







































Children and Teenagers 
ISSN 2576-3709 (Print) ISSN 2576-3717 (Online) 

Vol. 3, No. 2, 2020 
www.scholink.org/ojs/index.php/ct 

72 
 

Original Paper 

Dimensional Change Card Sorting of American Children: 

Marginalization-Related Diminished Returns of Age 
Shervin Assari1,2* 

1 Department of Family Medicine, Charles R Drew University of Medicine and Science, Los Angeles, 

CA 90059, USA 
2 Department of Urban Public Health, Charles R Drew University of Medicine and Science, Los Angeles, 

CA 90059, USA 
* Shervin Assari, assari@umich.edu; Tel.: +(734)-232-0445; Fax: +734-615-8739 

 

Received: November 2, 2020   Accepted: November 11, 2020   Online Published: November 23, 2020 

doi:10.22158/ct.v3n2p72       URL: http://dx.doi.org/10.22158/ct.v3n2p72 

 

Abstract 

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 association with DCCS for Black than for White children. This was documented by a 

significantly negative interaction between race and age on children’s DCCS scores. Conclusion: Age 

shows a weaker correlation with the cognitive flexibility of Black than of White children. A similar 

pattern can be seen when comparing low-income with high-income children. Conceptualizing race as a 

social factor that alters normal childhood development is a finding that is in line with MDRs. 



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Marginalization due to social stratification and racism interfere with the normal age-related cognitive 

development of American children. 

Keywords 

age, children, pre-adolescents, card sorting, cognitive flexibility, executive function 

 

1. Introduction 

Dimensional Change Card Sort (DCCS) is a useful tool to measure cognitive flexibility and executive 

function of children and adults (Zelazo, 2006), which are main components of cognitive performance 

Although overall, the DCCS is believed to generate a valid and reliable measure of cognition (Zelazo, 

2006), it is still unclear to what degree it can be applied to compare cognitive function across racial and 

ethnic groups. 

The association between race, socioeconomic status (Zelazo, 2006) (SES), and cognitive function is not 

only a scientific matter, but also a sensitive political issue(Herrnstein & Murray, 2010). Over the past 

several decades, there has been an ongoing political debate on whether it is appropriate to study race, SES, 

and cognitive performance. Specifically, whether race and SES correlate with cognitive function, and 

whether such effects are due to social forces or biological differences (Nisbett, 1995). Murray’s Bell 

Curve, that argued on the lower cognitive performance of Black individuals, generated an extreme 

backlash by the scientific community (Nisbett, 2009). In response to a biological claim, scientists 

questioned the validity of the argument that racial variation in cognitive performance is caused by 

biology (Nisbett, 1998, 2005; Nisbett et al., 2012a) and genetics (Brown & Day, 2006; Jensen, 1976; 

Nisbett et al., 2012b; Rushton & Jensen, 2005). Since then, the research community has provided 

considerable evidence suggesting that lower cognitive scores of Blacks compared to Whites may be an 

artifact and measurement bias as opposed to a true difference that reflects poor performance of Black 

people. Others have argued that we can always better measure cognitive function in White populations, 

rather than Blacks, which refutes any valid racial comparison of cognitive comparison across racial 

groups based on tests that are not well-validated in both groups (Nisbett, 2013; Turkheimer, Harden, & 

Nisbett, 2017). A recent research finding that cognitive scores predict the mortality of White but not 

Black people (Assari, 2020a) is another support for the argument that existing cognitive measures may 

fail to capture Black populations’ true cognitive performance (Assari, 2020a; Dotson, Kitner-Triolo, 

Evans, & Zonderman, 2009; Goldsmith, Darity Jr., & Veum, 1998; Nisbett, 2009). In addition, others 

have argued that low education quality, low SES, and other ecological reasons rather than a biological 

difference are in play (Rowe, Vesterdal, & Rodgers, 1998). Finally, Minorities’ Diminished Returns 

(MDRs) (Assari, 2017b; Assari, 2018) suggests that SES resources and potentials better translate to 

actual outcomes for Whites than for Blacks, again providing a social theory for explaining cognitive 

differences between race, particularly among middle-class families (Assari, 2018; Assari, 2018f, 2020b). 

