Dimensional Change Card Sorting of American Children: Marginalization-Related Diminished Returns of Age

Background: While age is associated with an increase in cognitive flexibility and executive functioning as a result of normal development during childhood, less is known about the effect of racial variation in children’s age-related cognitive development. The Marginalization-related Diminished Returns (MDRs) phenomenon suggests that, under racism, social stratification, segregation, and discrimination, individual-level economic and non-economic resources and assets show weaker effects on children’s development for marginalized, racialized, and minoritized families. Aim: We conducted this study to compare racial groups of children for age-related changes in their card sorting abilities. Methods: This cross-sectional study included 10,414 9-10-year-old American children. Data came from the Adolescent Brain Cognitive Development (ABCD) study. The independent variable was age, a continuous variable measured in months. The dependent variable was Dimensional Change Card Sort (DCCS) score, which reflected cognitive flexibility, and was measured by the NIH Dimensional Change Card Sort. Ethnicity, sex, parental education, and marital status were the covariates. Results: Older age was associated with higher DCCS score, reflecting a higher card-sorting ability and cognitive flexibility. However, age showed a weaker flexibility of Black than White can social that normal Marginalization due to social stratification and racism interfere with the normal age-related cognitive development of American children.


Aims
Built on the MDRs framework (Assari, 2018;Assari, 2018f, 2020b, we first estimated the overall effect of age on DCCS score, a proxy of cognitive flexibility. Then we compared racial groups of children for the effect of age on DCCS scores. Finally, we also compared groups based on household income for the effect of age on DCCS scores. We expected a positive association between age and DCCS (i.e., cognitive flexibility) overall. However, we expected this association to be weaker (diminished) for Black and low-income than for White and high-income children. Again, a similar finding in Black and low-income sub-group would be another evidence supporting our sociological explanation of racial differences in cognitive function (due to MDRs).

Design and Settings
This secondary analysis used a cross-sectional design and borrowed data from the Adolescent Brain Cognitive Development (ABCD) study (Alcohol Research: Current Reviews Editorial, 2018;Casey et al., 2018;Karcher, O'Brien, Kandala, & Barch, 2019;Lisdahl et al., 2018;Luciana et al., 2018). ABCD baseline data collection was conducted from 2016 in 21 sites across the United States. For more information on the ABCD study, consult here (Alcohol Research: Current Reviews Editorial, 2018; Auchter et al., 2018).

Participants and Sampling
The ABCD participants were 9-10-year-old children who were selected from multiple cities across the states. ABCD recruitment primarily relied on the US school system. For a detailed description of the sampling and recruitment in the ABCD, consult here (Garavan et al., 2018). Our analysis's eligibility was having valid data on all our study variables, including race, age, and cognitive flexibility. The analytical sample of this paper was 10,414.

Study Variables
The study variables included race, ethnicity, sex, age, household income, parental education, marital status, and cognitive flexibility. Race was self-identified: Blacks, Asians, Mixed/Other, and Whites (reference category). Parents reported the age of their children in months. The sex of the child was 1 for males and 0 for females. Parental marital status was reported by the parents and was 1 for married and 0 for others. Household income, reported by the parent, was a three-level categorical measure: less than 50K, 50-100K, and 100+K. Cognitive flexibility was evaluated by the Dimensional Change Card Sort (DCCS). This measure is one of the components of the NIH toolbox for assessment of neurological and behavioral function. The DCCS is an easily administered and widely used measure that evaluates cognitive flexibility and executive function for a wide range of ages. In this test, children are asked to sort a series of bivalent test cards, first according to one dimension (e.g., color), and then according to the other (e.g., shape). While children under three cannot properly switch and exhibit a pattern of www.scholink.org/ojs/index.php/ct Children and Teenagers Vol. 3, No. 2, 2020 76 Published by SCHOLINK INC. inflexibility similar to patients with prefrontal cortical damage, children older than five years of age can successfully switch when asked to. The performance score on the DCCS test provides a standard index of cognitive flexibility and executive function development. The DCCS is highly age-dependent and is impaired in children with psychiatric and developmental disorders such as autism, Attention-Deficit/Hyperactivity Disorder (ADHD), and schizophrenia (Zelazo, 2006).

Data Analysis
We used Data Exploration and Analysis Portal (DEAP) for data analysis. DEAP uses the R package for statistical calculations. We reported the mean (Standard Deviation [SD]) and frequency (%) of our variables overall and by race. We also performed the Chi-square and ANOVA for our bivariate analysis.
We used three mixed-effects regression models for multivariable modeling that allowed us to adjust to our data's nested nature. This was because participants are nested to families that are nested to sites and states. All models were performed in the overall sample. Model 1 did not have interaction terms. Model 2 included interaction terms between race and age. Model 3 included interaction terms between household income and age. In all models, the DCCS score was the outcome. Regression coefficient (b), SE, and p-value were reported. Our Appendices 1 and 2 show our variables distributions and also our modeling strategy.

