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.
Adolescence is a time of dramatic biological, social and cognitive change. Increases in sex hormones at the onset of puberty not only lead to rapid physical growth, but also influence the brain and behavior. In particular, the prefrontal cortex undergoes significant reorganization that promotes rapid gains in executive functioning—a core set of higher order skills involved in goal-directed behavior (Blakemore 2006). However, little research has examined how pubertal onset and progression may affect executive functioning trajectories. To address this gap, the current study sought to elucidate longitudinal associations between pubertal processes and two distinct executive functioning skills—attention and self-control—in boys and girls from ages 9.5. to 15.5 years-old. As executive functioning can have a powerful, long-term effect on positive adjustment and achievement (Moffitt et al. 2011), it is important to understand how normative physiological processes like puberty may hinder or promote the growth of executive functioning skills across adolescence.
Executive Functioning During Adolescence
Executive functioning refers to a broad range of cognitive abilities that allow people to plan, organize, and complete everyday tasks (Blakemore and Choudhury 2006). Executive functioning is often conceptualized as separable but interrelated skills involved in cognitive control, attention, inhibition, self-control and emotion regulation (Miyake and Friedman 2012). Further, prior research has suggested that executive functioning may fall along a hot–cold continuum with distinct developmental trajectories (Zelazo and Carlson 2012). Cold executive functioning is evoked under relatively decontextualized, non-affective situations, while hot executive functioning is elicited under motivational or emotional situations (Nejati et al. 2018). In nonclinical samples, cold executive functioning skills have been shown to predict school readiness (Ribner et al. 2017), better cognitive and academic functioning (Best et al. 2011), and higher socioeconomic status later in life (Lawson and Farah 2017), while poor hot executive functioning skills have been associated with common adolescent risk-taking behaviors such as reckless driving, unprotected sex, alcohol use, and drug experimentation (Prencipe et al. 2011).
Although most research to date points to the preschool years as a sensitive period for executive functioning development, longitudinal studies suggest that executive functioning skills are in an active state of development during adolescence as well (Arain et al. 2013). A theory known as the “dual systems” model of adolescent behavior (Casey et al. 2008) suggests that maturational disparities in the development of cold and hot executive functioning skills play an important role in adolescent development. Specifically, pubertal onset leads to a second reorganizational period in the brain, particularly in the prefrontal cortex—the region responsible for cognitive control—and subcortical regions (i.e., striatum, hippocampus and amygdala).
In the prefrontal cortex, pubertal onset triggers rapid synaptic pruning, leading to more efficient cognitive processing (Koolschijn et al. 2014). This suggests prefrontal cortex-controlled executive functioning skills (i.e., cold executive functioning) would improve throughout adolescence. For example, attention—an executive functioning skill commonly associated with inhibitory control in the prefrontal cortex—tends to show steady improvement from childhood to adulthood (Anderson et al. 2001). Gur and colleagues (2012) reported substantial improvement in attention skills from childhood to adulthood (ages 8–21; 3500 youth), with effect sizes exceeding 1.8 standard deviations. Similarly, researchers studying the development of attention in 8-year olds, 10-year olds, and adults found that there was significant improvement in accuracy and speed between the ages of 8 and 10, but that the adult group out-performed both child groups (Leon-Carrion et al. 2004) suggesting that attention skills continue to mature throughout adolescence, mirroring the development of the prefrontal cortex.
In subcortical regions, however, pubertal hormones may “over-activate” areas of the brain responsible for reward and emotion processing (Goddings et al. 2014). Thus, executive functioning skills involving the interaction of the limbic system and prefrontal cortex (i.e., hot executive functioning) may worsen in early adolescence as the overactive limbic system “overwhelms” the prefrontal cortex, resulting in the riskier behavior seen throughout adolescence (Casey et al. 2008). This theory is often applied to self-control, a key domain of executive functioning associated with a wide range of maladaptive behavior during adolescence such as risk-taking, substance abuse, and delinquency (Nigg 2016). Self-control involves the ability to control one’s impulses, emotions, and behaviors, as well as delay gratification. Ng-Knight and colleagues (2016) studied the development of self-control over three years in a sample of 1800 youth (mean age = 11), finding that self-control declined over early adolescence. Similarly, Shulman and colleagues (2015) found that the developmental trajectory of self-control follows a U-shaped trend, decreasing from ages 10–15 before rising again by ages 18–19. Therefore, it is possible that pubertal processes may be implicated in executive functioning fluctuations across adolescence.
The Pubertal Processes and Implications for Executive Functioning
Puberty consists of a series of biological events that begin with an increase in sex hormones in the brain and eventually leads to the development of secondary sex characteristics and reproductive competence (Patton and Viner 2007). Variation in pubertal onset has a strong genetic component but is also influenced by nutrition, body fat, and psychosocial stress (Arim et al. 2011). Pubertal timing (i.e., the individual differences in pubertal onset) plays an important role in psychological adjustment during adolescence. The maturation disparity hypothesis (Ge and Natsuaki 2009) suggests that children who develop earlier than their peers may encounter new environmental demands before they are emotionally prepared. Indeed, early pubertal timing has been associated with increased risky behavior and higher rates of psychopathology, particularly in girls (Mendle et al. 2010).
Although rarely studied, there may also be cognitive or social benefits of early maturation. For instance, Koerselman and Pekkarinen (2017) looked at the effect of pubertal timing on educational achievement and cognition, finding that early maturation relative to peers was associated with faster growth in cognitive skills. Early maturing youth who look older than same-aged friends may be exposed to new roles and responsibilities before the majority of their peers, giving them more opportunities to practice executive functioning skills important for academic achievement. Thus, while current research suggests these experiences expose early maturing youth to increased stress, it may also give them more opportunities for cognitive growth or social advantage. Further, since boys start puberty two years later than girls on average (Brix et al. 2018), they tend to express more satisfaction with pubertal changes (Bearman et al. 2006), and may receive more positive responses from peers and adults (Deardorff et al. 2007). Therefore, the consequences of early pubertal timing may be different for boys and girls (Deardorff et al. 2018).