Lack of predictive power of cognitive scores for Black people may also be due to MDRs (Assari, 2017b; 



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Assari, 2018). The MDRs reflect weaker health effects of personal assets and resources including SES, 

coping, and cognition for any marginalized group such as Black (Assari, 2018; Assari, 2018f), Hispanic 

(Assari, 2018e; Assari, 2019; Assari, Farokhnia, & Mistry, 2019; Shervin & Ritesh, 2019), Asian 

American (Assari, Boyce, Bazargan, & Caldwell, 2020b), and Native American (Assari & Mohsen 

Bazargan, 2019) people. 

Most of the research on MDRs have focused on economic resources such as parental education (Assari, 

Caldwell, & Bazargan, 2019), family income (Assari, Caldwell, & Mincy, 2018a; Assari, Thomas, 

Caldwell, & Mincy, 2018), and marital status (Assari & Bazargan, 2019). These studies have shown that 

economic resources generate fewer developmental, health, emotional, and behavioral outcomes for racial 

and ethnic minority group members than for Whites (Assari & Caldwell, 2018a; Assari, Caldwell, et al., 

2019; Assari, Caldwell, & Mincy, 2018a; Assari, Caldwell, & Mincy, 2018b; Assari, Thomas et al., 

2018). To give a few examples, high SES shows weaker effects on impulsivity (Assari, Caldwell, & 

Mincy, 2018a), depression (Assari & Caldwell, 2018a), anxiety (Assari, Caldwell, & Zimmerman, 2018), 

aggression (Assari, Caldwell et al., 2019), and substance use (Assari, Caldwell et al., 2019) for Black 

than for White children. As a result of these MDRs,  Black children with a high SES background does 

not greatly reduce their risk of impulsivity (Assari, 2020a; Assari, Caldwell, & Mincy, 2018a), reward 

sensitivity y (Assari, Akhlaghipour, Boyce, Bazargan, & Caldwell, 2020; Assari, Boyce, Akhlaghipour, 

Bazargan, & Caldwell, 2020), attention deficit hyperactivity disorder (ADHD) (Assari & Caldwell, 

2019a), obesity (Assari, Boyce, Bazargan, Mincy, & Caldwell, 2019), aggression (Assari, Caldwell et al., 

2019), chronic disease (Assari, Caldwell et al., 2019), anxiety (Assari, Caldwell, & Zimmerman, 2018), 

depression (Assari & Caldwell, 2018a), and suicide (Assari, Boyce, Bazargan, & Caldwell, 2020a), 

attention (Assari, Boyce, & Bazargan, 2020), school attachment (Assari, 2019b), and Grade Point 

Average (GPA) (Assari, 2019; Assari & Caldwell, 2019b; Assari, Caldwell et al., 2019).  

There are other non-economic resources essential for cognitive development as well, including 

neighborhood quality (Assari, 2016b), social network (Assari, 2017c), emotion regulation (Assari, 

2016a; Assari & Burgard, 2015; Assari, Moazen-Zadeh, Lankarani, & Micol-Foster, 2016), and coping 

(Assari, 2017a; Assari & Lankarani, 2016b). These all show weaker effects for Black than for White 

families, a pattern fully in line with the MDRs phenomenon. That is, non-economic resources may show 

weaker effects for Black than for White families. One of these resources and assets is age (Chalian, 

Khoshpouri, & Assari, 2019), which is the precursor of age-related cognitive development (Sowell, Delis, 

Stiles, & Jernigan, 2001; Sowell et al., 2004; Sowell, Thompson, Tessner, & Toga, 2001). In other 

age-dependent domains,  MDRs were also observed., meaning that age showed a weaker effect for 

Blacks than for Whites (Chalian et al., 2019). However, less is known about the MDRs of age-related 

changes in children’s cognitive development. If we observe weaker effect of age on cognitive 

performance in Black children, we have indirect support for a social, rather than a biological explanation 

for racial variation in cognitive function of children. 



