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Learning Objectives:

1. Describe the Virtual Reality (VR)-based tasks and machine learning (ML) approach used to predict academic performance in early adolescents.
2. Identify the classification algorithm and reported accuracy that best predicted teacher-rated academic performance from VR-derived behavioral data.

Mridula, T. V., & Muniyandi, M. (2026). Objective assessment of academic performance using virtual reality and machine learning in early adolescents. Child Neuropsychology. Advance online publication. https://doi.org/10.1080/09297049.2026.2653548

Learning Objectives:

1. Describe the association between neighborhood opportunity, as measured by the Child Opportunity Index (COI), and intelligence quotient (IQ) outcomes among youth presenting for neuropsychological evaluation.
2. Identify which Child Opportunity Index (COI) domains were significantly associated with intelligence quotient (IQ) outcomes and which domain was not.

Loblein, H., Nielsen, J., Godfrey, M., Strong, A., Gao, Y., Walsh, K. S., Sepeta, L., Gierdalski, M., & Berl, M. M. (2026). Child opportunity and cognitive development in a clinically referred sample. Child Neuropsychology. Advance online publication. https://doi.org/10.1080/09297049.2026.2672501

Learning Objectives:

1. Identify the executive function (EF) domains—Monitoring and Planning—found to have the highest centrality in a network model of executive functions and functional impairments among children with attention-deficit/hyperactivity disorder (ADHD).
2. Describe the bridge domains—Family and Inhibition—that most strongly connected the executive function and functional impairment communities in the network model.

Zhang, W., Cao, X., Wu, Z., Liu, J., Li, Y., Zhang, L., Wang, Y., Jackson, T., Xiang, Y.-T., & Yang, B. (2026). Inter-relationships between executive functions and functional impairments among children with ADHD: Findings from a network perspective. Child Neuropsychology. Advance online publication. https://doi.org/10.1080/09297049.2026.2638879

Learning Objectives:

1. Identify the three aspects of executive functioning (EF)—working memory (WM), problem-solving, and nonverbal fluency—found to significantly predict reading comprehension (RC) in children ages 8-12 after controlling for basic reading, listening comprehension, and IQ.
2. Explain how the study's findings challenge the Simple View of Reading by demonstrating that executive functioning (EF) predicts reading comprehension (RC) beyond basic reading ability and listening comprehension.

Imre, Z., & Kibby, M. Y. (2026). Should executive functioning be included in models of reading comprehension, and if so, which aspects? Child Neuropsychology. Advance online publication. https://doi.org/10.1080/09297049.2026.2636210

Learning Objectives:

1. Describe the overall strength and direction of the relationship between behavioral attention (ADHD symptomatology) and academic achievement, and explain how this relation is moderated by symptomatology type (inattention vs. hyperactivity/impulsivity) and rater type (teacher vs. parent), based on meta-analytic findings across 106 studies.
2. Explain how ADHD symptomatology relates differentially to academic skills of varying complexity, recognizing that inattention is more strongly associated with academic achievement than hyperactivity/impulsivity, and that higher-complexity skills (e.g., reading comprehension, written expression, math word problems) show stronger associations with behavioral attention than lower-complexity skills (e.g., decoding, spelling, math computation).

Gioia, A. R., Miciak, J., Peng, P., Gallagher, M. W., Williams, M. W., Farrell, A., Salentine, C., Boada, C., Dragoi, I., Harmouch, S., Ortiz-Jimenez, A., & Cirino, P. T. (2026). Behavioral attention and academic achievement: a comprehensive meta-analysis. Child Neuropsychology. https://doi.org/10.1080/09297049.2026.2634751

Learning Objectives:

1. Describe how adolescents with ADHD receiving methylphenidate treatment differ from healthy controls in social skills, social support, social intelligence, and peer relationships, including the finding that social impairments persisted despite ongoing pharmacological treatment.
2. Explain the mediating role of social intelligence in the relationship between social skills and peer relationships in adolescents with ADHD, and describe how executive dysfunction and ADHD symptom severity relate to social functioning within the study's proposed conceptual model.

Suzer Gamli, I., & Arici Gurbuz, A. (2026). Exploring the association between social skills, social intelligence, and executive dysfunction in adolescents with attention deficit hyperactivity disorder: a mediational analysis. Child Neuropsychology. https://doi.org/10.1080/09297049.2026.2632136

Learning Objectives:

1. Describe how the Measures of Executive Attention (MEA) battery addresses the task impurity problem by controlling for collateral linguistic and fine-motor demands, and explain how accounting for these abilities altered between-group differences (autistic vs. neurotypical children) on tasks such as visual search and working memory capacity in familiar conditions.
2. Identify the specific executive attention domains in which autistic children aged 8–14 showed weaknesses relative to neurotypical peers (cognitive flexibility and generative thinking in a graphical task; working memory capacity under novel, emotionally demanding conditions), and explain the relevance of task withdrawal patterns and emotion regulation to interpreting executive function performance in this population.

Anderle, F., Pasqualotto, A., Bentenuto, A., Venuti, P., & Benso, F. (2025). Assessing executive attention in autistic children: strengths, weaknesses and individual differences. Child Neuropsychology. https://doi.org/10.1080/09297049.2025.2600608

Learning Objectives:

1. Describe the sex-specific neurodevelopment trajectories identified in children aged 0–3 years using group-based trajectory modeling, including the differing number of trajectory classes for girls versus boys and the domains (adaptability in girls, language in boys) that primarily drive overall development.
2. Identify the perinatal factors associated with low developmental trajectories in boys and girls, including the shared risk factors (maternal gravidity of two or more, non-breastfeeding) and the factor (preterm birth) uniquely associated with low trajectory in girls.

Zhang, Z., Sheng, L., Yue, C., Qiao, X., Yuan, M., Liu, X., & Chen, X. (2026). Sex differences in neurodevelopment trajectories over the first 3 years. Child Neuropsychology. https://doi.org/10.1080/09297049.2025.2599855