Uncovering executive function profiles within interindividual variability: A data driven clustering exploration of design fluency in school-aged children
This study applied unsupervised machine learning to performances on a design fluency task, to identify distinct executive function (EF) profiles among 113 neurotypical Canadian children (62% female, 74% White, aged 7-13). By tracking design, repetition, and strategy production every minute for five minutes, the results revealed two profiles: Profile A generated fewer designs but exhibited a more stable production pattern across time. Profile B, initially more productive, showed a steeper increase in repetition errors and a decline in design production. These findings demonstrate how EF interindividual variability in neurotypical children can be captured through naturally emerging performance patterns, highlighting the value of temporal analysis in differentiating executive functioning profiles.