NCS-FO: Neurobehavioral integration of visual and semantic number knowledge and its role for individual variation in the math ability of children and adults

这个项目由匹兹堡大学的一组研究人员进行，将解决提高美国儿童和成人数学能力的需求。根据2015年美国国家教育进步评估，只有40%的四年级学生和33%的八年级学生的数学达到或超过熟练水平，只有大约30%的美国成年人能够在现实世界中完成基本的数学过程，比如看温度计和计算温度。如此糟糕的数学成绩给那些没有掌握基本数学技能就进入成年期的人带来了巨大的负担（比如在就业方面），并挑战了美国在全球经济中保持竞争力的能力，而全球经济是由美国公民的智力资本大力推动的。这个项目将研究数学成就背后的一项基本技能：通过将数字符号与它们所代表的数量联系起来来识别视觉数字符号的能力。利用功能性磁共振成像（fMRI）和行为测量，项目团队将描述数字知识的神经成分，并将测试数字网络中有助于“符号整合”和数学能力的通路。最后，通过研究成人和8岁儿童，他们将测试符号整合的神经基质是否随着年龄的增长而变化，如果是，这些变化是否与符号整合行为特征的变化和数学能力的个体差异相对应。总的来说，通过关注广泛使用但知之甚少的符号整合结构，所提出的工作将对数学能力理论产生广泛的影响，这些理论假设了这些潜在的过程，并将为未来研究数学学习轨迹和困难的数学学习者的补救策略提供信息。该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，这是一个由计算机与信息科学与工程（CISE）、教育与人力资源（EHR）、工程（ENG）和社会、行为和经济科学（SBE）联合支持的多学科项目。该项目的总体目标是调查符号整合是否是成人和儿童数学能力的基础。该项目利用更大和更成熟的文字识别文献来开发和测试数字处理的符号整合假设。该模型假设，形式数学能力部分依赖于视觉符号（即阿拉伯数字）与它们所代表的量的整合，通过直接（视觉-语义）和间接（视觉-语言，视觉-手动）途径。通过一项涉及100名成年人和125名8岁儿童的个体差异研究，一种创新的神经行为测量方法组合将被用于测试该模型。项目团队将开发一种新的神经成像协议，并将使用尖端的多变量方法来有效和广泛地识别和表征数字处理网络的神经成分。一组可能的区域包括涉及视觉（梭状回）、语言（角回）、手动（前额回）和语义（顶叶下皮层）数字编码的区域。此外，将从每个参与者获取静息状态数据，并用于提取数字知识的识别视觉、口头、手动和语义成分之间的连通性度量。一对行为任务将测量每个参与者的数字的视觉和语义代码（即符号整合）之间的联想强度。使用一般线性模型（GLM），研究者将测试直接（视觉-语义）和中介（视觉-语言-语义；视觉-手动-语义）通路对符号整合技能的显著贡献的预测。最后，对每个参与者的数学能力进行标准化测试。GLM将用于检验符号整合的个体差异与数学能力之间的预测正相关关系。总的来说，这项工作将对数学能力理论产生广泛的影响，并将为未来的数学学习和干预研究提供信息。

This project, conducted by a team of researchers at the University of Pittsburgh, will address the need to improve math abilities in American children and adults. According to the 2015 National Assessment of Educational Progress, only 40% of 4th graders, and 33% of 8th graders score at, or above, proficiency level in math, and only about 30% of US adults can complete basic mathematical processes in real-world scenarios such as looking at a thermometer and figuring out the temperature. Such poor math achievement outcomes impose significant burdens, such as in securing employment, on individuals who enter adulthood without achieving basic proficiency, and challenges the capacity of the US to remain competitive in a global economy that is strongly driven by the intellectual capital of its citizens. This project will investigate a foundational skill that underlies math achievement: the ability to recognize visual number symbols by connecting them with the quantities they represent. Using functional magnetic resonance imaging (fMRI) and behavioral measures, the project team will characterize the neural constituents of number knowledge and will test for pathways within this number network that contribute to this "symbolic integration" and math ability. Finally, by studying adults and 8-year-old children, they will test whether the neural substrates of symbolic integration change with age, and if so, whether these changes correspond to shifts in the behavioral profile of symbolic integration and individual difference in math ability. Overall, by focusing on the widely used, but poorly understood, construct of symbolic integration, the proposed work will have broad impact on theories of math ability that make assumptions about these underlying processes and will inform future studies examining math learning trajectories and remediation strategies for struggling math learners. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).The overarching objective of the project is to investigate whether symbolic integration is foundational to math ability in adults and children. The project leverages the larger and more established literature on word recognition to develop and test a symbolic integration hypothesis of number processing. This model posits that formal math ability rests in part upon the integration of visual symbols (i.e., Arabic numerals) with the magnitudes they represent, via both direct (visual-semantic) and indirect (visual-verbal, visual-manual) pathways. An innovative combination of neurobehavioral measures will be used to test the model, through an individual differences study involving 100 adults and 125 8-year-old children. The project team will develop a novel neuroimaging protocol and will use cutting-edge multivariate methods to efficiently and broadly identify and characterize the neural constituents of a number processing network. A likely set of regions includes those involved in visual (fusiform gyrus), verbal (angular gyrus), manual (precental gyrus), and semantic (inferior parietal cortex) coding of number. In addition, resting state data will be acquired from each participant, and used to extract a metric of connectivity between identified visual, verbal, manual, and semantic constituents of number knowledge. A pair of behavioral tasks will measure the associative strength between visual and semantic codes for number (i.e., symbolic integration) in each participant. Using general linear models (GLM), the investigators will then test the prediction that both direct (visual-semantic) and mediated (visual-verbal-semantic; visual-manual-semantic) pathways significantly contribute to symbolic integration skill. Finally, standardized measures of math ability will be obtained from each participant. A GLM will be used to test for a predicted positive relation between individual differences in symbolic integration and math ability. Overall, the work will have a broad impact on theories of math ability and will inform future studies of math learning and intervention.