition, stimulus ambiguity, and response-set overlap. Psychology and Aging, 16, 96–109. Mieszkowska, K., Łuniewska, M., Kołak, J., Kacprzak, A., Wodniecka, Z., & Haman, E. (2017). Home language will not take care of itself: Vocabulary knowledge in trilingual children in the United Kingdom. Frontiers in Psychology, 8. https://doi.org/ 10.3389/fpsyg.2017.01358. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex ‘‘frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7, 134–140. Morton, J. B., Bosma, R., & Ansari, D. (2009). Age-related changes in brain activation associated with dimensional shifts of attention: An fMRI study. NeuroImage, 46, 249–256. Müller, U., Steven Dick, A., Gela, K., Overton, W. F., & Zelazo, P. D. (2006). The role of negative priming in preschoolers’ flexible rule use on the Dimensional Change Card Sort task. Child Development, 77, 395–412. Munakata, Y., & Yerys, B. E. (2001). All together now: When dissociations between knowledge and action disappear. Psychological Science, 12, 335–337. Okanda, M., Moriguchi, Y., & Itakura, S. (2010). Language and cognitive shifting: Evidence from young monolingual and bilingual children. Psychological Reports, 107, 68–78. Paap, K. R., & Greenberg, Z. I. (2013). There is no coherent evidence for a bilingual advantage in executive processing. Cognitive Psychology, 66, 232–258.