Although most scholars have focused on individual differences in pubertal timing, youth also differ in how quickly they progress through puberty, often called pubertal tempo. The maturation compression hypothesis (Mendle et al. 2010) suggests that a faster pubertal tempo might present unique challenges for youth. For example, some studies suggest that faster tempo is associated with internalizing disorders, social difficulties, and substance abuse (Castellanos-Ryan et al. 2013), but no work to date has investigated whether pubertal tempo is associated with cognitive development. Given evidence that faster pubertal tempo is associated with higher circulating levels of estrogen and testosterone (Biro et al. 2014), and increases in pubertal hormones are associated with better executive functioning, moving through puberty faster could also result in faster growth in certain executive functioning skills.
Cross-sectional work has provided foundational knowledge of individual differences in executive functioning during adolescence, but it does not allow us to examine both inter- and intra-individual change in executive functioning across development. Further, although previous research suggests that pubertal onset drives rapid gains in executive functioning no work to date has examined whether individual differences in pubertal timing and tempo explains differences in executive functioning skills across the transition to adolescence.
To that end, this study examined longitudinal associations between pubertal timing and tempo and executive functioning trajectories in a longitudinal sample of youth over seven years (from ages 9.5–15.5). Using latent growth curve analysis, developmental trajectories of puberty (timing and tempo) and two specific executive functioning skills (attention and self-control) were modeled over time. Attention and self-control were selected for two key reasons. First, they represent the only two indicators of executive functioning that were measured repeatedly across the study (from age 9.5 to 15.5). Second, previous research has used self-control as an indicator of hot executive functioning (Zelazo and Carlson 2012) and attention as an indicator of cold executive functioning (Hongwanishkul et al. 2005), therefore the study also explored whether pubertal processes had differential relationships with executive functioning measures that are more affectively neutral (attention) or emotionally salient (self-control).
It was hypothesized that earlier pubertal timing and more rapid pubertal tempo would be associated with higher average levels and more rapid increases in attention skills (Fig. 1; hypothesis 1a, b) and worse average levels and slower increases in self-control skills (Fig. 1; hypothesis 2a, b). Given well-established sex differences in pubertal development (Wohlfahrt-Veje et al. 2016), the study also explored whether the associations between pubertal processes differed for boys and girls.
Data were drawn from the Eunice Kennedy Shriver National Institute of Child Health and Human Development’s (NICHD) Study of Early Child Care and Youth Development (SECCYD), a longitudinal study of childcare experiences, characteristics, and developmental outcomes from birth (1991) to adolescence (2008). Data were collected from ten research sites across the United States. Of the 8969 mothers who gave birth during the selected 24-hour sampling periods, a total of 1364 families met eligibility criteria (i.e., mother was over 18 years old, spoke English, was healthy, had a single baby and lived in a neighborhood less than an hour away from the research site) and agreed to become study participants. Comprehensive information about the study can be found at the Early Child Care Research Network (http://www.nichd.nih.gov/research/supported/seccyd/overview.cfm).
Participants for the current study included youth who participated in both Wave III (when pubertal assessments began) and Wave IV (n = 1099; 51% female). The analytic sample was 81.4% White, 11% Black and 7% were another race/ethnicity. In terms of socioeconomic status and household characteristics, youth had mothers with 14 years of education on average (i.e., some college), the average income-to-needs ratio was 4.37 (SD = 3.14), and 85% of sample lived in a two-parent household at age 9.5 (“baseline” for the current study).
Analyses comparing the demographic characteristics of children in the original SECCYD sample (n = 1,364) to those in the current study indicated that those excluded from the final analytic sample were more likely to be male (χ2(1364) = 3.94, p = 0.05), Black or Hispanic (χ2(1364) = 6.59, p = 0.01), have mothers with lower educational attainment, F(1, 1386) = 83.05, p < 0.01, and more likely to have lower family incomes at birth, F(1, 1295) = 60.42, p < 0.01. This pattern of missing data was expected based on previous research with the NICHD SECCYD data (Sabol and Hoyt 2017).
Within the analytic sample, only 25% of participants had puberty, attention, and self-control assessments at every time point (7 potential time points for puberty, 4 potential time points for executive functioning indicators), but over 62% of the analytic sample had at least three pubertal assessments and three executive functioning assessments across the seven years. Follow-up analyses revealed that the extent of missingness for these three key study constructs was not related to demographic characteristics such as maternal education, income-to-needs ratio, or family structure (Fs > 2.95, p’s < 0.05). Little’s test for missing completely at random for the pubertal and cognitive variables was run separately for boys and girls, and the assumption of missing completely at random was not rejected for either sex (females: χ2 (3180) = 3284.52, p = 0.09; males: χ2 (3068) = 3329.05, p = 0.07).
Starting at age 9.5, children’s pubertal development was assessed by nurse practitioners or physicians using Tanner staging criteria, where Stage I = prepubescent and Stage V = sexually mature. Staging criteria for girls were based on instructions from the American Academy of Pediatrics Manual: Assessment of Sexual Maturity Stages in Females and included criteria for breast bud palpation and pubic hair development. Staging criteria for boys followed the original Tanner methodology and included genital development and pubic hair development. All medical staff were experienced with Tanner staging and SECCYD protocols. Participants judged between two stages were assigned to the lower stage. When study children reached Stage V on both pubertal assessments (i.e., girls: pubic hair and breast development; boys: pubic hair and genital hair development), they were automatically assigned Tanner stage V for all subsequent assessments (i.e., no longer assessed). Importantly, there was limited variation within measurement occasion with Tanner staging during laboratory visits occurring on or close to participants’ half-birthday each year, starting at 9.5. Standard deviations within measurement occasions ranged from a low of 1.4 months to a high of 2 months. The frequency distribution of observed pubertal stage by age is displayed in Table 1.
Attention was measured by the Child Behavior Checklist (CBCL). The CBCL (Achenbach and Edelbrock 1991) was completed by the mother when the child was 9.5, 10.5, 11.5, and 15.5. The CBCL (ages 4–18) has 8 syndrome scales with 118 items used for 4 through 18 years of age. This scale has been standardized as well as validated with many samples of U.S. children (Achenbach and Ruffle 2000). Although the attention subscale of CBCL has traditionally been used with clinical samples, it has also been utilized as an indicator of cold executive functioning within the SECCYD dataset (Kim and Deater‐Deckard 2011).