Ethical Aspect
The ABCD study has an Institutional Review Board (IRB) approval, and all participants have provided assent or consent, depending on their age (Auchter et al., 2018). Given that our analysis was performed on fully de-identified data, our analysis was exempt from a full IRB review.

Descriptives
Overall, 10,414 9-10-year-old children were analyzed. Most participants were Whites (n = 6,897; 66.2%), followed by other/mixed race (n = 1,768; 17.0%), and Black (n = 1,515; 14.5%). Only 234 (2.2%) children were Asian.     Table 3 presents the results of three mixed-effects regression models in the overall sample. Model 1 showed a positive association between age and cognitive flexibility (Figure 1). Model 2 showed an interaction between age and race on cognitive flexibility. This interaction indicated that the boosting effect of age and cognitive flexibility is weaker for Black than for White children (Figure 2). Model 3 showed an interaction between age and household income on cognitive flexibility. This interaction indicated that the boosting effect of age and cognitive flexibility is larger for high income than for low-income children (Figure 3).

Discussion
This study showed a positive association between age and DCCS score (cognitive flexibility) overall; however, this association was stronger for White and high-income than for Black and low-income children. That is, while age boosts the cognitive flexibility for American children, this effect is weaker in Black and low-income than in White and high-income families. As a result, older Black children and older poor children have low cognitive flexibility, a pattern which is absent for White and high-income children. In White and high-income families, age shows a substantial boosting effect on cognitive flexibility. We argue that due to structural inequalities, age-related development of cognitive flexibility is hindered in Black and low-income children. income, marital status on behavioral risks such as aggression (S. , substance use , and mental health risks such as anxiety (Assari, Caldwell, & Zimmerman, 2018), depression , and suicide (Assari, Boyce, Bazargan, & Caldwell, 2020a) in Black children. These are all diminishing returns of economic resources for Black compared to White youth (Assari, 2018a(Assari, , 2018c(Assari, , 2019aAssari, Farokhnia et al., 2019). As we found similar results for race and income (MDRs in poor as well as Black families), the observed MDRs in Black families are attributed to social rather than biological processes.
A wide range of sociological and economic mechanisms explain the MDRs of age and economic resources on cognitive flexibility for Black related to White families (and also in low-income families).
at risk of impulsivity across the whole SES spectrum. This offers an explanation to why age, the main driver of development, shows weaker Black effects than White children.
While low SES and poor outcomes are one type of disadvantage in Black communities, MDRs reflect a qualitatively different set of disadvantages (Assari, 2017b;Assari, 2018). The former reflects unequal outcomes and opportunities, and the latter is reflective of low response to the presence of individual-level resources such as age and SES. It is due to the latter that policymakers may observe sustained inequality despite investments. To address the latter, there is a need to address the systemic causes of inequalities.
As a result of the combination of these two, Black families experience double jeopardies: not only resources such as SES are scarce, their influences are also hindered and dampened, given the many constrains in their environment (Assari, 2018;Assari, 2018f).
Multilevel economic and environmental mechanisms are in play that reduce the marginal returns of economic and non-economic resources and assets such as family SES and age (Assari, 2018;Assari, 2018f). MDRs are probably caused by social stratification, racism, and marginalization. These processes function across multiple societal institutions and levels (Assari, 2018;Assari, 2018f). Racial injustice, prejudice, and discrimination have historically interfered with the gain of resources and assets for the Black communities (Hudson, Sacks, Irani, & Asher, 2020;Hudson, Bullard et al. 2012;. Black children live in poorer neighborhoods and attend worse schools compared to their White counterparts, even when they are from the very same SES backgrounds (Assari, Boyce, Caldwell, Bazargan, & Mincy, 2020;. Another known cause of MDRs is childhood poverty (Bartik & Hershbein, 2018). As a result of environmental and structural injustice, we observe MDRs across resources, assets, outcomes, settings, and age groups. This paper broadens the effect of MDRs as it shows that it can also interfere with normal age-related cognitive development of Black children.

Limitations
The current study has some methodological shortcomings. First, because of a cross-sectional design, it is inappropriate for us to draw any causal inferences. However, age is a known determinant of cognitive

Conclusions
Relative to their White counterparts, Black children show lower cognitive flexibility at all age groups.
This Black-White gap is shaped by social forces rather than biological differences as we found the same pattern in low-vs high-income families. These diminished returns of age on cognitive development are important because cognitive flexibility is a driver for a wide range of education and economic outcomes later in life. To minimize the Black-White gap in brain development, there is a need to address societal barriers that cause MDRs of age and other economic and non-economic resources and assets in Black communities. There is a need for public and social policies beyond individual-level risk factors and address systemic, structural, and societal causes of inequalities. Enhancing quality of education in predominantly Black neighborhoods is needed.
Author Contributions: Single author.