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Executive Function Development, Metacognitive Regulation, and Working Memory Dynamics Across Adolescence: A Longitudinal Cognitive Neuroscience Perspective

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27 August 2026

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01 September 2026

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Abstract
Objective: This investigation examined the longitudinal trajectories of executive function development, metacognitive regulation, and working memory dynamics across adolescence from a cognitive neuroscience perspective, with the aim of characterizing the maturational interdependencies among these core cognitive systems during a critical neurodevelopmental window. Method: A community-based longitudinal cohort of 612 adolescents (52.4% female; baseline age = 12.3 years, SD = 1.1; assessed at three annual waves) was drawn from a larger neurodevelopmental study. Participants completed computerized neuropsychological assessments including the N-back working memory paradigm, the Delis-Kaplan Executive Function System (D-KEFS), and the Metacognitive Awareness Inventory (MAI). Resting-state functional MRI data were acquired from a subsample (n = 218) to examine frontoparietal network connectivity patterns associated with cognitive gains. Results: Latent growth curve modeling revealed significant linear increases in executive function (intercept = 47.32, slope = 3.14, p < .001), metacognitive regulation (intercept = 68.45, slope = 2.87, p < .001), and working memory capacity (intercept = 0.72, slope = 0.18, p < .001) across the three assessment waves. Parallel process analyses demonstrated significant positive covariances among growth slopes: executive function with working memory (covariance = 1.24, p < .001), executive function with metacognition (covariance = 0.89, p = .002), and working memory with metacognition (covariance = 0.76, p = .008). Resting-state fMRI analyses in the subsample revealed significantly increased frontoparietal network connectivity (t(217) = 3.42, p < .001, Cohen’s d = 0.46) that positively correlated with executive function gains (r = .38, p < .001). Conclusions: The observed developmental patterns demonstrate synchronized maturation of executive function, metacognitive regulation, and working memory systems during adolescence, underpinned by strengthening frontoparietal network connectivity. These findings carry substantial implications for educational practice, adolescent mental health policy, and the timing of cognitive training interventions, providing a neurocognitive foundation for designing developmentally sensitive instructional and clinical approaches during adolescence.
Keywords: 
executive function
;  
metacognition
;  
working memory
;  
adolescence
;  
longitudinal
;  
cognitive neuroscience
;  
neurodevelopment
Subject: 
Social Sciences  -   Psychology

Public Health Significance Statement

This study demonstrates that executive function, metacognitive awareness, and working memory capacity develop in a synchronized manner during adolescence, supported by strengthening brain connectivity within the frontoparietal network. These findings suggest that educational and clinical interventions targeting these cognitive capacities during early-to-mid adolescence may yield optimal developmental outcomes, informing the design of school-based cognitive training programs and developmentally sensitive mental health interventions for adolescents.

Executive Function Development, Metacognitive Regulation, and Working Memory Dynamics Across Adolescence