Behaviors were rated on 3-point scales from 0 (not true) to 2 (very true of child). Sample items include “Talks too much” and “Inattentive and easily distracted”. Items were reverse coded so that higher scores indicated fewer attention problems. The current study used the attention problems raw score based on the mean of the items. Raw scores were used in all analyses given that standardized scores lack information about mean level changes over time (Moeller 2015). The possible range for mother-rated attention problems was 0-22. Prior research focused on examining longitudinal measurement invariance attention in the NICHD SECCYD found that longitudinal stability of the CBCL subscale was very high, ranging from 0.65 to 0.96 (Grimm et al. 2009).
Mothers completed the parent-version of the Social Skills Rating System—a widely used inventory of child behavior—at ages 9.5, 10.5, 11.5 and 15.5 (Holmes et al. 2018). Self-control was measured by 10 items drawn from the self-control subscale of the social skills rating system (SRSS; Gresham and Elliot 1990). The self-control subscale included items reflecting behaviors that emerged in emotionally salient situations, such as responding to teasing or peer pressure appropriately, receiving criticisms well, and controlling tempers in difficult situations, making it an effective measure of hot executive functioning (Holmes et al. 2016).
Responses ranged from 0 (not at all) to 3 (very much) and sample items included “Controls temper when arguing with other children” and “Responds appropriately when pushed or hit by other children.” Items were reverse scored so that higher scores indicated better self-control. Internal reliability at all ages were acceptable (α = .73-.86) and prior work suggests longitudinal stability over time (Walthall et al. 2005). Means and standard deviations for each executive functioning indicator across ages 9.5-15.5 are presented in Table 1.
Socioeconomic status was assessed by a family’s income-to-needs ratio at age 9.5, computed by dividing the total family pre-tax income (as reported by mothers) by the poverty threshold, with higher scores indicating higher income. Given that pubertal timing varies significantly by race/ethnicity (Bleil et al. 2017), race/ethnic group membership at birth was also included as a time-invariant covariate. Mothers reported their child’s race/ethnicity (Black, non-Black Hispanic, Asian, American Indian or non-Hispanic White) at baseline. Due to low diversity in the SEEYCD sample (81% White), all race/ethnic groups other than the White group were collapsed into a single indicator of race/ethnicity (i.e., 1 = White and 0 = other race/ethnicity).
A series of linear growth curve models (LGCM) were run to examine the changes in pubertal development, attention, and self-control over 7 years. (In describing the analysis below—and in Fig. 1—the term executive functioning will be used to represent the two specific executive functioning indicators: attention and self-control.) LGCM is a Structural Equation Modeling analytic strategy that permits tests of both inter- and intra-individual change (Bub et al. 2007). The analysis was conducted over three stages. First, descriptive statistics of focal study variables were explored. Next, group and individual differences in puberty and executive functioning variables were examined using a series of unconditional simple growth models to test inter- and intraindividual differences in change over time. Each model—(1) genital (boys) or breast (girls) development, (2) pubic hair development, (3) attention, (4) self-control—was run separately for boys and girls (eight models total). All pubertal development models were estimated using yearly measures of Tanner Staging from ages 9.5 to 15.5. Attention and self-control were estimated using all available executive functioning data (i.e., ages 9.5, 10.5, 11.5 and 15.5). After establishing the adequacy of the unconditional simple models, covariates (i.e., race/ethnicity and income-to-needs) were included in a series of conditional simple models (eight in total, as described above).
Finally, a series of parallel process LGCMs were used to examine how trajectories of puberty and executive functioning (attention or self-control) were interrelated. As illustrated in Fig. 1, the four latent variables represent: (1) initial executive functioning level (intercept) at age 9.5; (2) executive functioning slope (average rate of change from age 9.5–15.5); (3) pubertal timing (intercept); and (4) pubertal tempo (slope; or the rate of individual change in puberty from age 9.5–15.5). Based on prior hypotheses, it was anticipated that initial executive functioning and pubertal timing as well as executive functioning slope and pubertal tempo in each parallel process model would correlate with each other. Whether initial executive functioning and pubertal timing would predict executive functioning slope or pubertal tempo was also explored; therefore, cross-lagged associations were added from the intercept to slope of each model.
In all models, the intercept and slope variables represented year-to-year change (within-person) in either Tanner Stage or executive functioning level and quantified intra-individual differences in pubertal slope (i.e., tempo) or executive functioning slope from age 9.5–15.5, respectively (Marceau et al. 2011). Factor loadings for the slopes were centered at age 9.5 (baseline) for boys and girls. Additionally, given sex differences in pubertal onset, both models were re-run with age centered at 10.5 for girls and 12.5 for boys (i.e., the average age of transition from pre-pubertal (Stage I) to pubertal (Stage II)) as a sensitivity test. The parameter estimates for pubertal timing and the executive functioning intercepts were fixed at 1. The parameter estimates for pubertal tempo and the executive functioning slopes accounted for varying time intervals between measures for the seven yearly puberty measurement points (0 = 9.5, 1 = 10.5, 2 = 11.5, 3 = 12.5, 4 = 13.5, 5 = 14.5, and 6 = 15.5), and the four available assessment periods for executive functioning measures (0 = 9.5, 1 = 10.5, 2 = 11.5, 6 = 15.5), in order to test for linear growth during the 7-year window. Although quadratic slopes were tested, they did not lead to a significant improvement in model fit; therefore, all models used a linear slope.
