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，并且与高比率有关 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， 广泛的卫生资源。设计有效的干预措施来补救MLD，并确定 认知和神经生物学特征是其有效性的基础，是向公众宣传的关键步骤 更广泛地说，不识数和学习障碍的健康负担。利用一个富有成效的、创新的、 高影响线的研究，我们建议调查神经认知机制的反应， 干预（RTI）旨在补救MLD儿童的核心和持续性认知障碍。 为了实现这一目标，我们将使用一个理论上激励的集成符号/非符号（iSNS） 采用随机对照设计进行干预，以增强符号和 量的非符号表示。我们将开发创新的计算模型来研究 潜在认知过程的个体差异，包括证据积累、对项目的敏感性 困难和表现监测，这是MLD儿童学习和大脑可塑性的基础。我们 中心假设是（1）iSNS将弥补数字问题解决缺陷，并加强潜在的 MLD儿童的认知过程，以及（2）神经表征的可塑性， 功能性大脑回路和网络的重新配置将有助于学习、记忆和迁移 MLD儿童至关重要的是，基于创新的系统神经科学方法，我们将利用 新的计算工具和定量网络分析的功能脑回路连接视觉空间 注意力，认知控制和记忆形成系统，以推进基础知识的 MLD儿童RTI的神经认知机制我们新方法的发现， 神经认知模型将对MLD的病因学、MLD的神经生物学、MLD的神经生物学和MLD的神经生物学产生重要影响。 更普遍的学习障碍，以及RTI个体差异的神经认知基础。 研究结果还将为学习中的个体差异提供新的见解，并对以下方面产生广泛的影响： 优化所有儿童的学习。更广泛地说，我们提出的研究将提供更深入的了解， 儿童学习、记忆和迁移（泛化）的动态神经认知过程 有学习障碍。

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.