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2. 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).  

 

3. Methods 

3.1 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). 

3.2 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. 

3.3 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 



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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). 

3.4 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. 

3.5 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. 

 

4. Results 

4.1 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 1 presents the descriptive data overall and by race. This table also compares racial groups for study 

variables. As this table shows, Black and mixed/other race participants had the lowest parental education 

and income. White and Asian children had the highest parental education and household income. DCCS 

score was also lower for Black than for White children. 

 

 

 

 

 

 



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Table 1. Descriptive Characteristics by Race (n = 10414) 

level All White Black Asian Other/Mixed p 

N 10,414 6,897 1,515 234 1,768 
 

 n (%) n (%) n (%) n (%) n (%)  

Sex       

    Female 4,996 (48.0) 3,254 (47.2) 760 (50.2) 117 (50.0) 865 (48.9) 0.128 

    Male 5,418 (52.0) 3,643 (52.8) 755 (49.8) 117 (50.0) 903 (51.1) 
 

Parental Education       

    <HS Diploma 385 (3.7) 145 (2.1) 123 (8.1) 6 (2.6) 111 (6.3) <0.001 

    HS Diploma/GED 862 (8.3) 327 (4.7) 340 (22.4) 3 (1.3) 192 (10.9) 
 

    Some College 2,674 (25.7) 1,462 (21.2) 600 (39.6) 18 (7.7) 594 (33.6) 
 

    Bachelor 2,766 (26.6) 2,057 (29.8) 230 (15.2) 65 (27.8) 414 (23.4) 
 

    Post Graduate Degree 3,727 (35.8) 2,906 (42.1) 222 (14.7) 142 (60.7) 457 (25.8) 
 

Married Family       

    No 3,165 (30.4) 1,415 (20.5) 1,058 (69.8) 33 (14.1) 659 (37.3) <0.001 

    Yes 7,249 (69.6) 5,482 (79.5) 457 (30.2) 201 (85.9) 1,109 (62.7)  

Household Income       

    <50K 2,997 (28.8) 1,259 (18.3) 999 (65.9) 36 (15.4) 703 (39.8) <0.001 

    50K-100K 2,974 (28.6) 2,104 (30.5) 335 (22.1) 54 (23.1) 481 (27.2)  

    >=100K 4,443 (42.7) 3,534 (51.2) 181 (11.9) 144 (61.5) 584 (33.0)  

Hispanic       

    No 8,451 (81.2) 5,737 (83.2) 1,439 (95.0) 215 (91.9) 1,060 (60.0) <0.001 

    Yes 1,963 (18.8) 1,160 (16.8) 76 (5.0) 19 (8.1) 708 (40.0)  

 Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)  

Age (Months) 118.95 (7.46) 119.03 (7.49) 118.88 (7.23) 119.40 (7.77) 118.65 (7.51) 0.192 

Card Sorting Score 97.10 (15.26) 98.29 (15.07) 91.31 (13.98) 102.36 (17.94) 96.73 (15.46) <0.001 

 

Table 2 presents the descriptive data overall and by levels of household income. From our participants, 

2,997 (28.8%) had a household income of <50K, 2,974 children (28.6%) had a househld income of 

between 50K and 100K, and 4,443 children (42.7%) were living in households with 100k+ income. 

Children in families with higher household income showed higher DCCS scores than low-income 

children. 