Mean intercepts and slopes were used to determine initial Tanner Staging and initial attention/self-control levels, as well as average rate of change in both constructs. Variance and covariance factors were examined to assess associations between timing, tempo, and executive functioning measures. All models were estimated in MPlus (Muthén and Muthén 1998) and used full maximum likelihood ratio to account for missing data and correct for nonresponse (Ferrer et al. 2008). The quality of the model and overall fit indices were evaluated by the comparative fit index (CFI), the Tucker Lewis index (TLI) and the root mean-square error of approximation (RMSEA). The CFI and TLI range from 0-1; values over 0.90 generally indicate good fit and values over 0.95 indicate excellent fit (Muthen 1983). RMSEA values under 0.08 are acceptable, values under 0.05 are considered good fit and values under 0.03 are considered excellent fit (Seltzer et al. 1994). The chi-square value for assessing fit is known to be sensitive to sample size and will often be significant with samples over 200 (Raykov 2000).
As shown in Table 1, the majority of girls reached Tanner Stage II for pubic hair development at age 10.5 (SD = 0.89), a proxy for pubertal onset (Noll et al. 2017), and then progressed through Tanner Stages (i.e., pubertal tempo) at an average rate of 0.96 stages per year (SD = 1.34), with similar estimates for breast development. The majority of boys entered Stage II (pubic hair development) at age 12.5 (SD = 0.79) and progressed through Tanner Stages at an average rate of 0.78 stages per year (SD = 1.29) with similar estimates for genital development. Executive functioning performance generally increased over adolescence, although there was little variation over time.
Simple LGCM: Puberty and Executive Functioning
The model fit estimating the intercept and slope for pubic hair development was acceptable (girls: χ2 (24) = 186.28, p = 0.001; CFI = 0.97; TLI = 0.97; RMSEA = 0.08, boys: χ2 (24) = 71.34, p = 0.001; CFI = 0.99; TLI = 0.99; RMSEA = 0.06). Across all models, the variance estimates of timing (girls: 7.11, p = 0.001, boys: 4.55, p = 0.001) and tempo (0.14, p = 0.001; boys: 0.30, p = 0.001) were significant, suggesting that there were individual differences in pubertal timing and tempo over adolescence. Estimates were similar in the models of breast and genital development.
Timing and tempo were negatively correlated for girls across both physical markers (pubic hair: r = −0.73, breast development: r = −0.53, p’s = 0.001). In the male sub-sample, timing and tempo were negatively correlated for pubic hair development in boys (r = −0.50, p = 0.001) but positively correlated for genital development (r = 0.19, p = 0.004). This suggests that earlier pubertal timing was associated with slower progression through Tanner Stages in the pubic hair models as well as the breast development model (for girls). In the boys’ genital development model, however, earlier pubertal timing was associated with faster pubertal tempo. After evaluating the growth pattern in the unconditional models, race/ethnicity and income-to-needs were added to each model. Across all models, White race/ethnicity was associated with later pubertal timing (e.g., β = −1.21, p = 0.009). Income to needs ratio was not associated with any differences in pubertal timing or slope.
The female and male attention models had acceptable fit (girls: χ2 (3) = 16.66, p = 0.001; CFI = 0.99; TLI = 0.97; RMSEA = 0.09; boys: χ2 (3) = 20.83, p = 0.001; CFI = 0.99; TLI = 0.97; RMSEA = 0.09), as did the female and male self-control models (girls: χ2 (3) = 12.53, p = 0.005; CFI = 0.99; TLI = 0.97; RMSEA = 0.04; boys: χ2 (3) = 1.65, p = 0.64; CFI = 0.99; TLI = 0.99; RMSEA = 0.01). Intercepts and slopes varied significantly from zero across all models (all p’s = 0.001) and the slopes showed linear growth over time (all p’s = 0.001). The variances for the intercept and slope in all four models were also significant, suggesting that there were individual differences in attention and self-control at the 9.5 assessment and that there were differences in the rate of growth in attention and self-control skills over time.
There was a significant negative correlation between the intercept and slope for attention (females: r = −0.45, males: r = −0.26, p’s = 0.001) and self-control (females: r = −0.18, males: r = −0.26, p’s = 0.001). This suggests that higher levels of attention and self-control at 9.5 were associated with slower growth in these same skills over adolescence. After the unconditional model was evaluated, the conditional model (with race/ethnicity and income-to-needs ratio) was estimated for all four models. In the female models, White race/ethnicity was positively associated with higher self-control skills (β = 1.20, p = 0.04) at age 9.5. Income-to-needs ratio was positively associated with initial attention (β = 0.12, p = 0.002) and self-control skills (e.g., β = 1.21, p = 0.04), such that youth with higher income-to-needs ratios had higher executive functioning skills. In the male models, only income-to-needs ratio was positively associated with initial attention (β = 0.13, p = 0.006) and self-control skills (e.g., β = 0.17, p = 0.001).
Parallel Process Models of Puberty and Executive Functioning
In the main analyses, a series of parallel process conditional LGCMs were run to examine the co-development of pubertal processes and executive functioning. All parallel process models had good fit to the data and the intercepts, slopes and variances were significantly different from zero. Parameter estimates and fit statistics for the parallel process models are presented in Table 2 (attention and puberty) and Table 3 (self-control and puberty).
Pubertal Development and Attention
In the male and female pubic hair models, there was a significant positive relationship between pubertal timing and the attention slope (female model: B = 0.18, p = 0.02; male model: B = 0.13, p = 0.04), with similar estimates for genital and breast development. This suggests that more advanced Tanner Staging was related to increased rate of improvement (slope) in attention over seven years (hypothesis 1). There was no evidence of any association between pubertal timing and the initial attention skills (intercept) in the male or female models. There was also no evidence of any association between pubertal tempo and the attention slope or between attention skills intercept and pubertal tempo (Table 2).
Pubertal Development and Self-control
Across all female models, pubertal timing was negatively associated with initial self-control skills (pubic hair: B = −0.12, p = .03; breast development: B = −0.12, p = .02), suggesting that more advanced Tanner Staging at earlier chronological age (i.e., early timing) was associated with worse self-control skills in girls. In the male models, pubertal timing was not significantly associated with initial self-control skills. Therefore, the results support hypothesis 2 for girls, but not for boys. All other associations between puberty and self-control were not statistically significant (Table 3). Finally, as a sensitivity analyses, the male models were re-run centered at average pubertal onset (12.5 for boys) and the female models were re-run centered at 10.5 (i.e., average age of pubertal onset). Each model showed the same pattern of results and similar effect sizes (see appendix for sensitivity analyses).
