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We leveraged nationally representative data from the Panel Study of Income Dynamics-Child Development Supplement (N = 3,562) and the Early Childhood Longitudinal Study (N = 18,174), to chart the development of working memory, indexed via verbal forward and backward digit span task performance, from 3 to 19 years of age. Results revealed non-linear growth patterns for forward and backward digit span tasks, with the most rapid growth occurring during childhood followed by a brief accelerated period of growth during early adolescence. We also found similar developmental trajectories on digit span task performance for males and females across the U.S. population. Together, this study highlights the relative importance of the childhood period for working memory development and provides researchers with a reference against which to compare the developmental changes of working memory in individual studies. From a practical perspective, clinicians and educators can also use this information to understand important periods of working memory growth using national developmental trends.

Adverse childhood experiences (ACEs) and sociopolitical stressors have negative consequences for young adults’ mental health. The current study examined how patterns of ACEs were associated with event-related clinical distress following the 2016 United States presidential election and tested whether difficulties in emotion regulation exacerbated ACE effects in 751 young adults. A latent class analysis (LCA) identified four classes of ACE exposure: Minimal Exposure (55.2%), Moderate Household Dysfunction (21.3%), High Verbal and Physical Abuse (17.8%), and Systemic Exposure (5.6%). Young adults in the Systemic Exposure class reported more event-related intrusion symptoms compared to all other classes. Lower levels of difficulty in emotion regulation were protective for classes with lower ACE exposure but were not relevant for classes with higher ACE exposure. Thus, ACEs may occur in unique constellations that have implications for sociopolitical stressors and mental health outcomes in young adulthood.

A challenge of early adolescence is the “developmental mismatch” between adolescents’ need for autonomy and the lack of opportunities to enact maturity via adult-like roles. We identified ways that...

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This study takes a person-centered approach to investigate White youths' racial contexts by utilizing a latent profile analysis among a sample of White adolescents (N = 323, ages 16-17; 52% female, 48% male; data collected 1996-1998). Racial contexts were composed of parent, peer, and school influences, which revealed three distinct profiles: a Race Conscious profile, a Race Silent profile, and a Low Race Engagement profile. These profiles predicted White adolescents' critical action during emerging adulthood, where adolescents in the Race Conscious profile were engaged in more critical action as compared to the other two profiles. These findings suggest that the racial contexts in which White adolescents develop have direct implications on their desire to work toward, and take action for, social change.

Averages dominate developmental science: There are representative groups, mean trajectories, and generalizations to typical children. Nearly all parents and teachers, however, eagerly proclaim that few youth are average; each child, adolescent, and young adult is unique. Indeed, individual youth are the focus of many eminent developmental theories, yet there is a shocking paucity of developmental methods-including study designs and analysis techniques-that truly afford individual-level inferences. Thus, the goal of this chapter is to explicate the advantages of an idiographic approach to developmental science, that is, an approach that provides insight into individual youth, often by studying within-person variation in intensive longitudinal data, such as densely coded observations, repeated daily or momentary assessments, and functional neuroimages. In three domains across development, the chapter illustrates the benefits of an idiographic approach by comparing empirical conclusions offered by traditional mean-based analysis techniques versus techniques that leverage the temporal and individualized nature of intensive longitudinal data. The chapter then concentrates on group iterative multiple model estimation (GIMME), which is an analysis technique that uses intensive longitudinal data to create youth-specific temporal networks, detailing how brain regions or behaviors are directionally related across time. The promise of GIMME is exemplified by applications to three different domains across development. The chapter closes by encouraging future idiographic developmental science to consider how research questions, study designs, and data analyses can be formed, implemented, and conducted in ways that optimize inferences about individual-not average-youth.