 

 



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Table 2. Descriptive Characteristics by Household Income (n = 10414) 

level All < 50K > =50K& < 100K > =100K p 

 
10,414 2,997 2,974 4,443  

 n (%) n (%) n (%) n (%)  

Race      

    White 6,897 (66.2) 1,259 (42.0) 2,104 (70.7) 3,534 (79.5) < 0.001 

    Black 1,515 (14.5) 999 (33.3) 335 (11.3) 181 (4.1)  

    Asian 234 (2.2) 36 (1.2) 54 (1.8) 144 (3.2)  

    Other/Mixed 1,768 (17.0) 703 (23.5) 481 (16.2) 584 (13.1)  

Hispanic      

    No 8,451 (81.2) 2,039 (68.0) 2,396 (80.6) 4,016 (90.4) < 0.001 

    Yes 1,963 (18.8) 958 (32.0) 578 (19.4) 427 (9.6)  

Sex      

    Female 4,996 (48.0) 1,461 (48.7) 1,423 (47.8) 2,112 (47.5) 0.582 

    Male 5,418 (52.0) 1,536 (51.3) 1,551 (52.2) 2,331 (52.5)  

Parental Education      

    <HS Diploma 385 (3.7) 361 (12.0) 22 (0.7) 2 (0.0) < 0.001 

    HS Diploma/GED 862 (8.3) 678 (22.6) 151 (5.1) 33 (0.7)  

    Some College 2,674 (25.7) 1,379 (46.0) 923 (31.0) 372 (8.4)  

    Bachelor 2,766 (26.6) 393 (13.1) 1,016 (34.2) 1,357 (30.5)  

    Post Graduate 

Degree 
3,727 (35.8) 186 (6.2) 862 (29.0) 2,679 (60.3)  

Married Family      

    No 3,165 (30.4) 2,001 (66.8) 791 (26.6) 373 (8.4) < 0.001 

    Yes 7,249 (69.6) 996 (33.2) 2,183 (73.4) 4,070 (91.6)  

 Mean (SD) Mean (SD) Mean (SD) Mean (SD)  

Age (Months) 118.95 (7.46) 118.59 (7.40) 118.78 (7.49) 119.31 (7.47) < 0.001 

Card Sorting Score 97.10 (15.26) 92.87 (14.03) 97.58 (14.91) 99.63 (15.69) < 0.001 

 

4.2 Multivariate Models 

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 



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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). 

 

Table 3. Mixed Effects Regressions overall (n = 10414) 

 
b SE p 

Model 1    

Age (Months) 0.08*** 0.02 < 0.001 

Race (Black) -4.25*** 0.50 < 0.001 

Race (Asian) 2.82** 1.01 0.005 

Race (Mixed/Other) -0.05 0.42 0.903 

Model 2    

Age (Months) 0.10*** 0.02 < 0.001 

Race (Black) 11.36# 6.90 0.100 

Race (Asian) -20.28 15.11 0.180 

Race (Mixed/Other) 4.87 6.26 0.437 

Race (Black) x Age -0.13* 0.06 0.023 

Race (Asian) x Age 0.19 0.13 0.126 

Race (Mixed/Other) x Age -0.04 0.05 0.432 

Model 3    

Age (Months) 0.03 0.04 0.407 

Race (Black) -4.24*** 0.50 < 0.001 

Race (Asian) 2.79** 1.01 0.006 

Race (Mixed/Other) -0.05 0.42 0.897 

Income (> =50K& < 100K) -1.00 6.13 0.871 

Income (> 100K)  -10.57# 5.63 0.060 

Income (> =50K& < 100K) x Age 0.02 0.05 0.636 

Income (> 100K) x Age 0.11* 0.05 0.022 

# p < 0.1 * p < 0.05  ** p < 0.01  *** p < 0.001 

 



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Figure 1. Association between Age and DCCS Score (Cognitive Flexibility) overall 

 
Figure 2. Association between Age and DCCS Score (Cognitive Flexibility) by Race 



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Figure 3. Association between Age and DCCS Score (Cognitive Flexibility) by Income 

 

5. 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. 