Early pubertal timing and fast pubertal tempo are often categorized as markers of psychosocial risk based on previous literature; however, few studies have investigated whether these pubertal processes may have a positive or negative influence on cognitive systems like executive functioning. This is especially surprising given theory (i.e., dual systems model; Steinberg 2010) and empirical evidence (Wasserman et al. 2017), which suggests that pubertal hormones may in fact drive gains and losses in executive functioning skills. In an effort to investigate these knowledge gaps, the current study examined longitudinal associations between pubertal development and two key indicators of executive functioning, attention and self-control.
The analyses suggested that while early developers (i.e., those with more advanced pubertal status for their chronological age) had similar mean levels of attention skills at age 9.5 as their on-time or late developing peers, they experienced a greater increase in attention skills over time (from age 9.5 to 15.5). This significant association between pubertal timing and the attention slope is a novel finding and was replicated for both boys and girls and across all indices of pubertal development (i.e., genital, breast and pubic hair development).
These findings highlight potential cognitive benefits to advanced maturation and compliment recent neurobiological research suggesting that the thinning of grey matter (i.e., synaptic pruning) in the prefrontal cortex at the onset of puberty is associated with more efficient cold executive functioning skills (Koolschijn et al. 2014). Indeed, neurocognitive studies have shown that thinner prefrontal cortexes are associated with higher scores on executive functioning tasks of working memory, attention, verbal learning, and problem solving (Squeglia et al. 2013). Therefore, early maturing youth may have thinner frontal cortices at a younger age compared to their peers, leading to faster growth in attention skills over time.
Psychosocial experiences may play an equally important role in this apparent developmental advantage in attention skills. Early developing youth often look older than same-aged friends and may therefore be exposed to new roles and responsibilities before the majority of their peers. For example, teachers may expect that kids who look older can handle more demanding cognitive tasks than their classmates. The perception of advanced competence (tied to physical appearance) and additional experiences with attention-related tasks (e.g., focusing on more complex academic tasks) at a younger age, may give early maturing youth a head start over their peers.
Despite the potential positive implications for attention skills, the results also suggest that early timing was associated with worse self-control in girls (replicated across both breast and pubic hair development models). These findings align with the dual systems theory—changes in brain regions associated with sensation-seeking and reward-taking at the onset of puberty may lead to worse hot executive functioning skills in girls, which may put early maturing girls on a riskier trajectory (Steinberg 2010). Epidemiological evidence suggests that early maturing adolescents are more likely to engage in risky behaviors, including early sexual initiation (Baams et al. 2015) and substance abuse (Hedges and Korchmaros 2016), and these findings also corroborate studies in which adolescents exhibited riskier decision-making behaviors in laboratory simulations (Icenogle et al. 2017). However, the present study extends these findings by demonstrating the importance of examining sex differences during puberty: there was no association between puberty and self-control in boys.
While male and female brains are overwhelmingly more alike than different, there are sex-specific differences in the trajectory of cortical brain volume development during puberty that may explain differing associations between puberty and self-control in the current study. Longitudinal studies have shown that cortical grey matter volume peaks one to three years earlier in females compared to males, while white matter in males grows more rapidly, resulting in increasingly larger volumes relative to females during adolescence (Peper et al. 2011). Furthermore, males show accelerated thinning of prefrontal cortex from age 12-14, while females show attenuated thinning of the prefrontal cortex during the same age range (Satterthwaite et al. 2014), suggesting that the adolescent male prefrontal cortex may be more efficient compared to puberty-matched females. This may help explain why at the same age, boys did not show any associations between self-control skills and pubertal processes. The mechanism behind these processes is still being investigated, but it is suspected that sex differences in brain development may be related to different hormone levels at different stages of pubertal maturation (Peper et al. 2011). Therefore, findings from the current study provide an important step in elucidating the impact of pubertal processes on the organization of female and male brains.
Overall, this study suggests that pubertal timing (but not tempo) may have a significant, sex-specific relationship with the development of hot and cold executive functioning skills in adolescence. Moreover, while early puberty may promote increased development in some cold executive functioning skills, such as attention or other inhibitory skills, it may also leave some early developing girls vulnerable to self-control deficits that could manifest in risky decision-making or problems with emotion regulation in the long-term. Given rises in sex-specific psychopathology during adolescence as well as increased rates of risky behavior (Wasserman et al. 2017), future work should explore how adults (e.g., parents, teachers) can help early maturing youth harness these cognitive advancements to help reduce risk in other domains. For example, can better attentional skills help reduce certain risky health behaviors? How may improved attentional capabilities improve social functioning and promote positive relationships with peers and adults?
The NICHD SECCYD dataset is one of the only studies with multiple measures of Tanner Stages and executive functioning, allowing us to employ longitudinal analyses of puberty, attention, and self-control. However, there are also several limitations associated with these data. First, since Tanner categorization is different for boys and girls (e.g., genital development vs. breast development), it was not possible to statistically test sex differences across models. Future work should include alternative pubertal assessments that allow researchers to explicitly test for sex differences.
Second, there were several limitations in regards to measurement. Although the SECCYD included several executive functioning measures (e.g., the balloon analogue risk task, the Stroop task), all executive functioning tasks with the exception of attention and self-control were only measured once or twice and therefore could not be modeled using the LGCM framework. Additionally, longitudinal measures of attention and self-control were not available from ages 12.5–14.5, so the executive functioning models may have been less sensitive to changes during these years. Although puberty was measured at every visit, it is also possible that yearly Tanner Staging is not precise enough to capture the nuances of pubertal tempo. (Indeed, pubertal tempo was not significantly associated with any measures of executive functioning in the current study.) More intensive and consistent sampling of both puberty and executive functioning (including multiple reporters and repeated objective tasks) across adolescence will be an important contribution to future studies.
