Interventions in math learning disabilities: cognitive and neural correlates

项目摘要

Project Abstract Mathematical learning disabilities (MLD) impact up to 14% of school-aged children, and are linked to high rates of morbidity and poorer health outcomes making it a significant public health concern requiring extensive health resources. Designing effective interventions to remediate MLD and identifying the cognitive and neurobiological features underlying their efficacy are critical steps for addressing the public health burdens of innumeracy and learning disabilities more broadly. Leveraging a productive, innovative, and high-impact line of research, we propose to investigate neurocognitive mechanisms underlying response to intervention (RTI) aimed at remediating core and persistent cognitive impairments in children with MLD. To achieve this goal, we will use a theoretically-motivated integrated symbolic/non-symbolic (iSNS) intervention with a randomized controlled design to enhance cross-format mapping between symbolic and non-symbolic representations of quantities. We will develop innovative computational models to investigate individual differences in latent cognitive processes, including evidence accumulation, sensitivity to item difficulty, and performance monitoring, that underlie learning and brain plasticity in children with MLD. Our central hypotheses are that (1) iSNS will remediate numerical problem-solving deficits and strengthen latent cognitive processes in children with MLD, and that (2) plasticity of neural representations and reconfiguration of functional brain circuits and networks will contribute to learning, retention, and transfer in children with MLD. Crucially, building on innovative systems neuroscience approaches, we will leverage novel computational tools and quantitative network analysis of functional brain circuits linking visuospatial attention, cognitive control, and memory formation systems to advance foundational knowledge of the neurocognitive mechanisms underlying RTI in children with MLD. Findings from our novel approach and neurocognitive models will have major implications for informing the etiology of MLD, the neurobiology of learning disabilities more generally, and the neurocognitive basis of individual differences in RTI. Findings will also provide new insights into individual differences in learning, with broad consequences for optimizing learning in all children. More broadly, our proposed studies will provide a deeper understanding of dynamic neurocognitive processes underlying learning, retention and transfer (generalization) in children with learning disabilities.