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

NCS-FO：视觉和语义数字知识的神经行为整合及其对儿童和成人数学能力个体差异的作用

• 批准号: 1734735
• 负责人: Melissa Libertus
• 金额: $ 96.63万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734735&HistoricalAwards=false
• 关键词: NCS; FO; Neurobehavioral; integration; visual

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.

该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成绩达到或超过熟练程度，只有大约30%的美国成年人可以在现实世界中完成基本的数学过程，例如看温度计并计算温度。这种糟糕的数学成绩给那些成年后没有达到基本熟练程度的人带来了巨大的负担，比如就业，并挑战了美国在由公民智力资本驱动的全球经济中保持竞争力的能力。这个项目将研究数学成就的基础技能：通过将它们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。使用功能性磁共振成像（fMRI）和行为测量，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年龄而变化，如果是这样，这些变化是否与符号整合的行为特征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，拟议的工作将对数学能力理论产生广泛的影响，这些理论对这些基本过程做出假设，并将为未来的研究提供信息，研究数学学习轨迹和补救策略。该项目由理解神经和认知系统的综合战略（NSF-NCS）资助，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）和社会，行为，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。该项目利用更大的和更成熟的文字识别文献，开发和测试数字处理的符号整合假设。这个模型假定，正式的数学能力部分取决于视觉符号的整合（即，阿拉伯数字）与它们所代表的量值，通过直接（视觉-语义）和间接（视觉-语言，视觉-手动）途径。一个创新的神经行为措施的组合将被用来测试模型，通过涉及100名成年人和125名8岁儿童的个体差异研究。该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参与视觉（梭状回）、言语（角回）、手动（脑前回）和语义（顶叶下皮层）编码的区域。此外，将从每个参与者处获取静息状态数据，并将其用于提取识别的数字知识的视觉、口头、手动和语义成分之间的连接性度量。一对行为任务将测量数字的视觉和语义代码之间的关联强度（即，符号积分）。使用一般线性模型（GLM），研究人员将测试预测，直接（视觉语义）和介导（视觉-言语-语义;视觉-手动-语义）路径显着有助于符号整合技能。最后，将从每个参与者那里获得数学能力的标准化测量。一个GLM将被用来测试一个预测的正关系之间的差异，在符号整合和数学能力。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。

项目成果

Numerical estrangement and integration between symbolic and non-symbolic numerical information: Task-dependence and its link to math abilities in adults

符号和非符号数字信息之间的数字疏远和整合：任务依赖性及其与成人数学能力的联系

• DOI: 10.1016/j.cognition.2022.105067
• 发表时间: 2022
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Ren, Xueying;Liu, Ruizhe;Coutanche, Marc N.;Fiez, Julie A.;Libertus, Melissa E.
• 通讯作者: Libertus, Melissa E.

Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain

• DOI: 10.1162/jocn_a_02008
• 发表时间: 2023-08-01
• 期刊: JOURNAL OF COGNITIVE NEUROSCIENCE
• 影响因子: 3.2
• 作者: Ren，Xueying;Libertus，Melissa E.
• 通讯作者: Libertus，Melissa E.