Finally, the SECCYD data does not represent the diversity of the U.S. (81% white, 83% above the poverty line), therefore, the results are not nationally representative. Additionally, according to the preliminary analyses, SECCYD had nonrandom attrition over time and is therefore not representative of children from low-income backgrounds, which limits generalizability. Relatedly, there was not enough statistical power to run a multi-group analyses examining whether pubertal processes played a different role in the development of executive functioning skills within specific race/ethnic groups. Race/ethnic minorities face different environmental stressors such as discrimination which can have important implications for both pubertal (Chumlea et al. 2003) and cognitive development (Cottrell et al. 2015). Prior research has shown that Black youth tend to begin puberty 1–3 years earlier than their White peers (Herbert et al. 2016), suggesting that these youth may face gains and losses in executive functioning development earlier than the sample in SECCYD. Additionally, low-income youth face unique environmental stressors as well (Raffington et al. 2018) and may not experience positive psychosocial environments that protect against changes in puberty and executive functioning. Future research with more diverse samples should elucidate whether the relationships between pubertal processes (timing and tempo) and executive functioning skills in this study replicates across various ethnic/racial groups. This is particularly important given the finding that early pubertal timing may confer some positive benefits for youth’s cognitive development.
Although early pubertal timing is usually considered a risk factor in the field of developmental science, the findings add to a growing body of work that calls for a more nuanced understanding of puberty. Analyses suggest that early pubertal timing was associated with faster improvements in attention skills across adolescence for all youth, but lower levels of self-control in girls (but not boys). Thus, these findings highlight the importance of examining how puberty intersects across varying neurobiological, cultural and social contexts (Deardorff et al. 2018). Early pubertal onset in supportive env ironments may be associated with positive outcomes, while stressful environments may induce more risky behavior. Overall, this work contributes to the growing literature on the complex interaction between puberty and executive functioning, underscores the importance of sex differences, and compels exciting new directions for future work in positive youth development and adolescent neuroscience.
Data and Sharing Declaration
The data that support the findings of this study are available from Inter-university Consortium for Political and Social Research but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. However, data are available from the authors upon reasonable request and with permission of Inter-university Consortium for Political and Social Research.
Achenbach, T. M., & Edelbrock, C. (1991). Child behavior checklist. Burlington VT: University of Vermont. Department of psychiatry.
Achenbach, T. M., & Ruffle, T. M. (2000). The Child Behavior Checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatrics in review, 21(8), 265–271.
Anderson, V. A., Anderson, P., Northam, E., Jacobs, R., & Catroppa, C. (2001). Development of executive functions through late childhood and adolescence in an australian sample. Developmental Neuropsychology, 20(1), 385–406. https://doi.org/10.1207/S15326942DN2001_5.
Arain, M., Haque, M., Johal, L., Mathur, P., Nel, W., Rais, A., & Sharma, S. (2013). Maturation of the adolescent brain. Neuropsychiatric Disease and Treatment, 9, 449–461. https://doi.org/10.2147/NDT.S39776.
Arim, R. G., Tramonte, L., Shapka, J. D., Dahinten, V. S., & Willms, J. D. (2011). The family antecedents and the subsequent outcomes of early puberty. Journal of Youth Adolescent, 40(11), 1423–1435. https://doi.org/10.1007/s10964-011-9638-6.
Baams, L., Dubas, J. S., Overbeek, G., & Van Aken, M. A. (2015). Transitions in body and behavior: a meta-analytic study on the relationship between pubertal development and adolescent sexual behavior. Journal of Adolescent Health, 56(6), 586–598.
Bearman, S. K., Presnell, K., Martinez, E., & Stice, E. (2006). The skinny on body dissatisfaction: a longitudinal study of adolescent girls and boys. Journal of Youth and Adolescence, 35(2), 217–229.
Best, J. R., Miller, P. H., & Naglieri, J. A. (2011). Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learning and Individual Differences, 21(4), 327–336. https://doi.org/10.1016/j.lindif.2011.01.007.
Biro, F. M., Pinney, S. M., Huang, B., Baker, E. R., Walt Chandler, D., & Dorn, L. D. (2014). Hormone changes in peripubertal girls. The Journal of Clinical Endocrinology & Metabolism, 99(10), 3829-3835. Blakemore, S. J. (2008). Development of the social brain during adolescence. The Quarterly Journal of Experimental Psychology, 61(1), 40–49. https://doi.org/10.1080/17470210701508715.
Blakemore, S. J., & Choudhury, S. (2006). Development of the adolescent brain: implications for executive function and social cognition. Journal of Child Psychology and Psychiatry, 47(3-4), 296–312. https://doi.org/10.1111/j.1469-7610.2006.01611.x.
Bleil, M. E., Booth-LaForce, C., & Benner, A. D. (2017). Race disparities in pubertal timing: implications for cardiovascular disease risk among african american women. Population Research and Policy Review, 36(5), 717–738.
Brix, N., Ernst, A., Lauridsen, L. L. B., Parner, E., Stovring, H., Olsen, J., & Ramlau-Hansen, C. H. (2018). Timing of puberty in boys and girls: a population-based study. Paediatric and Perinatal Epidemiology, 33(1), 70–78. https://doi.org/10.1111/ppe.12507.
Bub, K. L., McCartney, K., & Willett, J. B. (2007). Behavior problem trajectories and first-grade cognitive ability and achievement skills: a latent growth curve analysis. Journal of Educational Psychology, 99(3), 653–670. https://doi.org/10.1037/0022-06184.108.40.2063.
Casey, B. J., Getz, S., & Galvan, A. (2008). The adolescent brain. Developmental Review, 28(1), 62–77. https://doi.org/10.1016/j.dr.2007.08.003.
Castellanos-Ryan, N., Parent, S., Vitaro, F., Tremblay, R. E., & Séguin, J. R. (2013). Pubertal development, personality, and substance use: a 10-year longitudinal study from childhood to adolescence. Journal of Abnormal Psychology, 122(3), 782.
Chumlea, W. C., Schubert, C. M., Roche, A. F., Kulin, H. E., Lee, P. A., Himes, J. H., & Sun, S. S. (2003). Age at menarche and racial comparisons in us girls. Pediatrics, 111(1), 110–113.