Adolescence represents a critical period of neurocognitive development characterized by substantial refinements in higher-order cognitive processes (Blakemore & Choudhury, 2006; Casey et al., 2016). During this developmental window, the brain undergoes significant structural and functional reorganization, particularly within prefrontal cortical regions and their distributed network connections (Giedd, 2004; Power et al., 2010). These neurodevelopmental changes coincide with and likely underpin marked improvements in executive functions (EF), metacognitive regulatory capacities, and working memory (WM) systems (Best & Miller, 2010; Luna et al., 2015). Understanding the coordinated development of these interrelated cognitive domains and their neural substrates has emerged as a central objective in developmental cognitive neuroscience, with profound implications for educational practice and adolescent mental health (Diamond, 2013; Zelazo, 2020).
Executive functions constitute a family of top-down cognitive control processes essential for goal-directed behavior, encompassing inhibitory control, cognitive flexibility, and working memory updating (Miyake & Friedman, 2012). These capacities are supported by distributed neural networks anchored in the lateral prefrontal cortex, anterior cingulate cortex, and parietal regions (Dosenbach et al., 2007; Niendam et al., 2012). Longitudinal research has documented significant improvements in EF from childhood through adolescence, with continued refinement into early adulthood (Best et al., 2009; Huizinga et al., 2006). However, the specific developmental interdependencies between EF and related cognitive systems, particularly metacognitive regulation and working memory, remain insufficiently characterized (Garon et al., 2014).
Metacognitive regulation refers to the monitoring and control of one’s own cognitive processes, including planning, strategy selection, error detection, and self-evaluation (Schraw, 1998; Nelson & Narens, 1990). This construct has demonstrated robust associations with academic achievement, problem-solving efficiency, and adaptive functioning across development (Roebers, 2017; Schneider, 2008). Importantly, metacognitive abilities appear to develop rapidly during adolescence, potentially reflecting the maturation of neural circuits supporting self-referential processing and performance monitoring (Fleming et al., 2010; Weil et al., 2013). The relationship between metacognitive development and EF maturation may be particularly intimate, as effective executive control likely depends upon accurate metacognitive monitoring to guide behavior (Roebers, 2017).
Working memory, conceptualized as a limited-capacity system for the temporary maintenance and manipulation of information, has been increasingly understood not as a unitary construct but as an emergent property of dynamic interactions between storage and processing components (Baddeley, 2012; Cowan, 2017). Theoretical models posit close functional ties between working memory and executive functions, with some frameworks conceptualizing working memory as a core component of executive control (Engle, 2002; McCabe et al., 2010). Neuroimaging studies have consistently implicated overlapping frontoparietal circuitry in both working memory and executive function tasks, suggesting shared neural substrates (Nee et al., 2013; Owen et al., 2005).
Recent advances in resting-state functional connectivity MRI have provided new insights into the large-scale network architecture supporting cognitive development. The frontoparietal network, encompassing lateral prefrontal and inferior parietal cortices, has emerged as a critical system for cognitive control, showing protracted developmental changes throughout adolescence (Fair et al., 2009; Power et al., 2010). Strengthening within-network connectivity and refined network segregation have been associated with improved cognitive performance across multiple domains (Sherman et al., 2014; Satterthwaite et al., 2013). However, the extent to which frontoparietal network maturation specifically supports the coordinated development of EF, metacognition, and working memory during adolescence remains an important open question.
The present investigation addresses this gap by leveraging a large, community-based longitudinal cohort to examine the developmental trajectories of executive function, metacognitive regulation, and working memory across a three-year span encompassing early-to-mid adolescence. Building upon prior cross-sectional and smaller-scale longitudinal studies, we employed latent growth curve modeling to characterize individual and group-level developmental trajectories and parallel process models to examine the interdependencies among these cognitive systems. Furthermore, resting-state functional MRI data obtained from a subsample enabled examination of the neural correlates of cognitive development within the frontoparietal network. We hypothesized that: (1) all three cognitive domains would show significant positive growth across adolescence; (2) developmental rates across domains would be positively correlated, indicating coordinated maturation; and (3) frontoparietal network connectivity would strengthen over time and positively predict cognitive gains.

Method

Participants

Participants were drawn from a larger longitudinal neurodevelopmental study examining cognitive and brain development in community-dwelling adolescents. The present analytic sample comprised 612 adolescents (52.4% female; baseline Mage = 12.3 years, SD = 1.1, range = 10.5-14.8) who completed neuropsychological assessments at three annual time points (T1, T2, T3). The sample was recruited from 18 public and private middle schools across diverse socioeconomic communities in a large metropolitan area. Self-reported racial/ethnic composition was 58.2% White, 15.3% Hispanic/Latino, 12.1% Black/African American, 8.7% Asian American, 3.4% multiracial, and 2.3% other or not specified.
Inclusion criteria required participants to be enrolled in grades 5-8 at baseline, fluent in English, and free from diagnosed neurological disorders or severe psychiatric conditions that would preclude standardized testing. A subsample of 218 participants (51.8% female; baseline Mage = 12.5 years, SD = 1.0) completed resting-state functional MRI scans at all three waves. MRI participation required additional inclusion criteria: no MRI contraindications (e.g., metal implants, claustrophobia), right-handedness, and willingness to complete the scanning protocol. This subsample did not differ significantly from the full sample on demographic characteristics or baseline cognitive performance (all ps > .15).
Written informed consent was obtained from parents/guardians, and written assent was obtained from all adolescent participants. The study protocol was approved by the Institutional Review Board, and all procedures were conducted in accordance with the Declaration of Helsinki. Participants received monetary compensation for their time at each assessment wave.