Our finding is in line with MDRs of age on cognitive flexibility for Black children. This finding is in full 

harmony with what is already established on the MDRs of economic resources effect on children’s 

impulsivity (Assari, Akhlaghipour et al., 2020), reward responsiveness (Assari, Boyce, Akhlaghipour et 

al., 2020), impulsivity (Assari, Caldwell, & Mincy, 2018a), inhibitory control (Assari, 2020d), attention 

(Assari, Boyce, & Bazargan, 2020), and ADHD (Assari & Caldwell, 2019a) in Black families. Similar 

MDRs are also reported for the effects of family SES indicators such as parental education, household 



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income, marital status on behavioral risks such as aggression (S. Assari, C. H. Caldwell, et al., 2019), 

substance use (Assari, Caldwell et al., 2019), and mental health risks such as anxiety (Assari, Caldwell, 

& Zimmerman, 2018), depression (Assari & Caldwell, 2018a), 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, 2018c, 2019a; Assari, 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.  

The observed MDRs are not specific to one specific domain or outcome, suggesting that they are due to 

society but not culture, behavior, or biology. Thus, the decreased association between age and cognitive 

flexibility seen in Black children is not due to genetics, nor is it due to an innate difference in their 

cognitive ability.  This is evident because similar MDRs are shown for all marginalized groups with all 

types of marginalizing identities (Assari, 2017b; Assari, 2018). Thus, they are not only specific to Blacks 

(Assari, Thomas et al., 2018) but also to Hispanics (Assari, 2018e; Assari, 2019; Assari, Farokhnia et al., 

2019; Shervin & Ritesh, 2019), Asian Americans (Assari, Boyce, Bazargan, & Caldwell, 2020b), Native 

Americans (Assari & Bazargan, 2019), LGBTQs (Assari, 2019a), immigrants (Assari, 2020b), and even 

marginalized Whites (Assari, Boyce, Bazargan, Caldwell, & Zimmerman, 2020). These MDRs are also 

not specific to a particular age group, as documented for children (Assari, Caldwell, & Mincy, 2018a; 

Assari, Caldwell, & Mincy, 2018b; Assari, Thomas et al., 2018), adults (Assari, 2018a), and older adults 

(Assari & Lankarani, 2016a). Finally, these MDRs are relevant to economic resources such as SES 

(Assari, Preiser, Lankarani, & Caldwell, 2018), (Assari, Farokhnia et al., 2019), (Assari, Caldwell, & 

Mincy, 2018a), (Assari, 2018d), (Assari, Caldwell, & Zimmerman, 2018), and non-economic assets such 

as self-efficacy (Assari, 2017a; Assari & Lankarani, 2016b). This paper extends the MDRs literature to 

the effects of age on cognitive performance in Black families (Chalian et al., 2019). A recent study 

established similar results for immigrants compared to non-immigrants, again emphasizing that these 

effects are social rather than biological (Assari, Boyce, Bazargan, & Caldwell, 2020). 

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). 

Black families experience high levels of stress across all SES levels (Bowden, Bartkowski, Xu, & Lewis 

Jr., 2017). Social mobility is more taxing for Black than for White families (Chetty, Hendren, Kline, & 

Saez, 2014). At all SES levels, exposure (Assari, 2018b; Assari, Gibbons, & Simons, 2018a; Assari, 

Gibbons, & Simons, 2018b; Assari, Lankarani, & Caldwell, 2018; Assari & Moghani Lankarani, 2018) 

and vulnerability (Assari, Preiser et al., 2018) to discrimination is high for Black families. While low 

SES Black families struggle with food insecurity, poverty, and neighborhood disorder, high SES Black 

families experience discrimination due to proximity to Whites (Assari, Gibbons et al., 2018a; Assari, 

Gibbons et al., 2018b). As discrimination reduces the chance of healthy brain development (Assari & 

Caldwell, 2018b; Assari, Lankarani et al., 2018; Assari, Preiser et al., 2018), Black children may remain 



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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; Hudson, 

Neighbors, Geronimus, & Jackson, 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; Boyce, Bazargan, Caldwell, Zimmerman, & Assari, 

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. 