Cottrell, J. M., Newman, D. A., & Roisman, G. I. (2015). Explaining the black-white gap in cognitive test scores: toward a theory of adverse impact. Journal of Applied Psychology, 100(6), 1713–1736. https://doi.org/10.1037/apl0000020.
Deardorff, J., Hayward, C., Wilson, K. A., Bryson, S., Hammer, L. D., & Agras, S. (2007). Puberty and gender interact to predict social anxiety symptoms in early adolescence. Journal of Adolescent Health, 41(1), 102–104.
Deardorff, J., Hoyt, L. T., Carter, R., & Shirtcliff, E. A. (2018). Next steps in puberty research: broadening the lens toward understudied populations. Journal of Research on Adolescence, 29, 133-154.
Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial invariance and the specification of second-order latent growth models. Methodology (Gott), 4(1), 22–36. https://doi.org/10.1027/1614-2241.4.1.22.
Ge, X., & Natsuaki, M. N. (2009). In search of explanations for early pubteral timing effects on developmental psychopathology. Current Directions in Psychological Science, 18(6), 327–331.
Goddings, A.-L., Mills, K. L., Clasen, L. S., Giedd, J. N., Viner, R. M., & Blakemore, S.-J. (2014). The influence of puberty on subcortical brain development. NeuroImage, 88, 242–251.
Gresham, F. M., & Elliot, S. N. (1990). Manual for the social skills rating system. Circle Pines, MN: American Guidance Service.
Grimm, K. J., Pianta, R. C., & Konold, T. (2009). Longitudinal multitrait-multimethod models for developmental research. Multivariate Behavioral Research, 44(2), 233–258.
Gur, R. C., Richard, J., Calkins, M. E., Chiavacci, R., Hansen, J. A., Bilker, W. B., & Abou-Sleiman, P. M. (2012). Age group and sex differences in performance on a computerized neurocognitive battery in children age 8− 21. Neuropsychology, 26(2), 251.
Hedges, K., & Korchmaros, J. D. (2016). Pubertal timing and substance abuse treatment outcomes: an analysis of early menarche on substance use patterns. Journal of Child & Adolescent Substance Abuse, 25(6), 598–605.
Herbert, A. C., Ramirez, A. M., Lee, G., North, S. J., Askari, M. S., West, R. L., & Sommer, M. (2016). Puberty experiences of low-income girls in the united states: a systematic review of qualitative literature from 2000 to 2014. Journal of Adolescent Health, 60(4), 363–379. https://doi.org/10.1016/j.jadohealth.2016.10.008.
Holmes, C., Brieant, A., Kahn, R., Deater-Deckard, K., & Kim-Spoon, J. (2018). Structural home environment effects on developmental trajectories of self-control and adolescent risk taking. Journal of Youth and Adolescence, 48, 1–13.
Holmes, C. J., Kim-Spoon, J., & Deater-Deckard, K. (2016). Linking executive function and peer problems from early childhood through middle adolescence. Journal of Abnormal Child Psychology, 44(1), 31–42.
Hongwanishkul, D., Happaney, K. R., Lee, W. S., & Zelazo, P. D. (2005). Assessment of hot and cool executive function in young children: age-related changes and individual differences. Developmental Neuropsychology, 28(2), 617–644.
Icenogle, G., Steinberg, L., Olino, T. M., Shulman, E. P., Chein, J., Alampay, L. P., & Uribe Tirado, L. M. (2017). Puberty predicts approach but not avoidance on the iowa gambling task in a multinational sample. Child Development, 88(5), 1598–1614. https://doi.org/10.1111/cdev.12655.
Kim, J., & Deater-Deckard, K. (2011). Dynamic changes in anger, externalizing and internalizing problems: attention and regulation. Journal of Child Psychology and Psychiatry, 52(2), 156–166.
Koerselman, K., & Pekkarinen, T. (2017). The timing of puberty and gender differences in educational achievement IZA Discussion Papers 10889. Germany: IZA Institute of Labor Economics.
Koolschijn, P. C., Peper, J. S., & Crone, E. A. (2014). The influence of sex steroids on structural brain maturation in adolescence. PLoS ONE, 9(1), e83929 https://doi.org/10.1371/journal.pone.0083929.
Lawson, G. M., & Farah, M. J. (2017). Executive function as a mediator between ses and academic achievement throughout childhood. International Journal of Behavioral Development, 41(1), 94–104.
Leon-Carrion, J., García-Orza, J., & Pérez-Santamaría, F. J. (2004). Development of the inhibitory component of the executive functions in children and adolescents. International Journal of Neuroscience, 114(10), 1291–1311.
Marceau, K., Ram, N., Houts, R. M., Grimm, K. J., & Susman, E. J. (2011). Individual differences in boys’ and girls’ timing and tempo of puberty: Modeling development with nonlinear growth models. Developmental psychology, 47(5), 1389.
Mendle, J., Harden, K. P., Brooks-Gunn, J., & Graber, J. A. (2010). Development’s tortoise and hare: pubertal timing, pubertal tempo, and depressive symptoms in boys and girls. Developmental Psychology, 46(5), 1341–1353. https://doi.org/10.1037/a0020205.
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: four general conclusions. Current Directions in Psychological Science, 21(1), 8–14. https://doi.org/10.1177/0963721411429458.
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(1), 49–100. https://doi.org/10.1006/cogp.1999.0734.
Moeller, J. (2015). A word on standardization in longitudinal studies: don’t. Frontiers in Psychology, 6, 1389.
Moffitt, T. E., Arseneault, L., Belsky, J., Dickson, N., Hancox, R. J., Harrington, H., & Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2693–2698. https://doi.org/10.1073/pnas.1010076108.
Muthen, B. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22, 43-65.
Muthén, L. K., & Muthén, B. O. (1998). Mplus user’s guide (Version 7). Los Angeles, CA: Muthén & Muthén, 2004.
Nejati, V., Salehinejad, M. A., & Nitsche, M. A. (2018). Interaction of the left dorsolateral prefrontal cortex (l-DLPFC) and right orbitofrontal cortex (OFC) in hot and cold executive functions: evidence from transcranial direct current stimulation (tDCS). Neuroscience, 369, 109–123.