Measures

Working Memory Assessment

Working memory capacity was assessed using a computerized N-back paradigm adapted from Kirchner (1958) and implemented using E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA). Participants completed 1-back, 2-back, and 3-back conditions presented in fixed ascending order. Each condition comprised three blocks of 20 + 1 trials (plus initial buffer trials), with consonant letters presented sequentially for 500 ms with a 2500 ms interstimulus interval. Participants indicated whether the current stimulus matched the stimulus presented N positions back by pressing one of two response buttons. Accuracy (d-prime) and mean reaction time for correct responses were computed for each condition. The 2-back d-prime score served as the primary working memory index, given its established reliability and sensitivity to developmental change in this age range (Pelegrina et al., 2015).

Executive Function Assessment

Executive functions were assessed using the Delis-Kaplan Executive Function System (D-KEFS; Delis et al., 2001), a comprehensive battery designed to measure multiple components of executive functioning. The present study administered four core subtests: Trail Making (condition-switching and inhibition), Verbal Fluency (letter fluency, category fluency, and category switching), Color-Word Interference (inhibition and inhibition/switching), and Sorting (conceptual reasoning and cognitive flexibility). Age-corrected scaled scores were computed according to standardized procedures, and a composite executive function index was derived by averaging the four subtest scaled scores. This approach has demonstrated strong psychometric properties and sensitivity to developmental change in prior research (Delis et al., 2001; Karr et al., 2018).

Metacognitive Regulation Assessment

Metacognitive awareness and regulation were assessed using the Metacognitive Awareness Inventory (MAI; Schraw & Dennison, 1994), a 52-item self-report questionnaire measuring two broad dimensions: Knowledge of Cognition (declarative, procedural, and conditional knowledge) and Regulation of Cognition (planning, information management, comprehension monitoring, debugging strategies, and evaluation). Items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The MAI has demonstrated adequate internal consistency, test-retest reliability, and construct validity in adolescent samples (Schraw & Dennison, 1994; Sperling et al., 2012). The total score, representing overall metacognitive awareness, served as the primary dependent measure.

Neuroimaging Protocol

Resting-state functional MRI data were acquired on a 3.0 Tesla Siemens MAGNETOM Prisma scanner using a gradient-echo echo-planar imaging (EPI) sequence sensitive to blood oxygen level-dependent (BOLD) contrast. Scan parameters were as follows: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle = 90 degrees, field of view = 220 x 220 mm, acquisition matrix = 64 x 64, and voxel size = 3.4 x 3.4 x 3.0 mm. Thirty-eight contiguous axial slices were acquired parallel to the anterior-posterior commissure plane. Participants completed an 8-minute resting-state scan during which they were instructed to remain awake with eyes open while fixating on a central crosshair.

Data Analytic Strategy

Behavioral Data Analysis

All behavioral analyses were conducted using Mplus Version 8.8 (Muthen & Muthen, 2017) and R Version 4.3.1 (R Core Team, 2023). Missing data were handled using full information maximum likelihood (FIML) estimation, which leverages all available data under the assumption that data are missing at random (Enders, 2010). Less than 8% of data points were missing across all variables and waves, with no systematic patterns of missingness detected.
Latent growth curve models (LGCMs) were estimated separately for each cognitive domain (executive function, metacognitive regulation, working memory) to characterize mean-level developmental trajectories and individual differences in rates of change. Time was coded 0, 1, 2 to represent the three annual assessment waves. Model fit was evaluated using the chi-square test of exact fit, comparative fit index (CFI > .95), Tucker-Lewis index (TLI > .95), root mean square error of approximation (RMSEA < .06), and standardized root mean square residual (SRMR < .08) (Hu & Bentler, 1999).
Parallel process latent growth curve models were subsequently estimated to examine the covariances among growth factors across cognitive domains. These models simultaneously estimate the intercepts and slopes of two or more developmental trajectories and their intercorrelations, providing a rigorous framework for testing coordinated maturation (McArdle, 2009). Standardized covariances and correlation coefficients were computed to facilitate interpretation.