18

K. Crespo et al. / Journal of Experimental Child Psychology 188 (2019) 104663

Paap, K. R., Johnson, H. A., & Sawi, O. (2016). Should the search for bilingual advantages in executive functioning continue? Cortex, 74, 305–314. Paap, K. R., & Sawi, O. (2014). Bilingual advantages in executive functioning: Problems in convergent validity, discriminant validity, and the identification of the theoretical constructs. Frontiers in Psychology, 5. https://doi.org/10.3389/ fpsyg.2014.00962. Park, J., Ellis Weismer, S., & Kaushanskaya, M. (2018). Changes in executive function over time in bilingual and monolingual school-aged children. Developmental Psychology, 54, 1842–1853. Pelham, S. D., & Abrams, L. (2014). Cognitive advantages and disadvantages in early and late bilinguals. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 313–325. Poarch, G. J., & van Hell, J. G. (2012). Executive functions and inhibitory control in multilingual children: Evidence from secondlanguage learners, bilinguals, and trilinguals. Journal of Experimental Child Psychology, 113, 535–551. Poulin-Dubois, D., Blaye, A., Coutya, J., & Bialystok, E. (2011). The effects of bilingualism on toddlers’ executive functioning. Journal of Experimental Child Psychology, 108, 567–579. Prior, A., & Gollan, T. H. (2011). Good language-switchers are good task-switchers: Evidence from Spanish– English and Mandarin-English bilinguals. Journal of the International Neuropsychological Society, 17, 682–691. Prior, A., & MacWhinney, B. (2010). A bilingual advantage in task switching. Bilingualism: Language and Cognition, 13, 253–262. R Core & Team (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Reimers, S., & Maylor, E. A. (2005). Task switching across the life span: Effects of age on general and specific switch costs. Developmental Psychology, 41, 661–671. Rubin, O., & Meiran, N. (2005). On the origins of the task mixing cost in the cuing task-switching paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1477–1491. Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27, 763–797. Sebastián-Gallés, N., Albareda-Castellot, B., Weikum, W. M., & Werker, J. F. (2012). A bilingual advantage in visual language discrimination in infancy. Psychological Science, 23, 994–999. Sebastián-Gallés, N., Echeverría, S., & Bosch, L. (2005). The influence of initial exposure on lexical representation: Comparing early and simultaneous bilinguals. Journal of Memory and Language, 52, 240–255. Semel, E., Wiig, E. H., & Secord, W. A. (2003). Clinical evaluation of language fundamentals (4th ed.). San Antonio, TX: PsychCorp. Singh, L., Fu, C. S. L., Rahman, A. A., Hameed, W. B., Sanmugam, S., Agarwal, P., ... Rifkin-Graboi, A. (2015). Back to basics: A bilingual advantage in infant visual habituation. Child Development, 86, 294–302. Slot, P. L., & von Suchodoletz, A. (2018). Bidirectionality in preschool children’s executive functions and language skills: Is one developing skill the better predictor of the other? Early Childhood Research Quarterly, 42, 205–214. Soveri, A., Rodriguez-Fornells, A., & Laine, M. (2011). Is there a relationship between language switching and executive functions in bilingualism? Introducing a within-group analysis approach. Frontiers in Psychology, 2. https://doi.org/10.3389/ fpsyg.2011.00183. Spaulding, T. J. (2010). Investigating mechanisms of suppression in preschool children with specific language impairment. Journal of Speech, Language, and Hearing Research, 53, 725–738. Tse, C. S., & Altarriba, J. (2015). Local and global task switching costs in bilinguals who vary in second language proficiency. American Journal of Psychology, 128, 89–106. Verhagen, J., Mulder, H., & Leseman, P. (2017). Effects of home language environment on inhibitory control in bilingual threeyear-old children. Bilingualism: Language and Cognition, 20, 114–127. Vissers, C., Koolen, S., Hermans, D., Scheper, A., & Knoors, H. (2015). Executive functioning in preschoolers with specific language impairment. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01574. Waszak, F., Hommel, B., & Allport, A. (2003). Task-switching and long-term priming: Role of episodic stimulus–task bindings in task-shift costs. Cognitive Psychology, 46, 361–413. Welsh, M. C., Pennington, B. F., & Groisser, D. B. (2009). A normative-developmental study of executive function: A window on prefrontal function in children. Developmental Neuropsychology, 7, 131–149. White, L. J., Alexander, A., & Greenfield, D. B. (2017). The relationship between executive functioning and language: Examining vocabulary, syntax, and language learning in preschoolers attending Head Start. Journal of Experimental Child Psychology, 164, 16–31. Wiebe, S. A., Espy, K. A., & Charak, D. (2008). Using confirmatory factor analysis to understand executive control in preschool children: I. Latent structure. Developmental Psychology, 44, 575–587. Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3year-olds. Journal of Experimental Child Psychology, 108, 436–452. Wiig, E. H., Semel, E., & Secord, W. A. (2006). Clinical evaluation of language fundamentals-Fourth Edition, Spanish Version (CELF-4 Spanish). San Antonio, TX: Pearson Education. Wiseheart, M., Viswanathan, M., & Bialystok, E. (2016). Flexibility in task switching by monolinguals and bilinguals. Bilingualism (Cambridge), 19, 141–146. Woodard, K., Pozzan, L., & Trueswell, J. C. (2016). Taking your own path: Individual differences in executive function and language processing skills in child learners. Journal of Experimental Child Psychology, 141, 187–209. Wylie, G., & Allport, A. (2000). Task switching and the measurement of ‘‘switch costs”. Psychological Research, 63, 212–233. Yang, H., Hartanto, A., & Yang, S. (2016). The complex nature of bilinguals’ language usage modulates task-switching outcomes. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00560. Zelazo, P. D. (2006). The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nature Protocols, 1, 297–301. Zelazo, P. D., & Bauer, P. J. (Eds.). (2013). National Institutes of Health Toolbox–Cognitive Function Battery (NIH Toolbox CFB): Validation for children between 3 and 15 years. Monographs of the Society for Research in Child Development, 78(4), 150– 155.

K. Crespo et al. / Journal of Experimental Child Psychology 188 (2019) 104663

19

Zelazo, P. D., & Müller, U. (2002). Executive function in typical and atypical development. In U. Goswami (Ed.), Handbook of childhood cognitive development (pp. 445–469). Oxford, UK: Blackwell. Zelazo, P. D., Müller, U., Frye, D., & Marcovitch, S. (2003). The development of executive function in early childhood. Monographs of the Society for Research in Child Development, 68(3, Serial No. 274).