Ng-Knight, T., Shelton, K. H., Riglin, L., McManus, I. C., Frederickson, N., & Rice, F. (2016). A longitudinal study of self-control at the transition to secondary school: considering the role of pubertal status and parenting. Journal of Adolescence, 50, 44–55.
Nigg, J. T. (2016). Annual research review: on the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. Journal of Child Psychology and Psychiatry, 58, 361–383. https://doi.org/10.1111/jcpp.12675.
Noll, J. G., Trickett, P. K., Long, J. D., Negriff, S., Susman, E. J., Shalev, I., & Putnam, F. W. (2017). Childhood sexual abuse and early timing of puberty. Journal of Adolescent Health, 60(1), 65–71.
Patton, G. C., & Viner, R. (2007). Pubertal transitions in health. The Lancet, 369(9567), 1130–1139.
Peper, J. S., Hulshoff Pol, H. E., Crone, E. A., & van Honk, J. (2011). Sex steroids and brain structure in pubertal boys and girls: a mini-review of neuroimaging studies. Neuroscience, 191, 28–37. https://doi.org/10.1016/j.neuroscience.2011.02.014.
Prencipe, A., Kesek, A., Cohen, J., Lamm, C., Lewis, M. D., & Zelazo, P. D. (2011). Development of hot and cool executive function during the transition to adolescence. Journal of Experimental Child Psychology, 108(3), 621–637. https://doi.org/10.1016/j.jecp.2010.09.008.
Raffington, L., Prindle, J., Keresztes, A., Binder, J., Heim, C., & Shing, Y. L. (2018). Blunted cortisol stress reactivity in low–income children relates to lower memory function. Psychoneuroendocrinology, 90, 110–121.
Raykov, T. (2000). On the large-sample bias, variance, and mean squared error of the conventional noncentrality parameter estimator of covariance structure models. Structural Equation Modeling: A Multidisciplinary Journal, 7(3), 431–441. https://doi.org/10.1207/S15328007SEM0703_4.
Ribner, A. D., Willoughby, M. T., & Blair, C. B., Investigators, F. L. P. K. (2017). Executive function buffers the association between early math and later academic skills. Frontiers in Psychology, 8, 869.
Rogol, A. D., Clark, P. A., & Roemmich, J. N. (2000). Growth and pubertal development in children and adolescents: effects of diet and physical activity–. The American Journal of Clinical Nutrition, 72(2), 521S–528S.
Sabol, T. J., & Hoyt, L. T. (2017). The long arm of childhood: preschool associations with adolescent health. Developmental Psychology, 53(4), 752.
Satterthwaite, T. D., Shinohara, R. T., Wolf, D. H., Hopson, R. D., Elliott, M. A., Vandekar, S. N., & Gur, R. E. (2014). Impact of puberty on the evolution of cerebral perfusion during adolescence. Proceedings of the National Academy of Sciences of the United States of America, 111(23), 8643–8648.
Seltzer, M. H., Frank, K. A., & Bryk, A. S. (1994). The metric matters: the sensitivity of conclusions about growth in student achievement to choice of metric. Educational Evaluation and Policy Analysis, 16(1), 41–49.
Shulman, E. P., Harden, K. P., Chein, J. M., & Steinberg, L. (2015). Sex differences in the developmental trajectories of impulse control and sensation-seeking from early adolescence to early adulthood. Journal of Youth and Adolescence, 44(1), 1–17.
Squeglia, L. M., Jacobus, J., Sorg, S. F., Jernigan, T. L., & Tapert, S. F. (2013). Early adolescent cortical thinning is related to better neuropsychological performance. Journal of the International Neuropsychological Society, 19(9), 962–970.
Steinberg, L. (2010). A dual systems model of adolescent risk-taking. Developmental Psychobiology, 52(3), 216–224. https://doi.org/10.1002/dev.20445.
Tsukayama, E., Toomey, S. L., Faith, M. S., & Duckworth, A. L. (2010). Self-control as a protective factor against overweight status in the transition from childhood to adolescence. JAMA Pediatrics, 164, 631–635.
Walthall, J. C., Konold, T. R., & Pianta, R. C. (2005). Factor structure of the social skills rating system across child gender and ethnicity. Journal of Psychoeducational Assessment, 23(3), 201–215.
Wasserman, A. M., Crockett, L. J., & Hoffman, L. (2017). Reward seeking and cognitive control: using the dual systems model to predict adolescent sexual behavior. Journal of Research on Adolescence, 27(4), 907–913.
Wohlfahrt-Veje, C., Korsholm Mouritsen, A., Hagen, C. P., Tinggaard, J., Grunnet Mieritz, M., Boas, M., & Main, K. M. (2016). Pubertal onset in boys and girls is influenced by pubertal timing of both parents. The Journal of Clinical Endocrinology and Metabolism, 101, jc20161073.
Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: development and plasticity. Child Development Perspectives, 6(4), 354–360.
This research uses data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (NICHD SECCYD), a longitudinal, multi-site prospective project directed by a steering committee and funded through a series of cooperative agreements (U10s and a U01). We thank the principal investigators and families who participated in this study. Information on how to obtain the SECCYD data files is available on the NICHD SECCYD website (https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00233). No direct support was received from NICHD for this analysis and the views expressed in this article are the authors alone. The authors also gratefully acknowledge feedback from Dr. Terri Sabol (Northwestern University) and Dr. Ann Higgins (Fordham University) on early drafts of this manuscript.
NC and LTH conceived of the study. NC performed the statistical analysis and drafted the manuscript; LTH oversaw and provided feedback on the statistical analysis and helped draft and edit the manuscript. Both authors read and approved the final manuscript.
Conflict of Interest
The authors declare that they have no conflict of interest.
All research reported on in the manuscript was conducted in compliance with APA ethical principles. The study consisted of secondary analyses of de-identified data, and therefore did not require formal consent or ethics board approval.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Chaku, N., Hoyt, L.T. Developmental Trajectories of Executive Functioning and Puberty in Boys and Girls. J Youth Adolescence 48, 1365–1378 (2019). https://doi.org/10.1007/s10964-019-01021-2