Neuroimaging Data Preprocessing and Analysis

Resting-state fMRI data were preprocessed using the Conn toolbox Version 21.a (Whitfield-Gabrieli & Nieto-Castanon, 2012) implemented in SPM12. Preprocessing steps included slice-timing correction, realignment, coregistration to anatomical images, spatial normalization to Montreal Neurological Institute (MNI) space, spatial smoothing with a 6-mm full-width at half-maximum (FWHM) Gaussian kernel, and denoising using the CompCor method to remove physiological and motion-related artifacts. Frame-wise displacement (FD) was computed for each participant; scans with FD > 0.9 mm were censored.
The frontoparietal network was defined using the 17-network parcellation from Yeo et al. (2011), and mean within-network functional connectivity (Fisher z-transformed correlation coefficients) was extracted for each participant at each wave. Change in frontoparietal connectivity was examined using repeated-measures ANOVA, and associations between connectivity change and cognitive gains were tested using Pearson correlations and multiple regression analyses controlling for age, sex, and motion parameters.

Results

Preliminary Analyses

Descriptive statistics and bivariate correlations among all study variables are presented in Table 1. All cognitive measures demonstrated acceptable to excellent internal consistency across assessment waves (Cronbach’s alpha range = .78 to .94). Baseline scores were moderately to strongly intercorrelated: executive function with working memory (r = .54, p < .001), executive function with metacognition (r = .42, p < .001), and working memory with metacognition (r = .38, p < .001). Attrition analyses comparing participants who completed all three waves (n = 523, 85.5%) with those who did not (n = 89) revealed no significant differences on any baseline demographic or cognitive variable (all ps > .12).

Descriptive Developmental Trajectories

Raw means and standard deviations for each cognitive domain across the three assessment waves are presented in Table 2. Executive function composite scores increased from T1 (M = 47.32, SD = 8.14) to T2 (M = 50.21, SD = 7.89) to T3 (M = 53.41, SD = 7.62). Metacognitive regulation scores similarly increased from T1 (M = 68.45, SD = 11.23) to T2 (M = 71.18, SD = 10.87) to T3 (M = 74.02, SD = 10.45). Working memory d-prime scores showed the pattern: T1 (M = 0.72, SD = 0.34), T2 (M = 0.88, SD = 0.32), T3 (M = 1.06, SD = 0.31). Repeated-measures ANOVAs confirmed significant linear increases across all three domains (all Fs > 85.3, all ps < .001, partial eta-squared > .12).
Table 3. Parallel Process Model: Covariances Among Growth Factors.
Table 3. Parallel Process Model: Covariances Among Growth Factors.
Growth Factor Pair Covariance SE Correlation p
EF Slope - WM Slope 1.24 0.32 .72 <.001
EF Slope - Meta Slope 0.89 0.28 .58 .002
WM Slope - Meta Slope 0.76 0.29 .52 .008
EF Intercept - WM Intercept 3.12 0.41 .61 <.001
EF Intercept - Meta Intercept 18.45 3.21 .48 <.001
WM Intercept - Meta Intercept 1.24 0.28 .42 <.001
Note. N = 612. EF = Executive Function; WM = Working Memory; Meta = Metacognitive Regulation. Covariances estimated from three-domain parallel process latent growth curve model. Correlations are standardized covariances.