 

6. 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 

development. The direction of the association between age and cognitive flexibility is from age to 

cognitive performance, not vice versa. Still, the findings reported here should be interpreted as 

correlations, not causes. To establish stronger causal evidence, we need to use longitudinal data with 

multiple observations of cognitive performance over time. Such research will help us map changes and 

trajectories of cognitive function that occur as the child ages to structural barriers that surrounds Black 

and low-income families. Our expectation is that the age-related trajectories in cognitive development 

would only be hindered in Black children who receive fewer cognition-promoting stimuli, live in worse 

neighborhoods, and attend worse schools when compared to their White counterparts. 



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Similarly, we only tested the MDRs of age. Previous work had established MDRs of family SES on 

cognitive and emotional outcomes (Assari, Caldwell, & Mincy, 2018a), (S. Assari, 2020c; Assari, 

Akhlaghipour et al., 2020; Assari, Boyce, Akhlaghipour et al., 2020). Future research should test if 

MDRs in any other domain, such as SES, can explain the observed MDRs of age. In addition, we only 

controlled for family-SES, and all our confounders were individual-level. It is imperative to control for 

contextual and neighborhood-level indicators as well as physical and mental health status. For example, 

we did not control for autism, ADHD, and other conditions that can interfere with cognitive development 

of children. Finally, we did not study how these MDRs emerge or change over time. Such research is 

necessary if we wish to find a window of opportunity for intervention. More research is needed on how 

the observed MDRs-related Black-White inequalities in cognitive performance narrow, maintain, or 

widen over time and how they contribute to the future Black-White gap in educational and economic 

success. 

 

7. 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. 

Acknowledgment: Author thanks Luke Sorensen for his contribution to the draft of the paper.  

Funding: Shervin Assari is supported by the National Institutes of Health (NIH) grants 5S21MD000103, 

D084526-03, CA201415 02, DA035811-05, U54MD008149, U54MD007598, and U54CA229974.  

Conflicts of Interest: The authors declare no conflict of interest. 

DEAP Acknowledgment: DEAP is a software provided by the Data Analysis and Informatics Center of 

ABCD located at the UC San Diego with generous support from the National Institutes of Health and the 

Centers for Disease Control and Prevention under award number U24DA041123. The DEAP project 

information and links to its source code are available under the resource identifier RRID: SCR_016158.  

ABCD Funding: Data used in the preparation of this article were obtained from the Adolescent Brain 

Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). 

The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners 



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under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, 

U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, 

U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of federal partners is 

available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete 

listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. This 

manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or 

ABCD consortium investigators. ABCD consortium investigators designed and implemented the study 

and/or provided data but did not necessarily participate in analysis or writing of this report. 

 

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www.scholink.org/ojs/index.php/ct                        Children and Teenagers                        Vol. 3, No. 2, 2020 

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Published by SCHOLINK INC. 

 

Appendices 
Appendix 1. Distribution of Predictor (a), Outcome (b), Qnantiles (c), and Residuals (d) 

 

 

 

 

a                            b                            c                     d 

 

Appendix 2. Model Formula 

Model 1 

nihtbx_cardsort_agecorrected ~ age + race.4level + sex + high.educ.bl + married.bl + 

household.income.bl + hisp 

Model 2 

nihtbx_cardsort_agecorrected ~ age + race.4level + sex + high.educ.bl + married.bl + 

household.income.bl + hisp + age x race.4level 

Model 3 

nihtbx_cardsort_agecorrected ~ age + race.4level + sex + high.educ.bl + married.bl + 

household.income.bl + hisp + age x household.income.bl 

All Models: Random: ~(1|abcd_site/rel_family_id) 

 

 


	Figure 1. Association between Age and DCCS Score (Cognitive Flexibility) overall