Stage 1 Registered Report. During adolescence, increases in pubertal hormones lead to reproductive maturity and drive changes in cognitive development. However, little is known about how to best characterize interindividual differences in hormone concentrations. The goal of the proposed study is to examine the antecedents and consequences of membership in empirically derived hormone subgroups. Data will be drawn from over 11,000 youth (approximately 50% male, ages = 9 -11 years) from the Adolescent Brain Cognitive Development study.Latent profile analysis stratified by sex will be used to derive profiles of hormone exposure based on basal testosterone, dehydroepiandrosterone (DHEA), and estradiol (girls only). Profile membership will then be related to pubertal status, race/ethnicity, socioeconomic status, and body mass index (BMI). Additionally, profile membership will be examined as a prospective predictor of adolescents’ cognition (i.e., inhibitory control, visuospatial processing, decision-making) assessed two years later. Findings will clarify disparate associations between hormones and behavior in adolescence and underscore the importance for youth-centered approaches that focus on patterns of individual characteristics.

As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.

Executive functioning (EF) is fundamental to positive development. Yet, little is known about how to best characterize constellations of EF skills that may inform disparate associations between EF and behavior during adolescence. In the current study, cross-validated latent profile analysis (LPA) was used to derive profiles of EF based on measures of inhibitory control, working memory, and cognitive flexibility using data from 11,672 youth (52.2% male, mean age = 9.91 years) in the Adolescent Brain and Cognitive Development study. Four meaningful EF profiles emerged from the data representing Average EF, High EF, Low Inhibitory Control, and Low EF. Boys, youth from low-income households, and early developing youth were more likely to be in profiles distinguished by lower EF. Profile membership also predicted differences in externalizing, internalizing, and other problem behaviors assessed one year later. Findings indicate that youth may have distinct constellations of EF skills, underscoring the need for person-centered approaches that focus on patterns of individual characteristics.

Previous research has used cortisol, the major hormonal byproduct of the hypothalamic pituitary adrenal (HPA) axis system, to explore how environmental stressors influence daily physiological functioning. Most of the research focused on diurnal cortisol has examined specific cortisol markers, with little consideration of how different components of the diurnal pattern may co-occur. Morning level, cortisol awakening response (CAR), bedtime level, as well as the diurnal slope and total cortisol exposure throughout the day (area under the curve; AUC), are five common parameters of diurnal HPA axis functioning that have been individually linked to physical and mental health outcomes, with mixed results. The current study introduces a novel approach to capture heterogeneity in HPA axis activity by using latent profile analysis to generate empirically-derived, theoretically supported diurnal cortisol profiles based on all five indicators. We analyzed salivary cortisol data from 278 young adults during a time of heightened sociopolitical stress – the 2016 U.S. presidential election – and examined whether profiles differentially predicted mental health trajectories across six months. Findings suggest that a specific combination of cortisol parameters (i.e., flat slope, high AUC, and high CAR) may predict worse mental health risk over time. Overall, this work suggests that diurnal cortisol profiles likely reflect distinct physiological underpinnings with unique health consequences that may not be observed by studying individual cortisol parameters.

Elections present unique opportunities to study how sociopolitical events influence individual processes. The current study examined 286 young adults’ mood and diurnal cortisol responses to the 2016 U.S. presidential election in real-time: two days before the election, election night, and two days after the election of Donald Trump, with the goal of understanding whether (and the extent to which) the election influenced young adults’ affective and biological states. Utilizing piecewise trajectory analyses, we observed high, and increasing, negative affect leading up to the election across all participants. Young adults who had negative perceptions of Trump’s ability to fulfill the role of president and/or were part of a non-dominant social group (i.e., women, ethnic/racial minority young adults) reported increased signs of stress before the election and on election night. After the election, we observed a general “recovery” in self-reported mood; however, diurnal cortisol indicators suggested that there was an increase in biological stress among some groups. Overall, findings underscore the role of macro-level factors in individuals’ health and well-being via more proximal attitudes and physiological functioning.

The current study investigated patterns in executive functioning (EF) skills in adolescents (N = 991; 48.8% male; 81% White; mage = 14.91) and examined how EF profiles were associated with adolescent wellbeing. Eight EF skills, comprising foundational, complex, and affective EF, were assessed via self-rated EF, parent-reported EF, and neuropsychological tasks. A latent profile analysis (LPA) revealed four distinct EF profiles representing unique constellations of higher, lower, or mixed performance across all eight EF skills. When compared across domains, profiles characterized by higher foundational and complex EF skills exhibited positive, contemporaneous associations with academics, mental health, and health behaviors. Profiles characterized by discordant or mixed performance also exhibited unique strengths, such as positive peer relationships. Findings indicate that person-centered approaches to examining EF may reveal important sources of heterogeneity that have functional significance for health and wellness.

