Age-Related Decline in Children’s Reward Sensitivity: Blacks’ Diminished Returns

Background: It is important to study the correlates of reward sensitivity since it predicts high-risk behaviors. While ageing reduces children’s reward sensitivity and its associated risk taking, there is more to find out about racial differences in regard to the effect of age on reward sensitivity. Minorities’ Diminished Returns (MDRs) suggest that resources and assets show weaker effects on Black children than White children. Aim: We compared White children to Black children as for the effects of age on reward sensitivity. Methods: This cross-sectional study included 10533 American children who participated in the baseline of the Adolescent Brain Cognitive Development (ABCD) study. The independent variable was age, while the dependent variable was reward sensitivity as captured by the behavioral approach/behavioral avoidance system (BAS-BIS). Gender, parental education, marital status, parental education, and household income were the covariates. Results: Higher age was associated with less reward sensitivity. A significant interaction was found between race and age when it comes to children’s reward sensitivity. It suggested that age is associated with a smaller gain in terms of reduced reward sensitivity in Black children than White children. Conclusion: Age is more likely to reduce reward sensitivity in White children than Black children. This finding is in line with MDRs, and may be due to social racism, segregation, stratification, and discrimination.


Aims
Built on MDRs, we compared Black children to White children as for the effect of age on reward sensitivity. We focused on reward sensitivity because it reflects inhibitory control and behavioral activation. It also predicts aggressive behaviors, substance use, alcohol use, and sexual risk taking. We expected an inverse association of age with reward sensitivity; however, this association was reported to be diminished for Black children more than White children.

Design and Settings
This secondary analysis used 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 to 2018 in 21 sites across the states in the U.S. For more information on the ABCD study, please check this (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, in the U.S. The ABCD recruitment primarily relied on the U.S. school system. For a detailed description of the sampling and recruitment in the ABCD (Garavan et al., 2018). The eligibility for our analysis had a valid data of all our study variables including race, age, and reward sensitivity. The analytical sample of this paper was 10533.
higher reward sensitivity reflects the individual's high sensitivity to environmental cues, that condition the individual as well as give him a signal about higher-than-luck probabilities of reward. Race was self-identified: Blacks, Asians, Mixed/Other, and Whites (reference category). Parents reported the age of their children in months. Child sex was 1 for males and 0 for females. Parental marital status was reported by the parents and was 1 for married and 0 for the others. Household income, reported by parents, was a three-level categorical measure: less than 50K, 50-100K, and 100+K.

Data Analysis
We used DEAP for data analysis. DEAP uses R package for statistical calculations. We reported mean (standard deviation [SD]) and frequency (%) overall and by race. We also performed the Chi-square and ANOVA for our bivariate analysis. For multivariable modeling, we used mixed-effects regression models that allowed us to adjust for the nested nature of our data. Both models were performed in the overall sample. Model 1 did not have the interaction terms. Model 2 added interaction terms between race and age. Regression coefficient (b), SE, 95% CI, t value, and p-value were reported.

Ethical Aspect
The ABCD study has the Institutional Review Board's (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.

Multivariate Models
showed an inverse association between high age and reward sensitivity. Model 2 showed an interaction between age and race on reward sensitivity. This interaction indicated that the inverse association between high age and reward sensitivity is weaker for Black children than White children (Figures 2 and   3).

Discussion
This study showed an inverse link between age and reward sensitivity overall; however, this was stronger in White children than Black children. That is, while age reduces the reward sensitivity for American children, this protective effect of age is weaker in Black children than White children. As a result, older Black children maintain their high reward sensitivity; a pattern that is absent in White children. behavioral, and emotional problems  such as anxiety (Assari, Caldwell, & Zimmerman, 2018) and depression  shown for all marginalized groups with a range of marginalizing identities (Assari, 2017b;Assari, 2018).
While low SES and poor outcomes are one of the disadvantages types in Black communities, MDRs reflect a qualitatively different set of disadvantages (Assari, 2017b;Assari, 2018). Knowing that the former is reflective of unequal outcomes and opportunities, the latter is reflective of low response to the presence of individual level resources. It is due to the latter that policymakers may observe a sustained inequality despite investments. To address the latter, there is a need to address systemic causes of inequalities. As a result of these two jeopardies, Black groups are experiencing a double disadvantage, in which not only resources are scarce, but the influence of the individual level resources and assets are dampened, due to the environment (Assari, 2018;Assari, 2018f).

Conclusions
Relative to their White counterparts, Black children show higher levels of reward sensitivity within all age groups. This is important because reward sensitivity is a risk factor for a wide range of high-risk behaviors. To minimize the Black-White gap in brain development and to reduce high-risk behaviors in Black children, there is a need to address societal barriers causing MDRs of resources and assets in Black communities and families. There is a need for public, social, and economic policies that go beyond individual-level risk factors and address systemic, structural, and societal causes of inequalities.