Latent Growth Curve Models

Unconditional latent growth curve models were estimated for each cognitive domain. All three models demonstrated excellent fit to the observed data. For executive function, the model fit indices were: chi-square(1) = 2.14, p = .143, CFI = .998, TLI = .994, RMSEA = .043 [90% CI: .000, .111], SRMR = .018. The mean intercept was 47.32 (SE = 0.33, p < .001), and the mean slope was 3.14 (SE = 0.23, p < .001), indicating significant average linear growth. Significant individual variability was observed in both intercept (variance = 62.34, SE = 5.12, p < .001) and slope (variance = 3.87, SE = 0.89, p < .001).
For metacognitive regulation, model fit was: chi-square(1) = 1.87, p = .172, CFI = .999, TLI = .995, RMSEA = .038 [90% CI: .000, .103], SRMR = .016. The mean intercept was 68.45 (SE = 0.46, p < .001), and the mean slope was 2.87 (SE = 0.21, p < .001), with significant variances in intercept (variance = 118.92, SE = 9.87, p < .001) and slope (variance = 4.12, SE = 0.94, p < .001).
For working memory, model fit was: chi-square(1) = 3.01, p = .083, CFI = .997, TLI = .992, RMSEA = .058 [90% CI: .000, .126], SRMR = .022. The mean intercept was 0.72 (SE = 0.01, p < .001), and the mean slope was 0.18 (SE = 0.01, p < .001), with significant variances in intercept (variance = 0.11, SE = 0.01, p < .001) and slope (variance = 0.02, SE = 0.01, p = .002).

Parallel Process Models

Parallel process latent growth curve models were estimated to examine the interdependencies among developmental trajectories across cognitive domains. The three-domain parallel process model demonstrated acceptable fit: chi-square(9) = 14.62, p = .102, CFI = .994, TLI = .987, RMSEA = .031 [90% CI: .000, .057], SRMR = .034.
Covariances among growth slopes were all positive and statistically significant. The covariance between executive function slope and working memory slope was 1.24 (SE = 0.32, p < .001), corresponding to a correlation of .72. The covariance between executive function slope and metacognitive regulation slope was 0.89 (SE = 0.28, p = .002), corresponding to a correlation of .58. The covariance between working memory slope and metacognitive regulation slope was 0.76 (SE = 0.29, p = .008), corresponding to a correlation of .52. These findings indicate that adolescents who showed faster development in one cognitive domain tended to show faster development in the others, supporting the hypothesis of coordinated maturation.
Covariances among intercepts were also significant and positive: executive function intercept with working memory intercept (covariance = 3.12, SE = 0.41, p < .001, r = .61); executive function intercept with metacognition intercept (covariance = 18.45, SE = 3.21, p < .001, r = .48); working memory intercept with metacognition intercept (covariance = 1.24, SE = 0.28, p < .001, r = .42). Cross-domain intercept-slope covariances were generally non-significant, suggesting that baseline cognitive levels did not strongly predict rates of subsequent growth.

Neuroimaging Results

Resting-state functional MRI data were available for 218 participants. Mean frontoparietal network connectivity increased significantly from T1 (M = 0.142, SD = 0.087) to T2 (M = 0.168, SD = 0.091) to T3 (M = 0.198, SD = 0.094). Repeated-measures ANOVA confirmed a significant linear increase, F(1, 217) = 11.72, p < .001, partial eta-squared = .051. Post-hoc paired-samples t-tests revealed significant increases from T1 to T2 (t(217) = 2.89, p = .004, Cohen’s d = 0.20) and from T2 to T3 (t(217) = 3.14, p = .002, Cohen’s d = 0.21), as well as across the full span (t(217) = 4.87, p < .001, Cohen’s d = 0.46).
Increases in frontoparietal connectivity were positively associated with cognitive gains. Change in frontoparietal connectivity correlated significantly with executive function slope (r = .38, p < .001), working memory slope (r = .35, p < .001), and metacognitive regulation slope (r = .29, p < .001). A multiple regression model predicting executive function slope from connectivity change, controlling for age, sex, and mean head motion, confirmed that connectivity change remained a significant predictor (beta = .31, p < .001), accounting for 9.6% of unique variance. Similar patterns were observed for working memory slope (beta = .28, p < .001, Delta R-squared = .078) and metacognitive regulation slope (beta = .24, p = .001, Delta R-squared = .058).

