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

该项目由匹兹堡大学的一组研究人员进行，旨在解决提高美国儿童和成年人数学能力的需求。根据2015年全国教育进步评估，只有40%的四年级学生和33%的八年级学生的数学水平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达到基本熟练程度的成年个人带来了巨大的负担，比如在确保就业方面，并挑战了美国在一个由公民智力资本强烈推动的全球经济中保持竞争力的能力。这个项目将调查数学成就的基础技能：通过将视觉数字符号与它们所代表的量联系起来识别它们的能力。利用功能磁共振成像(FMRI)和行为测量，该项目团队将表征数字知识的神经成分，并将测试这个数字网络中有助于这种“符号整合”和数学能力的途径。最后，通过对成年人和8岁儿童的研究，他们将测试符号整合的神经基础是否会随着年龄的增长而变化，如果是的话，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。总体而言，通过关注广泛使用但鲜为人知的符号整合结构，这项拟议的工作将对对这些潜在过程做出假设的数学能力理论产生广泛影响，并将为未来考察数学学习轨迹和针对有困难的数学学习者的补救策略的研究提供信息。这个项目是由理解神经和认知系统的综合策略(NSF-NCS)资助的，这是一个多学科项目，由计算机和信息科学与工程(CEISE)、教育和人力资源(EHR)、工程(ENG)和社会、行为和经济科学(SBE)的主管部门联合支持。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。该项目利用关于单词识别的更大和更成熟的文献来开发和测试数字处理的符号整合假说。这一模型认为，形式的数学能力部分取决于视觉符号(即阿拉伯数字)与它们所代表的大小的整合，通过直接(视觉-语义)和间接(视觉-语言，视觉-手动)两种途径。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，将使用神经行为测量的创新组合来测试该模型。该项目团队将开发一种新的神经成像方案，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那些涉及数字的视觉(梭形回)、言语(角回)、手写(前脑回)和语义(顶下皮质)编码的区域。此外，休眠状态数据将从每个参与者获得，并用于提取已识别的数字知识的视觉、语言、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中数字的视觉和语义代码(即符号整合)之间的关联强度。使用一般线性模型(GLM)，研究人员将检验直接(视觉-语义)和中介(视觉-言语-语义；视觉-手动-语义)路径对符号整合技能有显著贡献的预测。最后，将从每个参与者那里获得数学能力的标准化测量。GLM将被用来测试符号整合的个体差异与数学能力之间的预测正相关关系。总体而言，这项工作将对数学能力理论产生广泛影响，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.