Cognitive flexibility (CF) is critical for humans to perform complex tasks and ensures that humans exhibit appropriate behaviour in response to changing environments. The ability to shift thinking, adapt behaviour, and apply knowledge in new contexts is a core executive function that supports problem solving, self-regulation and lifelong learning. It is especially crucial during key developmental periods where the brain is rapidly changing, and later in adulthood to help maintain cognitive health. In a fast-evolving world. CF is essential for navigating uncertainty, developing new skills and thriving in dynamic work and social environments. Yet, current education and training systems often do not adequately support the development of flexible thinking, highlighting the need for more evidence-based interventions that can strengthen CF across the lifespan.
Successful implementation of cognitive flexibility involves several sub-domains within executive functions. Prior research on cognitive flexibility has portrayed it as various aspects of human cognition ranging from a cognitive skill related to set-shifting, or a by-product of cognitive processes, to part of the cognitive system. In previous work, the investigators clarified the structure of cognitive flexibility through a large behavioural study, resulting in a dual-factor model comprising distinct but complementary dimensions that show unique associations with critical outcome variables. The first factor, CF1 (shifting type flexibility) refers to the ability to switch efficiently between rules and tasks. The second factor, CF2 (strategy type flexibility) reflects the capacity to adapt problem-solving strategies based on contextual cues. Notably, previous work shows that these two forms of CF relate to different learning outcomes. Shifting flexibility predicts reading ability, while strategy flexibility is associated with mathematical proficiency, problem-solving, academic skills and creativity. These findings suggest that different forms of CF may support distinct aspects of learning and raise the possibility that training-related gains in CF may generalise beyond the trained tasks to other cognitive and behavioural domains. Improvements may emerge on tasks that closely overlap with the trained processes, for example those that use similar task structures (near transfer) or extend to more dissimilar tasks (far transfer).
Current flexibility interventions and neuroimaging studies examining CF commonly utilise tasks that tap on to executive functions such as task-set switching or the Dimensional Change Card Sort (DCCS) task. These tasks primarily target CF1 shifting type flexibility. Although effective, one concern related to using these tasks is that it does not tap merely into cognitive flexibility but also activate other executive functions such as inhibition and working memory. Hence, this reduces the precision and specificity of these tasks as training tools. The present project proposed structure learning as a more fundamental and apt training approach. It involves seeking patterns in the stochastic presentations of stimuli, without the need for explicit feedback and is in itself a basic building block for cognitive flexibility, particularly CF2 strategy flexibility.
In the educational context, structure learning is analogous to patterning, a crucial cognitive ability that underpins mathematical and reading skills. Prior research has demonstrated a close relationship between pattern understanding and cognitive flexibility. Hence, structure learning training could potentially be beneficial in improving one's cognitive flexibility. Furthermore, emerging evidence has demonstrated that domain-general training of structure learning skills produced learning that transfer well beyond the learning task (far transfer). However, there is also a paucity in studies that examined whether structure learning training per se could produce generalisable improvements in cognitive flexibility.
The present study aims to address this gap by examining whether cognitive flexibility can be trained using a structure learning intervention, compared with an active control condition (shifting related training) and a passive no-training control. This study will assess whether these interventions produce changes in neural markers and behavioural performance associated with potential gains in CF. Behavioural changes will be examined in terms of their generalisation beyond the trained tasks, specifically whether they show near and far transfer across cognitive and learning-related outcomes, and whether these effects differ for shifting and strategy related training.
Outcome variables are defined a priori and organised into prespecified cognitive domains. Corrections for multiple comparisons will be applied within each domain.