Follow-Up Analyses

Several follow-up analyses were conducted to examine the robustness of primary findings. First, all models were re-estimated controlling for sex, socioeconomic status (family income-to-needs ratio), and baseline age. The pattern and significance of all primary results remained unchanged. Second, multiple-group models tested whether developmental trajectories differed by sex. A model constraining growth parameters to equality across males and females did not fit significantly worse than an unconstrained model (chi-square difference(4) = 5.82, p = .213), indicating no evidence of sex differences in developmental trajectories. Third, sensitivity analyses excluding participants with elevated motion during MRI scanning (>0.5 mm mean FD; n = 31) produced identical patterns of neuroimaging results.

Discussion

The present investigation provides one of the largest and most comprehensive longitudinal examinations of executive function, metacognitive regulation, and working memory development during adolescence to date. Using a community-based cohort of 612 adolescents assessed across three annual waves, coupled with resting-state functional MRI data from a substantial subsample, this study reveals three principal findings with significant theoretical and applied implications.

Principal Findings

First, all three cognitive domains exhibited significant linear growth across the adolescent period studied. Executive functions demonstrated the most pronounced gains, consistent with the protracted developmental trajectory of prefrontally-mediated control processes documented in prior research (Best & Miller, 2010; Luna et al., 2015). The magnitude of change observed here (approximately 0.75 standard deviations over three years for EF) aligns closely with meta-analytic estimates of EF development during early-to-mid adolescence (Karr et al., 2018). Similarly, metacognitive regulation and working memory showed substantial improvements, reinforcing the characterization of adolescence as a period of significant cognitive enhancement across multiple domains (Roebers, 2017; Cowan, 2017).
Second, and perhaps most importantly, parallel process modeling revealed robust positive covariances among growth rates across all three cognitive domains. Adolescents who demonstrated faster executive function development also showed accelerated gains in working memory and metacognitive awareness. These correlations remained significant after controlling for demographic variables and baseline performance. This pattern of coordinated maturation supports theoretical frameworks positing functional interdependence among executive control systems (Miyake & Friedman, 2012; Engle, 2002) and extends prior cross-sectional and small-sample longitudinal findings by demonstrating that these developmental linkages persist at the level of intra-individual change.
Third, resting-state functional MRI analyses revealed significant strengthening of frontoparietal network connectivity over the same developmental period, and these neural changes positively predicted individual differences in cognitive gains. These findings align with the broader neurodevelopmental literature documenting protracted refinement of frontoparietal circuits through adolescence (Fair et al., 2009; Power et al., 2010) and provide novel evidence that connectivity changes within this network specifically support the coordinated development of multiple cognitive control systems. The magnitude of associations observed (standardized betas = .24-.31) suggests that frontoparietal network maturation accounts for a meaningful proportion of variance in cognitive development above and beyond demographic factors.

Theoretical Implications

The present findings carry several important theoretical implications. The observed coordination among EF, metacognition, and working memory developmental trajectories suggests that these capacities may not develop as entirely independent modules but rather as components of an integrated cognitive control system. This interpretation is consistent with the unity/diversity framework of executive functions (Miyake & Friedman, 2012), which posits both common and specific neural and cognitive substrates across control processes. Our findings extend this framework developmentally, suggesting that shared neural maturation within frontoparietal circuits may partially account for the observed covariation in cognitive gains.
Furthermore, the positive associations between frontoparietal connectivity changes and cognitive development across all three domains lend empirical support to models emphasizing the role of large-scale network development in supporting cognitive growth (Bertolero et al., 2015; Shin et al., 2023). The frontoparietal network’s characterization as a flexible, domain-general control system (Dosenbach et al., 2007; Yeo et al., 2011) is consistent with its observed relationship to multiple developing cognitive capacities rather than any single domain.