Societal events – such as natural disasters, political shifts, or economic downturns – are time-varying and impact the learning potential of students in unique ways. These impacts are likely accentuated during the COVID-19 pandemic, which precipitated an abrupt and wholesale transition to online education. Unfortunately, the individual-level consequences of these events are difficult to determine because the extant literature focuses on single-occasion surveys that produce only group-level inferences. To better understand individual-level variability in stress and learning, intensive longitudinal data can be leveraged. The goal of this paper is to illustrate this by discussing three different techniques for the analysis of intensive longitudinal data: (1) regression analyses; (2) multilevel models; and (3) person-specific network models, (e.g., group iterative multiple model estimation; GIMME). For each technique, a brief background in the context of education research is provided, an illustrative analysis is presented using data from college students who completed a 75-day intensive longitudinal study of cognition, somatic symptoms, anxiety, and intellectual interests during the 2016 U.S. Presidential election – a period of heightened sociopolitical stress – and strengths and limitations are considered. The paper ends with recommendations for future research, especially for intensive longitudinal studies of online education during COVID-19.

Puberty is marked by substantial increases and emerging sex differences in psychological disorders and risky behaviors. However, few studies have examined these effects beyond adolescence, and the previous literature has been dominated by samples of White girls. The current study examines the broadest known set of health sequelae related to traditional pubertal markers and peer-relative pubertal timing in a representative sample of 14,545 U.S. youth from the National Longitudinal Study of Adolescent to Adult Health. Maturational timing was assessed by age at menarche for girls and physical development for boys (e.g., facial hair, voice change), and then categorized as early (1 SD below mean), on-time, or late (1 SD above mean) within-sex. Early and late peer-relative timing was assessed by a self-report of looking “much older” or “much younger” than one's peers. We examined psychological (depressive symptoms, antisocial behavior), behavioral (number of sex partners, drug use, physical activity, screen time, sleep hours), and physical health (self-reported health, BMI) outcomes during adolescence and young adulthood in a series of sex-stratified regression analyses using survey weights and a comprehensive set of sociodemographic covariates. Results indicated that, overall, earlier pubertal timing (i.e., maturational timing and peer-relative timing) put both girls and boys at risk during adolescence, while later timing was protective. However, longitudinal models revealed mixed results. For instance, early maturational timing was associated with higher young adult BMI (girls: β = 0.139, p, Highlights • Early or late puberty predicts health, but there is little longitudinal research. • Perceived pubertal development relative to peers also predicts health outcomes. • Links between pubertal timing and long-term health were more pervasive for girls. • Adolescent psychological symptoms and health behaviors explain long-term links. • Results point to the need for high-quality puberty education early in life.

There are substantial changes in executive functioning during adolescence that may correspond with the onset and progression of puberty. The current study examines associations between pubertal development (timing and tempo) and changes in specific executive functioning skills (i.e., attention and self-control) across the transition from childhood to adolescence (ages 9.5–15.5) using data from the Study of Early Child Care and Youth Development (1099 youth; 52% female, 81% White, 83% above the poverty line). The findings indicated that early maturation was associated with faster increases in attention skills over adolescence for both boys and girls. Further, early maturation predicted worse self-control among girls but not boys. This study provides new insights on executive functioning during the transition to adolescence—a period of both vulnerability and opportunity.

This study took a life course approach to examine associations among family income from birth to age 15, and adolescent health and well-being. Utilizing latent growth mixture modeling, we identified four distinct family income trajectories based on changes in low-income status (family income ≤200% of the federal poverty line) over 15 years, which in turn related to their outcomes in adolescence. More specifically, youth living in a consistent state of higher income from birth to age 15 (Consistent Higher Income) reported better health and behavioral outcomes than youth in the Consistent Low Income, Increasing Income, or Decreasing Income groups. Furthermore, despite tending to have relatively high-income levels at the beginning of life, the Decreasing Income group showed several risky behavioral and health patterns, including more sexual risk-taking and high blood pressure. Results underscore the importance of studying changes in family income across childhood and adolescence.