Unsupervised Clustering of Executive Function Patterns in Children with Exceptional Cognitive Abilities

The objective of this study was to identify and characterize distinct executive function profiles among children with exceptional cognitive abilities using unsupervised clustering techniques. This quantitative, cross-sectional study was conducted with a sample of school-aged children with documented exceptional cognitive abilities in South Africa. Executive functions were assessed using a multi-method battery comprising performance-based measures of working memory, inhibitory control, cognitive flexibility, planning, and sustained attention, alongside parent- and teacher-rated executive function scales. Following data screening and standardization, unsupervised clustering analyses were performed using hierarchical and partition-based algorithms to identify naturally occurring executive function patterns. Model fit and cluster validity were evaluated using internal validity indices, and cluster stability was examined through resampling procedures. Unsupervised clustering analyses supported a three-cluster solution, revealing statistically distinct executive function profiles. Multivariate comparisons indicated significant between-cluster differences across all executive function domains, with large effect sizes observed for working memory, inhibitory control, and cognitive flexibility. Cluster membership was significantly associated with classroom engagement and behavioral self-regulation indicators, while age differences across clusters were nonsignificant. The findings demonstrate substantial heterogeneity in executive functioning among children with exceptional cognitive abilities, highlighting the presence of globally advanced, selectively strong, and asynchronous executive function profiles. These results underscore the value of person-centered, data-driven approaches in understanding cognitive regulation in high-ability populations and emphasize the need for individualized assessment and educational support. The objective of this study was to identify and characterize distinct executive function profiles among children with exceptional cognitive abilities using unsupervised clustering techniques. This quantitative, cross-sectional study was conducted with a sample of school-aged children with documented exceptional cognitive abilities in South Africa. Executive functions were assessed using a multi-method battery comprising performance-based measures of working memory, inhibitory control, cognitive flexibility, planning, and sustained attention, alongside parent- and teacher-rated executive function scales. Following data screening and standardization, unsupervised clustering analyses were performed using hierarchical and partition-based algorithms to identify naturally occurring executive function patterns. Model fit and cluster validity were evaluated using internal validity indices, and cluster stability was examined through resampling procedures. Unsupervised clustering analyses supported a three-cluster solution, revealing statistically distinct executive function profiles. Multivariate comparisons indicated significant between-cluster differences across all executive function domains, with large effect sizes observed for working memory, inhibitory control, and cognitive flexibility. Cluster membership was significantly associated with classroom engagement and behavioral self-regulation indicators, while age differences across clusters were nonsignificant. The findings demonstrate substantial heterogeneity in executive functioning among children with exceptional cognitive abilities, highlighting the presence of globally advanced, selectively strong, and asynchronous executive function profiles. These results underscore the value of person-centered, data-driven approaches in understanding cognitive regulation in high-ability populations and emphasize the need for individualized assessment and educational support.

Keywords:

Executive functions , cognitive flexibility , exceptional cognitive ability , unsupervised clustering , child development