Interventions in math learning disabilities: cognitive and neural correlates

项目摘要 数学学习障碍(MLD)影响高达14%的学龄儿童，并与高比率有关 发病率和较差的健康结果使其成为一个重大的公共卫生问题，需要 丰富的卫生资源。设计有效的干预措施来补救MLD，并确定 其功效背后的认知和神经生物学特征是向公众演讲的关键步骤 更广泛地说，不算数和学习障碍造成的健康负担。利用高效、创新和 高影响力的研究，我们建议研究潜在的神经认知机制 旨在纠正MLD儿童核心和持续性认知障碍的干预(RTI)。 为了实现这一目标，我们将使用理论上有动机的综合符号/非符号(ISNS) 采用随机对照设计进行干预以增强符号和符号之间的跨格式映射 量的非符号表示法。我们将开发创新的计算模型来研究 潜在认知过程中的个体差异，包括证据积累、对项目的敏感性 学习困难和表现监控是MLD儿童学习和大脑可塑性的基础。我们的 中心假设是：(1)iSNS将弥补数值解决问题的缺陷，并增强潜在性 MLD儿童的认知过程，以及(2)神经表征和 大脑功能回路和网络的重新配置将有助于学习、保持和迁移 在患有MLD的儿童中。至关重要的是，在创新的系统神经科学方法的基础上，我们将利用 连接视觉空间的脑功能回路的新型计算工具和定量网络分析 注意，认知控制和记忆形成系统，以促进基础知识的 MLD儿童RTI潜在的神经认知机制。我们新方法的发现和 神经认知模型将对揭示MLD的病因、MLD的神经生物学 更一般的学习障碍，以及RTI个体差异的神经认知基础。 这些发现还将为学习中的个体差异提供新的见解，并对 优化所有儿童的学习。更广泛地说，我们建议的研究将提供对 儿童学习、保持和迁移(概括)的动态神经认知过程 有学习障碍的人。

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.