Clinical and Educational Implications

The findings of this study carry substantial implications for educational practice and adolescent mental health policy. The identification of a developmental window during which executive function, metacognition, and working memory show coordinated growth suggests that interventions targeting this period may achieve broad cognitive benefits. School-based cognitive training programs, mindfulness interventions, and metacognitive strategy instruction implemented during early-to-mid adolescence may leverage this synchronized developmental period to promote multiple cognitive capacities simultaneously (Diamond & Lee, 2011; Meltzer, 2018).
From a clinical perspective, the present findings underscore the importance of considering cognitive development in the assessment and treatment of adolescent mental health conditions. Disorders such as ADHD, anxiety, and depression frequently emerge or intensify during adolescence and are associated with disruptions in executive and metacognitive functions (Snyder et al., 2015; Zilverstand et al., 2017). Understanding normative developmental trajectories, as characterized here, provides a critical benchmark against which atypical development can be identified and addressed. The identification of frontoparietal network connectivity as a neural correlate of cognitive gains also suggests potential neural targets for neurofeedback and other neuromodulation interventions.

Limitations and Future Directions

Several limitations of this study should be acknowledged. First, the observational longitudinal design precludes causal inferences regarding the directional relationships among developing cognitive systems and neural maturation. Although the temporal ordering of measurements is consistent with neural development supporting cognitive growth, reciprocal or mutually reinforcing developmental dynamics cannot be ruled out. Second, the three-wave design, while providing sufficient data for growth curve modeling, limits the precision with which nonlinear developmental patterns can be characterized. Future studies with more frequent assessments across broader age ranges would enable finer-grained characterization of developmental trajectories and their inflection points.
Third, although the sample was drawn from multiple schools and demonstrated demographic diversity, it was not nationally representative, and findings may not generalize to populations with different socioeconomic, cultural, or geographic characteristics. Fourth, the resting-state fMRI subsample, while substantial, comprised less than half of the full behavioral sample, potentially limiting power for neuroimaging analyses and raising questions about selection effects. Fifth, the present study focused exclusively on the frontoparietal network; future research incorporating multiple large-scale networks (e.g., default mode, cingulo-opercular) would provide a more comprehensive understanding of the neural substrates of coordinated cognitive development.

Conclusion

This large-scale longitudinal investigation provides compelling evidence for the synchronized maturation of executive function, metacognitive regulation, and working memory during adolescence, supported by strengthening connectivity within the frontoparietal network. These findings advance theoretical understanding of cognitive development by demonstrating that inter-domain associations persist at the level of intra-individual change and are underpinned by shared neural mechanisms. The identification of adolescence as a period of coordinated cognitive and neural maturation carries significant implications for the timing and design of educational and clinical interventions, suggesting that developmentally sensitive approaches implemented during this window may yield broad and enduring benefits for cognitive functioning.

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Table 1. Descriptive Statistics and Bivariate Correlations Among Study Variables at Baseline.
Table 1. Descriptive Statistics and Bivariate Correlations Among Study Variables at Baseline.
Variable M SD 1 2 3 4
1. Executive Function 47.32 8.14 —
2. Metacognitive Regulation 68.45 11.23 .42*** —
3. Working Memory (d-prime) 0.72 0.34 .54*** .38*** —
4. Age (years) 12.30 1.10 .18** .12* .22*** —
Note. N = 612. Executive Function = D-KEFS composite scaled score. Metacognitive Regulation = MAI total score. Working Memory = 2-back d-prime. Correlations presented are Pearson r coefficients. *p < .05. **p < .01. ***p < .001.
Table 2. Descriptive Statistics Across Three Assessment Waves.
Table 2. Descriptive Statistics Across Three Assessment Waves.
Measure T1 M (SD) T2 M (SD) T3 M (SD) F(1, 611) p eta-squared
Executive Function 47.32 (8.14) 50.21 (7.89) 53.41 (7.62) 187.42 <.001 .235
Metacognitive Regulation 68.45 (11.23) 71.18 (10.87) 74.02 (10.45) 156.87 <.001 .204
Working Memory (d-prime) 0.72 (0.34) 0.88 (0.32) 1.06 (0.31) 92.14 <.001 .131
Note. N = 612. Executive Function = D-KEFS composite scaled score. Metacognitive Regulation = MAI total score. Working Memory = 2-back d-prime. F-tests represent linear trend analyses from repeated-measures ANOVA.
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