Fluid Intelligence describes the ability to solve complex problems under novel conditions and predicts success in a wide range of areas. The overarching aim of this thesis was to explore the cognitive processes necessary for the completion of complex cognitive tasks in children with and without intellectual disabilities. Cognitive segmentation, the ability to separate a complex problem into component parts, is vital for the successful completion of fluid intelligence tasks (Duncan, 2013). In the first empirical chapter, Chapter 2, I describe the development of a child-appropriate measure of cognitive segmentation that is used in the experimental work in Chapters 4 and 5. Two versions of the task were developed. In one, participants were presented with a traditional 2x2 matrix reasoning problem and asked to draw the missing matrix item in a response box below. In the second, the problem was broken down into its component features across three separate cells, reducing the need for participants to segment the problem. The task development process took 12 months and involved the creation of multiple stimuli, several rounds of revisions, and two pilot sessions. This study presents the first attempt to develop a child-appropriate version of Duncan et al.’s (2017) cognitive segmentation task. Chapter 3 outlines the methodology used in experimental Chapters 4 and 5. The second empirical chapter, Chapter 4, builds on Duncan et al.’s (2017) work to test whether cognitive segmentation improves performance on complex fluid intelligence problems in children aged 6-10 years in the same way as it does for some adults. The effects of age were explored to test whether younger children fail to segment more than older children. The influence of additional within-task characteristics (e.g., the number and type of rules in each item) on performance were also investigated. The results revealed that cognitive segmentation is crucial for problem-solving success in children, irrespective of age and ability. The implications of the within-task characteristics analyses for the design of fluid intelligence tasks are discussed. The aim of the third empirical chapter, Chapter 5, was to explore the relative contribution of cognitive segmentation to fluid reasoning alongside other cognitive processes commonly implicated in problem-solving, namely working memory and processing speed. I hypothesised that performance would be correlated across all tasks and that each would predict performance on a fluid reasoning task. The main question of interest was whether each cognitive skill would make unique contributions to fluid intelligence, and crucially whether cognitive segmentation contributed anything above and beyond working memory and processing speed. The results revealed that cognitive segmentation does make a significant, unique contribution to problem-solving, above that accounted for by the other cognitive processes.
The final empirical chapter, Chapter 6, presents a phenotyping study of a group of individuals characterised by intellectual disability (ID); individuals with a rare de novo mutation on the STXBP1 gene. This was the first study conducted in my PhD, which inspired my interest in understanding the processes involved in “intelligence”. It is presented as the last empirical chapter for coherence. Together, these studies highlight the challenges faced by individuals with ID and demonstrate that breaking problems down (cognitive segmentation) can aide problem-solving in children of all abilities. They underscore the importance of breaking complex problems down in the classroom, and re-structuring multi-step tasks into separate independent steps to support children’s learning and classroom success. Using these techniques with individuals with ID may help them complete the everyday challenges they face.